rafmacalaba/datause-encoder-data huggingface dataset:
# Table D.4: **Regression Results** – Household Savings

This table reports the effect of debt relief on total household savings. Within a panel, each column reports results from a separate regression. The dependent variable is total self-reported savings at the time of the survey. Regressions in panel A are estimated using the full sample of surveyed households. Panels B.1 and B.2 report restricted sample robustness checks. Panel B.1 reports estimates from a robustness sample consisting of housheolds with audited and matching land records. Panel B.2 reports estimates from a reduced bandwidth sample, which excludes observations in the top and bottom 25% of observations to either side of the program cutoff. Interview effects include interviewer and month-of-interview dummies. Respondent controls include gender, age, years of education, household size, log of pre-program land owned, log of pre-program total debt. Robust standard errors, in parentheses, are clustered by bank and district. + p _<_ 0.10 ** p _<_ 0.05 *** p _<_ 0.01.

|_Panel A: Full Sample_|Model 1<br>(1)|Model 2<br>(3)|Model 3<br>(5)|Model 4<br>(7)|
|---|---|---|---|---|
|100% Relief|-0.25|-0.06|-0.12|-0.07|
||[0.17]|[0.16]|[0.17]|[0.16]|
|Observations|2,346|2,346|2,225|2,346|
|R-squared|0.134|0.122|0.139|0.122|
|_Panel B: Robustness Samples_<br>Panel B.1: audited sample|||||
|100% Relief|-0.12|0.02|-0.05|0.02|
||[0.32]|[0.25]|[0.27]|[0.25]|
|Observations|1,510|1,510|1,510|1,510|
|R-squared|0.152|0.128|0.146|0.128|
|Panel B.2: reduced bandwidth sample|||||
|100% Relief|-0.20|-0.02|-0.03|-0.03|
||[0.20]|[0.20]|[0.21]|[0.21]|
|Observations|1,710|1,710|1,611|1,710|
|R-squared|0.146|0.135|0.160|0.135|
|Interviewer effects|Yes|Yes|Yes|Yes|
|Bank*district effects|No|No|Yes|Yes|
|Local linear control function|Yes|Yes|Yes|No|
|Quadratic control function|No|No|No|Yes|
|Households with positive|savings (treatm|ent/control)|2,112|(1,248/864)|
|Households with zero|savings (treatm|ent/control)|279|(166/113)|

59
31 Table 2. Black Market Exchange Rate Premia, Weighted Average Across 59 Developing Countries, 1960 to 1993 (percent)

||1960-69|1970-79|1980-89|1991-93|
|---|---|---|---|---|
|Region:|||||
|North & Sub-Saharan Africa|23|45|75|23|
|South & East Asia|233|25|15|6|
|East Europe & Central Asia|277|231|238|5|
|Latin America & Caribbean|13|34|89|9|
|All developing countries in<br>sample|144|77|76|9|

Source: Estimated using country data compiled by Easterly (2006) using the sum of export and import revenues as weights.
Appendix Table 5: Harvest and productivity for cocoa plots at baseline

||Total harvest<br>($)|Rubber<br>harvest ($)|Cocoa harvest<br>($)|Other crops<br>harvest ($)|Total yield<br>($/ha)|Rubber yield<br>($/ha)|Cocoa yield<br>($/ha)|Other crops<br>yield ($/ha)|
|---|---|---|---|---|---|---|---|---|
|Seedling Subsidy|-199.78**|-98.66**|-89.23*|5.37|-22.25|-16.93***|8.37|-3.71|
||(84.01)|(39.85)|(52.08)|(20.89)|(22.97)|(6.45)|(18.45)|(5.82)|
|Couples' Training|182.40*|51.70|106.27|42.16|17.57|11.58**|1.54|6.29|
||(97.90)|(35.19)|(69.60)|(32.70)|(26.26)|(5.49)|(21.53)|(7.48)|
|Seedling Subsidy + Couples'<br>Training (beta)|-17.38|-46.96|17.04|47.52*|-4.68|-5.35|9.91|2.57|
|H0: Seedling Subsidy +<br>Couples' Training = 0 (p-val)|0.82|0.14|0.76|0.09|0.81|0.35|0.55|0.73|
|Observations|1050|1050|1050|1050|1050|1050|1050|1050|
|Mean for control group|758.20|170.08|460.86|85.85|184.06|27.25|120.97|23.16|
|Mean for individual training|520.07|69.11|357.54|81.34|158.78|10.83|124.84|20.07|

Robust and clustered standard errors in parentheses. All values are winsorized at 99%. Household-level regressions.

