rafmacalaba/datause-encoder-data huggingface dataset:
# **What Does “Entrepreneurship” Data** **_Really_ Show?  A Comparison of the Global Entrepreneurship Monitor and World Bank Group Datasets**

Zoltan J. Acs School of Public Policy, George Mason University Fairfax, VA, USA Max Planck Institute of Economics Jena, Germany <u>zacs@gmu.edu</u> Sameeksha Desai Max Planck Institute of Economics Jena, Germany University of Missouri Kansas City, USA <u>desai@econ.mpg.de</u> Leora F. Klapper The World Bank, Development Research Group Washington, DC, USA <u>lklapper@worldbank.org</u>

> * The opinions expressed do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.  We thank Paul Reynolds for valuable comments.  We received valuable feedback from participants at the 2007 GEM Conference and the 2008 Babson College Entrepreneurship Research Conference.
**Table A4: Regression results for currently attending school, girls only**

||(1)|(2)|(3)<br>Currently|
|---|---|---|---|
|VARIABLES|Currently<br>Attending School|Currently<br>Attending School|<br>Attending<br>School|
|Survey year: 2016|0.150|0.123***|0.063|
||(0.150)|(0.037)|(0.151)|
|Survey year: 2017|0.043|0.162**|-0.032|
||(0.124)|(0.075)|(0.090)|
|Inflation*Post|-0.077|||
||(0.086)|||
|Inflation*distance to school more than 5 hours*Post|-0.202***|||
||(0.030)|||
|Food Inflation*Post||-0.136***||
|||(0.041)||
|Food inflation* distance to school more than 5||||
|hours*Post||-0.745***||
|||(0.063)||
|Non-food Inflation*Post|||-0.020|
||||(0.063)|
|Non-food inflation* distance to school more than 5 hours*Post|||-0.159***|
||||(0.020)|
|Conflict|0.000|0.000|0.000|
||(0.000)|(0.000)|(0.000)|
|Distance to school: More than 5 hours (Ref: < 30||||
|minutes)|0.103***|0.0992***|0.110***|
||(0.034)|(0.032)|(0.036)|
|Female household head|-0.0511**|-0.0538**|-0.0513**|
||(0.023)|(0.023)|(0.023)|
|Education level of household head_Primary|-0.075|-0.074|-0.076|
||(0.086)|(0.085)|(0.086)|
|Education level of household head_Secondary|0.080|0.076|0.078|
||(0.062)|(0.060)|(0.062)|
|Education level of household head_University|0.086|0.088|0.082|
||(0.058)|(0.059)|(0.058)|
|Education level of household_Other|-0.431***|-0.392***|-0.437***|
||(0.052)|(0.047)|(0.056)|
|Constant|0.423***|0.426***|0.424***|
||(0.042)|(0.041)|(0.041)|
|Observations|3,344|3,344|3,344|
|R-squared|0.064|0.066|0.063|
|*** p<0.01, ** p<0.05, * p<0.1.||||
|Notes: Robust standard errors in parentheses.||||

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Figure A7: Labor force participation rate <!-- Start of picture text --> 2015<br>Total<br>Men<br>Women<br>Richest<br>Q4<br>Q3<br>Q2<br>Poorest<br>Non Poor<br>Poor<br>0 20 40 60 80 100<br>Percent<br>In the labor force / active<br>Outside the labor force / inactive<br><!-- End of picture text --> <!-- Start of picture text --> 2016<br>Total<br>Men<br>Women<br>Richest<br>Q4<br>Q3<br>Q2<br>Poorest<br>Non Poor<br>Poor<br>0 20 40 60 80 100<br>Percent<br>In the labor force / active<br>Outside the labor force / inactive<br><!-- End of picture text --> Source: Authors’ calculations based on HFSSS 2015 and 2016 Figure A8: Employment and enrollment status <!-- Start of picture text --> 2015<br>Total<br>Men<br>Women<br>Richest<br>Q4<br>Q3<br>Q2<br>Poorest<br>Non Poor<br>Poor<br>0 20 40 60 80 100<br>Percent<br>Employed Employed and Enrolled Unemployed<br><!-- End of picture text --> <!-- Start of picture text --> 2016<br>Total<br>Men<br>Women<br>Richest<br>Q4<br>Q3<br>Q2<br>Poorest<br>Non Poor<br>Poor<br>0 20 40 60 80 100<br>Percent<br>Employed Employed and Enrolled Unemployed<br><!-- End of picture text --> 20
and 1.7 percent, respectively. Although the trend line shapes appear similar, their means and variances are significantly different. The mean calculated from unweighted data is larger, suggesting that big economies grew less than small ones, as indicated above. Both small and large economies experienced high growth variation, however. The average SD of South Africa’s growth is 2.41 percent; Nigeria’s average is 5.15 percent; and the simple average of African countries is 2.26 percent.

