rafmacalaba/datause-encoder-data huggingface dataset:
- 15 -

To estimate Equations (1) to (3) simultaneously, the conventional three-stage least squares estimation method (Zellner and Theil, 1962) is adopted. For computational simplicity this model needs to assume linearity all over the system, despite the dichotomy of _D_<sup>_TE_</sup> . But all the equations can be estimated together to conclude the ultimate effects of competition, entry and technical considerations.

In addition, several different estimation approaches are used to estimate two of the three equations in the system. To estimate Equations (2) and (3) together, the two-stage probit least squares model is applied (Maddala, 1983). Notably, _D_<sup>_TE_</sup> is endogenous and dichotomous, while _n_ is a positive number.<sup>17</sup> Therefore, the two-stage approach is taken where the probit and ordinary least squares (OLS) models are first performed for _D_<sup>_TE_</sup> and ln _n_ , respectively, and then the standard errors are corrected by the estimated individual variance-covariance matrices and a correlation between the two equation.18 The system will explain how bidders make an entry decision and how the auctioneer reacts to their entry via requiring quality standards.

To estimate Equations (1) and (2) simultaneously, the conventional instrumental variable technique is applied. In this case, the endogeneity of _D_<sup>_TE_</sup> is presumed insignificant. Although this may not hold in the formal model as will be shown, it roughly reveals how bidders choose to enter into a particular type of auction, and given that, whether or not the auction is pro-competitive.

Finally, the treatment effect model is used to estimate Equations (1) and (3), because _D_<sup>_TE_</sup> is a binary endogenous choice in the equilibrium bid. This will indicate the possible impact of technical considerations on project procurement costs under the assumption that _n_ is fixed.

> 17 Technically, in addition, the second endogenous variable _n_ is typical of count data. To avoid further complication, however, _n_ is treated as a continuous positive variable.

> 18 To identify the system of equations, one exogenous variable, _CAID_ , is included in the equation of _DTE_ at the second stage. Note that in the two first-stage regressions both _WAID_ and _CAID_ are used as instruments.
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of maternal and child health outcomes, the capacity of primary health care personnel, geographic location, health care budget, and physical infrastructure of facilities. In one district of Sughd region, a pre-pilot of the PBF program was implemented, and it was subsequently removed from the study sample.<sup>4</sup> The research team selected control districts in the two regions. The selection was guided by two goals: 1) selection of districts that are similar in terms of the number of facilities and doctors per capita, and 2) achieving the same number of catchment areas in each region. Two control districts were chosen in the Sughd region and seven in the Khatlon region, as can be seen in the map presented in Figure 1.<sup>5</sup> Additional districts were added to the project after 2015, but the assignment of the control districts was maintained.<sup>6</sup> The Tajikistan Demographic and Health Survey 2012 was used to test whether the parallel trends assumption can be rejected between 2008 to 2012. We created annual indicators for timely antenatal care, receiving at least four antenatal consultations and child vaccination coverage. We failed to reject the parallel trend hypothesis for all indicators. This analysis is presented in Appendix 2. In Appendix Table A2, we present baseline outcome by treatment group and tests for differences between the groups. The tests show that the PBF and control districts were overall similar before the launch of the PBF intervention.

# Data

Large-scale household and facility-based surveys were conducted before the launch of the program and after about three years of implementation. Appendix 3 provides more detailed information on the sampling and survey design. The baseline survey was conducted from November 2014 to July 2015.<sup>7</sup> Facility-based surveys were conducted in 108 rural health centers in the seven PBF district

> 4 Spitamen district was selected as a pre-pilot district. The remaining treated districts are Mastcho, J. Rasulov and Devashtich in Sughd region and Yavan, Farkhor, J. Balkhi and Kubodiyon in Khatlon region.

> 5 The control districts in Sughd region are Asht and Konibodom. The selected districts in Khatlon region are Temurmalik, Vakhsh, Pyanj, Jilikul, A. Jomi, Khuroson and Kumsangir.

> 6 We could not identify other interventions that were introduced in the control districts in the duration of the study through other sources.

