rafmacalaba/datause-encoder-data huggingface dataset:
border, the BD in cotton income mainly stems from a higher output on the Cˆote d’Ivoire side because producer prices are only slightly higher (90 vs. 85 CFA franc per kg in Mali). Further, this Ivorian advantage is specific to the border area; both national official sales and survey figures show that the two countries produced roughly the same quantities of cotton (see table 2). In fact, the Mali side of the border was not yet producing significant quantities of cotton. In this country, cotton production historically began more northward, around the city of Koutiala, and mostly took off in the mid-1970s. As in Cˆote d’Ivoire, it was strongly regulated and subsidized by the State through a parastatal company ( _Compagnie Malienne de D ́eveloppement des Textiles_ ). We can again explain the BD in consumption among rural villages by the discontinuity in cash crop income. As in the case of Ghana (with cocoa and coffee), when controlling for cotton income, the BD in cash expenditures decreases from +117 USD to an insignificant -13. Unreported results also show that richer farmers invest in cattle. We identify BDs in the number of cows owned by the household (+1.76, s.e.= 0.32) as well as in the number of goats and sheep (+0.53, s.e.=0.10). Differences in public health policies may also be involved because the fight against parasitic diseases (river blindness or onchocerciasis, sleeping sickness or trypanosomiasis) was undertaken much earlier in Cˆote d’Ivoire.

At the Guinea border, although almost no cocoa and approximately half as much coffee is grown on the Guinea side, the BD in cash crops income is small but statistically significant (+16 USD). In comparison with the Ghana and Mali cases, we argue that the absence of a large difference in cash crop income may account for why no border discontinuity is found in consumption per capita or in children’s stunting.

33
Table 9: Local spillovers on investments and productive assets

||(1)<br>Total start-up<br>capital<br>(all activities)|(2)<br>Value of<br>productive assets<br>(all activities)|
|---|---|---|
|Panel A. Pooled Estimates|||
|Treatment (ITT)<br>PDS Lasso selected controls|1091.27<br>(5120.99)<br>Yes|10762.98<br>(7751.74)<br>Yes|
|Department X (Urban/Rural)|Yes|Yes|
|Mean in Control|25358.29|59658.83|
|Observations|1,102|1,102|
|Panel B. Treatment Arm Estimates|||
|VSLA (T1) (ITT)|-3209.52<br>(5080.75)|6500.30<br>(9057.50)|
|Cash Grant with repayment (T2) (ITT)|3823.40<br>(6901.26)|12489.14<br>(9932.08)|
|Cash Grant (T3) (ITT)|3055.09<br>(7695.99)|15203.18<br>(9364.66)|
|PDS Lasso selected controls|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|
|Mean in Control|25358.29|59658.83|
|p-value T1=T2|0.27|0.54|
|p-value T2=T3|0.93|0.79|
|p-value T1=T3|0.38|0.35|
|p-value T1=T2=T3|0.44|0.63|
|Observations|1,102|1,102|

Robust standard errors clustered at locality level. Start-up capital and value of assets are in CFA franc and winsorized at 99%. The specification includes control variables chosen using post double selection (PDS) Lasso based on the set in Table A6. _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01
Table C3: Workers’ Adjustment to GDP Decline in Firms’ Destinations using Alternative Worker Outcomes

||Average<br>months worked<br>following Yagan (2019)|Average<br>real wages<br>following Yagan (2019)|Indicator for<br>being fred<br>(Defnition 1)|Indicator for<br>being fred<br>(Defnition 2)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|GFC frm shock*2009|-0.0544***|-28.6391***|0.0003*|0.0003*|
||(0.0108)|(6.3102)|(0.0002)|(0.0002)|
|GFC frm shock*2010|-0.0441***|-28.4306***|0.0000|0.0000|
||(0.0104)|(6.3088)|(0.0002)|(0.0002)|
|GFC frm shock*2011|-0.0186*|-28.3811***|-0.0002|-0.0002|
||(0.0098)|(6.2924)|(0.0002)|(0.0002)|
|GFC frm shock*2012|-0.0192**|-28.2186***|-0.0003|-0.0003|
||(0.0089)|(6.2794)|(0.0002)|(0.0002)|
|GFC frm shock*2013|-0.0089|-27.9682***|0.0001|0.0001|
||(0.0084)|(6.2490)|(0.0002)|(0.0002)|
|GFC frm shock*2014|-0.0163**|-27.6736***|-0.0000|0.0000|
||(0.0083)|(6.1863)|(0.0002)|(0.0002)|
|GFC frm shock*2015|-0.0088|-27.0546***|0.0002|0.0002|
||(0.0088)|(6.1000)|(0.0003)|(0.0003)|
|GFC frm shock*2016|-0.0081|-26.9920***|0.0000|0.0000|
||(0.0089)|(6.0603)|(0.0003)|(0.0003)|
|GFC frm shock*2017|-0.0121|-26.2731***|0.0002|0.0002|
||(0.0087)|(5.9758)|(0.0003)|(0.0003)|
|Observations|3,046,074|3,046,074|2,463,396|2,463,396|
|R-squared|0.3118|0.4228|0.0161|0.0162|
|2-digit industry X Year FE|Yes|Yes|Yes|Yes|
|State X Year FE|Yes|Yes|Yes|Yes|