* p<0.1 ** p<0.05 *** p<0.01

Note: Baseline control variables in all regressions: Age of household head, Household size, Number of adults, Number of children, Dependency ratio, Married planter, Polygamous relationship, Matrilineal ethnicity, Planter has IGA, Asset index, Credit constrained, Share of plots owned, Average distance to plots (minutes), Number of plots, Average plot size. Contemporaneous control variables in all regressions: Rubber-cocoa intercrop, Any other intercrop.

4
Countries that failed to productively mobilize the (temporary)<sup>transfer of</sup> purchasing power, through either terms of trade changes<sup>or capital inflows, formed</sup> unbalanced trade integration that accumulated debt without<sup>biilding bases for higher</sup> future export earnings. Faced with debt crisis, forced austerity, and import compression, these economies had their long-term productivity growth and export earning capacity hindered severely. Chart 4 shows the correlation between speed<sup>of integration into world</sup> markets and GDP growth for 18 major LMIC economies. Countries with successful<sup>trade</sup> integration are seen in the upper right quadrant, while economies with unbalanced integration are situated in the lower right quadrant.

Chart 4. Speed of Trade Integration and GDP Growth, 1970-1992 (18 major developing economies) <!-- Start of picture text --> 10<br>Correlatl n Coeffica nt +0.5h<br>_ 8- 8. i _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ~~~~~~~~~~cnina tiorea<br>MI- nin Thaland<br>L. ~~~~~~++<br>s. tSaudl A bia<br>________ Algeria +In Mexico<br>0.<br>O ~ ~ Ngea| ipp nos<br>Cl 2- V ID v ranl<br>/ | F~SU 'Sou hAfdcand<br>00<br>-6 -4 -2 0 2 4 6<br>Speed of Integration (percent)<br>Source: Intemational Economics Department, World Bank.<br><!-- End of picture text -->

4
important for individuals’ labor market position as reflected in their earnings and employment chances. For individuals education also has important other returns, for instance, in terms of their health (see Grossman 2006 for a review). But investments in education are also regarded as an engine for countries’ prosperity.

Our analysis of financial schemes fits into the normative economics framework in which efficiency and equity serve as guidelines. In the next section we outline this analytical framework. While this framework serves as reference point, we do not judge financial arrangements purely in the terms of this framework. In addition, we also pay attention to empirical studies that inform us about the effectiveness of various financial arrangements. In reviews of financial arrangements in the field of education, the empirical analysis confines itself often to a description of alleged success stories. We will move away from that custom and instead focus on empirical results coming from studies that use credible strategies to identify the causal impacts of certain policies. By emphasizing this, we will also show that much is still unknown. In section 2 we will also elaborate a bit on the criteria that can be used to judge the credibility of empirical findings.

After the sketch of the analytical framework in section 2, section 3 applies this framework to various financial arrangements. That section starts with a description of financial instrument that can stimulate successful early learning (basic and secondary education). Successful early learning can be important for later learning in the presence of dynamic spillovers (“learning begets learning”). It then follows with an analysis of cost-sharing schemes, including various loans schemes, human capital contracts and financial incentives for students.

3
# **Figure 1. Cumulative spending multiplier +/- 90 confidence intervals. Single multiplier. OECD countries.**

Panel A. OECD countries. Using spending forecast errors <!-- Start of picture text -->
1.5<br>1<br>0.5<br>0<br>-0.5<br>0 1 2 3 4<br>Cumulative multiplier<br><!-- End of picture text -->

<!-- Start of picture text --> Semester<br><!-- End of picture text --> Panel B. Developing countries. Using spending growth rate <!-- Start of picture text -->
1.5<br>1<br>0.5<br>0<br>-0.5<br>0 1 2 3 4 5 6 7 8<br>Cumulative multiplier<br><!-- End of picture text -->

**Quarter**
# **Introduction**

The HIV/AIDS epidemic is expected to have profound impacts on an array of economic and demographic outcomes. Most empirical studies of the impact of high levels of adult mortality caused by HIV/AIDS and other diseases focus on economic outcomes such as poverty and human capital outcomes for orphans.<sup>1</sup> There are, of course, other important channels through which HIV/AIDS may have long-run effects on the remaining household members, including demographic behaviors. There are fewer studies of the behavioral impact of HIV/AIDS on demographic outcomes such as fertility and marriage among surviving family members.<sup>2</sup> Using longitudinal data from Tanzania, this study examines the effect of parental death or other adult deaths in households on subsequent transition into marriage for a cohort of children from the baseline who become young adults by 2004.