Figure 2: GDP per capita growth <!-- Start of picture text --> Figure 2a: Unweighed data<br>8<br>4<br>0<br>8<br>-4<br>4<br>0<br>-4<br>-8<br>1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br>Growth Trend Cycle<br><!-- End of picture text --> <!-- Start of picture text --> Figure 2b: Weighted data<br>4<br>4<br>0<br>2<br>-4<br>0<br>-2 -8<br>-4<br>1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br>Growth Trend Cycle<br><!-- End of picture text -->

# **_Comparing Sub-Saharan Africa with Other Developing Regions_**

The African growth story looks bleak when compared with the growth performances of other developing countries. Whereas all other regions experienced significant income improvements, Africa’s income shrank between the 1970s and the 2000s (see table 1). Africa is always among the weakest growth performers, although there has been some catching up recently.

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**Table 10: Determinants of non‐compliance, between firm**

||**All**<br>**categories**|**Disclosure**<br>**categories only**|**Child**<br>**labor**|**Compensation**|**Contracts**|**Discrimination**|**Forced**<br>**Labor**|**Freedom of**<br>**association**|**Occupational safety**<br>**and health**|**Work**<br>**time**|
|---|---|---|---|---|---|---|---|---|---|---|
|Firm size|‐0.015***|‐0.012***|‐0.040***|‐0.029***|‐0.027***|‐0.001**|0.000|‐0.009***|‐0.016***|‐0.023***|
||(0.001)|(0.004)|(0.005)|(0.003)|(0.005)|(0.001)|(0.000)|(0.003)|(0.003)|(0.006)|
|Firm age|‐0.000|‐0.001|‐0.013**|0.002|‐0.001|‐0.001*|‐0.000|0.002|0.003|‐0.011**|
||(0.001)|(0.004)|(0.005)|(0.003)|(0.005)|(0.000)|(0.000)|(0.003)|(0.003)|(0.005)|
|Baseline|0.078***|0.080***|0.144***|0.125***|0.122***|0.004***|‐0.000*|0.036***|0.115***|0.104***|
|noncompliance|(0.002)|(0.007)|(0.013)|(0.007)|(0.009)|(0.001)|(0.000)|(0.006)|(0.006)|(0.011)|
|Length of|‐0.035***|‐0.042***|‐0.031***|‐0.035***|‐0.067***|‐0.000|0.000|‐0.025***|‐0.061***|‐0.051***|
|participation|(0.001)|(0.004)|(0.005)|(0.003)|(0.005)|(0.000)|(0.000)|(0.003)|(0.003)|(0.006)|
|Constant|0.119***|0.129***|0.132***|0.095***|0.110***|0.008***|0.003|0.164***|0.221***|0.160***|
||(0.003)|(0.009)|(0.017)|(0.008)|(0.011)|(0.002)|(0.002)|(0.011)|(0.008)|(0.014)|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm FE|No|No|No|No|No|No|No|No|No|No|
|Observations|240,805|32,934|8,937|35,928|22,455|52,332|13,473|26,946|64,269|16,465|
|R‐squared|0.016|0.017|0.063|0.035|0.038|0.002|0.002|0.024|0.021|0.023|

Note: Non‐compliance=1. Firm size, firm age, baseline non‐compliance, and length of participation in program are all identified as a dummy variable equal to 1 if the firm is above the median. Standard errors in parentheses. * = 10% significance, ** = 5% significance, *** = 1% significance. Source: Author’s calculations using data from ILO‐IFC Better Work Vietnam.

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respectively). The error term _"_ is a random shock. Standard errors are clustered by country.