_Note:_ Robust standard errors clustered at the firm level in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. All specifications control for vectors of worker and firm characteristics as of 2007 described in Section III. FE stands for fixed effects.

47
# **_A.3. Supplementary Tables_**

# **Table A.3.1: Results of Simulation 1 by region, income group and lending type. Effect on Learning-Adjusted Years of Schooling (LAYS)**

||||Post-COVID-19<br>||
|---|---|---|---|---|
|Aggregate|Baseline|Optimistic|Intermediate|Pessimistic|
|EAP|8.7|8.5|8.2|7.8|
|ECA|10.4|10.1|9.7|9.3|
|LAC|7.7|7.4|7.1|6.8|
|MNA|7.6|7.4|7.1|6.7|
|NAC|11.4|11.2|10.8|10.3|
|SAR|6.2|5.9|5.7|5.5|
|SSF|4.9|4.7|4.5|4.3|
|Grand Total|7.9|7.6|7.3|7.0|
|HIC|10.7|10.5|10.1|9.6|
|UMC|8.0|7.7|7.4|7.1|
|LMC|6.3|6.1|5.8|5.6|
|LIC|4.5|4.3|4.1|4.0|
|Grand Total|7.9|7.6|7.3|7.0|
|Part I|10.9|10.6|10.2|9.8|
|IBRD|8.1|7.8|7.5|7.2|
|IDA/Blend|5.4|5.2|5.0|4.8|
|Grand Total|7.9|7.6|7.3|7.0|

Source: Authors’ calculation. Results expressed in Learning-Adjusted Years of Schooling (LAYS), Simulation 1 results based on latest available LAYS of 157 countries (unweighted average).

45
16 Table 2: Effect. of Agroeclmtic Endowments, Innfrstructura, Banks and Pricee on Agrcultural Output, **Wage,** Rural Employmnt and Population

