Bhorat - University of Southern California



The Impact of Increase in Minimum Wages on Consumer Perceptions of Service:A Deep Learning Analysis of Online Restaurant ReviewsAbstractWe study the impact of a mandated increase in minimum wages on consumer perceptions of multiple dimensions of service quality in the restaurants industry. When faced with a mandated increase in minimum wages, firms might reduce the number of workers leading to poor consumer service. Alternatively, more motivated workers might improve consumer service. Based on textual data from 533,022 online reviews for 2,870 restaurants, we estimate a deep learning Word2Vec model to extract service quality perceptions. We then exploit a natural experiment in the County of Santa Clara, wherein only the city of San Jose legislated a 25% minimum wage increase in 2013. Using measures of perceived service quality as the dependent variable, we estimate a difference-in-difference model. We find an improvement in perceived service quality of San Jose restaurants in terms of reduced delays, slowness and unfriendliness. This decrease is restricted to independent restaurants and does not extend to chains. This finding is consistent with agency theory based predictions of greater incentives to improve service in independent restaurants. We discuss implications for consumers, restaurants and policy makers.Key words: Service quality, minimum wages, deep learning methods, text analysis, natural experiments, agency theory.1. IntroductionMost countries in the world including the United States, China, Japan and Germany have long legislated minimum hourly wage. The purpose of such legislation is to protect workers against unduly low pay. It also serves as one of several policy tools to potentially reduce inequality and poverty (International Labor Organization, 2015). The impact of such legislation on economic and labor outcomes have been studied by academics and policy makers for decades. Across several industries, geographies and time periods, minimum wage legislation has been shown to impact inequality (Autor, Manning and Smith 2016), employment levels (Aaronson and French, 2007), firm value (Bell and Machin 2018), other wages (Grossman 1983), and non-wage benefits (Bhorat, Kanbur and Stanwix 2014). Despite this rich body of research, there is lack of consensus about the directions and magnitudes of these effects. Across academia, media, policy makers, and the general public, there are strong differences in opinion about the advantages and drawbacks of such legislation. In contrast to the positive view that minimum wages reduce poverty and inequality, improve living standards, and boost morale, opponents opine that setting minimum wages increases unemployment especially of unskilled and inexperienced workers, and increases the cost of doing business. Even though minimum wage legislation has been intensely scrutinized and debated, there is very little academic research or discussion on how such legislation might impact the behavior or experiences of consumers who interact with minimum wage workers. We investigate the impact of increases in minimum wage increases by thousands of firms on various dimensions of the perceived service quality of those firms, as captured by the opinions of the firms’ consumers. Especially, we view these effects as another angle to consider when measuring the economic impact of minimum wage.Our empirical research context is the US restaurants industry. Direct labor costs are about 30% of total operating costs of US restaurants, and about 33% of restaurant workers are paid within 10% of the minimum wage. Two-thirds of workers in the United States, earning the minimum wage or less in 2016, were employed in service occupations, mostly in food preparation and serving related jobs. For these reasons, several researchers have focused on this industry to measure minimum wage effects (see Section 2 for details). We measure service quality by text analysis of 533,022 online reviews for 2,870 unique restaurants posted during 2008-2017 (see Section 3 for details). Traditional approaches to measure service quality, such as surveys and focus groups, might be expensive, time consuming, and potentially subject to recall biases and demand effects (Netzer et al. 2012). Unlike such approaches which rely on primary data collected over a short period of time and for a limited number of firms, our data are available over many months, and for a large number of firms. Therefore, relative to primary data collection, our approach is especially useful for studying the impact of temporally distant events (such as past legislations) by comparing periods before and after such events.To analyze text, we use a state-of-the-art unsupervised deep learning model (Mikolov et al. 2013). Unlike applications where a label is associated with text, and the modeling objective is label prediction for new text (i.e. supervised learning), unsupervised learning uncovers latent relationships in the text. As analysts of text, our interests often center on topics (and therefore words) that occur rarely in the text corpus, which in our context is the set of consumer reviews of restaurants in our context. Topic models such as the Latent Dirichlet Allocation (Blei et al, 2001), which estimate co-occurrence patterns in a corpus using latent topics, have been increasingly used in marketing (Tirunillai and Tellis 2014, Puranam et al. 2017). One challenge with such topic models is that the estimated topics tend to generally focus on most commonly discussed topics. It is difficult to ensure that the topic model will identify rare topics of interest. We resolve this issue by using a word of focal interest (i.e. one seed word pertaining to one dimension of service quality, such as reliability), and automatically identifying other related words using an appropriate distance measure. Specifically, the Word2Vec model (Mikolov et al. 2013) learns a d-dimension latent vector representation for each word in the vocabulary. These vector representations are referred to as “embeddings”. Semantic similarity between two words is captured by the cosine distance between their respective embeddings. We expect that the semantic relationships between words are domain specific, and consequently we estimate the word vectors from our specific dataset. The model yields a list of semantically proximate words for the seed word of substantive interest. For example, timely, attentiveness and speedy might be semantically proximate to “responsiveness”. This list of words then defines the “topic” associated with the seed word. In the next step, we measure the pairwise distance of each word in a consumer review from the “topic” associated with the seed word, and proximate words. The mean of these distances across all words in a review is the distance of the review from the topic, and serves as our dependent variable of interest. Since the distance measure takes into account not just the seed word, but other proximate words, reliance of results on our choice of seed words in minimized. Reviews which are at greater distance from the topic of interest, are semantically less similar to the topic.Given our interest in multiple service quality dimensions, we analyze several dependent variables for each review, each representing the distance of the review from one dimension of service quality. Seed words for our analysis are rooted in the service quality literature. SERVQUAL (Parasuraman et al, 1988 and 1991) is a framework that outlines five dimensions of service quality. SERVPERF (Cronin and Taylor, 1993) operationalizes this framework for restaurants. Based on this framework, we focus our analysis on the following major dimensions of service quality of restaurants: tangibles, reliability, responsiveness, assurance and empathy. Descriptions of these service dimensions and seed words associated with each dimension appear in Table 1. For robustness, we also simultaneously extract discussions of price from consumer reviews. This ensures that service quality measurement is not conflated with measures of satisfaction or dissatisfaction with (potentially changing) prices. Following our approach of identifying seed words for the different dimensions of service quality, we use price as a seed word to indicate the price dimension. ====Insert Table 1 Here====Estimation of causal effects of minimum wage increases is difficult in part because of lack of exogenous variations in wages (i.e. wage increases might be correlated with service quality due to unobservables), unavailability of high quality instruments, and infeasibility of conducting field experiments with meaningful wage increases. To deal with this, we exploit the following natural experiment. The state of California requires employers to pay full state minimum wage before tips to tipped employees. Exclusions based on the number of employees of the business are not permitted. California is divided into 58 counties, one of which is the County of Santa Clara (henceforth CSC). CSC has a population of 1.92 million (2015 Census), is home to Silicon Valley, and is estimated to have the third highest per capita GDP across all cities in the world. CSC is comprised of 8 cities (San Jose, Cupertino, Los Altos, Milpitas, Mountain View, Palo Alto, Santa Clara and Sunnyvale). Of these cities, San Jose, a city with a population of over 1 million, increased hourly minimum wage by 20% from $8 to $10 effective March 11, 2013. This is due to an ordinance which aims to raise the minimum wage to $15 over time. The remaining 7 cities did not witness any change from the statewide rate of $8 per hour in 2013. Since the scope of the wage increase was limited to San Jose, we analyze data from San Jose and the remaining 7 cities of CSC separately. That is, the remaining cities serve as a useful contrast and as a geographically proximate control group (see Allegreto and Reich (2018) who also use the same treatment group and control group). For this