rafmacalaba/datause-encoder-data huggingface dataset:
has reduced fertility for Ethiopian women.

Earlier literature (Ainsworth et al., 1996; Lam and Duryea, 1999; Schultz, 1994, 1997) has documented the negative relationship between schooling and fertility that exists in the data. To identify the effect of education, Osili and Long (2008) used school construction in Nigeria, and Keats (2018) exploited a discontinuity around the implementation of FPE in Uganda. Both articles found that education led to a reduction in fertility of between 0.263 and 0.36 births for each additional year of schooling. Ozier (2018) also showed that access to secondary school reduced teen pregnancy in Kenya, and Zenebe Gebre (2018) found that an FPE reform in Malawi led to reductions in fertility through the age of 25. Although these articles all found a similar relationship between schooling and fertility, the mechanisms vary. In addition to the timing of marriage and evidence of increased labor market productivity, Keats (2018) also found evidence of increased use of contraceptives. In Malawi, Zenebe Gebre (2018) documented strong evidence of an increase in the use of contraception and a move away from agricultural employment.

Outside of Africa, the evidence of a causal relationship between schooling and fertility has been more mixed. Fort et al. (2016) discovered evidence of a positive causal relationship between schooling and fertility in continental Europe but a negative relationship in the United Kingdom, and Clark and Bono (2016) found that school quality in the United Kingdom had a significant positive impact on women’s earnings and a negative effect on fertility. Exploiting discontinuities at starting ages, McCrary and Royer (2011) found no evidence that schooling affected the probability of motherhood.

The results of this article are significant in three ways. First, the article presents an application of a difference-in-differences identification strategy that can be used in a variety of settings to study national-level reforms with a minimal amount of pre-reform information needed for identification. Measuring the impact of national-level removal of user fees was one of the key categories found to need further study in the 3ie systematic review of education policy (Snilstveit et al., 2016). Second, this article finds strong evidence of a negative relationship between schooling and fertility, and investigates the detailed pathways through which this relationship develops. Third, the article finds significant evidence of positive returns to schooling, through both reduction in fertility and improvement in labor market outcomes. This result suggests that increased schooling generated by the removal of school fees led to lasting increases in education that bettered the day-to-day lives of Ethiopian women.

# **2 Background and Education Reform**

After 17 years of military and communist rule, the Ethiopian People’s Revolutionary Democratic Front took power in 1991 and quickly established a transitional government (Ofcansky and Berry, 1993). This govern2
WPS4102 <mark>I</mark> MPACT <mark>E</mark> VALUATION <mark>S</mark> ERIES <mark>N</mark> O <mark>. 9</mark>

**Conditional Cash Transfers and Female Schooling:**

**The Impact of the Female School Stipend Program on Public School**

**Enrollments in Punjab, Pakistan**

Nazmul Chaudhury Dilip Parajuli The World Bank

# Abstract

Instead of mean-tested Conditional Cash Transfer (CCT) programs, some countries have implemented gender-targeted CCTs to explicitly address intra-household disparities in human capital investments. This study focuses on addressing the direct impact of a female school stipend program in Punjab, Pakistan – Did the intervention increase female enrollment in public schools? To address this question, we draw upon data from the provincial school censuses 2003 and 2005. We estimate the _net_ growth in female enrollments in grade 6-8 in stipend eligible schools. Impact evaluation analysis, including difference-and-difference (DD), triple differencing (DDD) and regression-discontinuity design (RDD), indicate a modest but statistically significant impact of the intervention. The preferred estimator derived from a combination of DDD and RDD empirical strategies suggests that the average program impact between 2003 and 2005 was an increase of six female students per school in terms of absolute change and an increase of 9 percent in female enrollment in terms of relative change. A triangulation effort is also undertaken using two rounds of a nationally representative household survey before and after the intervention. Even though the surveys are not representative at the sub-provincial level, the results corroborate evidence of the impact using school census data.

# World Bank Policy Research Working Paper 4102, December 2006

_The Impact Evaluation Series has been established in recognition of the importance of impact evaluation studies for World Bank operations and for development in general. The series serves as a vehicle for the dissemination of findings of those studies. Papers in this series are part of the Bank’s Policy Research Working Paper Series. 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._ __________________________________ The authors would like to thank Jishnu Das, Michelle Riboud, Tahseen Sayed and an anonymous referee for their valuable comments and suggestions on the paper.
_Table 3: Soft vs. Hard Definition (Bilateral 5-Year Fixed Effects and Year Fixed Effects Included)_

