rafmacalaba/datause-encoder-data huggingface dataset:
(2003) or Haltiwanger and Schweiger (2005); or to examine a particular dimension of the investment climate, e.g. corruption – Hellman et al. (2000); property rights -- Brunetti (1998); Johnson et al. (2002); finance – Beck et al. 2005.

One of the appealing features of firm rankings of constraints is that they implicitly combine an assessment of the severity of the conditions with the importance of that area to the firm’s operations. If long delays or costs are in a critical area, this should be reflected in the reported constraints. Thus perceived constraints incorporate a measure of the impact of an issue which should be important in identifying reform priorities. In this sense they are closer to Carlin et al. (2006)’s notion of subjective measures as Lagrangian multipliers. And the perceived impact of issues will influence firms’ decision whether to undertake investments, hire workers or expand production. Managers make these decisions with an eye to what they expect in the future, including their assessment of the investment climate. Perceptions can thus have real effects.

_Concerns about using subjective responses_ While the comparison of constraints thus has many appealing features, there are also several potential difficulties in making such comparisons across individuals, let alone countries (e.g. Sudman et al. 1996, Tanur (1992)). Simply comparing absolute scores can be misleading as the italicized paragraph at the beginning of the paper illustrates. Potential shortcomings in comparing subjective responses include:<sup>4</sup> _1. Specificity of responses: whether respondents answer the specific question or provide a broader evaluation of conditions._ To attribute the respondents to the

> 4 Additional limitations to relying solely on survey responses: they only include incumbent firms; reflect respondents’ views, not broader social interests; and they ignore the costs (absolute and relative) of addressing constraints.

7
countries over the sample period,<sup>25</sup> the results imply that decreases in the rate of motorization contributed to less than a 3 percent decline in occupant fatalities/VKT.<sup>26</sup> **_Road Infrastructure._** Road building is found to increase road safety, as suggested by the formal models, but trends in road conditions (as proxied by the road length-time trend interaction (ln(RD)*t)) offset this effect. The results in the Base Model indicate that the elasticity with respect to the road network decreases in magnitude from about - 0.39 ( _0.16_ ) in the early 1960s to approximately zero (0.02 ( _0.13_ )) by the mid-1990s.<sup>27</sup> Since total route length does not decrease over time, increases in the road-time interaction (ln(RD)*t) could reflect an aging or deterioration of the existing road network, if repairs are not kept up. Alternatively, if the building of new roads is positively correlated with road maintenance and improvements (lane widening, etc.) on existing routes, then the positive coefficient on the road length-time trend interaction may suggest that trends in road improvements have led to an increase in risk taking behavior on the part of drivers in response to presumably safer roads conditions—driving faster on straighter, wider roads (or highways).<sup>28</sup> This result is consistent with Noland’s (2003b) finding that road infrastructure improvements—additional lane-miles, lane widening and changes in geometric design—may even lead to increases in traffic fatalities.<sup>29</sup> Since the interpretation of the route length-time trend interaction is not clear, measuring the net

> 25 For example, the annual growth rate of the vehicle fleet fell from 5.9% in 1965 to 1.9% in 2002 in Austria, from 6.0% to 1.1% in Denmark, and from 5.0% to 2.0% in the United States.

> 26 Appendix B of Kopits (2004) shows the sensitivity analysis of the Base Model results with respect to the specification of this variable and the countries included in the sample.  In general, the coefficient increases in magnitude as more years are included in the average growth rate and if South Korea is dropped from the sample. All other coefficient estimates remain stable across specifications.  In the interest of maintaining the largest sample size possible, we continue to use VEHGROWTH in our estimation.

> 27 The magnitude of the coefficient on total route length (and, hence, the overall elasticity) approximately doubles in magnitude when LIVER is included in the estimation (Model 3 in Table 7).

28 Mahalel and Szternfeld (1986) hypothesize that drivers may also feel safer on wider roads and reduce cautionary behavior.

> 29 Similarly, using monthly data for 18 counties in Norway, Fridstrom and Ingebrigsten (1991) also found improvements to the national road network had no beneficial effect on safety rates.