> 7 The facility-based survey was conducted in November to December 2014 prior to the launch of the PBF program. The household survey was conducted from March to July 2015 because of unforeseen delays related to procurement of tools for anthropometric measures and obtaining of administrative data needed for the sample framework. Respondents reported about care received in the preceding two years, so we expect the timing to have negligible

7
# How Can Micro and Small Enterprises in Sub-Saharan Africa Become More Productive? The Impacts of Experimental Basic Managerial Training

Yukichi Mano,<sup>a</sup> Alhassan Iddrisu,<sup>b</sup> Yutaka Yoshino,<sup>c</sup> and Tetsushi Sonobe<sup>a</sup> _Keywords: Africa, Ghana, industrial development, survival clusters, management training, randomized experiment_ JEL classification: M31, M41, O14, C93 a Foundation for Advanced Studies on International Development (FASID), Japan. b The Ministry of Finance and Economic Planning, Ghana. c The World Bank
Table 11: Development Accounting

|Sector|Country FE|Contribution to|
|---|---|---|
|||log of Sales per<br>worker<br>TFPR|
|Overall|Y|0.23<br>0.24|
||N|0.28<br>0.31|
||Sector X Country|0.23<br>0.25|
|Agriculture|Y|0.30<br>0.33|
||N|0.44<br>0.50|
|Manufacturing|Y|0.27<br>0.26|
||N|0.33<br>0.30|
|Services|Y|0.20<br>0.24|
||N|0.26<br>0.28|

Notes : The table reports the contribution of _Sj_ to the cross-establishment dispersion in productivity, (log) sales per worker, and TFPR, as discussed in the text. Cross-establishment dispersion is measured by gap between the establishments in 90<sup>_th_</sup> and 10<sup>_th_</sup> deciles of the distribution of relevant variable. The first three rows correspond to the estimates from the first three columns of Table 8. Rows 4 through 9 report contributions from sectoral regressions reported in Table 12.

Table 12: Productivity and Technology Sophistication - Across Sectors

||||ln(Sales p|er worker)|||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_Kj_|0.342<sup>_∗∗∗_</sup>|0.442<sup>_∗∗∗_</sup>|0.234<sup>_∗∗∗_</sup>|0.281<sup>_∗∗∗_</sup>|0.218<sup>_∗∗∗_</sup>|0.244<sup>_∗∗∗_</sup>|
||(0.023)|(0.024)|(0.008)|(0.009)|(0.011)|(0.012)|
|_Hj_|0.507<sup>_∗∗_</sup>|0.825<sup>_∗∗∗_</sup>|0.164<sup>_∗∗∗_</sup>|0.604<sup>_∗∗∗_</sup>|0.165<sup>_∗∗∗_</sup>|0.586<sup>_∗∗∗_</sup>|
||(0.227)|(0.236)|(0.061)|(0.063)|(0.058)|(0.060)|
|_Sj_|0.648<sup>_∗∗∗_</sup>|1.023<sup>_∗∗∗_</sup>|0.584<sup>_∗∗∗_</sup>|0.701<sup>_∗∗∗_</sup>|0.458<sup>_∗∗∗_</sup>|0.587<sup>_∗∗∗_</sup>|
||(0.088)|(0.086)|(0.023)|(0.025)|(0.030)|(0.030)|
|Constant|5.402<sup>_∗∗∗_</sup>|3.067<sup>_∗∗∗_</sup>|6.471<sup>_∗∗∗_</sup>|6.326<sup>_∗∗∗_</sup>|7.502<sup>_∗∗∗_</sup>|7.410<sup>_∗∗∗_</sup>|
||(0.419)|(0.233)|(0.124)|(0.108)|(0.420)|(0.139)|
|N|825|825|6032|6032|6189|6189|
|R-squared|0.716|0.577|0.480|0.327|0.382|0.186|
|2 Dig. Sector FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country FE|Yes|No|Yes|No|Yes|No|
|Data|Agri.|Agri.|Manu.|Manu.|Serv.|Serv.|

Notes : All regressions estimated using establishment-level sampling weights. *, ** and *** denote 10%, 5% and 1% significance respectively.