||1|2|3|4|5|6|7|
|---|---|---|---|---|---|---|---|
||Baseline<br>– all<br>positive<br>trade|Baseline<br>with per<br>capita<br>GDP|Only<br>countries<br>receiving<br>AfTF /1/|Recipients<br>only;<br>trade with<br>recipients<br>& ROW|Recipients<br>with<br>developing<br>world only|Recipients<br>with<br>developing<br>& bilateral<br>donor; no<br>intra-<br>donor<br>trade|Base-line<br>(2) with<br>lags|
|**_Soft Aid _**<br>|**_Definition _**<br>|<br>||||||
|**ln_itfa**|**0.004***|**0.004***|**0.068****|**0.024*****|**0.004**|**0.004**|**0.024*****|
|**ln_etfa**|**-0.002**|**-0.002**|**0.057***|**-0.009**|**-0.007****|**-0.002**|**0.000**|
|L.ln_itfa|||||||0.046***|
|L2.ln_itfa|||||||0.020***|
|L.ln_etfa|||||||0.002|
|L2.ln_etfa|||||||0.004|
|**_Hard Aid_**<br>|**_Definitio_**<br>|**_n_**<br>||||||
|**ln_itfa**|**0.004***|**0.004**|**-0.012**|**0.015*****|**0.005**|**0.004**|**0.017****|
|**ln_etfa**|**0.002**|**0.002**|**0.047****|**0.001**|**0**|**0.001**|**-0.001**|
|L.ln_itfa|||||||0.058***|
|L2.ln_itfa|||||||0.003|
|L.ln_etfa|||||||-0.005|
|L2.ln_etfa|||||||0.006*|

Robust standard errors in brackets; includes bilateral 5 year fixed effects, year effects * significant at 10%; ** significant at 5%; *** significant at 1% See appendix tables for all coefficients

# **IV.3. Robustness Checks and Analysis**

A robustness check, as well as to compare with other research, is to examine the results using a standard gravity model specification without the fixed effects specification. Table 4 indicates that a more standard approach to the gravity specification (including various dummy variables and distance for example) gives somewhat different results. Coefficients on the aid variables tend to be larger, particularly when trade between recipients is considered (compare column (3) and (5), and aid flows appear to be negatively related to imports when lags are considered (column 7). Thus, specification is an important concern when examining the potential role for aid to enhance trade.

15
Appendix Table A4 compares our study sample to respondents from the nationally representative INEGI survey, both in Mexico and in Tlaxcala. In Panel A we include all farmers, while Panel B restricts the sample to rainfed farmers. Study farmers have lower yields than both the national and the Tlaxcala sample. In terms of agricultural practices, study farmers are less likely to use hybrid seeds relative to the national average (but comparable to the Tlaxcala sample) and are more likely to have used fertilizer and herbicide than either of the INEGI samples. They are also more likely to have used extension services in the past. Panel B shows similar patterns, and both panels suggests that farmers with greater experience and perhaps those interested in improved inputs were more likely to select into the study.

# **3 Experimental Design**

The five experimental arms are a combination of three components: (a) soil analysis and recommendations (individual or averaged), (b) extension services and (c) and in-kind grants (conditional or unconditional) as outlined in the introduction. To summarize:

- **T1** : _Individualized or plot-level_ soil analysis, input recommendations, extension services; inflexible in-kind grant.

- **T2** : _Averaged or cluster-level_ soil analysis, input recommendations, extension services; inflex-

- ible in-kind grant. (i.e. same as **T1** but with averaged instead of individual soil analyses and recommendations).

- **T3** : Averaged soil analysis, input recommendations, extension services; _flexible_ in-kind grant. (i.e. same as **T2** but with a flexible instead of inflexible in-kind grant).

- **T4** : Averaged soil analysis, input recommendations; extension services. (i.e. same as **T3** or **T2** but with _no in-kind grant_ ).

- **C** : No intervention. Control Arm received averaged soil analysis and recommendations the year after the intervention ended (in early 2016).

Budgetary constraints prevented us from using a full factorial design with 18 experimental arms.<sup>19</sup> As mentioned before, we chose to include agricultural extension services in all treatment arms because a pilot in the same study area with a comparable sample had suggested limited value of the soil analysis and recommendations without the extension services, as farmers appeared to greatly value the ability question and discuss the recommendations with the extension agents. Grants were provided in-kind (rather than in cash) for three reasons. First, because agro-dealers did not typically stock fertilizers in the blends required by the recommendations,

> 19These follow from the three possible choices of soil analysis and recommendations, two choices for extension services, and three choices for the grant intervention.