There are competing theories of the impact of HIV/AIDS on transitions into marriage. Higher prime-age adult mortality can influence marriage patterns in expectation of mortality outcomes ( _ex ante_ ) or in response to realized illness or death of household members or parents ( _ex post_ ). If HIV/AIDS is seen as introducing more risk, then marriage might be an _ex ante_ strategy - a means of geographic diversification and risk sharing among household members (see the discussion in McCarthy _et al_ ., 2006; Rosenzweig and Stark, 1989). Likewise, lower life expectancy due to the epidemic might encourage people to marry at younger ages. In HIV/AIDS affected communities, marriage might be seen as a way to protect against contracting the disease.<sup>3</sup> Bracher _et al._ (2003), for example, is motivated by the idea that in Malawi and elsewhere in the region marriage is increasingly believed to protect young women from HIV since it may reduce

> 1 See references in Beegle _et al_ . (2006a) for poverty/income studies; see references in Foster and Williamson (2000) and Beegle _et al._ (2006c) for studies on education and health outcomes for orphans.

> 2 The behavioral impact on fertility could be described as an indirect effect rather than direct which constitutes biological effects of HIV and STIs on fecundity. See UN (2002) for a review of studies on the indirect effect often drawing on focus group discussions; also see Taiwo (2006) who examines the impact of orphan fostering on fertility in Malawi and Ainsworth _et al._ (1998) who examine fertility patterns in households affected by prime-age adult deaths in Tanzania.

> 3 Contrary to this perception, it is actually unclear empirically if or how marriage protects young women from contracting AIDS. For example, Clark (2004) shows that adolescent married women in Kenya and Zambia face a higher probability of having contracted HIV than their unmarried counterparts.

1
- Bouwman, A., Kram, T., Goldewijk, K., 2006. Integrated Modelling of Global Environmental Change: an Overview of Image 2.4. Tech. rep., Netherlands Environmental Assessment Agency, Bilthoven, The Netherlands.

- Bulte, E., van Soest, D., Van Kooten, G., Schipper, R., 2002. Forest Conservation in Costa Rica when Nonuse Benefits are Uncertain but Rising. American Journal of Agricultural Economics 84 (1), 150–160.

- Cai, Y., Judd, K. L., 2010. Stable and Efficient Computational Methods for Dynamic Programming. Journal of the European Economic Association 8 (23), 626–634.

- Cai, Y., Judd, K. L., Thain, G., Wright, S. J., 2013. Solving Dynamic Programming Problems on a Computational Grid. Working Paper 18714, National Bureau of Economic Research.

- Cassman, K., Grassini, P., van Wart, J., 2010. Crop Yield Potential, Yield Trends, and Global Food Security in a Changing Climate. In: Hillel, D., Rosenzweig, C. (Eds.), Handbook of Climate Change and Agroecosystems. Imperial College Press, pp. 37–51.

- Cassman, K. G., 1999. Ecological Intensification of Cereal Production Systems: Yield Potential, Soil Quality, and Precision Agriculture. Proceedings of the National Academy of Sciences 96 (11), 5952–5959.

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- Collins, W., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Hinton, T., Jones, C., Liddicoat, S., Martin, G., et al., 2008. Evaluation of the HadGEM2 Model. Hadley Center Tech. Note 74.

- Conrad, J., 1997. On the Option Value of Old-growth Forest. Ecological Economics 22 (2), 97–102.

- Conrad, J., 2000. Wilderness: Options to Preserve, Extract, or Develop. Resource and Energy Economics 22 (3), 205–219.

- Costanza, R., d’Arge, R., De Groot, R., Farber, S., Grasso, M., Hannon, B., Limburg, K., Naeem, S., O’Neill, R., Paruelo, J., et al., 1997. The Value of the World’s Ecosystem Services and Natural Capital. Nature 387 (6630), 253–260.

- Cranfield, J., Eales, J., Hertel, T., Preckel, P., 2003. Model Selection when Estimating and Predicting Consumer Demands using International, Cross Section Data. Empirical Economics 28 (2), 353–364.