57
data for about 90% of the potential universe throughout the period we consider. No comparison over time is possible for social protection spending, since we have only one data point for each country. When contrasting the HCI or its components with spending values, we use the most recent data point available from each country. Health data are most dense. Specifically, 98% of countries have data from 2016, meaning that the vast majority of countries have data from the most recent possible year. For education, however, the most recent observation of spending data ranges in years from 2000 to 2016 (with 52% from 2016 and 86% from 2010 and more recent). Social protection spending data are from 2009-2016. In short, only in very selected cases do measures of the outcomes and the spending refer to the same year. The government domestic expenditure on health, education, and social protection in the most recent year is available for 124 countries and is used in our estimates of “social sector spending”.

# B. Data density

High-quality data are the prerequisite to understand the relationship between social sector spending and human capital outcomes. In this section we discuss how spending data availability has evolved over time and across regions. For this purpose, we divide the time period under consideration into four sub-periods (2000-2003, 2004-2007, 2008-2011, 2012-2016), with the different length of the last one explaining the difference in the number of potential observations.

Data density for domestic current health spending has been consistently high, with a coverage of at least 96% of all potential observations throughout the entire time period (Table 2). Remarkably, low-income countries (LICs) have seen a significant increase from 88% in 2000-2003 to 93% in 2011-2016. The picture is completely different when we look at the density of spending data for education, with coverage peaking at 67% in the time period between 2008 and 2011 (Table 2). Perhaps even more worryingly, we do not observe a stable upward trend in data availability: in 2012-2016 the coverage went down to 61%. Also for education, LICs display a positive trend (from 50% in 2000-2003 to 71% in 2012-2016), while high-income countries display an opposite trend (from 88% in 2000-2003 to 71% in 2012-2016). Information on spending in social protection is low across the spectrum, with an average density of 11%. In 2008-2011 there were only 21 observations, while there were 102 observations in 2012-2016.

6
**_Figure 11_** _- Guarantee allocation in 2019 vs COVID-19 programs_

_(In USD million and as a percentage of guarantees allocation in 2019)_

|**Peru**|**Paraguay**|**Brazil**|
|---|---|---|
|16,000<br>**106,235%**|400<br>**68,498%**|16,000<br>**7,916%**|
|14,000|350|12000<br>14,000|
|12,000|300|,|
|10,000|250|10,000|
|8,000|200|8,000|
|4,000<br>6,000|100<br>150|4,000<br>6,000|
|2,000|50|2,000|
|0|0|0|
|**Uruguay**|**Chile**|**Colombia**|
|500<br>**1,982%**|10,000<br>**1,112%**|4,000<br>**160%**|
|100<br>200<br>300<br>400|2,000<br>4,000<br>6,000<br>8,000|1,000<br>1,500<br>2,000<br>2,500<br>3,000<br>3,500|
|0||500|
||0|0|
|**Mexico**|**Ecuador**|**Argentina**|
|12,000<br>**140%**|60<br>**123%**|500<br>**97%**|
|10,000|50|400|
|4,000<br>6,000<br>8,000|20<br>30<br>40|200<br>300|
|2,000|10|100|
|0|||
||0|0|

**Source:** Own elaboration, credit to SMEs obtained from the Financial Access Survey (IMF) and from the MSME Finance Gap Report (IFC) if missing.

**Note:** Heat map bar depicts the credit to SMEs covered by PPCG as percentage of the financial system credit to SMEs as in Figure 4. Two of the three PCGs in Argentina did not implement COVID-19 programs. We couldn’t compile complete information for _Banco de Desarrollo Productivo_ (BDP, Bolivia), and _Sociedad Administradora de Fondos de Garantía Reciproca_ (SAFGR, Honduras). In the case of BDP its guarantees granted by the end of 2020 totaled less than USD 1 million, and around USD 33 million in SFGR in Honduras. Besides, the guarantees granted by FSG in El Salvador stood at USD 38 as of 2019.

20
# **5.7 Do regulations influence the various margins of labor reallocation differently?**

So far we have focused on the effects of regulations in labor and product markets on overall job reallocation. In this section we want to explore whether such regulations have a different impact on the different margins of reallocation, namely on job flows due to the entry and exit of firms in the market and those due to reallocation among incumbents (see Table 15).<sup>18</sup> Column (1) shows that cross-cell variation in job reallocation by entering and exiting firms in the Latin American countries is very similar to the variation observed in the United States, which is the reason for the close link between cross industry/size variation in job reallocation between the United States and the Latin American countries (see Table 14), given that the coefficient on U.S. job reallocation for continuing firms in Latin America is much lower in magnitude (see column (2) of Table 15). The link between cross industry/size differences in job reallocation is not as close between the United States and the EU and transition countries, and there is not much difference in the strength of the link for entering/exiting businesses and for continuing businesses.