# **4 Results: Inequality**

## **4.1 Overview of Results**

Our fist set of results are reported in Table 2. The dependent variable ranges from the poorest segments of the population (model 1, which models the income share of the bottom 10% of the population) to the wealthiest (model 10, which models the top 10%).

|||**Expla**|**ining c**|**hanges **|**in inco**|**me sh**|**ares**||||
|---|---|---|---|---|---|---|---|---|---|---|
||(1)<br>0-10|(2)<br>10-20|(3)<br>20-30|(4)<br>30-40|(5)<br>40-50|(6)<br>50-60|(7)<br>60-70|(8)<br>70-80|(9)<br>80-90|(10)<br>90-100|
|CBI|-0.75<sup>_⇤⇤_</sup>|-0.71<sup>_⇤⇤_</sup>|-0.56<sup>_⇤⇤_</sup>|-0.47<sup>_⇤⇤_</sup>|-0.36|-0.24|-0.11|0.06|0.42|2.72<sup>_⇤_</sup>|
||(0.28)|(0.27)|(0.26)|(0.24)|(0.23)|(0.22)|(0.20)|(0.20)|(0.33)|(1.36)|
|GDP (log)|0.32|0.46|0.35|0.26|0.23|0.28|0.38|0.47|0.38|-3.12|
||(0.33)|(0.31)|(0.34)|(0.37)|(0.39)|(0.39)|(0.36)|(0.32)|(0.38)|(2.26)|
|Population (log)|0.66|0.45|0.35|0.30|0.28|0.24|0.01|-0.25|-0.30|-1.74|
||(0.56)|(0.50)|(0.50)|(0.49)|(0.49)|(0.50)|(0.51)|(0.56)|(0.76)|(2.91)|
|Country FE|X|X|X|X|X|X|X|X|X|X|
|Year FE|X|X|X|X|X|X|X|X|X|X|
|Observations|726|726|726|726|726|726|726|726|726|726|
|_R_<sup>2</sup>|0.27|0.31|0.29|0.27|0.24|0.23|0.21|0.15|0.09|0.28|
|Mean of DV|2.30|3.87|4.96|5.97|7.04|8.26|9.78|11.89|15.44|30.48|
|SD of DV|1.06|1.32|1.36|1.33|1.26|1.14|0.94|0.64|0.89|8.01|

Table 2: E↵ect of CBI on inequality. The dependent variable is the share of income as a percentage of total income, divided by decile. For instance, the first column represents the percentage of total income earned by the bottom 10% of the population. Standard errors clustered by country. Symbols: _⇤_ : _p <_ 0 _._ 1; _⇤⇤_ : _p <_ 0 _._ 05; _⇤⇤⇤ p <_ 0 _._ 01.

We find suggestive evidence that the poorest were negatively a↵ected by CBI. The e↵ect was worst for the bottom 10%, but it was negative and statistically significant for the entire bottom 60% of the population. The e↵ect was about nil for the 60-70% and 70-80% brackets. The top-20% saw their share of income increase (and the top-10% even more so). We note here the importance of the year fixed e↵ects, which take into account secular trends toward higher inequality.

To show how remarkably monotonic this e↵ect is, we report the e↵ect of a one-standard deviation increase in CBI (about 0.2 points) for each income group in Figure 2. To situate the magnitude of the e↵ect, the within-country standard deviation of income in the bottom bracket is about 0.3.

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Corbo, V. and K. Schmidt-Hebbel (1991): “Public Policies and Saving in Developing Countries,” Journal ofDevelopment Economics 36 (July): 89-115.

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Our results are consistent with other reviews of subsets of the preprimary experimental and quasi-experimental literature that find significant positive impacts on cognitive skills during early childhood (Nores & Barnett, 2010; Duncan & Magnuson, 2013; Yoshikawa et al., 2013; McCoy et al., 2017; van Huizen & Plantenga, 2018) and larger impacts for disadvantaged children (Yoshikawa et al., 2013; van Huizen & Plantenga, 2018) by using narrative, vote-tallying, and meta-analytic techniques to aggregate evidence. Our review is unique in that we limit our focus to center-based preprimary education programs (rather than more holistic packages of services that intervene at earlier stages and often include intensive, home-based coaching for parents).

In contrast to other studies and reviews that document fade-out (Yoshikawa et al., 2013; Duncan & Magnuson, 2013), we find persistence of an advantage in cognitive skills after the preprimary period, although it is possible that individual studies (and therefore more narrative or vote-tallying reviews) lack sufficient statistical power to detect effects as small as 0.07 sd. Persistence in an advantage in executive function and social-emotional skills, however, is consistent with the hypothesized mechanism behind “sleeper effects,” or gains in health and earnings observed in adulthood when test score advantages dissipate or disappear after the preprimary period (Heckman et al., 2010; Bailey et al., 2020a; Cascio & Schanzenbach, 2014).