purpose, we estimate a difference-in-difference model on all reviews of restaurants located in CSC in our dataset which were posted in a 9-month period before, and 9-month period after the date of the wage increase. This model employs deep learning based measures of perceived service quality as the dependent variable. Model estimation is based on a subset of the 533,022 reviews employed for estimating dimensions of service quality. The 18-month window of analysis leads to a dataset of 24,488 reviews from San Jose before the wage increase, and 27,921 reviews from San Jose after. These represent reviews of 1,232 restaurants. As controls, we employ reviews from the other 7 cities of CSC. These include 10,705 reviews before the wage increase, and 12,138 reviews after. These represent reviews of 528 restaurants in the other 7 cities.Geographical proximity reduces the possibility of biased estimates due to unobservables which might affect San Jose differently from the other cities in CSC. To isolate the causal effects of the wage increase on consumer opinions of service quality in restaurants in San Jose, we control for differences in characteristics between San Jose and the remaining cities, characteristics of reviewers who post restaurant reviews, observed review level characteristics, and for unobserved restaurant level characteristics (via restaurant level fixed effects). In sum, we combine current methods in text analysis from computer science with causal inference techniques. Deep learning methods are new to the marketing literature. We are also unaware of textual analysis applications in the vast and inter-disciplinary literature on minimum wages. 1.1 Research Questions and Agency TheoryOur first research question is whether minimum wage increase leads to an increase or decrease in consumer opinions of service quality. The direction of this effect is a priori unclear. A restaurant might react to forced increases in minimum wages by reducing the number of employees (McBride 2017). Indeed, in 2016, McDonald’s announced the nationwide rollout of automated kiosks to replace cashiers, as a way to reduce costs due to minimum wage increases (Rensi 2016). Several other chain restaurants are currently contemplating replacing minimum wage workers with robots, explicitly to deal with rising labor costs (Taylor 2018). A reduction in employee strength might lead to lower service quality. For example, with fewer employees, it might take more time for an order to be transported from the kitchen to the dining area. A kiosk, or other substitutes, might not be perceived to be as courteous and responsive as a waiter. This line of logic suggests a negative effect of minimum wage increases on service quality. On the other hand, greater wages might lead to greater motivation of employees, and might also increase the restaurant’s ability to hire more productive or experienced workers. It has been found in at least one industry study that when employers transitioned to paying a living wage to its employees, they experienced “significantly lower rates of staff turnover, reputational benefits, reduction in sick leave, better motivated staff and an increase in productivity.” A majority of employers noticed better quality of work, and employees agreed that their work had improved after the wage increase. This suggests a positive impact on service quality. Therefore, the direction of impact on service quality is an empirical question.We do not expect the impact of wage increases on service quality to be the same across all restaurants. An important aspect of the restaurant industry is the difference in ownership structures of chain and independent restaurants, and resulting differences on incentives and managerial abilities (Brickely and Dark 1987, Lafontaine 1992, Lafontaine and Shaw 2005, and Shepard 1993). Our second research question is whether the impact of minimum wage increase on consumer opinions of service quality, differs across chain and independent restaurants. Chain restaurants have two forms of ownership - they are either corporate-owned or owned by a franchisee. Independent restaurants are more likely to be owned and/or supervised by the founder. Per agency theory, corporate-owned chain outlets have the least incentive and ability to monitor service outcomes since the store manager is a salaried employee (who herself might also face incentive issues). Franchisee outlets keep residual profits, and therefore are more motivated to improve service outcomes. They also have greater ability to observe employees if they are locally based. However, to enable ease of franchising and to maintain brand quality, many national chains are built on process-driven cultures with little room for local variance in quality. In contrast, industry reports suggest independents’ success is affected by providing a better customer experience via service and differentiation (e.g. varied menu) than the more bland but reliable national chains. Independents have greater ability to monitor employees (on visible attributes of service) compared to chains. They also have a higher incentive to monitor for higher service quality provision as a requirement for higher-paid workers, especially if accompanied by higher pass-through prices. Given their smaller size and differentiated presence, pricing changes might be easier implement with higher wages. Based on all these insights, we distinguish between chain and independents in our analysis of minimum wage effects on perceived service quality. We expect greater effects on service quality of independents. These questions have important implications for several stakeholders including marketers, consumers and policy makers. A positive effect on service quality would provide an additional rationale for increasing minimum wages - one that is unrelated to employment or living standards of employees, but related to consumer satisfaction and welfare. On the other hand, a negative effect would provide robust evidence consistent with the notion that minimum wage employees are being replaced with less service-oriented substitutes. A positive effect of increased wages on service quality should give pause to managers considering replacing minimum wage employees, and provide a rationale based on business outcomes, for lowly paid employees to demand wage increases. Finally, in the face of some evidence that minimum wage increases could lead to price increases (Allegretto and Reich 2018), a positive effect on service quality could incentivize consumers to remain loyal to restaurants affected by the legislation, despite having to pay greater prices. We find that consumers discuss topics associated with delays, slowness and unfriendliness to a lower extent after the wage increase in San Jose restaurants where workers had higher minimum wages. This change in topic discussion is consistent with improved service quality. Consistent with agency theory discussed above, this service quality improvement is restricted to independent restaurants, and does not extend to chain restaurants in that city. Next, we discuss the relevant streams of literature and how our work is positioned relative to each stream. Section 3 presents the textual data. In section 4, we discuss the deep learning model, the difference-in-difference specification, and specific estimation challenges. Section 5 presents the results from the model and their implications. Section 6 concludes. 2. Literature ReviewOur work is related to the literatures on the impact of minimum wages, customer satisfaction, text mining and service quality. The impact of minimum wage increases on employment is a politically fraught and an economically complex question. We discuss a small set of papers to highlight the key issues. There are several theory models (e.g., Aaronson and French, 2007) on the impact of minimum wage increase on employment and total earnings of employed workers. These models show the employment effect varies as a function of labor and product market competition, factor substitutability, and other factors. There is a large stream of empirical work in this area with conflicting findings, and a fierce debate on reasons for these conflicting findings. Especially, there are debates on the appropriate control group when estimating the effect of the “treatment” of minimum wage increases, and controlling for heterogeneity if pooling units subjected to the regulation, across various geographies. As mentioned, several studies have analyzed the restaurant industry with its heavy reliance on minimum-wage workers. Card and Krueger (1994) find that a minimum wage increase leads to higher employment in limited service fast-food chains. Dube et al. (2010) examine both limited-service and full-service restaurants (with differing fraction of minimum wage workers and price elasticity of product demand). They also find positive employment and earnings effects. On the other hand, several researchers find negative employment effects. Among these are Neumark and Wascher (2000) and Aaronson et. al. (2008), who look at limited-service restaurants. The employment effect of minimum wages is not the focus of our study. However, we draw on this literature for its distinction between types of restaurants and the importance of finding the right control group to infer causality.Our work is more closely related to two other papers on the impact of minimum wage increases on marketing outcomes. Allegretto and Reich (2018) estimate whether minimum wage increases are passed on to consumers via increased price. They invoke the estimate of Orkent and Alston (2012) that demand for restaurant products is relatively price inelastic (-0.71). Therefore, restaurants faced with forced increases of minimum wages might prefer to not reduce employment, but instead increase prices by a small amount. The alternative of reducing employment might lead to greater reduction of profits. Using internet-based menus of restaurants tracked before and after wages increases, they