Column (3) of Table 15 shows the results of estimating the job flow regressions for entering and exiting firms, controlling for labor and product market regulations corrected by the degree of enforcement and differentiating the impact of both by firm size. Column (4) does the same for continuing firms. The results suggest a negative and statistically significant effect of labor market regulation (interacted with U.S. job reallocation) on labor mobility generated by entering and exiting firms for all but large firms. The coefficients are also more than twice as large in magnitude as the corresponding coefficients in column (4) of Table 14, and they are about the same magnitude for micro, small and medium entering and exiting firms. However, in order to correctly assess the magnitude of the impact, we need to remember that the magnitude of job reallocation varies significantly by size class. Taking that into account, note that stringent labor market regulation has the biggest impact on job reallocation by micro entering and exiting firms: it is associated with a 4.6 percentage points drop in job reallocation by such firms. The impact on small, medium and large firms is lower: stringent labor market regulation is associated with a 2.5, 2.2 and 0.5 percentage points drop in job reallocation, respectively.

The estimated effects of product market regulation (interacted with U.S. job reallo-

> 18We focus on the combined flows due to entry and exit of firms because of the very high correlations between entry and exit across industries in most countries. This in turn suggests that entries and exits are largely part of a creative destruction process in which entry and exit reflect within sector reallocation reflecting idiosyncratic differences across firms within sectors (see Bartelsman et al. [2004] for evidence based on the same dataset used in this paper, as well as Geroski [1991], Baldwin and Gorecki [1991]).

41
**Table 6: Test of Complementarity Between Parent’s Education and Occupation in Children's Years of** **<u>Schooling</u>**

|||**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|---|
|||**Standard specification**|**Marginal**<br>**parents' ed**<br>**farm and**<br>**house**|**effect of**<br>**ucation in**<br>**non-farm**<br>**holds**|**Test of equality between**<br> **Columns (2) and (3)**|
|||||**Non-**||
||||**Farm**|**farm**|**Z-score**|
|**Panel 6A:**<br>**Children-Pa**|**rents**|||||
|**_Daughters_**||||||
||OLS|**-0.04**<br>-0.23|**1.05**<br>8.26***|**0.88**<br>5.02***|0.77|
|**Matching**<br>**estimator**|A-I<br>matching<br>BC-RM||**1.05**<br>7.18<br>**1.00**<br>7.03***|**0.83**<br>4.27<br>**0.96**<br>4.93***|0.9<br>0.16|
|**Propensity**<br>**score**<br>**weighted**<br>**estimator**|NIPW<br>MB-NIPW||**1.01**<br>(0.76,<br>1.22)<br>**0.93**<br>(0.62,|**0.88**<br>(0.55,<br>1.14)<br>**1.01**<br>(0.44,|0.27<br>-0.15|
||||1.23)|1.57)||
|**_Sons_**||||||
||OLS|**-0.15**<br>-0.86|**1**<br>10.74***|**0.7**<br>4.64***|1.71*|
|**Matching**|A-I<br>matching||**1.05**<br>10.31|**0.66**<br>3.81|1.94**|
|**estimator**|BC-RM||**0.91**<br>8.21***|**0.86**<br>5.52***|0.26|
|**Propensity**<br>**score**|NIPW||**1.04**<br>(0.86,<br>1.19)|**0.63**<br>(0.34,<br>0.91)|2.12**|
|**weighted**|||**1.12**|**0.59**||
|**estimator**|MB-NIPW||(0.77,<br>1.34)|(0.05,<br>1.04)|1.74*|

# **_Notes_**

- 1) For the column (1), the dependent variable is years of schooling of children generation, the regression includes the family background occupation dummy, family education dummy, the interaction of the two dummies, and the controls; but the table only reports the coefficient of the interaction of two dummies;

- 2) For the column (1), family background occupation dummy = 1  if off-farm, =0 otherwise, family background education dummy = 1 if more than primary schooling, 0=otherwise;