Finally, our results, along with current coverage rates, suggest that levels of preprimary investment may be suboptimal, particularly in low- and middle-income countries. When offered services, families do elect to send their children to preprimary education centers. The significant average effects for cognitive and social-emotional skills and the lack of any differences across high and low- and middle-income countries show that preprimary education does provide better conditions for the development of skills than children’s counterfactual options of remaining at home or in informal care settings, even in contexts with low state capacity. This improvement in skills could help counter the inequities in skill development that children bring with them when they enter primary school (Fernald et al., 2011; Naudeau et al., 2011; Duncan and Magnuson, 2013; Schady et al., 2014). The high benefit-to-cost ratios indicate high returns to preprimary investments, and the persistence of impacts beyond the preprimary period suggests that investments in preschool can play a role in improving the effectiveness of primary education (Johnson & Jackson, 2019), possibly by enhancing executive functions and social-emotional skills that help children learn (Cunha and Heckman, 2007) or by reducing heterogeneity in the 6
Walle and Mu (2007), so we only summarize them here. We find a number of significant explanatory variables for program placement. Consistent with official selection criteria, communes with a higher total population and a larger share of ethnic minority population were more likely to participate in the project. Communes in Thai Nguyen, Nghe An, and Binh Thuan were more likely to get the project than communes in Tra Vinh. A few characteristics that may indicate higher living standards or local development had a significant negative effect on the probability of participation ─ namely, the share of the adult population working in private enterprises, the school enrollment rate and having an Agricultural Bank branch. Yet, other proxies for income had no effect ─ including, the presence of a market and predicted average commune consumption expenditures. Finally, among measures of transport and accessibility, a national road passing through the commune, the presence of passenger transport, a higher density of roads, and a higher distance to the province center all reduced the probability of participation, while a railroad passing through the commune improved it. As there is imperfect overlap in the estimated propensity score for project and non-project communes, we limit the sample to the common support, ending up with 94 project and 95 non-project communes for the rest of the analysis. Using the predicted propensity scores to match communes, we achieve a close balancing of the initial observed commune characteristics for the two samples.<sup>20</sup>

# **_5.2 Average treatment effects_**

We assess impacts of the road project on a set of outcome variables (introduced in Table 1) that we deem relevant to local market development and to testing TILD. In

> Walle and Mu (2007).

> 20  Following Rosenbaum and Rubin (1985), we carried out a balancing test using the standardized mean difference ─ the difference in covariate means in project and non-project communes as a percent of the standard deviation in the full sample. This drops significantly from 14% before to 9% after matching.

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# Table **10** System Estimation of Four Equations: Three Stage Least Square'

|Dependent Variable: Growth||Dependent Variable: Government Size||
|---|---|---|---|
|Independent Variables||Independent Variables||
|Constant|0.146***|Constant|-1.780***|
||(0.038)||(0.851)|
|Dummy for 1974-83|-0.009*|Dummy for 1974-83|0.031|
||_(0.005)_||(0.083)|
|Dummy for 1984-93|-0.009*|Dummy for 1984-93|0.074|
||_(0.005)_||(0.089)|
|Initial Per Capita GDP|-0.022***|Per capita GDP|0.135|
||(0.004)||(0.113)|
|Initial Schooling|0.005<br>(0.006)|Age Dependency Ratio|0.267<br>(0.240)|
|Population Growth|-0.333|Urbanization Ratio|-0.095|
||(0.269)||(0.099)|
|Investment/GDP|0.001|Relative Price of Government<br>Consumption|-0.887***|
||(0.006)||(0.191)|
|Government Size|-0.037***|Openness|0.091|
||(0.012)||(0.094)|
|Quality of Bureaucracy|0.059|Terms of Trade Changes|-1.021|
|(0 worst, I best)|(0.044)||(1.976)|
|Government Size * Quality of<br>Bureaucracy|0.031<br>(0.024)|Openness * Terms of Trade Changes|-1.020<br>(1.976)|
|Policy Distortion|-0.006***|Years Since Independence|-0.139|
||(0.002)||(0.135)|
|Terms of Trade Changes|0.180***|Political Freedom|-0.029|
||(0.054)|(I high, 7 low)|(0.022)|
|Latin America Dummy|-0.018***<br>(0.005)|Federal Dummy|-0.110<br>(0.108)|
|Sub-Saharan Africa Dummy|-0.026***<br>(0.009)|Latin America Dummy|-0.229***<br>(0.084)|
|Socialist Dummy|-0.026***<br>(0.009)|East Asia Dummy|-0.304***<br>(0.126)|
|Number of Observations|91||91|
|System R-squaredb|0.7420|||

' Standard errors are in parentheses. The instruments are: initial per capita GDP; initial per capita GDP squared; federal dummy; initial income inequality; initial social spending/GDP; socialist dummy; Latin America dummy; Sub-Saharan Africa dummy; constant; decade dummy variables; previous five year lagged variables of policy distortion, investment/GDP; relative price of government consumption, openness; political freedom, government size, and growth; lagged government size* quality of bureaucracy; and lagged openness*terms of trade changes.