find that prices increased as a result f minimum wage increases. This result holds even in border areas between higher and lower minimum wage cities, where competition might have been expected to bid away price differentials. They find that the price increase magnitude is similar to the previous mark-ups. This implies no negative employment effects. They also do not find any competitiveness effects, based on their analysis of border areas. A recent working paper on the impact of minimum wage increases on a marketing related outcome is Chakrabarty et al. (2017). They examine whether there is a change in restaurant hygiene (using hygiene violations data) due to minimum wage increase. Their hypothesis is that higher labor costs lead to employment cuts and fewer workers, and/or cost-cutting in other ways. They find an increase in less severe hygiene violations, but not in the most severe violations. They conclude that consumers face less hygienic conditions when minimum wages increase. Unlike Allegretto and Reich (2018) and Chakrabarty et al. (2017), we do not estimate price pass-through or health violations. Our interest is in consumer perceptions, and for that we utilize data from thousands of consumer reviews. To the extent that service perceptions might be confounded by price perceptions, we jointly estimate a price topic with service topics in our text analysis. Our difference-in-difference regression of service topics are robust to the inclusion of price topic as a control variable. We view our analysis as being broader and more relevant to marketing research with its focus on service attributes. To the best of our knowledge, ours is the first paper to study the effect of any wage change by a firm on any kind of consumer behavior or experience. The closest stream of research in marketing to our work, is on the relationship between firm routines and customer satisfaction. Anderson et al. (1997) discuss whether customer satisfaction and firm costs and productivity are positively or negatively correlated. It is possible that improving customer satisfaction reduces costs such as those of returns, new customer acquisition, etc. Alternatively, improving customer satisfaction might increase costs e.g. of customer support, IT etc. They find that for services in particular, customer satisfaction comes with increased costs. Note that for our context, an increase in minimum wages will increase per worker cost. The impact on total cost is unclear since firms might reduce employment, so whether minimum wage increase leads to greater or lower service quality cannot be predicted based on this stream of research. In fact, even if total labor costs increase, higher costs are not a sufficient condition for higher customer satisfaction. So the direction of the effect of minimum wage increases is an open empirical question.In related work, researchers have examined the link between employee satisfaction (especially front-line employees) and business outcomes, including customer satisfaction (e.g., Schneider et. al. 1998, Kamakura et. al. 2002, Simon et. al. 2009). The predominant conclusion is a positive correlation between employee and customer satisfaction. Zablah et al. (2016) find that while attention has been focused on how satisfied front-line employees can improve customer satisfaction, there might be larger effects from satisfied customers to satisfied front-line employees. In our empirical context, it is possible that increases in wages are more than offset by increased work demands, resulting in lower employee satisfaction. It is also possible that higher wages result in hiring of more motivated current or new employees (e.g. via the efficiency wage hypothesis in labor economics, Shapiro and Stiglitz 1984). This might result in more satisfied workers. Therefore, the link between minimum wage increase and customer satisfaction (or customer perceptions of quality) is perhaps more complex than the relationship between employee satisfaction and customer satisfaction. We differ from these papers as follows- first, we are able to extract via text analysis multiple dimensions of customer perceptions of service quality, while controlling for price perceptions. Second, our natural experiment allows us to infer causal effects on customer perceptions of service quality; we do not have any confounds of reverse causality effect of customer perceptions on higher performance of front-line employees.Another stream of directly relevant research is text mining methods in marketing. Our paper is related to the literature on extracting useful information from large masses of text of reviews (Decker and Trusov 2010, Archak et. al. 2011, Ghose et al. 2012). Lee and Bradlow (2011) automate extraction of phrases from consumer reviews, which are then rendered into word vectors which record the frequencies with which words appear in the corresponding phrase. Phrases are clustered together according to their similarity, measured as the distance between the word vectors. Importantly, they demonstrate the validity of text-mined measures of product attributes, in comparison with more traditional measures. Netzer et al. (2012) use a similar approach, with the difference that they define similarity between products based on their co-mention in the data. Tirunillai and Tellis (2014) and Puranam et. al. (2017) apply the Latent Dirichlet Allocation (LDA) model on consumer reviews to infer the latent dimensions of product quality. Büschken and Allenby (2016) propose an LDA model that makes use of the sentence structure of reviews, leading to improved prediction of consumer ratings. In this paper, we propose an alternative method for extracting topics from text (see section 4 for details). Unlike existing methods in marketing, our Word2Vec model (Mikolov et al, 2013) of deep learning is well suited for identifying rare topics of interest. Our objective is not to summarize the text corpus into key attributes, but instead to hone in on a specific multidimensional construct of interest: service quality. Other approaches using topic modeling yield qualitatively poorer construct representations in our context (see section 4 for details).Finally, as discussed earlier, we rely on the literature in marketing on service quality to identify dimensions of service quality to be investigated. 3. DataOur primary source of data is Yelp, a California-based multinational corporation which publishes online reviews of local businesses, and is commonly used by consumers in the United States to post and view reviews of restaurants. Per , an independent research firm, was among the top 70 most popular websites in the United States in July 2014, the time period of the data. By the end of 2017, the website had well over 100 million reviews. For the purpose of estimating word vectors using deep learning methods, we utilize data on all restaurant reviews posted on this website from the time period January 1st, 2008 to April 3rd, 2017, for all cities in CSC. This dataset has 533,022 reviews. It covers 962 chain restaurants (those restaurants with at least two outlets in California), with 160,866 reviews from 76,774 reviewers with an average review length of 56.19 words and an average rating of 3.40 (out of 5). In addition, the data includes 1,908 independent restaurants, with 372,156 reviews, from 136,110 reviewers with an average review length of 56.94 words and an average rating of 3.65 (out of 5).Our substantive interest is in estimating the effect of a wage increase in May 2013. As mentioned, for this purpose, we estimate a difference-in-difference model on all reviews of restaurants located in CSC in our dataset which were posted in a 9-month period before, and 9-month period after the date of the wage increase. Our results are robust to shorter and longer time windows. Very long time windows increase the likelihood that differences in time varying unobservables across the treatment and control units might affect our results. Also, the effect of wage increase on service quality, and its reflection in consumer reviews, might take a few months. So very short time windows might not be sufficient for our purpose. We now discuss the appropriateness of using the other 7 cities of CSC as control units. As mentioned, these are geographically close to San Jose, and are mostly urban, much like San Jose. More importantly, wages across San Jose and other cities are similar. For the year 2013, weekly wages were $361 in San Jose, and $394 in other cities (Allegretto and Reich 2018). Weekly wages in San Jose and other cities for limited service restaurants averaged $312 and $319 respectively. Weekly wages in San Jose and other cities for full service restaurants averaged $400 and $435 respectively. Furthermore, both areas experienced similar unemployment trends (see Allegretto and Reich 2018 for a more detailed comparison). We now describe reviews of restaurants in San Jose. Means of ratings (on a 5-point scale) decreased slightly from 3.60 before the wage increase to 3.57 after. However, since ratings are a composite metric capturing several constructs, not just service quality, this trend is not particularly insightful for our research context. The website allows readers of a review to indicate whether it is “useful”, “funny” and “cool”. On average, a review posted before the wage increase received 1.15 mentions of being “useful”, 0.58 mentions of being “funny” and 0.57 mentions of being “cool”. The means (per review) of these mentions for reviews posted after the wage increase are 1.07, 0.50 and 0.51 for mentions of “useful”, “funny” and “cool”. Similar to ratings, it is unclear if these metrics capture service quality in any discernible way, but do serve as control variables in our model. Detailed summary statistics appear in Table 2. ====Insert Table 2 Here====Next we discuss the frequency of occurrence of seed words used to model the five dimensions of service quality (Table 3). As model free evidence of the positive effect of the wage increase, occurrence frequency (per review) of the words “knowledge” and “attention” increased by at least 5% after the wage increase; and occurrence frequency of the words “wait”, “slow” and “impolite” decreased by at least 5%. However, summary statistics do not paint an entirely positive picture. Occurrence frequencies of the words “dirty” and “delay” increased after the wage increase, and those of the words “clean” and “fast” decreased. This indicates that service quality might have been negatively impacted for at least some dimensions. Differences across chain and independent restaurants in the occurrence frequencies of seed words provide interesting model free insights. While the occurrence of the word “mistake” decreased by 11% after the wage increase among independents, it increased by 9% among chain restaurants. Occurrence of the word “impolite” decreased by 56% among independents, but only 13% among chains, suggesting stronger effects of wage increases among independents. However, these metrics ignore other words which are semantically similar to the seed words, do not account for CSC-specific time varying unobservables (for which we employ reviews from other CSC cities as controls), do not control for restaurant characteristics and might be confounded with changing perceptions of price.