- 3) For all columns, the control variables are children's age, children's age squared, mother's age, mother's age squared, father's age, father's age squared;

- 4) Column (2) and (3) break the full sample into two subsamples based on family occupation background, for each subsample, the treatment is family education background high, which is more than primary schooling;

- 5) A-I matching=Abadie and Imbens (2002)

- 6) BC-RM = Biased corrected radius matching;

- 7) NIPW = normalized inverse probability weighted propensity score estimator of Hirano and Imbens (2001);

- 8) MB-NIPW = minimum-biased normalized inversed probability weighted estimator;

- 9) The computation uses STATA program written by Millimet and Tchernis (2013);

- 10) For NIPW and MB-NIPW, 90% confidence interval is given in parentheses, which is boostrapped using 250 replications;

- 11) ***, **, and * Denotes statistical significance at 1%, 5%, and 10% level;
# **<u>1. Introduction</u>**

While most education research focuses on the effects of material inputs, class size, or school organization, the most important input in the education production function may be children’s study effort. Yet the impact of performance incentives to boost student effort has been relatively unexplored in both economics and education. There are a number of reasons to think that children’s study effort may often be suboptimally low in the absence of performance incentives. For one, even in the presence of large future returns to education in the labor market, individual study effort may be too low in a lifetime sense since adolescents typically have far higher time discount rates than adults (Greene 1986, Nurmi 1991). In a second, related point, agents with time inconsistent preference, such as hyperbolic discounting, may under-invest in activities like studying which have immediate costs and mainly long-run payoffs.

This paper examines the impact of the Girls Scholarship Program (GSP), introduced in rural Kenyan primary schools in 2001. The project provided awards for 13-15 year old girls amounting to approximately US$38 per winner over two years – a large sum in this region, where annual per capita income is only US$360 (World Bank 2002). Scholarship program schools were randomly selected, allowing us to attribute differences in educational outcomes between the program and comparison groups to the scholarship program. We employ data on student and teacher school attendance, and students’ selfesteem, attitudes toward school, and study patterns to isolate the behavioral mechanisms through which program incentives impact education.

There are three main findings. First, program school girls had significantly higher test scores than comparison school girls on average, and this result is nearly identical over the two years of scholarship competition. Second, program test score impacts persisted for one full year after the competition, evidence of medium-run program benefits that are unlikely to be simply the result of cramming for a single exam, or “teaching to the test”. Third, there is evidence the program generated substantial positive classroom externalities: in the larger of the two study districts (Busia district) there were significant test gains for boys (boys were all ineligible for the scholarship), as well as for girls with low initial scores, who had little chance at winning the award. The existence of externality benefits greatly increases 1
Table 8: Split IV regression of the effects of IT use on firm-level productivity: Low competition with China

|Dependent variable: log(sales/worker)|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Computers-per-worker|0.995|1.613|1.639|1.454|1.642|1.391|
||(1.356)|(1.681)|(1.731)|(1.818)|(1.844)|(1.772)|
|F-frst stage|8.5|5.7|6.1|5.5|5.5|5.7|
|Sargan|0.90|0.98|0.72|0.67|0.75|0.90|
|Share of labor with Internet|1.075|0.954|1.019|1.046|1.327|0.806|
||(1.262)|(1.245)|(1.328)|(1.377)|(1.471)|(1.781)|
|F-frst stage|11.3|12.0|12.0|11.0|11.0|6.7|
|Sargan|0.92|0.83|0.66|0.47|0.39|0.29|
|Share of labor with computer|0.517|0.475|0.353|-0.238|-0.447|-0.391|
||(1.472)|(1.422)|(1.144)|(1.277)|(1.281)|(1.267)|
|F-frst stage|5.4|6.0|10.3|8.3|8.3|8.6|
|Sargan|0.71|0.76|0.98|0.93|0.98|0.78|
|Controls|||||||
|Age|No|Yes|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|Yes|
|FDI share|No|No|No|No|Yes|Yes|
|Time efects|No|No|No|No|No|Yes|
|Observations|672|672|672|672|672|672|

Robust standard errors in parentheses

* _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 All specifications include firm’s fixed effects.

Source: Authors’ calculations using data from ENTIC, INEGI 23