|Explanatory<br>Variable|Aggregate<br>crop output|Agricultura<br> employment|l Nonfarm<br>employment|Agricultural<br>real wage|
|---|---|---|---|---|
|Aggregate real crop price (laggod)<br>5|0.194|0.160|0.204|0.002|
||(9.161)*|<br>(3.244)|-<br>(2.186)<br>i|<br>(0.144)|
|Real fertilizer pricea|-0.214|0.244|0.012|0.185|
||(-3.989)0|<br>(0.716)|<br>(0.032)|<br>(8.23So|
|Road|0.06|0.074|0.241|0.03C|
||(2.017).|(0.782)|(2.559).|(1.697).|
|Government Irrigationa|0.06S|0.014|0.070|-0.038|
||(1.738)*|<br>(0.220)|<br>(1.071)|<br>(-1.658)|
|Regulated marketes|0.202|0.06S|-0.048|-0.021|
||(12.804)*|<br>(1.637)|* (-1.282)|<br>(-1.746).|
|Comme<br>rcial bankeS|0.039|-0.06|0.292|0.044|
||(8.642)*|<br>(-2.694)|o (10.942)|*<br>(6.532)e|
|Primary echoolo'|0.177|0.172|-0.527|-0.288|
||(2.498)*|<br>(1.214)|<br>(-8.572)|. (-4.433).|
|Rural electrificatlon&|0.112|-0.061|0.112|0.061|
||(6.399).|<br>(-1.686)|.<br>(8.084)|.<br>(4.561).|
|Year|-0.019|1.562|0.279|0.045|
||(-6.659)e|<br>(1.894)|<br>(O.81)|(4.904)o|
|Rainfall x1O0|0.071|||0.186|
||(3.293).|||(1.968)e|
|Soil moleture capacity|-0.079|-12.760|-9.741|-0.089|
||(-1.059)|<br>(-0.542)|<br>(-0.652)|<br>(-0.480)|
|Urbandltance|-0.0004|0.156|0.025|0.001|
||(-0.548)|<br>(0.941)|<br>(0.282)|<br>(0.922)|
|Length of ralny**season**|0.142|5U.896|29.818|-0.07|
||(1.752).|<br>(2.095)|*<br>(1.790)|* (-0.322)|
|Excess rainmonths|0.087|16.583|86.769|0.042|
||(0.478)|<br>(0.768)|<br>(2.831)|e<br>(0.218)|
|Cool winter months|0.058|-39.866|18.447|0.364|
||(0.886)|<br>(-2.024)|. (1.072)|<br>(2.234).|
|Flood potential|-0.082|1.265|-8.60|-0.057|
||(-1.815)|§<br>(0.168)|<br>(-0.798)|<br>(-0.915)|
|Irrigation<br>potential|0.011|2.264|1.156|0.019|
||(8.606)*|<br>(2.890)|.<br>(1.699)|*<br>(2.486)*|
|Constant|1.089|-288.294|-100.866|0.970|
||(2.448).|<br>(-1.856)|* (-1.004)|<br>(0.607)|
|F-statistic|93.011|12.608|46.816|68.88a|
|Hausman-Wu (chi-square)|27.576|19.721|19.450|25.088|
|Number of observotlons|1786|170|170|1785|

Note: t-Statistics **are** in parentheses. Asterisk **refers** to significance level of 10 percent or bettor on **a** two-tail test. § **refers** to significance level of 10 percent on **a** single-tall test. * Coefficients **are** In elasticity form at the variable means.
Figure 1 <!-- Start of picture text --> NIGEfRIA c'<br>ANAMBRA WATER SUPPLY AND SANITATION PROJECT z _<br>Onitsho Wator Supply Campownt<br>i |, r ' t / .^ e / \ t; ,,~.- A '<br>ftoA~ ~ ~ ~ ~ ~ ~ ~~~~~~~~~~~~~~~~~~~~tJ~<br>J \s / * .................... ~~~~~~.t ,"1.>,"t *, - ' \ tSw_ G....tt_. Pt <<br>i-ba.b *-0 C.-I<br>s~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ S..d'qT; , \ .<br>g; .,wt "_ ~~~~\ .. . T ........ z; . .. P .. .. f ........._<br>s-,-- , '~_ _<br><!-- End of picture text -->
# **4. Education and Employment**

The potential contribution of education to improving labor productivity, lowering income inequality and promoting growth and development is well recognized, and virtually all governments set aside a substantial portion of their public expenditure budget for education. Since independence, the nations of sub-Saharan Africa have invested heavily in the education sector. The achievements in the sector have been impressive both in absolute terms and in relation to other sectors. However, primary enrollments have stagnated or even declined recently, and the quality of education has apparently declined. These reversals have occurred in an era of mounting fiscal austerity, population growth and strained political climate, all of which have hurt the education sector, and the deterioration in educational services have exacerbated the regions' economic and social problems. Between 1970 and 1980, while the world's population grew at an average annual rate of 1.9 percent, Africa's population grew at 2.9 percent (World Bank, 1993, P. 18), and is projected to grow even faster at a rate of 3.2 percent a year, while growth rates are declining in other parts of the world. Africa's rapid population growth creates serious problems for education. To keep pace with the growth of school-age children, more schools, teachers, books etc. are required.

Extensive experience from Africa and elsewhere provide strong evidence that increased investment in education can yield broad economic benefits-including higher incomes and lower fertility. It can accelerate the growth process, and act as a complement to other growth inducing factors. However, public expenditure on education, both absolutely and as a percentage of GNP has fallen in many countries. The economic evidcnce based on micro and macro-level data indicate significant returns to education. The micro-level analysis examine the relation between an individuals education level and his/her productivity in the labor market as a wage worker or self-employed worker; or by linking the education of individuals to the outcomes of household behavior, such as fertility, child mortality etc. The macroeconomic evidence relates to the growth of national economies to prior investments in education controlling for other factors. Even though there is an enormous literature on estimating rates of return to schooling and on the job training, there are few empirical studies from sub-Saharan Africa (Ghana, Kenya). This paper build on the earlier paper on returns to education in Ghana (Glewwe, 1988). While Glewwe's (1988) work was based on GLSS 2, we look at the returns to education during the period 1987-91. This survey includes two time periods, before and after the educational reforms in Ghana, and can help us better understand whether education pays in the labor market..
Policy Research Working Paper 6630