8
**TABLE 4.3: Number and Percentage of Households Discouraged in each Public Agency, by income, education and sex**

|**Public Agency**|**low**<br>**income**|**medium**<br>**income**|**high**<br>**income**|**primary**<br>**education**|**secondary**<br>**education**|**university**<br>**education **|**male **|**female **|**TOTAL**|
|---|---|---|---|---|---|---|---|---|---|
|Secretaria Municipal de Transito|2.6|4.4|6.8|0.6|2.4|5.1|4.4|1.7|3.1|
|Direccion Nacional de Immigraciones|3.7|6.9|6.8|1.2|3.6|6.8|5.1|3.8|4.5|
|Notarias Publicas|4.7|5.2|5.1|2.4|4.9|5.3|4.5|5.1|4.8|
|ReNIEC|4.8|5.5|5.1|3.0|4.3|8.4|6.7|4.3|5.5|
|Registros Publicos|6.2|8.0|8.5|6.0|6.7|6.7|7.3|5.8|6.6|
|Policia Nacional de Peru|8.3|7.4|3.4|3.0|8.1|9.6|9.1|7.0|8.1|
|Escuelas o universidades estatales|8.8|8.5|6.8|4.2|8.1|11.4|9.4|8.3|8.8|
|Hospitales públicos y Centros de Salud|9.0|9.9|6.8|8.4|9.3|8.9|8.2|10.0|9.1|
|Banco de la Nacion|8.6|11.6|11.9|5.4|9.0|11.2|9.7|9.0|9.4|
|Area de rentas de su municipalidad|9.2|11.6|6.8|9.6|8.6|10.7|9.4|9.5|9.4|
|Empresa de Agua y desague|11.7|10.7|6.8|13.9|11.6|10.4|11.2|11.6|11.4|
|EsSalut|11.3|14.0|13.6|3.0|13.1|12.5|12.3|11.5|11.9|
|SUNAT|14.9|20.9|18.6|4.2|15.9|20.5|19.1|13.3|16.3|
|**% HHdiscouraged in at least one agency**|**61.4**|**67.5**|**59.3**|**45.2**|**61.4**|**68.8**|**65.7**|**58.5**|**62.3**|
|**% HHdiscouraged in at least two agencies**|**23.2**|**29.2**|**30.5**|**13.3**|**23.9**|**31.1**|**27.1**|**23.2**|**25.2**|

Total number of households is 1696 EsSalut: Seguredad Social en Salud SUNAT: Superintendencia Nacional de Administración Tributaria ReNIEC: Registro Nacional de Identificación y Estado Civil 28
Table 6: Eigendecomposition, Eigenvalues for Principal Components

||||Pri|ncipal Compone|nts|||
|---|---|---|---|---|---|---|---|
||(1)<br>Component 1|(2)<br>Component 2|(3)<br>Component 3|(4)<br>Component 4|(5)<br>Component 5|(6)<br>Component 6|(7)<br>Component 7|
|2007|2.90|1.05|1.03|1.00|0.64|0.38|0.00|
|2008|2.84|1.13|1.05|0.97|0.64|0.37|0.00|
|2009|2.83|1.09|1.04|0.99|0.68|0.37|0.00|
|2011|2.81|1.12|1.03|0.97|0.70|0.38|0.00|
|2012|2.78|1.11|1.03|0.91|0.79|0.38|0.00|
|2013|2.80|1.11|1.01|0.95|0.78|0.34|0.00|
|2014|2.82|1.06|1.03|0.96|0.78|0.33|0.00|
|2015|2.74|1.08|1.03|0.99|0.77|0.38|0.01|
|2016|2.75|1.03|1.02|1.00|0.78|0.42|0.00|
|2017|2.75|1.07|1.05|0.96|0.74|0.41|0.02|
|2018|2.79|1.07|1.04|0.98|0.76|0.36|0.01|
|2019|2.78|1.11|1.01|0.95|0.76|0.39|0.02|
|2020|2.79|1.11|1.04|0.92|0.75|0.36|0.03|
|2021|2.76|1.12|1.05|0.94|0.80|0.34|0.00|

_Notes:_ Based on data from EH 2007-2021.