20
# Table IV(c)

|Comparing Election Effec<br>[t-statistics|ts on Sales<br>in parenth|vs Producer<br>esis]|Taxesa|
|---|---|---|---|
||Sales Tax|Trade Tax|Excise Tax|
|Independent Variables:||||
|Scheduled election year|-1.31|-1.08|-1.40|
||[-0.99]|[-2.20]|[-2.29]|
|State domestic product [SDP]|0.01|0.01|0.02|
||[3.31]|[6.79]|[7.04]|
|Share of agriculture in SDP|-33.77|-4.18|-25.38|
||[-2.87]|[-1.59]|[-2.65]|
|Proportion of rural population|-362.57|-81.72|-215.62|
||[-5.27]|[-2.70]|[-6.99]|
|Number of Observations|383|381|394|
|R-sq|0.89|0.80|0.89|

a. All taxes and SDP are in 1973 rupees per capita. Regressions include year and state effects, and are reported with robust standard errors.

42
Obviously, each employer has different perceptions and expectations toward engineering graduate skills. The respondents‘ perceptions and expectations may arbitrarily influence the ratings of the satisfaction and importance levels because of, for instance, wording and orderings of questions. Therefore, many economists are skeptical about the meaningfulness of the answers from so-called ―subjective questions‖. We acknowledge this subjective element of this analysis. However a growing literature within different strands of economics, such as happiness and competitiveness use subjective survey data for econometric and/or psychometric analysis. In addition, management and marketing professionals employ a battery of satisfaction surveys, e.g., employee satisfaction surveys, customer satisfaction surveys, etc. to inform key decisions. We therefore follow the advice of Bertrand and Mullainathan (2001) who argues that subjective measures may be helpful as explanatory variables with due diligence to the interpretation of the results.

# **4. Characteristics of Respondents**

This section shows descriptive statistics of the respondents (employers). The surveyed employers are those that hire engineering graduates. The summary of descriptive statistics of 157 employers is presented in Table A2-1 in Annex 2.

**_Size_** : Half of respondents are large companies with more than 500 employees, while the other half is equally divided into medium (with between 100 and 500 employees) and small employers (with less than 100 employees).  Given that a half of the employers in our sample are large companies with more than 500 employees, we may have oversampled large companies. As a result, the outcomes of the survey may reflect more views of the larger companies.

**_Location_** : More than 40% of the responding employers are from the North region where Delhi is located, and 27% and 19% are from West and South regions, respectively.  Statewise, Delhi dominates the sample (27%), and Maharashtra where Mumbai is located accounts for 19%.  Other major states have a share of 38% in total; Uttar Pradesh 8.9% (the most populous state), Gujarat 8.9% (one of the fastest economically growing states), Haryana 8.3% (a large area of the state is included in the National Capital Region), Karnataka 7.0% (one of the economically progressive states), and Andhra Pradesh 5.1% (one of the top IT exporting states).

**_Sector_** : The survey covers almost 20 sectors of industries.  As discussed in the Introduction, IT, Infrastructure, and Power sectors show increasing demand of qualified engineering graduates.  These sectors have the highest share of employers in the sample after ―Other‖.  One third of the companies answered that their sectors do not belong to any of the listed sectors in the questionnaire and selected ―Other‖.  This ―Other‖ is further disaggregated into mining, other service activities, and professional, scientific and technical activities by using the responses from another question ― _Please specify the major economic activity of your firm_ ‖.

**_Foreign Capital_** : Twenty percent of the responding firms were established with foreign capital.

10
to wealth accumulation are also likely to be related to societal system affiliation and the norms within that group.

# **6 Income and Wealth Mobility**

Why are the top income earners in SSA not able to accumulate wealth despite high saving rates? Indeed, top income earners in SSA save at rates as high as the top income earners in the United States (see Section 3.3). However, they are not able to accumulate as much wealth as their US counterparts. Specifically, the top 1% income earners hold 4% of total wealth in rural Malawi and 11% in urban Malawi, while this figure is 26% in the US (see Section 3.4). In this context, note that wealth accumulation is the result of past saving behavior and, hence, at saving rates similar to those of the US, the top income earners in SSA will accumulate sizeable wealth only if they remain at the top for a period of time comparable with their US counterparts. Therefore, a simple — way to reconcile these two facts high saving rates but relatively low wealth accumulation for the top income earners in SSA — is through the study of income (and wealth) mobility.

