rafmacalaba/datause-encoder-data huggingface dataset:
|Ta|ble4:Labor forc<br>|e participation, b<br>|yrace andimmig<br>|rationstatus,for<br>|eachgenderand <br>|educationcatego<br>|ry separately<br>||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|||Ma<br>|les<br>|||Fem<br>|ales<br>||
||Less than<br>HighSchool|High School|Some college|College and<br>above|Less than<br>HighSchool|High School|Some college|College and<br>above|
|Dependent variable|In the labor<br>force|In the labor<br>force|In the labor<br>force|In the labor<br>force|In the labor<br>force|In the labor<br>force|In the labor<br>force|In the labor<br>force|
|White bornout US|0.0045|-0.0277***|-0.0338***|-0.0282***|-0.0208***|-0.0876***|-0.0890***|-0.0936***|
||[0.0045]|[0.0033]|[0.0027]|[0.0016]|[0.0046]|[0.0035]|[0.0031]|[0.0027]|
|Blackborn inUS|-0.1202***|-0.1028***|-0.0623***|-0.0316***|-0.0333***|-0.0508***|-0.0147***|0.0309***|
||[0.0024]|[0.0017]|[0.0015]|[0.0017]|[0.0024]|[0.0019]|[0.0016]|[0.0019]|
|Blackbornout US|0.0082|-0.0588***|-0.0613***|-0.0427***|0.1113***|0.0199***|0.0051|0.0166***|
||[0.0069]|[0.0062]|[0.0051]|[0.0042]|[0.0068]|[0.0061]|[0.0052]|[0.0054]|
|Asianborn inUS|-0.0538***|-0.0396***|-0.0211***|-0.0203***|0.0071|-0.0002|0.0175***|-0.0046|
||[0.0161]|[0.0079]|[0.0050]|[0.0032]|[0.0158]|[0.0095]|[0.0060]|[0.0047]|
|Asianbornout US|-0.0266***|-0.0578***|-0.0942***|-0.0604***|0.0165***|-0.0791***|-0.1058***|-0.1246***|
||[0.0044]|[0.0041]|[0.0033]|[0.0017]|[0.0038]|[0.0040]|[0.0035]|[0.0024]|
|Hispanic born inUS|-0.0704***|-0.0684***|-0.0386***|-0.0155***|-0.0586***|-0.0418***|-0.0111***|0.0115***|
||[0.0030]|[0.0025]|[0.0021]|[0.0022]|[0.0030]|[0.0027]|[0.0023]|[0.0029]|
|Hispanic bornout US|-0.0320***|-0.1111***|-0.0867***|-0.0822***|-0.0577***|-0.1310***|-0.0961***|-0.1284***|
||[0.0020]|[0.0029]|[0.0028]|[0.0032]|[0.0022]|[0.0030]|[0.0032]|[0.0041]|
|Observations|593401|1003587|963513|912345|552861|1070422|1122287|903624|

Robust standard errors in brackets. * Significant at 10%; ** significant at 5%; *** significant at 1%. Data from the 2000 Census IPUMS data (5% sample), population aged 25-65. The probit regressions (marginal effects shown) also include 5 year age group dummies and region dummies, as well as indicators for being married, disabled and in school, the number of children under age 5 in the household and the logarithm of total personal income earned by the other household members. “White natives” is the omitted dummy. The regressions use the sampling weights provided with the IPUMS data.

32
based quotas, primarily in India (Basant & Sen, 2020; Bertrand et al., 2010; Cassan, 2019) and Israel (Alon & Malamud, 2014); and geographic quotas, in Sri Lanka (de Silva et al., 2021). Most of the literature indicates that affirmative action is instrumental in increasing access to education in historically underrepresented populations.

Public universities in Afghanistan operate under a stringent centralized system. Admission to a public university is determined solely by the applicants’ scores on the annual nationwide entrance exam for public universities, known as the _Kankor_ examination. Female applicants tended to score lower than their male counterparts in the Kankor. This disparity in performance is attributable to the systematic underinvestment and cultural barriers Afghan women face in accessing post-secondary education (Bamik, 2018; Hayward & Babury, 2015; Najam & Johnston, 2023), which is exacerbated by conflict (Darwish & Wotipka, 2022). In 2016, the Afghan government introduced a gender quota for admissions to address gender disparities and promote women’s long-term labor participation. The policy expanded until the Taliban’s return. This policy allocated seats at the enrollment unit level ( _concentrations_ ) by gender in particular universities and courses of study. These concentrations served as the _treated units_ . Consequently, the admissions competition was gender-specific—female applicants competed with other females, and male applicants competed with other males—for gender-designated seats in treated concentrations. Conversely, the other concentrations maintained open competition<sup>1</sup> (hereafter, _untreated units_ ).