# **Abstract**

Given their widespread presence in rural and poor areas, post offices can play a leading role in advancing financial inclusion. Yet little is known about the type of clients that post offices reach through their financial service offerings as compared with clients of traditional financial institutions (such as commercial banks). This paper documents and analyzes account ownership patterns at post offices in comparison with traditional financial institutions, using the Global Financial Inclusion Indicators (Global Findex) database, which collects data on account ownership at post offices in 60 countries where postal accounts are offered. Controlling for a host of individual characteristics and country fixed effects, the paper finds that post offices are relatively more likely than traditional financial institutions to provide accounts to individuals who are most likely to be from financially vulnerable groups, such as the poor, less educated, and those out of the labor force. The paper also uses data from the Universal Postal Union to explore the degree to which different postal business models and the size of the postal network help explain differences in account ownership patterns. The results suggest that post offices can boost account ownership by acting as cash-merchants for transactional financial services, such as electronic government and remittance payments, and that partnerships between the post office and other financial institutions coincide with a higher bank account penetration. The paper also finds that the size of the postal network matters; the larger the network-relative to the network of traditional financial institutions—the more likely it is that adults have an account at the post office.

This paper is a product of the Finance and Private Sector 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 lklapper@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
Supplemental Table 5. The association between baseline receptive vocabulary and follow-up Luo receptive vocabulary

||l1|l2|l3|l4|l5|l6|
|---|---|---|---|---|---|---|
||b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|
|Receptive vocabulary in English (_z-_score)|0.2780****|||0.1125**||0.0857*|
||0.052|||0.0445||0.0461|
||<0.001|||0.0121||0.0641|
|Receptive vocabulary in Swahili (_z-_score)||0.3340****|||0.1472****|0.1269***|
|||0.0481|||0.0437|0.0455|
|||<0.001|||0.0008|0.0056|
|Receptive vocabulary in Luo (_z-_score)|||0.5168****|0.4761****|0.4590****|0.4359****|
||||0.0443|0.0474|0.0474|0.0492|
||||<0.001|<0.001|<0.001|<0.001|
|Constant|–0.01|–0.0028|–0.0037|–0.0067|–0.0038|–0.0061|
||0.049|0.0477|0.0423|0.0424|0.0419|0.0421|
||0.8383|0.9524|0.9295|0.8742|0.9279|0.8857|
|_R_-squared|0.0797|0.1066|0.2645|0.2759|0.2819|0.2882|
|N. of cases|442|442|442|442|442|442|

Notes. Receptive English, Swahili, and Luo vocabulary scores are age-adjusted _z-_ scores for children ages 2 to 6 years, measured using separate assessments based on the British Picture Vocabulary Scale (BPVS). Baseline and follow-up were conducted approximately 5 weeks apart. * _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01, **** _p_ < 0.001.
We can try to follow where the 6.5 percent of respondents who no longer claim religious reasons for refusal went<sup>21</sup> . On average, 3% actually decided to apply for the loan, an additional 0.8% said they did not trust the offer, 1.4% continued to refuse the loan but on economic grounds instead of religious grounds, and the remaining 1.3% changed their response to a generic reason for refusal.

We can further use these shifts to compare the experimental measures of the importance of religious considerations to the survey-based measures that are more widely used. Of those in the control group that claimed religious reasons for not applying for the loan, about one third of them shifted into applying for the loan, about one third continued to claim religious reasons for not applying, and the final third changed their answer to something else, close to evenly across economic reasons and to generic reasons. Together this implies that nearly two thirds of those that claim religious reasons for not taking out loans seem to act consistent with religious objections being the main reason for not applying for a loan, while the remaining third shift to a different excuse when the religious angle is addressed. This suggests that survey data on financial exclusion due to religious objections may be overestimating the importance of this characteristic by a third.

# _Alternative Interpretations_

This study has several limitations. The label of “sharia-compliant” may do more than merely represent adherence to religious considerations. We identify six potential alternatives.