====Insert Table 3 Here====Summary statistics of reviews for restaurants in CSC cities other than San Jose appear in Tables 4 and 5. Means of restaurant ratings, review lengths, and indication of whether reviews are “useful”, “funny” and “cool” are similar across the treatment and control group, both before and after the wage increase. Changes in the frequency of occurrence of some seed words such as “clean” and “menu” follow similar trends for San Jose and for other cities. On the other hand, while the frequency of occurrence of “dirty” increased after the wage increase for San Jose, this frequency decreased for other CSC restaurants. ====Insert Tables 4 and 5 Here====4. ModelWe describe first our text analysis model and then the differences-in-differences regression model that employs output from text analysis as the dependent variable.4.1. Deep Learning Text AnalysisWe first discuss the motivation for employing a deep learning model. Service quality is known to be a multi-dimensional construct. Based on the service quality literature, we are interested in 5 dimensions of service quality, which are captured by 17 seed words (these appear in Table 1). Each seed word describes a sub-dimension or topic of substantive interest. The objective of our text analysis is to obtain robust quantitative estimates of the extent to which each review in our data, represents each of the 17 topics of interest. As mentioned earlier, our main motivation for employing deep learning methods is that descriptors of service quality are not discussed very frequently in our data. This is typical for online reviews. Puranam et al. (2017) find that as many as 200 topics are discussed in a set of restaurant reviews from New York City, with the most discussed topic accounting for less than 5% of the overall corpus. In our dataset, several topics such as “clean”, and its antonym “unclean”, are rarely discussed. Typical topic models used in marketing are not very well suited to investigate the occurrence of rare topics of interest. To demonstrate this, we estimated two topic models from the marketing literature: Latent Dirichlet Allocation and Non Negative Matrix Factorization. Words occurring with high probability in major topics estimated from each of these models, are presented in Appendixes 1 and 2. We find that none of the estimated topics cleanly captures service quality. Discussion of service quality, as estimated from these models, is spread across several topics, and is absent in other topics. This highlights the need for an alternate modelling approach. Our modelling approach has two major steps. In the first step, we adopt a principled data-based method to define the 17 topics of interest. In the next step, we estimate the distance of each review from each topic of interest, such that the model output (and the input for the differences-in-differences analysis) is a set of 17 parameters for each review, capturing the extent to which each review represents each topic of service quality. One simple way forward for the first step could to assume that the seed word is an adequate measure of the topic of interest. In the second step, we could then estimate the distance of each review from each of the 17 seed words. This has the obvious drawback that subjectivity in the researcher’s choice of seed words could affect the results. Our model deals with this issue by automatically identifying words which are semantically similar to a seed word. For example, for the seed word “fast”, the model could potentially identify the following semantically similar words or phrases: quick, speedy, relatively fast, fairly quick, etc. The “topic” of interest is then defined not just by one seed word, but by a set of semantically close words, estimated from the data. To the extent that the words comprising a topic are semantically similar, adding or dropping a few words from this set of words which define a topic, does not affect results, thus ensuring robustness. We also estimate a “price” topic using “price” as a seed word. Note that seed “price” is general enough to allow semantic similarity to words like cheap, expensive, and cost (of the meal; not of production). We focus the rest of the discussion on measurement of service topics since these are the main focus on our paper. Expectedly, similar logic and steps follow for estimation of the price topic as for any of the service topics. For the purpose of estimating topics, the Word2Vec model first learns a d-dimension latent vector representation for each word in the vocabulary V, i.e. the set of all unique |V| words across all reviews in the dataset. These vector representations are referred to as “embeddings”. Semantic similarity between two words is then measured in terms of the cosine distance between their respective embeddings. We expect that the semantic relationships between words are domain specific, consequently we estimate the word vectors in our dataset, instead of employing word lists or dictionaries.From an estimation perspective, the central idea is to set up a prediction task for each focal word, taking into account the immediate neighboring words in a “context window”. For example, in the sentence “The quick brown fox jumped over the lazy dog”, suppose the word “fox” is the word of focal interest. A context window of 3 would involve the 3 words appearing immediately before, and the 3 words appearing immediately after the focal word. These are {the, quick, brown, jumped, over, the}. These context words are also termed outside words. Starting from the first word in the vocabulary, every word is sequentially treated as the focal word. The prediction task can be set up in at least two distinct ways. The first is to predict the focal word, given the context window. The second, is to predict the words in the context window, given a focal word. The former is referred to as the Continuous Bag of Words model and the latter is referred to as the skip-gram model. The latter specification has been found to perform better on semantic accuracy (Mikolov et al. 2013a) and on estimating representations for rare words. Consequently, we focus on the skip-gram model, and now present the formal model specification.The prediction task is as follows - given the focal word “jumped” in the example sentence above, predict all words in a window of m = 3 words – i.e. quick, brown, fox, over, the, and lazy. In the skip-gram model each focal word w is associated with two vectors – vw and uw . These are the “input” and “output” vector representations of the word w respectively. The outside words (words in the context window other than the focal word) in the context window of size 2m are indexed as o. uo is the embedding of the other word in the context window. In the above example, suppose the focal word w is fox. Then the word fox has two representations – as an input vector and as an output vector. One example of a context word is quick, consequently uo is the vector associated with quick. Another context word is over.A corpus is a concatenation of all documents in the dataset; in our case, the corpus comprises all reviews. The length of the corpus is T and each position in the corpus is indexed by t. The objective function is to maximize the average log probability of observing the words given context, across all T positions in the corpus.Jθ=1T t=1T-m≤j≤m,j ≠ 0 logpwt+jwt (1)The probability for an outside word o i.e. wt+j =o in a context for a focal word f (i.e. wt =f), located at position t is evaluated as a logit function of the dot product between the two embeddings:p(o|f)=exp(uo'vf)w=1|V|exp(uw'vf)(2)The model has a total of 2d|V| parameters. This is the dimension of the embedding times the two vectors estimated for each word. And, while it yields a simple analytical form for maximization, the summation in the denominator (over all the words in the vocabulary) is computationally problematic. For example, we have approximately 34.3 million positions in the corpus, and a vocabulary of 11,121 words.An alternative model specification incorporates the idea that the model parameters from (1) should be able to help distinguish the observed focal word (f) and context word (o) combinations in the data from those combinations generated by a noise distribution. This approach is called noise contrastive evidence (Gutmann and Hyvarinen 2012). For each focal word observed in the data, k context words (o) are drawn from a noise distribution (q(o)) and labeled as 0 (i.e. as coming from noise). This set of k context words are referred to as “negative samples” as these words are not necessarily observed in the context window of the focal word. All the f and o combinations observed in the data are labeled as 1. The estimation task is to predict data from noise. The probability that a particular f and o combination is drawn from noise is: (Label=0|f,o)=k×q(o)expuo'vf+k×q(o) (3)Similarly, the probability that a particular observation is observed from data is given by:(Label=1|f,o)=expuo'vfexpuo'vf+k×q(o) (4)The reformulated log-likelihood is:θ=f,o?Datalogplabel=1f,o +k Eo'~qo log?