In this paper, I examine the short-term efficacy of gender quotas on women’s matriculation<sup>2</sup> , especially those from low socioeconomic status—proxied by districts’ average nightlight data—in Afghan public universities. Specifically, I explore variations across study units and over time to assess the causal impact of gender quotas on the share of matriculated female students and score-related outcomes using the difference-in-differences (DD) methods. The causality of the DD estimates relies on the central parallel

> 1Admission is solely based on applicants’ Kankor score.

> 2Kankor participants receive a single admission offer from the administering authority. The terms ‘matriculation,’ ‘admission,’ and ‘enrollment’ are used interchangeably.

2
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TABLE A7: Impacts of text messages on ECD Outcomes, by treatment variations

||(1)<br>ECD full sample|(2)<br>ECDyoung|(3)<br>ECD Old|
|---|---|---|---|
|Panel A: By treatment variation (thematic focus)||||
|Nutrition & Stimulation|-0,00|-0,05|0,02|
||(0,04)|(0,07)|(0,05)|
|Nutrition|-0,02|0,02|-0,05|
||(0,04)|(0,08)|(0,05)|
|Stimulation|0,02<br>(0,04)|0,00<br>(0,08)|0,02<br>(0,05)|
|Observations|2485|774|1711|
|Panel B: By treatment variation (targeted caregiver)||||
|Mother|0,03|0,05|0,02|
||(0,04)|(0,08)|(0,05)|
|Father|-0,01|-0,01|-0,01|
||(0,04)|(0,08)|(0,05)|
|Mother and Father|-0,02|-0,06|-0,02|
||(0,04)|(0,07)|(0,05)|
|Observations|2485|774|1711|

_Note:_ All outcome variables are standardized using the mean and standard deviation of control households. In column 1, the dependent variable is the first principal component of the 4 Denver sub-components for children below 36 months and the first principal component of all 7 tests for children 36-84 months old. In column 2, the dependent variable is the principal component for the four Denver sub-components for children aged 12 to 35 months. In column 3, the dependent variable is the first principal component of all 7 tests (4 Denver sub-components plus TVIP, Memory and self-control) of children aged 36 to 83 months. All the regressions include controls for the stratification variables, child age and gender, enumerator fixed effects, and a dummy variable for a set of households surveyed in 2016, as well as village fixed effect. Standard errors reported in parentheses.<sup>_∗_</sup> p<0.1,<sup>_∗∗_</sup> p<0.05,<sup>_∗∗∗_</sup> p<0.01 55
variables, we also expect that the general development of the country, the occurrence of a systemic banking crisis, and the level of economic growth will affect the relative use of bankruptcy.

# **3. Data and Summary Statistics**

The number of total commercial bankruptcy filings was collected from government and private sources around the world for all available years between 19901999.7 We include the sum of all firms that file for liquidation or reorganization under the bankruptcy code. This measures the total use of the bankruptcy law and the judicial system to resolve corporate financial distress. In order to compare the relative use of bankruptcy cross-country, we normalize the number of bankruptcy filings. We use the total number of firms, as provided by Djankov, La Porta, Lopez-de-Silanes, and Shleifer (2002) and official country statistical handbooks.<sup>8</sup> Tables 1 and 2 show some summary statistics for the countries in our sample panel.

To explain the relative use of bankruptcy, we include as explanatory variables measures of macroeconomic performance, financial structure, efficiency of judicial system, other institutional measures, and the specific creditor rights discussed above. We expect that the number of failed firms depends on a country's current and expected 7 See Appendix 1 for the country sources. In part because, there is variation across countries in the definition and implications of bankruptcy, we include all legal proceedings designed to either liquidate or rehabilitate an insolvent firm. Results were qualitatively robust to analyzing only liquidation procedures for those countries that identified those numbers separately.

8 For seven countries, only the total number of manufacturing firms is available. For these countries we extrapolate the total number of firms by sector and legal origin (English, French, etc.). All empirical results are robust to the exclusion of these countries.

13
Table 3: Probit Estimates of relationship between Wife Characteristics and Domestic Violence

|Dependent variable = 1(Ever|Been Beaten)||||
|---|---|---|---|---|
|(1)|(2)|(3)|(4)|(5)|
|Work<br>0.017|-0.253***|0.442**|0.119***|0.207*|
|[0.036]|[0.078]|[0.181]|[0.045]|[0.124]|
|Age|0.003*<br>[0.001]||||
|Work X Age|0.011***||||
||[0.003]||||
|Age First Marriage||-0.008|||
|||[0.006]|||
|Work X Age First Marriage||-0.026**|||
|||[0.012]|||
|Education|||-0.015***<br>[0.005]||
|Work X Education|||-0.021***<br>[0.008]||
|Migrant||||0.035<br>[0.044]|
|Work X Migrant||||-0.190*|
|||||[0.115]|
|Work X Migrant X Began Work After Migrating||||0.007<br>[0.044]|
|Observations<br>1,322|1,322|1,321|1,322|1,321|
|_Standard errors in brackets, clustered at the level of the village. *** p<0.01, *_|_* p<0.05, * p<0.1_||||
Policy Research Working Paper 6770