17
**Annex 3. Replications of Malaysia-focused analyses about the similarity of skills required in very-low, low, medium, and highly digital occupations for the occupational skills profile, Cambodia, Thailand, and Vietnam**

**Table A.3.1. Share of skills categories across digital occupation levels in the occupational skills profile, Cambodia, Malaysia, Thailand, and Vietnam (replication of data behind figure 6)**

||**Oc**|**cupati**<br>**pro**|**onal ski**<br>**file**|**lls**||**Cam**|**bodia**|||**Mal**|**aysia**|||**Tha**|**iland**|||**Vie**|**tnam**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Di**|**gital o**<br>**lev**|**ccupati**<br>**els**|**on**|**D**|**igital o**<br>**lev**|**ccupati**<br>**els**|**on**|**D**|**igital o**<br>**lev**|**ccupat**<br>**els**|**ion**|**Di**|**gital o**<br>**lev**|**ccupat**<br>**els**|**ion**|**D**|**igital o**<br>**le**|**ccupati**<br>**vels**|**on**|
|**Skills**|**VL**|**L**|**M**|**H**|**VL**|**L**|**M**|**H**|**VL**|**L**|**M**|**H**|**VL**|**L**|**M**|**H**|**VL**|**L**|**M**|**H**|
|**Digital**|4.8|9.9|15.0|38.3|5.0|9.8|15.5|32.7|4.8|9.4|15.9|33.3|4.5|10.1|15.3|37.1|4.6|9.7|14.8|34.4|
|Advanced digital|0.8|1.6|3.0|15.8|1.0|1.2|2.7|8.7|0.8|1.6|3.1|13.2|1.1|1.3|3.1|12.6|1.1|1.3|3.0|14.5|
|Intermediate digital|1.0|2.6|5.0|13.3|1.5|3.2|5.2|17.5|1.1|2.9|3.8|14.3|1.0|3.1|4.8|15.8|1.1|3.0|5.1|13.6|
|Basic digital|2.9|5.6|7.1|9.3|2.5|5.5|7.6|6.5|2.9|5.0|9.0|5.9|2.4|5.7|7.4|8.8|2.4|5.4|6.7|6.2|
|**Cognitive**|10.9|11.9|10.3|9.4|9.9|10.7|10.1|8.6|10.5|11.6|10.1|8.3|10.5|11.0|9.5|8.8|10.3|11.3|10.2|8.9|
|Thinking|0.6|1.3|1.3|2.0|0.4|1.0|1.0|1.6|0.6|1.2|1.2|1.8|0.4|1.1|1.2|1.8|0.4|1.1|1.2|2.0|
|Communication|5.6|6.0|4.7|3.9|5.2|5.6|4.8|3.5|5.5|5.7|4.6|3.4|5.5|5.4|4.4|3.6|5.3|5.6|4.8|3.7|
|Organization|4.4|4.5|4.0|3.4|4.2|3.9|4.2|3.4|4.3|4.5|4.1|3.0|4.4|4.2|3.7|3.2|4.4|4.3|4.1|3.1|
|**Socioemotional**|11.0|7.9|6.4|6.7|10.0|7.1|5.8|7.6|12.2|7.8|5.9|5.5|9.9|7.1|5.8|6.8|9.9|7.2|6.2|6.1|
|Emotions|0.5|0.5|0.6|0.7|0.7|0.3|0.8|0.5|0.4|0.4|0.6|0.4|0.3|0.4|0.6|0.6|0.3|0.4|0.7|0.4|
|Relationships|7.9|5.7|4.3|3.6|5.6|5.4|3.8|3.4|8.9|5.7|4.1|3.3|6.3|5.3|4.0|3.4|6.2|5.4|4.2|3.5|
|Personal growth|2.6|1.7|1.5|2.4|3.7|1.4|1.2|3.7|2.8|1.6|1.2|1.8|3.3|1.4|1.2|2.8|3.4|1.4|1.2|2.2|
|**Other**|73.4|70.4|68.3|45.6|75.0|72.4|68.6|51.2|72.5|71.2|68.1|52.8|75.1|71.8|69.4|47.3|75.2|71.8|68.8|50.6|
|**Total**|100|100|100|100|100|100|100|100|100|100|100|100|100|100|100|100|100|100|100|100|

_Source:_ Authors’ calculations based on SEAD data.

_Notes_ : “VL”, “L”, “M”, and “H” respectively stands for “Very low”, “Low”, “Medium”, and “High”. The share of skills splits between digital, cognitive, and socioemotional, and other skills. Digital skills levels and cognitive and socioemotional skills subsets are subcategories counted in the total.