Table 17 shows the economic mobility patterns we find. To discuss mobility we focus on the fraction of households that leave a given income or wealth quantile. We focus on two cases, the United States from 2001-2007 PSID data reported in D ́ıaz-Gim ́enez et al. (2011) and Uganda, for which we have available panel ISA data for two waves: 2005-06 and 2009-10.<sup>75</sup> First, we find much less persistence in the bottom and the top of the income distribution in Uganda than in the United States. Precisely, in rural Uganda, 51% of households in the top quintile leave it within four years, whereas this figure is 46% in urban Uganda; that is, there is more income persistence in urban areas than in rural areas. Instead, in the United States only 23% of households at the top quintile leave it within four years; that is, the income earners at the top quintile in both rural and urban Uganda are likely to leave that quintile at least twice as fast as their US counterparts. This implies a larger downward mobility in SSA than in the United States. Second, focusing on the bottom of the distribution, we also find larger upward mobility in the SSA than in the United States. Specifically, around 70% of households in the bottom 20% of the income distribution leave that quintile in both rural and urban Uganda, whereas only 23% of households at the bottom of the income distribution move upwards from that quintile in the United States. Third, wealth mobility is large in absolute terms, suggesting a substantial amount of wealth risk. At the same time, wealth is more persistent than income. For instance, while 71% of households move upwards from the bottom income quintile in rural Uganda within a span of four years, this

> 75We transform the mobility statistics for the 6-year span reported in D ́ıaz-Gim ́enez et al. (2011) for the PSID data into 4-year span statistics using simple averages to make the mobility statistics between the United States and Uganda comparable.

40
**Table 10: Impact of Business Registration Reform on Wage Work**

||||Depende<br>|nt variable:<br>|||
|---|---|---|---|---|---|---|
||Wage ear|ner dummy|Low<br>wage ear|-risk<br>ner dummy|Hig<br>wage ear|-risk<br>ner dummy|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Reform dummy (SARE)|-0.0020|-0.0006|0.0051|0.0064**|-0.0071*|-0.0070***|
||(0.0027)|(0.0023)|(0.0030)|(0.0027)|(0.0035)|(0.0021)|
|Primary education dummy||0.0563***||0.0034||0.0529***|
|||(0.0040)||(0.0032)||(0.0041)|
|Secondary education dummy||0.1182***||-0.0096**||0.1278***|
|||(0.0066)||(0.0046)||(0.0075)|
|High school education dummy||0.1006***||-0.0892***||0.1898***|
|||(0.0080)||(0.0064)||(0.0083)|
|University education dummy||0.2356***||-0.1395***||0.3751***|
|||(0.0112)||(0.0087)||(0.0151)|
|Female dummy||-0.2509***||-0.1447***||-0.1062***|
|||(0.0093)||(0.0104)||(0.0075)|
|Age||-0.0047***||-0.0061***||0.0013***|
|||(0.0003)||(0.0002)||(0.0003)|
|Married dummy||-0.0808***||-0.0955***||0.0147***|
|||(0.0074)||(0.0060)||(0.0034)|
|Census variables*time trend|No|Yes|No|Yes|No|Yes|
|R-squared|0.003|0.119|0.007|0.078|0.011|0.113|
|No. of observations|1,636,225|1,636,225|1,636,225|1,636,225|1,636,225|1,636,225|

_Note:_ Standard errors in parentheses (clustered at municipality level). Regressions include quarter and municipality fixed effects. Census variables are log GDP per capita, log number of economic establishments per 1000 capita, log fixed assets per capita, and log investment per capita from the 1999 Economic Census, converted to per capita levels using population data from the 2000 Demographic Census. Significance levels: * 10%, ** 5%, *** 1% 43
# Appendix Table 2A