# **Abstract**

Whether the negative relationship between farm size and productivity that is confirmed in a large global literature holds in Africa is of considerable policy relevance. This paper revisits this issue and examines potential causes of the inverse productivity relationship in Rwanda, where policy makers consider land fragmentation and small farm sizes to be key bottlenecks for the growth of the agricultural sector. Nationwide plot-level data from Rwanda point toward a constant returns to scale crop production function and a strong negative relationship between farm size and output per hectare as well as intensity of labor use that is robust across specifications. The inverse relationship continues to hold if profits with family labor valued at shadow wages are used, but disappears if family labor is rather valued at village-level market wage rates. These findings imply that, in Rwanda, labor market imperfections, rather than other unobserved factors, seem to be a key reason for the inverse farm-size productivity relationship.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at dali1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **4.0 Multi-Level Models**

A brief introduction to the choice of method is necessary. Previous civil war studies have used a variety of methods such as Poisson, linear, and logistic regression (Collier, 1999-2001; Fearon and Laitin, 2003; Sambanis, 2001). Fearon and Laitin’s use of logistic regression is structured to provide probabilities of a positive outcome (e.g. probability that war will occur) yet it is not appropriate for time dependent data. Because of the nature of time-series data, observations of countries over time are dependent; not accounting for this temporal dependence can bias statistical models.

Mixed multilevel models (MML) can be used to explore and predict factors that lead to civil war onset and instability. MML models are a variant of multiple regression that allow for both random and fixed effects and a hierarchical distribution of variance. The basic idea of MML models is that both individual and group aspects explain the dependent variable (Sniders and Bosker, 1999). To ignore clustered relationships (such as pupils in schools, patients in hospitals, and country years in countries) risks overlooking the impact and importance of group effects while also potentially invalidating many of the traditional statistical techniques used for studying relationships. The basic MML model, described in equation 1, is designed around random intercepts. The intercept is able to vary throughout the levels producing a deviation of each level. In turn, this deviation is used to create the variance accounted for on each level. The benefits of using multilevel models are: 1) that hierarchical frameworks “encourage a systemic analysis of how covariates affect an outcome variable”; 2) correct biases in parameter estimates common in clustered data; 3) correct standard errors, confidence intervals and significance tests; 4) create decomposed variance and covariance for each level (Guo and Zhao, 2000: 444). The assumptions underlying MML models are that the variance on each level is higher than zero and the log relative risk follows a normal distribution (Langford et al. 1999).

For this model, observations are clustered into three levels. The level one units are countryyears (e.g. each year from 1945-2001 in France, Mali from 1960-2001, etc). Each country-year is associated with a country, which is nested in a region.

15
capability to alter their energy technology in the short run, they will choose a more variable energy input in the baseline period versus the CDM implementation period, and in particular choose a higher energy input in the baseline period, as a “less sophisticated” (and cheaper) energy technology is chosen in this period. In the CDM implementation period it will be opposite, as the energy technology will then be relatively more sophisticated when it can be altered in the short run.

**Figure 1: Emissions under baseline (** **_EP_ ) and in the CDM implementation period (** **_EC_ ), in cases with constant** **_H_ , and endogenous** **_H_ , respectively**

<!-- Start of picture text --> 5.0<br>Ep (endog. H)<br>4.5<br>Ec (endog. H)<br>4.0 Ep (constant H)<br>Ec (constant H)<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>0 2 4 6 8 10 12 14 16 18 20<br>Emission price<br>Source: Own simulations<br>Notes:<br>Emissions in the case of no CDM are normalized to unity.<br>Parameter values applied for simulations: β = 0.5 , γ = 1.25, λ = 0.5, and z = 20.<br>Emission<br><!-- End of picture text --> The practical implication of this result is that when firms are foreseeing CDM financing, and may choose their baselines, firms will tend to choose a baseline (pre-CDM) energy technology that is less efficient than otherwise (chosen in the absence of CDM financing; in our model, prevailing under case B). Drawing also on conclusions from Section II.2 above, where not all firms seeking CDM financing were assumed to obtain it, one could also here easily visualize cases where inefficient energy technologies are established and retained over time, due to availability of the CDM option, also in firms that do not actually obtain CDM financing.