46
group, perhaps reflecting a high degree of fertilizer use, expansion of irrigated land, widespread use of multiple-cropping and the introduction of new seed varieties and other technological improvements. <mark>By contrast, c</mark> ereal yields in the SSA region remain about one-fifth of those in highincome countries throughout the period. <mark>Overall, closing the ‘yield gap’ on</mark> currently cultivated areas <mark>appears to be one way of increasing global agricultural output in a sustainable manner (Foley</mark> _<mark>et al.</mark>_ <mark>2011). Some economists argue that the fragmentation and offshoring of production associated with the</mark> New Globalization (Baldwin 2016) may offer new opportunities for poor farmers to be integrated into the global production network.<sup>13</sup> <mark>Fukase and Martin (2016) estimated the following simple food production model in cereal equivalents as a function of income and land endowment per capita using a large data set for 19802009:</mark> where z is CE production per capita, _x_ is PPP GDP per capita in 2005 constant prices, _l_ is hectares of agricultural land per capita.<sup>15</sup> The exponent on the income per capita term is positive as higher agricultural productivity, associated with the higher economy-wide productivity that increases income levels, raises agricultural production. The positive relationship between income and agricultural production is likely to reflect not only higher yields per hectare but also better infrastructure and marketing know-how associated with higher income. The exponent on the agricultural land is positive as land-abundant countries tend to produce and export land-intensive commodities such as agricultural products.

<mark>Using the parameter values from Equation (8), Figure 8 shows the estimated CE production curves evaluated at different land endowments of selected countries, namely, the United States (land abundant), Japan (land scarce) and China (relatively land scarce) together with the global CE consumption curve (adopted from Figure 10abc, Fukase and Martin 2014). The figure suggests</mark>

> 13 For instance, by joining supermarket value chains, small farmers in Madagascar benefited from spillovers from the productivity of technology for rice ( <mark>Minten, Randrianarison and Swinnen 2009).</mark>

> 14 Standard errors are in brackets.

> 15 Following Rask and Rask (2011), hectares of agricultural land per capita are defined as a sum of arable land, land in permanent crops, and one-third of land in permanent pasture.

19
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||F|amine Contr|ol||Death Rate|s|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|YRD|0.176**|0.171***||-3.549|-10.545***||
||(0.062)|(0.060)||(4.362)|(3.201)||
|Guerrilla||0.018|||-0.335||
|||(0.040)|||(1.273)||
|YRD×GuerrillaNo [_b_0]|||0.151**|||-10.050***|
||||(0.063)|||(3.221)|
|YRD×GuerrillaYes [_b_1]|||0.200***|||-11.154***|
||||(0.060)|||(3.173)|
|Other Control Variable|Yes|Yes|Yes|Yes|Yes|Yes|
|Obs.|15|51|51|14|51|51|
|Adjusted-_R_<sup>2</sup>|0.485|0.171|0.191|0.501|0.566|0.571|
|_H_0 :_b_0 = _b_1|||0.244|||0.271|

Table 14: The Effects of Power Structure on Famine Control and Death Rates during the 1959-1961 Chinese Famine in Fujian, Border-Sharing Counties Only.

Notes: White standard errors are in parenthesis. Intercept not reported. Columns (1) and (4) use subsample of FA3 counties and their borders sharing counties. Columns (2) and (5) use subsample of guerrilla counties and their borders sharing counties. Other control variables include Ln_GVOPC_52 (Ln_GVOPC_78), Ln_pop_52 (Ln_pop_78), Share of Plain (%) and Distance to Xiamen.

qualitatively and quantitatively, are very similar to our results from the full sample. For instance, the YRD effect on annual growth rates in the period 1978-1998 is now 1.96 percentage point (vs 2.10 percentage point in Column 6 of Table 6), and on the growth rates in the whole period of 1952-1998 is 0.97 percentage point (vs. 0.91 percentage point in Column 3 of Table 6). This is quite remarkable, since the estimation sample is now only a quarter of the previous, already quite small, sample.

In Columns (2) and (5), we use the 51 neighboring counties with and without guerrilla in the estimation. When we examine the effect of both YRD-affiliation and guerrilla presence, again we find the results qualitatively and quantitatively similar for the growth rates in both periods. For instance, the effect of YRD on the growth rate in 1978-1998 is 1.55 percentage point (vs. 1.93 percentage point in Column 6 of Table 7), and that of guerrilla is 1.37 percentage point (vs. 1.52 percentage point in Column 3 of Table 7).

In Columns (3) and (5) where we allow for the interactive effect of YRD and guerrilla presence, there are some differences in results. Relative to FA3 counties, YRD counties without guerrilla presence no longer has statistically significant advantage in growth rates, though the advantage remains and the magnitude remains sizable — being 0.98 (vs. 1.46 in Column 7 of Table 8) for 19878-98. However, relative to FA3 counties, YRD counties with guerrilla presence still exhibits sizable and significant advantage in growth, 1.06 (vs. 1.29 in Column 3 of Table 8) for 1952-98, and 2.65 (vs. 3.18 in Column 7 of Table 8) for 1978-1998.

In Table 14, we report similar results for both famine control and death rates in 1959-1961 using border-sharing 39