(1-pLabel=0f,o')(5)This objective function maximizes the probability of observing the data, while minimizing the probability of word combinations generated from noise. Note the second term still requires a sum over all |V|. This intensive computation is substituted with a Monte Carlo approximation.θ=f,o?Datalogplabel=1f,o +kf?Datao' ~q(o) 1k log pLabel=0f,o' (6)Mikolov et al. (2013), assume that the context words are drawn from a uniform distribution of size |V|. This leads to the following simplified objective function.θ=f,o?Dataσ(uo'vf) +f?Datao' ~q(o) σ(-uo''vf) (7)Here σ (.) is simply the logit transform. The first term in (7) is simply the normalized cosine distance between the two word vectors. The second term measures a similar distance between the focal word and words unlikely to be associated with the focal word. The model searches for a set of parameters (the vector representations of each word) that simultaneously accounts for the frequency with which all pairs of words co-occur. It has also been shown that, under certain assumptions, this approach is equivalent to matrix factorization. There are some parallels with multidimensional scaling (MDS) in that similarity measures are preserved under a d-dimensional representation. Unlike MDS, the similarity scores are not known, though co-occurrence patterns are known.The model is estimated using TensorFlow (an application for deep learning, see Abadi et al, 2016). It is a model development framework, similar to WinBugs used by Bayesian researchers. The estimation of the parameters is by maximizing likelihood. Given the large number of parameters, we make use of the “Adam” optimizer (an adaptive momentum optimizer, see Kingma and Ba 2014). The algorithm is a first-order gradient-based optimization scheme for stochastic objective functions. Although the number of parameters in our model are fewer than in the LDA model (and is independent of the size of the corpus), we do need to select a few hyperparameters, namely, the window size (m), the number of negative samples and the dimension for the embeddings (d). We select a maximal window size of 10, 10 negative samples and 100-dimension embeddings based on the perplexity of the model and the judged similarities of the word embeddings. Our results are robust to the choice of estimation choices such as using a context window of size 5 and using embeddings of sizes 50, 100 or 150. Details are available and are not presented for brevity. Finally we discuss our choice of seed words. Table 1 lists the seed words selected for each service dimension. In some instances where more than one possible obvious candidate was available, multiple seeds were used. For example we have selected both wait and delay as seed words. In theory, we can also explicitly chose the valence of each attribute and measure both positive and negative valence for each attribute. For example, we include both fast and slow in the seed words for reliability. However, incorporating valence in graphical models and factorization methods is uncommon, complex and requires additional assumptions. The model described above yields a list of semantically proximate words for each of 17 service seed words (and one for price, included for its potential role as control variable to separate service quality from price perceptions). This list of words defines the topic associated with the seed word. As mentioned earlier, in the next step, we measure the average distance of each review from the seed word and use that as our measure of interest. For each word of a review, we measure the mean cosine distance of that word from each word of the topic (the seed word and 19 semantically similar words). The mean of these distances (across the 20 words of the topic), gives us the distance of that word from the topic. We then compute the mean (across words of the review) of this mean distance, to obtain the distance of the review from the topic. We repeat this for each of 17 topics. As a result, for each review we obtain 17 measures, representing the distance of the review from the 17 topics of interest. These serve as the dependent variable for the next stage of analysis. We also calculate similar measures of price perceptions, and run the causal difference-in-difference analysis below including these price perceptions as control variables. Note this is over and above factoring out any price perceptions confound in the service perception measurement, as described in this section. Since the difference-and-difference results are similar in direction and magnitude with and without the inclusion of the price topic as a control variable, in the discussion below we discuss results without the inclusion of price topic measure. We have also examined if there is any impact of wage increase on price topic as a dependent variable. We do not find any systematic changes in price discussion; this is possibly consistent with the small 1.45% price changes found post-policy by Allegretto and Reich (2018), and the relatively price inelastic nature of restaurant demand in the United States (Okrent and Alston 2012). 4.2: Difference-in-Differences Regression ModelIn an attempt to identify the causal effect of the wage increase on the distances of reviews from 17 topics of service quality, we implement a “difference-in-differences” methodology. We calculate the causal effect of a treatment (i.e. the wage increase) on the outcome variable (distance from a topic of service quality) by comparing the average change in the outcome variable for the treatment group (CSC restaurants in San Jose) to the average change for the control group (CSC restaurants outside San Jose). We regress the outcome variable on two main effects (the effect of belonging to the treatment group on the outcome, and the effect of the treatment on the outcome), the interaction of these two effects, and several control variables, as follows:PerQualir =Consti+ β1SanJoser+β2Postr+β3SanJoser×SanJoser+β4Ratingr+ β5Useful.Countr + β9Cool.Countr + β9Funny.Countr + Restaurant Dummies +β10review_lengthr+?i+?r (8)The subscripts i indexes the reviewer and r the reviews. The dependent variable PerQualir represents the cosine distance of review r from one of the 17 topics of service quality. For each review, there are 17 measures, 1 for each topic, leading to 17 equations. SanJoser is the dummy variable which accounts for the effect of belonging to the treatment group. It controls for unobserved factors which might affect perceived service quality measures of San Jose restaurants and other CSC restaurants differently. Postr is the dummy variable which accounts for the effect of the treatment. It is possible that at the time of the implementation of the wage increase, there were unobserved events which affected service quality perceptions of all restaurants in CSC. The main effect of Postr controls for how service quality perceptions for all restaurants changed after the wage increase. The coefficient of interest is β3, the coefficient on the interaction of SanJoser and Postr. As controls, we include the review-specific counts of the words “cool”, “funny” and “useful”, and the length of the review. It is possible that longer reviews, cooler reviews, funnier reviews and more useful discuss service quality to a greater or lower extent. Restaurant specific fixed effects, and reviewer specific intercepts, account for restaurant specific unobservables and unobserved reviewer characteristics respectively. Error terms are assumed IID and normally distributed. All parameters are topic-specific. The identifying assumption is that after controlling for a) all observed review characteristics, b) unobserved reviewer level characteristics (e.g., some reviewers might write more about service quality than others), c) unobserved differences across San Jose and other cities in the same county, and d) unobserved differences in service quality between the time period before and after the wage hike, which affect all restaurants in the county; any differences in changes in our measures of service quality across San Jose and other cities, are due to the wage increase. 