**b** System R-squared is not an appropriate measure of goodness of fit with instrumental variable regressions.

46
We first reclassify the 2,175 core cities (of more than 100,000 population) into 16 regions following the regional classification employed in the 2009 World Development Report (World Bank 2009): Australia and New Zealand, Central America and Caribbean, Central Asia, Caucasus and Turkey, Eastern Africa, Eastern Europe and Russia, Middle Africa, North America, Northeast Asia, Northern Africa, South America, Southeast Asia and Pacific, Southern Africa, Southern Asia, Western Africa, Western Asia, and Western Europe. In each WDR region, we group its constituent cities into quintile subgroups according to their relative city population size in 2000, and compute in each quintile group the mean values of each of the regressors of Table 9. These quintile mean values represent region-specific average attributes of each quintile group cities. For example, the largest quintile group cities in Western Africa is assumed to share the same city-specific attributes and dynamics (such as previous growth rates of city population and market potential), while conditioned by country-specific exogenous growth paths (such as projected national population growth, and urbanization rates).

We repeat the same region-specific quintile grouping for 8,301 broader cities of more than 50,000 population. Based on its quintile group, each city in this broader set is then assigned the growth attributes (listed in Table 9) extrapolated from corresponding core city statistics in the same quintile group of the same region. In this way we identify city growth dynamics of each 8,301 broader cities, and sequentially project city population growth rates and corresponding city population sizes backward (from 2000 to 1970) and forward (from 2000 to 2050).

In the third and final stage, we harmonize our projections with UN Population Division national urban population projections (to year 2050) as these are the most widely used national estimates of future urban and rural population. We adjust city population projections ( city popij,tadj. proj ) such that national urban population growth rates are the same as the UN Population Division’s country projections. Specifically, city popij,tadj. proj = city popini. projij,t × ∑ k ∈ _<u>j</u>_ city popini. projini. projkj,2000 × urban popUNPDUNPDj,t . ∑ city popkj,t urban pop j,2000 k ∈ _j_ 35
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|Slope of the terrain where each firm exists|_SLOP_|343|0.88|0.67|0.06|4.40|
|---|---|---|---|---|---|---|
|Factor share: Labor|SL|345|0.28|0.22|0.00|0.98|
|Energy|SE|345|0.06|0.11|0.00|0.87|
|Fuel|SF|345|0.04|0.09|0.00|0.97|
|Water|SW|345|0.01|0.02|0.00|0.12|
|Transport|ST|345|0.13|0.16|0.00|0.92|
|Communications|SC|345|0.01|0.04|0.00|0.50|

Source: Authors’ survey data.

# **IV. MAIN ESTIMATION RESULTS**

First of all, the ordinary least squares regression is performed: The result is shown in **Table 6** . The result is potentially biased because of the possible endogeneity problem of infrastructure placement. To deal with this problem, the IV regression model is estimated. Three variables are instrumented: _RAIL_ , ln _Z_ and ln _WT_ . The results are broadly similar and consistent with prior expectation, although some variables have the different statistical significance and magnitude. The Hausman exogeneity test statistic is estimated at 4.88, of which the _p_ -value is 0.18. Although the test statistic is below the conventional critical value, these three variables may or may not be exogenous. According to the overidentifying restriction test, the selected instruments are considered to be valid.

The use of rail, _RAIL_ , is considered highly endogenous, while the inventory and transport input price variables can be treated as exogenous. Two more IV models are estimated under different endogeneity assumptions ( **Table 7** ). When _WT_ is excluded from the endogenous variables, the exogeneity test statistic is estimated at 3.55, which is still below the critical value. However, the exogeneity test can be rejected when only _RAIL_ is instrumented. The test statistic is above the 10 percent critical value according to the chi squared distribution with a degree of freedom of one. Therefore, it is likely that _RAIL_ is endogenous and the other two variables are exogenous.