First, if an individual does not trust financial institutions, the sharia-compliant label may signal trustworthiness and thus increase the take-up rate by consumers. This issue was the motivation for the independent auxiliary experiment on the branding of the conventional loan. In this auxiliary experiment, we learn that the known brand name (Jordan Microfinance Company) generates 2.2 percentage point (se=1.1) higher take-up rates than the unknown brand name (Tathmeer) for a conventional loan, all else constant. This is important because branding does matter, and that the control group in the main test (Jordan Microfinance Company conventional loan) did perform better than an unknown brand, suggesting that respondents did, at least to some extent, trust the financial institution. If trust were a simple binary variable, this would allow us to argue that the sharia-compliant loan (marketed under the

> 21 To do this we need to first assume that the same type of people are not shifting their reasons for refusing the loan for any reason other than the religious aspect of the product. This requires people’s non-religious preferences to be stable in the face of a choice between a conventional and religious product.

21
of nightlight is indicative of dire economic situations and disruptions in public services (S ̈anger et al., 2022). Nightlight provides a rough indicator. However, given the lack of granular metrics at the applicant and/or district levels in Afghanistan, nightlight data provides a plausible indicator of economic development at the district level. Therefore, I cannot differentiate between applicants’ socioeconomic statuses at the individual level and instead rely on district classifications. I used the two-year pre-treatment average luminosity by district as a binary indicator to identify low-SES applicants, operationalized as districts with luminosity lower than the mean (0.2678 nanoWatts/cm2/sr) in the pre-treatment period. I used this indicator to examine the impact of quotas on the share of female students from low SES. To achieve this objective, I successfully matched 95.4%<sup>19</sup> of all matriculated applicants (336,151) to their districts of origin. I accomplished this by merging student records at the district level based on school and province names (i.e., the province from which they participated in the Kankor exam) with the applicant’s high school districts using the comprehensive school database from the MoE.

# **5 Empirical Strategy**

The gender quota implementation started in 2016 and expanded in the following years. The quota distributed the available seats at some concentrations based on gender but maintained open competition at other concentrations. Thus, I exploited the variation across concentrations and over time to study the causal impact of the gender quota on the share of matriculated female students and score-related outcomes in treated units using the following DD model shown in Equation 1:

> 19The reasons for the 4.6% of unmatched applicants include multiple schools located in different districts within the same province. However, labels for these schools are entered identically without any unique district information, making it difficult to identify the exact school from which an applicant graduated in the matriculation dataset. Additionally, applicant school names are missing, either because they were not entered, or the applicants completed their high school studies in neighboring countries (e.g., Pakistan and Iran).

16
- 3 -

productivity in connection to climate and soil conditions (e.g., Global Agro-ecological Zones (GAEZ) system developed by the FAO and the International Institute for Applied Systems Analysis (IIASA)). These data provide information on crop suitability at the detailed spatial level. On the infrastructure side, the new economic geography literature is building a solid body of knowledge on regional distribution and disparity of infrastructure endowments, identifying missing links and bottlenecks (see, for instance, World Bank (2009) and (2010)).

There are however only a few empirical studies that statistically link these two different sources of spatial data on the agriculture and infrastructure sides, excepting a number of more recent works, such as Dorosh, Wang, You and Schmidt (2012). This paper examines the agricultural potential of East Africa, namely, Burundi, Kenya, Rwanda, Tanzania and Uganda, through an examination of the two different sources of spatial data. Despite the currently high international commodity prices, in particular in the traditional export crops, such as coffee and cotton, these East African countries are still struggling to improve agricultural productivity. This paper specifically aims at: (i) generating spatial agricultural production and potential data for the region; (ii) developing spatial data to show transport accessibility in each locality; and (iii) developing an empirical model to link these data and analyze the relationship between agriculture production and transport infrastructure investment.

The remaining sections are organized as follows: Section II describes our spatial agriculture data. Section III develops an empirical model and describes our infrastructure data. Section IV discusses our main estimation results and some policy implications. Section V concludes.

# **II. SPATIAL PRODUCTION ALLOCATION MODEL (SPAM) UPDATE**

The current paper relies on a spatial production allocation model (SPAM) developed by the International Food Policy Research Institute (IFPRI) for generating highly disaggregated crop-specific production data. The SPAM is a spatial model to allocate crop production