||Domain 1 Inde<br>(1)<br>Agree/Stro|x: Job Satisfa<br>(2)<br>nglyAgree|ction<br>(3)<br>Strong|(4)<br>lyAgree|(5)<br>Incl.|(6)<br>Neutral|
|---|---|---|---|---|---|---|
|Treatment|‐0.580**|‐0.572**|‐0.56**|‐0.523**|‐0.430|‐0.449|
||(0.251)|(0.245)|(0.270)|(0.255)|(0.300)|(0.298)|
|Effort Median|0.480*|0.461*|‐0.0045|‐0.0223|0.507**|0.435*|
||(0.259)|(0.268)|(0.284)|(0.267)|(0.209)|(0.233)|
|Treatment X Effort Median|‐0.0636|‐0.0552|0.193|0.183|‐0.0595|‐0.00981|
||(0.378)|(0.370)|(0.376)|(0.347)|(0.369)|(0.372)|
|Male||0.319||0.0158||0.139|
|||(0.226)||(0.225)||(0.176)|
|Part‐Time||‐0.199||‐0.148||‐0.321*|
|||(0.174)||(0.126)||(0.159)|
|No Training||0.372||‐0.344||0.468|
|||(0.364)||(0.349)||(0.363)|
|Less than University Degree||0.141||1.164**||‐0.183|
|||(0.389)||(0.453)||(0.435)|
|University Degree||0.0399||0.0743||‐0.0581|
|||(0.205)||(0.134)||(0.198)|
|>= 60 students in class||0.00453||0.0846||0.158|
|||(0.219)||(0.198)||(0.231)|
|Age||‐0.0213||‐1.65e‐05||‐0.0080|
|||(0.0132)||(0.0154)||(0.0137)|
|Number of Subjects teaching thi|s Term|0.110||0.154||‐0.0963|
|||(0.124)||(0.134)||(0.110)|
|Constant|0.0677|0.310|0.233|‐0.138|‐0.0225|0.364|
||(0.209)|(0.580)|(0.238)|(0.421)|(0.207)|(0.644)|
|Observations|348|348|349|349|348|348|
|R‐squared|0.061|0.087|0.032|0.088|0.042|0.060|

Standard errors in parentheses, clustered by school. *** p < 0.01, ** p < 0.05, * p < 0.10.

33
For profits, we find comparable results that the magnitude is higher in administrative data than in the AAA tool, but the difference in this case is statistically significant for Ghana. However, the difference in magnitudes is not statistically different in Uganda. Overall, these findings confirm that if we believe administrative data to be the closest “true” business performance indicator, then the AAA tool comes very close to matching it in terms of magnitude and precision. Note that all traditional measures of sales and profits have significantly lower magnitude than AAA estimates with AAA sales of 1,608 USD and 1,306 USD in Ghana and Uganda compared to 1,384 USD and 1,205 USD, respectively; and AAA profits of 374 USD and 215 USD in Ghana and Uganda compared to 245 USD and 92 USD, respectively.

Furthermore, as discussed earlier administrative data for the vast majority of small firms in developing countries simply does not exist and would be very expensive and time-consuming to collect. In comparison, the AAA methodology provides a compelling alternative, offering comparable value much more efficiently and cheaply.

# **3.5. Method or Medium?**

As a final test of the validity of the AAA tool, we explicitly conduct a field experiment in Ghana to assess the measurement methodology. Specifically, we randomly assign half the survey respondents from a baseline business survey the AAA electronic tool and the other half an identical paper version of the tool.<sup>10</sup> Both survey instruments include the same set of questions and steps for measuring firm sales and profits. All surveyors received identical training and each surveyor implemented the electronic tool or the paper tool to a respondent on a randomized basis; each surveyor was randomly assigned 50% electronic instruments and 50% paper instruments to complete (thus the only thing that differed was the 'approach' of using an electronic tool). This approach allows us to examine differences between the questioning techniques (reported versus anchored/adjusted) while isolating any effects due to the electronic tool versus paper tool. Moreover, by comparing outcomes across the two versions of the tool, we can differentiate whether gains in precision are attributable to the medium of conducting surveys (electronic versus paper) or to the AAA method.

> 10 Table 1 includes summary statistics for the sample that was assigned the electronic version of the AAA tool.

24
experiment with all combinations of caste statuses in the principal and agent roles. We found no statistically significant caste difference in sharing. In fact, slightly more principals shared when both principal and agent were high caste than when both were low caste.

Another hypothesis that would explain the caste difference in behavior in the Stag Hunt experiment is a low sense of self-efficacy among low-caste men compared to high-caste men. That hypothesis could also explain a pattern that emerged in our vignette-based survey, which is that low-caste men were much less likely than high-caste men to want to respond violently to the transgressions described in the vignettes. We conducted a second survey to test for caste differences in self-efficacy and found no evidence that low-caste men have lower self-efficacy than high-caste men.