# **4. Remedies**

The basic remedy for the troubles outlined in sections above would be for the CDM regulator to set a predetermined baseline tied to “objective” criteria, and based on an (hopefully unbiased) assessment of the firm’s “objectively correct” emissions. This would make the 14
show, using French regional data, that infrastructure investment decisions are often politically driven and are therefore likely to depart from efficiency considerations.

# **Main Methodological Issues**

The main problem that has plagued early studies and has been deemed responsible for the sometimes unrealistic results displayed is the potential endogeneity of infrastructure indicators. Such endogeneity has three main origins.<sup>33</sup> First, measurement error problems have already been discussed above and the main conclusion is that they seriously weaken the case for using public capital figures as proxy for infrastructure.

The second issue is that of potential unobserved effects or omitted variables. Unobserved effects arise if specific geographical units, countries or regions, have characteristics that lead them both to have higher performance (growth, productivity, etc.) and to invest more in infrastructure and if these aspects are unobserved to the econometrician. In most cases, it is plausible that some of these unobserved effects are time invariant, so the issue may be addressed with fixed effects estimation techniques in the context of panel data. Holtz-Eakin (1994) and Garcia-Milà et al. (1996) revisit the evidence on US states and find that once fixed effects are accounted for, no significant effect of public capital remains. Table 4 indeed shows that panel data specifications incorporating fixed effects do find negative or inconclusive results much more often.

**<u>Table 4</u>**

|||**Results**||
|---|---|---|---|
||**-1 **|**0**|**1**|
|**Fixed effects**||||
|**No (41)**|2.4%|22.0%|75.6%|
|**Yes (47)**|8.5%|40.4%|51.1%|

It may also the case that omitted factors are time varying ones, in which case instrumental variables are needed. Straub (2007) discusses how instrumental variables may be constructed using data about policy in neighboring countries or state.

Finally, perhaps the more serious problem is that of reverse causation, and indeed it is often argued that the large estimates obtained by early papers were due to the neglect of this issue. This has mainly been addressed by using lagged values of the independent variables as instruments, a less than perfect solution in the context of the relatively small samples that characterize infrastructure studies. Romp and de Haan (2005) survey the macro-econometric literature and indicate that when reverse causation considerations were taken into account, the magnitude of estimates was approximately reduced to one third of Aschauer’s initial estimates, an indication of the importance of the issue. Cost function studies have also been used in an attempt to go around this problem, but they are in principle more suited to industry-level data and long panel because of their very data-demanding nature (see discussion in Romp and de Haan, 2005).

> 33 See Wooldridge (2002) for more details.

24
percent of overall price variability.<sup>23</sup> It increases to 27.7 and 21.3 percent in Niger and Burkina Faso respectively, where agriculture is also mainly rain‐fed and highly seasonal. While the bulk of intra‐annual price variability is not related to seasonal fluctuations, for a number of crops (maize) and countries (especially in the Sahel), its contribution appears nonetheless non‐ negligible.

Thirdly, we ask, is the seasonality we find in African food markets is excessive? Some seasonality in prices is to be expected when production is seasonal, given storage costs. But what should count as excessive? Most of the products considered are non‐traded in the sense that only small quantities cross national borders. However, this is not true of either maize or rice and for these two commodities the national seasonal gaps can be compared with those on the relevant international market. White maize predominates in human consumption through most of Africa rather than the yellow maize typically consumed in the developed world. The Johannesburg futures market (SAFEX) provides the reference price for white maize in southern and east Africa. This price is quoted in rand. For rice, the most commonly used reference price is the Bangkok spot price (5 per cent broken) which is quoted in US dollars. In both cases, we use monthly prices over the 13 year period 2000‐12.<sup>24</sup> Seasonality is well defined in both price processes, with the dummy variables specification preferred in each case. The estimated seasonal gaps are 12.2 percent for SAFEX white maize and 5.1 percent for Bangkok rice.<sup>25</sup> These statistics are to be compared with the average maize and rice seasonal gap estimate of 33.1 percent and 16.6 percent respectively. In both cases, seasonality is on average two and a half to three times as acute in local African markets as on the relevant international market. Moreover, the local African seasonal _R_<sup>2</sup> coefficients for maize

> 23 These statistics may show an upward bias in small samples (section 3), though the country fixed effects also helped control for this.

> 24 ‐ Sources: Johannesburg Stock Exchange, <u>https://www.jse.co.za/downloadable</u>

> <u>files?RequestNode=/Safex/PriceHistory/Spot%20Months%20on%20all%20Grain%20Products</u> and IMF, _International Financial Statistics_ .

> 25 In Kaminski et al (2016) we used a slightly different model selection criterion which favored the dummy specification for these two price series. This gave slightly narrower seasonal gaps.

22