5. ResultsFirst we present the top 10 closest words to each of the 17 seed words for service and for price, estimated from the deep learning model (Table 6). Several of the closest words are antonyms of the associated seed words (e.g. fast and speedy), which provides face validity to our text model. Other close words are specific to our research context of service in restaurants, which validates our decision to not use dictionaries for defining topics. For example, the words “bathroom” and “bathrooms” are estimated to be close to the seed word “clean”, and the words “refill” and “wave” are estimated to be close to the seed word “attention.” Although these are not antonyms, bathrooms are likely more salient for the cleanliness of restaurants, and waving is a common way to attract the attention of the waiter. ====Insert Table 6 Here====We now present parameter estimates from the difference in difference model (Equation 8), which aims to estimate the causal effect of the wage increase on text based measures of each of 17 dimensions of perceived service quality. Our first question is whether minimum wage increase leads to an increase or decrease in consumer opinions of service quality. We find that perceived service quality of San Jose restaurants changes along three dimensions, as compared to control units (Table 7). The coefficients of the interaction of SanJoser and Postr are negative for the topics delay, slow and unfriendly. Reviews of restaurants in San Jose showed a larger negative change in the representation of these topics after the wage increase, than reviews of restaurants in other cities of CSC did. This is of course, welcome news for consumers and restaurants. Delay and slowness are known to measure the reliability of service quality; our results provide unique evidence that greater wages lead to more reliable service quality. These results are consistent with the notion that greater wages motive employees to serve better, or that they increase the restaurant’s ability to hire more productive or experienced workers. ====Insert Table 7 Here====Interestingly, there is no significant change in the discussion of those topics of service quality which have positive connotations (such as clean, fast, courteous and friendly). One possible reason is that workers who provide poor service (e.g. those who are delayed, slow and unfriendly) are likely to be paid less (and minimum wages) than workers who provide good service (e.g. clean and fast workers). Since the wage increase does not depend on prior service quality, but only on existing wages, the regulation disproportionately raised the wages of workers who were providing poor service (and were paid less prior to wage increase). Workers providing good service might have been paid above minimum wage prior to the legislated change, and hence their wages are not affected by the legislation. Consequently, their (good) service levels might remain unaffected. It is also worth noting that the service dimensions affected by the wage increase are easily observable to consumers (e.g. fast service is easier to observe than worker knowledge) or can be more easily attributed to workers (e.g. courtesy can be more easily ascribed to a restaurant worker than the menu items of that restaurants). This suggests a potential qualifier for service effects of wage increases: wage increases lead to positive service perceptions of observable and easily attributable worker service quality. This nuanced insight is enabled by the multidimensional service quality measures. Other parameter estimates from this analysis show that ratings and counts of useful, cool and funny significantly predict the discussion of almost all service topics. Reviews scoring high on the topics clean, menu and prompt, are also associated with more positive ratings. Greater discussion impoliteness elicits more “useful” votes, as compared to the discussion of other topics. Greater discussion of promptness and slowness elicits more “cool” votes. Reviews of restaurants based on San Jose discuss two service dimensions less than other reviews: delay and knowledge. 5.1 Impact of Increased Wages on Independent Restaurants and Chain RestaurantsOur second research objective is to investigate whether the effect of wage increase varies across chain restaurants and independent restaurants. As discussed in section 1.1, agency theory suggests that the impact of wage increase of service quality should be greater in independent restaurants, which have greater incentive and ability to monitor service outcomes. To explore this, we estimated the difference in difference model separately, first on all reviews of independent restaurants (in San Jose and other cities) and then on all reviews of chain restaurants (in San Jose and other cities). Results from these analyses appear in Tables 8 and 9. ====Insert Tables 8 and 9 Here====Separate analysis of independent and chain restaurants provides several unique insights. The negative effects of wage increase we reported earlier on the discussions of three dimensions of service quality (delay, slow and unfriendly) are even stronger (i.e., more negative) for independent restaurants. Furthermore, we find negative effects of wage increase on three other dimensions of service quality. These are dirty, wait and impolite. In all, wage increase negatively affects six dimensions of service quality. Negative effects on six negatively valenced dimensions of service quality suggests that the impact of wage increase is quite positive. Consistent with earlier results, aspects of service quality that are not easily observable (e.g. knowledgeable) or are easily attributable to workers (e.g. menu) remain unaffected by wage increases. Estimation of the difference-in-differences model with data from chain restaurants does not suggest a causal effect of wage increase on any dimension of service. In other words, improvement in perceptions of service quality in San Jose restaurants after the wage increase, are driven by independent restaurants only. These results hold even after controlling for discussion of price in the regression specification. Comparison of results for the two types of restaurants provides empirical support to our theory that corporate-owned chain outlets have lower incentive and ability to monitor service outcomes, than independents. Although franchisee outlets among chain restaurants might be motivated to improve service outcomes, the imposition of process-driven service guidelines across most major US chains leaves relatively less scope for improvement than independent restaurants. It is also possible that independent restaurants are able to improve service outcomes more easily than chain restaurants due to their smaller size, and greater operational flexibility. Another possibility which explains the null effect for chains is that typical consumers of chain restaurants do not expect service improvements due to wage increases. If this is indeed true, and if workers in chain restaurants are aware of this, then they do not have incentives to improve service. Our data suggest that this is unlikely. The frequency of occurrence of the 17 seed words describing service quality in reviews posted before the wage increase, does not vary much across chains and independents (Tables 3 and 4). Different service expectations across restaurant types should have led to differences in the levels of discussion of service dimensions across chains and independents. Our objective is not to rule out all but one explanation; we expect several mechanisms might be driving this difference in the effect size across chain and independent restaurants. Our results suggest that agency theory based mechanisms are likely the larger driver of these effects; mechanisms based on differences in consumers’ service expectations appear to be less likely to cause our results. 6. Implications and ConclusionsWe first discuss the implications of our research for consumers. First, consumers who value timeliness, speed and friendliness will be well-served by increases in minimum wages since these service qualities improve in independent restaurants. Second, consumers who are sensitive to other aspects of service such as the extensiveness of the menu, the likelihood of mistakes by restaurants workers and the knowledge of restaurant personnel will probably not notice a change a service levels with increases in minimum wages. Given the small price increase associated with the wage increase documented in a previous study (Allegretto and Reich 2018), such consumers do not have much to lose by continuing to visit restaurants experiencing wage hikes. Third, consumers who choose independent restaurants over chain restaurants can expect greater improvements in service levels, not just in terms of reduced delay, slowness and unfriendliness, but also in terms of reduced dirtiness, waiting and impoliteness. Our results bring positive news to restaurants experiencing greater labor costs due to mandated wage increases, even more so for owners and managers of independent restaurants. The debate on the higher minimum wage legislation has focused solely on greater labor costs and prices, without considering the potential demand side upsides. Other than profit impact, our dimension-specific estimates of service quality are useful for owners and managers. Service perceptions are more likely to improve along dimensions that are easily observable to consumers, perhaps suggesting the need to manage other service dimensions. Given the limited impact of wage increase on service perceptions of chain restaurants, corporate managers and franchises of such outlets might view these results as a reason to defocus their attention from mandated wage increases.Policy makers might view these results as providing directionally positive evidence of minimum wage increases on consumer welfare. This research could serve as an impetus to further investigate the consumer welfare implications of labor cost increases in restaurants and other service industries. They might also note that not all effects of wage increases are positive. Our results are also of interest to researchers in marketing. Marketing applications of deep learning methods for text mining are at a nascent stage. As we show in our analysis, these methods can be useful and are easily implementable. Researchers in customer satisfaction, and agency theory might also be encouraged to use vast online ratings data and natural experiments for causal inference.We conclude with a discussion of areas where this work can be extended. First, we were unable to obtain robust data on employee satisfaction. Establishing a text-analysis multi-attribute based link between front-line employee satisfaction and perceived customer service is a logical next step, especially given the rich literature in this area. Second, we do not have data on differences in pre-policy wages in chains and independents. Nor do we have information on franchising within chains in our data location and time period. Extending data on these dimensions would allow us to test more fine-grained employee and owner incentive effects. Third, we introduce an unsupervised deep learning application to text analysis in the Marketing domain. 