This paper contributes to the rapidly growing literature on the interaction between culture and economic behavior (see, for example, Greif, 2006; B ́enabou, 2008; Herrmann, Th ̈oni and G ̈achter, 2008; Hoff and Stiglitz, 2010, 2016; Hoff, Kshetramade and Fehr, 2011; Nunn, 2012; Jackson and Xing, 2014; World Bank, 2015). Our contribution is to show that culture can block the emergence of efficient conventions in a strategic setting in which prior experimental work with US subjects found that the outcome was almost always efficient. To the best of our knowledge, this is the first paper to link culture to the ability to form efficient conventions in coordination games of common interest.

Numerous studies, including those cited above, have shown that people from different cultures will behave differently in otherwise identical situations, and also that behavior can depend on seemingly irrelevant details of context (Kahneman and Tversky, 1984). An explanation that is broadly accepted in behavioral economics, psychology, sociology, and anthropology is that human decision making is carried out using a set of mental tools, which have been variously referred to and characterized as schemas and mental models (Swidler, 1986; D’Andrade, 1995; Nisbett and Cohen, 1996; DiMaggio, 1997; Fiske et al., 1998). In brief, humans process information and interpret the world around them by matching their observations to a relatively sparse set of known patterns, which may lead them to select a course of action by automatically invoking a rule of thumb. The particular patterns and rules of thumb that an individual uses are influenced by history and experience. Thus, individuals who share a common experience will tend to process information and interact with the world in a similar manner.

“Culture” in general usage is a broad and amorphous concept, and it has acquired different technical meanings in different disciplines. In our view, any reasonable definition of culture for an economist should include those common and distinct elements of how a group of individuals perceive, interpret, and interact with the wider economy. The culture of honor, in particular, has been described as such a “mediative concept” by the anthropologist 5
industries grow faster in financially developed markets (Rajan and Zingales (1998)), and that firms in industries dominated by small firms grow faster in more financially developed markets (Beck et al. (2008)). Studies have also found that industries in which entry is more important exhibit less growth in countries with restrictions to firm entry ( Klapper, Laeven and Rajan (2004)). Finally, Micco and Pagés (2006) show that industries that are inherently more volatile create fewer jobs and are less developed in countries with very restrictive hiring and firing regulations.

While improving on cross-country studies, the former studies suffer from three potential shortcomings that can be addressed in our study. First, in most cases variation in business environment conditions is captured with country level variables that reflect _de jure_ regulations or conditions, that is, the procedures and costs that would be incurred if firms fully complied with what is on the books. However, there can be large gaps between what is on the books and what is experienced on the ground. This is particularly true in lower income countries and those with higher levels of corruption. (Hallward-Driemeier and Aterido (2007); Kaufmann, Kraay and Mastruzzi (2007)). In that regard, it is desirable to have measures of de facto regulation.

Second, it is important to explore variation in the business environment not only across countries but also within country boundaries—across sub-national areas and especially, across firm size and ownership.<sup>4</sup> Having disaggregated variables allows for hypotheses to be tested without having to assume that one country is the benchmark or “best” case against which to compare other countries.

Third, while most studies look at the effects of individual dimensions of the investment climate, our focus is on regressions that combine multiple dimensions in a single regression. This not only addresses potential bias in the estimates, it allows us to test directly which dimensions have the biggest impact on firm performance.

A number of recent studies make use of the World Business Environment Survey (WBES)<sup>5</sup> —a firm-level dataset with 4,000 firms across 54 countries—to study the effect of

> 4 A few studies assess differences in business environment conditions within countries. Besley and Burgess (2004) and Ahsan and Pagés (2009) exploit differences in labor market regulations across Indian states to assess how different labor market regulations affect economic performance across Indian states.  Almeida and Carneiro (2009) assess the effect of variation of enforcement in labor regulations within Brazil.

> 5 Despite their similar names, the World Business Environment Survey (WBES), a one off survey in 1999, is not part of the larger and ongoing World Bank firm survey effort, the Enterprise Surveys (ES).  The earlier

6
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