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Brown (2016), “A Cross-Lagged Test of the Association Between Customer Satisfaction and Employee Job Satisfaction in a Relational Context,” Journal of Applied Psychology, May, 101(5)Table 1: Dimensions of Service Quality, and Seed WordsDimensionsDescriptionsSeed WordsTangiblesComfort and cleanliness of the dining areaClean, dirtyAttractive and readable menuMenuReliabilitySincere interest in correcting anything that is wrongMistakeServe customers in the time promisedWait, delay, fast, slowServe customer’s food exactly as it was orderedOrderedResponsivenessProvide prompt and quick servicePrompt, seatedAssuranceConsistently courteous with customersCourteous, polite, impolite, friendly, unfriendlyHave the knowledge to answer customers’ questions such as menu items, their ingredients, and methods of preparationsKnowledgeEmpathyGive customers personal attentionAttentionTable 2: Summary Statistics of Reviews for San Jose Restaurants ?Before Wage Increase After Wage IncreaseTotal ?Indepen-dentChainAll Indepen-dentChainAll Number of reviews16,7877,70124,48819,0778,84427,92152,409Number of unique reviewers9,9885,18412,86011,7986,27215,40425,009Number of restaurants reviewed6984831,1817094861,1951,232Rating3.67 (1.23)3.45 (1.31)3.60 (1.26)3.66 (1.26)3.38 (1.36)3.57 (1.30)3.59 (1.28)Review length59.84 (47.51)61.05 (48.83)60.22 (47.93)56.55 (46.06)57.05 (45.90)56.71 (46.01)58.35 (46.95)Number of mentions: "Useful"1.182 (2.83)1.07 (2.14)1.15 (2.63)1.11 (2.34)0.96 (2.09)1.07 (2.26)1.10 (2.44)Number of mentions: "Funny"0.58 (2.31)0.58 (1.78)0.58 (2.16)0.50 (1.69)0.52 (1.65)0.51 (1.67)0.54 (1.92)Number of mentions: "Cool"0.59 (2.33)0.53 (1.60)0.57 (2.13)0.51 (1.73)0.48 (1.63)0.50 (1.70)0.53 (1.91)Table 3: Frequency of Occurrence of Seed Words in Reviews of San Jose Restaurants?Before Wage IncreaseAfter Wage IncreaseTotal?Independ.ChainAllIndepend.ChainAllOccurrence frequency (per review) for seed words for TANGIBLESClean0.0508 (0.2401)0.0551 (0.2585)0.0521 (0.2460)0.0453 (0.2311)0.0475 (0.2320)0.0460 (0.2314)0.0489 (0.2384)Dirty0.0116 (0.1225)0.0171 (0.1449)0.0133 (0.1300)0.0132 (0.1325)0.0185 (0.1609)0.0148 (0.1421)0.0142 (0.1366)Menu0.1608 (0.4769)0.1424 (0.4585)0.1551 (0.4713)0.1449 (0.4525)0.1244 (0.4142)0.1384 (0.4408)0.1462 (0.4554)Occurrence frequency (per review) for seed words for RELIABILITYWait0.1458 (0.4453)0.2274 (0.6123)0.1714 (0.5502)0.1352 (0.4381)0.2165 (0.5919)0.1609 (0.4935)0.1659 (0.4990)Delay0.0004 (0.0189)0.0005 (0.0279)0.0004 (0.0221)0.0007 (0.0280)0.0002 (0.0150)0.0005 (0.0247)0.0005 (0.0235)Fast0.0561 (0.2540)0.1160 (0.4064)0.0749 (0.3113)0.0536 (0.2462)0.0871 (0.3398)0.0642 (0.2797)0.0692 (0.2949)Slow0.0283 (0.1809)0.0306 (0.1875)0.0290 (0.1830)0.0258 (0.1752)0.0292 (0.1831)0.0268 (0.1777)0.0278 (0.1802)Ordered0.2718 (0.6371)0.2623 (0.6030)0.2688 (0.6266)0.2595 (0.6073)0.2398 (0.5745)0.2532 (0.5972)0.2605 (0.6111)Mistake0.0043 (0.0743)0.0062 (0.0881)0.0049 (0.0789)0.0038 (0.0730)0.0067 (0.0900)0.0048 (0.0788)0.0049 (0.0788)Occurrence frequency (per review) for seed word for RESPONSIVENESSPrompt0.0038 (0.0611)0.0038 (0.0613)0.0038 (0.0612)0.0035 (0.0592)0.0036 (0.0600)0.0035 (0.0594)0.0036 (0.0603)Occurrence frequency (per review) for seed words for ASSURANCECourteous0.0027 (0.0545)0.0029 (0.0545)0.0028 (0.0545)0.0025 (0.0511)0.0035 (0.0591)0.0028 (0.0537)0.0028 (0.0541)Polite0.0073 (0.0883)0.0054 (0.0753)0.0067 (0.0845)0.0063 (0.0797)0.0047 (0.0703)0.0058 (0.0768)0.0062 (0.0805)Impolite0.0001 (0.0109)0.0002 (0.0161)0.0001 (0.0127)5.2419 (0.0072)0.0002 (0.0150)0.0001 (0.0103)0.0001 (0.0115)Friendly0.1060 (0.3255)0.1023 (0.3156)0.1048 (0.3224)0.0972 (0.3103)0.0907 (0.3093)0.0952 (0.3100)0.0997 (0.3159)Unfriendly0.0015 (0.0499)0.0024 (0.0496)0.0018 (0.0498)0.0014 (0.0396)0.0023 (0.0486)0.0017 (0.0427)0.0017 (0.0461)Knowledge0.0010 (0.0327)0.0006 (0.0254)0.0009 (0.0306)0.0011 (0.0339)0.0010 (0.0318)0.0011 (0.0333)0.0010 (0.0320)Occurrence frequency (per review) for seed word for EMPATHYAttention0.0066 (0.0835)0.0075 (0.0964)0.0069 (0.0878)0.0093 (0.1017)0.0099 (0.1090)0.0095 (0.1040)0.0083 (0.0968)Table 4: Summary Statistics of Reviews for Restaurants in CSC Cities other than San Jose ?Before Wage Increase After Wage IncreaseTotal ?Indepen-dentChainAll Indepen-dentChainAll Number of reviews7,9912,71410,7059,1762,96212,13822,843Number of unique reviewers5,6062,2347,0076,6152,4538,14713,718Number of restaurants reviewed327182509332181513528Rating3.56 (1.25)3.40 (1.33)3.52 (1.27)3.55 (1.27)3.33 (1.38)3.50 (1.30)3.51 (1.29)Review length59.28 (48.13)58.36 (46.85)59.04 (47.80)57.75 (47.84)54.30 (41.98)56.91 (46.50)57.91 (47.12)Number of mentions: "Useful"1.15 (2.41)1.00 (2.25)1.11 (2.37)1.18 (2.58)0.87 (1.87)1.11 (2.43)1.11 (2.40)Number of mentions: "Funny"0.55 (1.79)0.52 (1.80)0.54 (1.79)0.51 (1.80)0.44 (1.57)0.50 (1.75)0.52 (1.77)Number of mentions: "Cool"0.53 (1.71)0.47 (1.64)0.52 (1.70)0.49 (1.85)0.38 (1.44)0.46 (1.76)0.49 (1.73)Table 5: Frequency of Occurrence of Seed Words in Reviews of CSC Restaurants not in San Jose?Before Wage IncreaseAfter Wage IncreaseTotal?Independ.ChainAllIndepend.ChainAllOccurrence frequency (per review) for seed words for TANGIBLESClean0.0561 (0.2480)0.0459 (0.2374)0.0535 (0.2454)0.0501 (0.2327)0.0554 (0.2512)0.0514 (0.2374)0.0524 (0.2412)Dirty0.0116 (0.1304)0.0165 (0.1513)0.0128 (0.1360)0.0086 (0.1096)0.0221 (0.1725)0.0119 (0.1280)0.0123 (0.1318)Menu0.1660 (0.4773)0.1464 (0.4579)0.1610 (0.4725)0.1455 (0.4581)0.1491 (0.4758)0.1464 (0.4625)0.1533 (0.4672)Occurrence frequency (per review) for seed words for RELIABILITYWait0.1357 (0.4274)0.1791 (0.4905)0.1467 (0.4446)0.1434 (0.4398)0.1592 (0.4522)0.1473 (0.4429)0.1470 (0.4437)Delay0.0002 (0.0158)0.0007 (0.0271)0.0003 (0.0193)0.0006 (0.0255)0.0010 (0.0317)0.0007 (0.0272)0.0005 (0.0238)Fast0.0593 (0.2570)0.0732 (0.2962)0.0628 (0.2675)0.0555 (0.2460)0.0759 (0.3211)0.0605 (0.2665)0.0616 (0.2670)Slow0.0285 (0.1876)0.0412 (0.2511)0.0317 (0.2057)0.0300 (0.1907)0.0440 (0.2298)0.0334 (0.2010)0.0326 (0.2032)Ordered0.2781 (0.6259)0.2792 (0.6284)0.2784 (0.6265)0.2756 (0.6117)0.2473 (0.5946)0.2686 (0.6077)0.2732 (0.6166)Mistake0.0040 (0.0688)0.0069 (0.0876)0.0047 (0.0740)0.0041 (0.0807)0.0036 (0.0606)0.0040 (0.0763)0.0043 (0.0752)Occurrence frequency (per review) for seed word for RESPONSIVENESSPrompt0.0065 (0.0804)0.0055 (0.0740)0.0062 (0.0788)0.0052 (0.0721)0.0043 (0.0659)0.0050 (0.0706)0.0055 (0.0746)Occurrence frequency (per review) for seed words for ASSURANCECourteous0.0043 (0.0660)0.0040 (0.0634)0.0042 (0.0654)0.0034 (0.0589)0.0013 (0.0366)0.0029 (0.0543)0.0035 (0.0597)Polite0.0061 (0.0796)0.0073 (0.0854)0.0064 (0.0811)0.0088 (0.0980)0.0087 (0.0930)0.0088 (0.0968)0.0076 (0.0898)Impolite0.0003 (0.0193)0.000 (0.000)0.0002 (0.0167)0.0003 (0.0233)0.0003 (0.0183)0.0003 (0.0222)0.0003 (0.0198)Friendly0.1077 (0.3304)0.0960 (0.3093)0.1047 (0.3252)0.1016 (0.3187)0.0924 (0.2999)0.0994 (0.3142)0.1019 (0.3194)Unfriendly0.0007 (0.0316)0.0022 (0.0469)0.0011 (0.0361)0.0013 (0.0361)0.0030 (0.0607)0.0017 (0.0434)0.0014 (0.0402)Knowledge0.0008 (0.0335)0.0018 (0.0428)0.0011 (0.0361)0.0011 (0.0346)0.0010 (0.0317)0.0011 (0.0339)0.0011 (0.0349)Occurrence frequency (per review) for seed word for EMPATHYAttention0.0075 (0.0946)0.0095 (0.0973)0.0080 (0.0953)0.0069 (0.0870)0.0090 (0.0983)0.0074 (0.0899)0.0077 (0.0925)Table 6: Top Words (or Phrases) Semantically Proximate to Seed WordsService Quality DimensionsSeed WordsTop 10 Closest Words ( in terms of cosine distance)TangiblesCleanwell_maintained spotless nicely_decorated bathroom_clean clean_modern clean_bathrooms bathrooms_clean tidy clean_bright clean_spaciousDirtyfilthy unsanitary tables_sticky wiped grimy unclean dirty_floors wipe dirty_sticky sanitaryMenuitems menus extensive_menu listed menu_extensive descriptions extensive specials item selectionsReliabilityMistakemade_mistake error wrong screwed corrected messed argued correct incorrectly insistedWaitwaiting waits minute_wait long waited busy hour min ended_waiting arriveDelaydelayed delays arrive ended_waiting apologetic apologies backed takes_longer unacceptable receivingFastquick speedy fast_efficient efficient fairly_quick relatively_fast inexpensive quickly quick_efficient relatively_quickSlowextremely_slow understaffed spotty slower inattentive incredibly_slow busy staffed forgetful disorganizedOrderedorders ordering ordered ready pick ask placing_order point correct placed_orderResponsivenessPrompttimely attentive speedy courteous friendly_prompt prompt_friendly friendly_attentive efficient fast_efficient prompt_courteousAssuranceCourteouspolite friendly prompt attentive helpful friendly_helpful friendly_efficient friendly_courteous cheerful efficientPolitecourteous friendly helpful cheerful attentive respectful cordial smiling pleasant welcomingImpoliteacted rude extremely_rude unfriendly rude_unfriendly unwelcoming condescending rudeness ignored_us ignoredFriendlyfriendly_helpful courteous polite welcoming cheerful nice friendly_attentive friendly_welcoming helpful friendly_efficientUnfriendlyrude extremely_rude grumpy unwelcoming rude_unfriendly indifferent inattentive surly attitude unhappyKnowledgeknowledgeable knowledgable suggestions informative recommendations educated genuine asked_questions ben answer_questionsEmpathyAttentionflag someone_attention water_refill ignored flag_someone wave_someone refills_water flag_server refill_water wavePricePriceprices pricing expensive pricey quality value bit_pricey priced cost little_priceyTable 7: Results of Difference in Difference Analysis (All Restaurants)Service topicPost X SanJoseSanJosePostRatingUsefulCountCoolCountFunnyCountRev. LengthClean-0.024 (0.052)1.012 (0.962)-0.040 (0.044)0.804 (0.011)-0.049 (0.010)0.043 (0.016)-0.092 (0.014)-0.021 (0.000)Dirty-0.054 (0.036)-0.559 (0.667)0.049 (0.031)-0.738 (0.007)-0.023 (0.007)-0.033 (0.011)0.028 (0.010)0.000 (0.000)Menu-0.017 (0.042)-0.842 (0.774)-0.018 (0.036)0.519 (0.009)-0.039 (0.008)0.076 (0.013)-0.126 (0.011)-0.007 (0.000)Wait-0.104 (0.052)-1.721 (0.973)0.142 (0.045)0.098 (0.011)-0.028 (0.010)0.031 (0.017)-0.116 (0.014)-0.008 (0.000)Delay-0.092 (0.043)-1.639 (0.800)0.172 (0.037)-0.780 (0.009)0.024 (0.008)0.038 (0.014)-0.132 (0.012)0.011 (0.000)Fast-0.053 (0.054)0.040 (0.993)0.004 (0.046)0.708 (0.011)-0.045 (0.010)0.036 (0.017)-0.086 (0.014)-0.023 (0.000)Slow-0.129 (0.047)-1.289 (0.875)0.151 (0.040)-0.424 (0.010)-0.069 (0.009)0.092 (0.015)-0.128 (0.013)-0.010 (0.000)Ordered-0.047 (0.058)-1.655 (1.072)0.075 (0.049)-0.129 (0.012)-0.066 (0.011)0.079 (0.018)-0.119 (0.015)0.000 (0.000)Mistake-0.063 (0.044)-1.226 (0.812)0.097 (0.037)-1.166 (0.009)0.026 (0.009)-0.046 (0.014)-0.004 (0.012)0.014 (0.000)Prompt-0.031 (0.052)-0.564 (0.969)0.072 (0.044)0.841 (0.011)-0.037 (0.010)0.111 (0.017)-0.197 (0.014)-0.018 (0.000)Courteous-0.028 (0.051)-0.689 (0.956)0.085 (0.044)0.881 (0.010)0.018 (0.010)0.010 (0.016)-0.132 (0.014)-0.018 (0.000)Polite-0.054 (0.050)-0.669 (0.940)0.126 (0.043)0.443 (0.010)0.019 (0.010)-0.040 (0.016)-0.074 (0.013)-0.013 (0.000)Impolite-0.061 (0.034)-0.880 (0.619)0.120 (0.028)-0.847 (0.007)0.046 (0.007)-0.094 (0.011)0.036 (0.009)0.002 (0.000)Friendly-0.034 (0.062)-0.581 (1.148)0.031 (0.053)1.314 (0.013)-0.028 (0.012)0.021 (0.020)-0.126 (0.016)-0.029 (0.000)Unfriendly-0.084 (0.040)-0.578 (0.735)0.113 (0.034)-0.871 (0.008)0.011 (0.008)-0.053 (0.013)-0.017 (0.011)-0.005 (0.000)Knowledge0.007 (0.035)-2.374 (0.651)0.018 (0.030)0.298 (0.007)0.022 (0.007)-0.003 (0.011)-0.028 (0.009)-0.002 (0.000)Attention-0.058 (0.039)-1.106 (0.729)0.157 (0.033)-0.802 (0.008)0.007 (0.008)-0.018 (0.012)-0.051 (0.011)0.007 (0.000)Note: parameters in bold are significant at the 5% levelTable 8: Results of Difference in Difference Analysis (Independent Restaurants)Service topicPost X SanJoseSanJosePostRatingUsefulCountCoolCountFunnyCountRev. LengthClean-0.054 (0.060)0.035 (0.718)-0.030 (0.050)0.802 (0.013)-0.064 (0.012)0.050 (0.019)-0.081 (0.016)-0.021 (0.001)Dirty-0.091 (0.042)-1.542 (0.495)0.077 (0.035)-0.749 (0.009)-0.028 (0.008)-0.041 (0.013)0.040 (0.011)0.001 (0.000)Menu-0.029 (0.048)-1.722 (0.576)-0.028 (0.040)0.508 (0.010)-0.041 (0.009)0.082 (0.015)-0.125 (0.013)-0.008 (0.000)Wait-0.143 (0.060)-1.958 (0.715)0.144 (0.050)0.090 (0.013)-0.034 (0.012)0.036 (0.019)-0.108 (0.016)-0.009 (0.000)Delay-0.101 (0.050)-0.480 (0.594)0.163 (0.042)-0.787 (0.010)0.019 (0.010)0.032 (0.016)-0.111 (0.014)0.011 (0.000)Fast-0.089 (0.062)-2.044 (0.741)0.020 (0.052)0.688 (0.013)-0.053 (0.012)0.040 (0.019)-0.077 (0.017)-0.024 (0.000)Slow-0.168 (0.055)-1.248 (0.652)0.162 (0.046)-0.455 (0.011)-0.078 (0.011)0.088 (0.017)-0.112 (0.015)-0.010 (0.000)Ordered-0.069 (0.067)-4.137 (0.805)0.093 (0.057)-0.155 (0.014)-0.070 (0.013)0.091 (0.021)-0.123 (0.018)0.000 (0.000)Mistake-0.074 (0.051)-1.795 (0.608)0.114 (0.043)-1.180 (0.011)0.031 (0.010)-0.057 (0.016)0.005 (0.014)0.014 (0.000)Prompt-0.042 (0.061)0.718 (0.724)0.052 (0.051)0.832 (0.013)-0.050 (0.012)0.116 (0.019)-0.183 (0.016)-0.019 (0.000)Courteous-0.035 (0.060)0.063 (0.712)0.067 (0.050)0.894 (0.012)0.005 (0.011)0.014 (0.019)-0.115 (0.016)-0.019 (0.000)Polite-0.064 (0.058)-0.206 (0.698)0.113 (0.049)0.448 (0.012)0.004 (0.011)-0.034 (0.018)-0.058 (0.016)-0.014 (0.000)Impolite-0.080 (0.039)0.556 (0.459)0.129 (0.032)-0.840 (0.008)0.041 (0.007)-0.101 (0.012)0.050 (0.010)0.002 (0.000)Friendly-0.059 (0.072)-0.657 (0.860)0.024 (0.060)1.318 (0.015)-0.045 (0.014)0.034 (0.022)-0.116 (0.019)-0.030 (0.000)Unfriendly-0.109 (0.046)-0.204 (0.546)0.118 (0.038)-0.884 (0.010)0.002 (0.009)-0.055 (0.014)0.000 (0.012)-0.005 (0.000)Knowledge0.031 (0.041)-0.244 (0.486)-0.017 (0.034)0.308 (0.009)0.026 (0.008)-0.001 (0.013)-0.031 (0.011)-0.002 (0.000)Attention-0.084 (0.045)-0.996 (0.539)0.148 (0.038)-0.810 (0.009)-0.001 (0.009)-0.026 (0.014)-0.028 (0.012)0.007 (0.000)Note: parameters in bold are significant at the 5% levelTable 9: Results of Difference in Difference Analysis (Chain Restaurants)Service topicPost X SanJoseSanJosePostRatingUsefulCountCoolCountFunnyCountRev. LengthClean0.044 (0.103)-0.846 (0.755)-0.071 (0.090)0.823 (0.019)-0.004 (0.020)0.030 (0.033)-0.126 (0.027)-0.020 (0.001)Dirty0.033 (0.073)-1.826 (0.531)-0.021 (0.063)-0.715 (0.014)-0.013 (0.014)-0.018 (0.023)0.003 (0.019)-0.001 (0.000)Menu0.032 (0.084)-1.375 (0.616)-0.023 (0.073)0.549 (0.016)-0.028 (0.017)0.091 (0.027)-0.146 (0.022)-0.006 (0.000)Wait0.017 (0.108)-0.407 (0.788)0.107 (0.094)0.129 (0.020)-0.007 (0.021)0.020 (0.035)-0.147 (0.028)-0.009 (0.001)Delay-0.069 (0.088)-1.765 (0.640)0.186 (0.076)-0.777 (0.016)0.034 (0.017)0.061 (0.028)-0.188 (0.023)0.010 (0.000)Fast0.056 (0.107)-2.953 (0.783)-0.050 (0.093)0.766 (0.020)-0.021 (0.021)0.033 (0.034)-0.116 (0.028)-0.023 (0.001)Slow-0.022 (0.095)-0.878 (0.691)0.106 (0.082)-0.352 (0.018)-0.048 (0.019)0.115 (0.030)-0.172 (0.024)-0.011 (0.000)Ordered0.016 (0.114)-3.707 (0.829)0.004 (0.099)-0.073 (0.021)-0.053 (0.023)0.089 (0.037)-0.135 (0.029)0.001 (0.001)Mistake-0.031 (0.087)-3.222 (0.640)0.043 (0.076)-1.145 (0.016)0.010 (0.017)-0.021 (0.028)-0.025 (0.022)0.014 (0.000)Prompt0.001 (0.104)0.454 (0.759)0.112 (0.090)0.862 (0.019)-0.004 (0.021)0.118 (0.033)-0.242 (0.027)-0.018 (0.001)Courteous-0.008 (0.103)-0.393 (0.752)0.124 (0.090)0.850 (0.019)0.051 (0.020)-0.011 (0.033)-0.174 (0.026)-0.018 (0.001)Polite-0.024 (0.102)-0.848 (0.743)0.147 (0.089)0.436 (0.019)0.055 (0.020)-0.076 (0.033)-0.110 (0.026)-0.013 (0.001)Impolite-0.014 (0.068)-1.075 (0.497)0.105 (0.059)-0.872 (0.013)0.052 (0.013)-0.104 (0.022)0.013 (0.017)0.001 (0.000)Friendly0.042 (0.122)-0.863 (0.888)0.027 (0.106)1.324 (0.023)0.022 (0.024)-0.019 (0.039)-0.158 (0.031)-0.028 (0.001)Unfriendly-0.028 (0.080)-1.456 (0.582)0.111 (0.069)-0.847 (0.015)0.033 (0.016)-0.070 (0.026)-0.046 (0.021)-0.006 (0.000)Knowledge-0.073 (0.070)1.400 (0.513)0.109 (0.061)0.269 (0.013)0.012 (0.014)-0.009 (0.023)-0.018 (0.018)-0.001 (0.000)Attention-0.006 (0.081)0.048 (0.590)0.169 (0.07)-0.786 (0.015)0.025 (0.016)-0.007 (0.026)-0.103 (0.021)0.007 (0.000)Note: parameters in bold are significant at the 5% levelAppendix A1: Topics Estimated with Latent Dirichlet Allocation Topic0: food place ordered didn bad got wasn really tasted taste better even came good back much one go eat neverTopic10: good sauce place fries shrimp wait wings really got spicy ordered order seafood also pretty go hot came much tryTopic1: pizza good place cheese great toppings one pizzas crust really love sauce slice order go also ordered best large freshTopic11: always place food love ve go great service good come time wait order usually never best favorite one times eatTopic2: place one don food people re go ve restaurant now know time location going ll think want see way evenTopic12: food good place chicken great indian delicious also try restaurant really best dishes fresh go one spicy tried rice definitelyTopic3: good ordered dinner came salad steak restaurant nice menu dish meal great dessert also us service really one pasta $Topic13: good burrito food tacos mexican place salsa carne_asada super taco meat one chicken go really sauce burritos also best greatTopic4: pho place good vietnamese noodles broth soup dim_sum restaurant bowl beef meat also pretty one really rice places order serviceTopic14: us food table came service wait time minutes order got one server seated asked didn ordered took waitress waiter peopleTopic5: great food service place friendly staff amazing delicious us back definitely restaurant nice excellent love best awesome experience good madeTopic15: food good service place pretty price great restaurant really better nice decent quality stars go bad ok prices much averageTopic6: sandwich sandwiches bread good place salad fresh one also lunch love go chicken $ cheese really meat great ve deliciousTopic16: ramen good place bowl broth really noodles tea also got pretty one spicy soup try pork little flavor small $Topic7: sushi good place rolls roll fish fresh really also rice pretty sashimi one great $ definitely japanese service salmon goTopic17: breakfast good place coffee food great really ordered got came service try also menu nice wait definitely delicious pretty pancakesTopic8: meat good chicken bbq sauce side place ribs really beef rice food also korean got pretty ordered came spicy plateTopic18: burger good place fries drinks bar great burgers happy_hour drink really pretty beer $ nice food also go one menuTopic9: chicken good food dish dishes thai soup spicy rice ordered chinese beef restaurant sauce fried_rice also curry really place noodlesTopic19: order said food asked back one told go didn got us never even time ordered went don service manager customer_serviceAppendix A2: Topics Estimated with Non-Negative Matrix FactorizationTopic0: don go order one know re want eat people muchTopic 10: chicken fried wings salad curry teriyaki butter rice ordered dryTopic1: pizza crust toppings pizzas cheese slice thin_crust delivery slices sauceTopic 11: place try nice recommend clean go definitely small awesome lookingTopic2: great service friendly staff awesome atmosphere definitely delicious amazing drinksTopic 12: ramen broth noodles pork bowl egg salty santouka soup JapaneseTopic3: sauce rice spicy ordered dish dishes also beef soup meat Topic 13: food service restaurant fast quality chinese excellent friendly indian priceTopic4: sushi rolls roll fresh fish sashimi quality japanese tempura salmonTopic 14: burrito super tacos carne_asada mexican salsa burritos taco orange_sauce meatTopic5: good pretty service price overall prices though decent little portionsTopic 15: time wait first long come back next every lunch tryTopic 6: pho broth vietnamese noodles bowl meat beef places spring_rolls soupTopic 16: sandwich sandwiches bread cheese lunch fresh meat bbq steak saladTopic 7: us came table got asked minutes ordered didn server orderTopic 17: love delicious amazing favorite yummy also absolutely coming awesome everythingTopic 8: burger fries burgers cheese sweet_potato bacon garlic bun five_guys toppingsTopic 18: best ve one tried far san_jose bay_area amazing places favoriteTopic 9: always friendly staff favorite fresh never order usually come goTopic 19: really nice enjoyed liked also super lot got didn enjoy ................
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