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29
**Tables**

# **Table 1: State-level differences**

||**India**|**Bihar**|**Odisha**|**Tamil Nadu**|
|---|---|---|---|---|
|GDP per-capita at current prices|Rs. 74,380|Rs. 33,199|Rs. 59,229|Rs. 128,366|
|GDP per-capita at 2004-2005 prices|Rs. 39,904|Rs. 16,801|Rs. 26,531|Rs. 66,635|
|GDP per capita in $ at current market exchange rates|$1,627|$682|$1,150|$2,464|
|Rank (among 33 states and union territories of India) for GDP|-|33|27|8|
|SC population|16.20%|8.20%|16.50%|20.00%|
|ST population|8.60%|1.30%|22.80%|1.10%|
|Literacy|74.04%|63.82%|73.45%|80.33%|
|Literacy among SCs|66.10%|48.65%|69.02%|73.26%|
|Urban Population|31.16%|11.30%|16.68%|48.45%|
|Sex-ratio|943|996|979|916|
|Female literacy|65.46%|53.33%|64.36%|73.86%|

Source: GDP numbers are from the Ministry of State Planning and Implementation (2016). Retrieved from http://statisticstimes.com/economy/gdp-of-indian-states.php (Nov 8, 2016). Demographic numbers are from the Census of India (2011).
the state of rights in those countries. Of the 100 countries that are included, there are some gaps in early years or pre-independence. However, by 2010, only Niger’s laws regarding property rights to unmarried men and women could not be located.

**Table 1 : Regional and Income Representation of Countries (1960)**

||**Number of**||**Quartiles of Initial**|**Income Levels**||
|---|---|---|---|---|---|
|**Region**|**Countries**|**Lowest**|**Lower Middle**|**Upper Middle**|**Upper**|
|**EAP**|12|5|3|3|1|
|**ECA**|10|0|4|6|0|
|**LAC**|15|0|1|11|3|
|**MENA**|8|0|5|3|0|
|**OECD**|17|0|0|2|15|
|**SAR**|5|5|0|0|0|
|**SSA**|33|16|14|2|1|
|**Total**|**100**|**26**|**27**|**27**|**20**|

# **Approach**

The legal indicators cover the period 1960-2010. The aim is to look at how changes in these indicators affect outcomes in women’s and girls’ economic opportunities, education, health, and empowerment. However, before analyzing the time dimension, we first start with the cross-country relationships between a large set of outcomes and each of the legal indicators. Table 2 presents the cross-country variation in the legal indicators and their association with outcomes. In each regression, each country appears once, generally in the year of its most recent household survey. Each of the legal indicators is entered in a separate regression, so each cell in Table 2 represents the results of different estimations.

A number of country characteristics are controlled for, including GDP per capita and the sectoral composition of the economy. The size of the agricultural, industry or service sectors can 14
markets can be thought of as being in a ‘P4P only’ advertised treatment, 7 in a ‘FW only’ advertised treatment, and 4 in a ‘Mixed’ advertised treatment.<sup>10</sup> Empirically, we consider the Mixed treatment as a separate arm; we estimate a corresponding advertisement effect only as an incidental parameter.

This first-tier randomization was accompanied by an advertising campaign to increase awareness of the new posts and their associated contracts.<sup>11</sup> In November 2015, as soon as districts revealed the positions to be filled, we announced the advertised contract assignment. In addition to radio, poster, and flyer advertisements, and the presence of a person to explain the advertised contracts at District Education Offices, we also held three job fairs at TTCs to promote the interventions. These job fairs were advertised through WhatsApp networks of TTC graduates. All advertisements emphasized that the contracts were available for recruits placed in the 2016 school year and that the payments would continue into the 2017 school year. Applications were then submitted in December 2015. In January 2016, all districts held screening examinations for potential candidates. Successful candidates were placed into schools by districts during February–March 2016, and were then assigned to particular grades, subjects, and streams by their head teachers.

**Second-tier randomization: Experienced contracts** Our aim in the second tier was to randomize the schools to which REB had allocated the new posts to contracts. A school was included in the sample if it had at least one new post that was filled and assigned to an upper-primary grade.<sup>12</sup> Following a full baseline survey, schools were randomly assigned to either P4P or FW. Of the 164 schools in the second tier of the experiment, 85 were assigned to P4P and 79 were assigned to FW contracts.

> 10This randomization was performed in MATLAB by the authors. The Mixed advertised treatment arose due to logistical challenges detailed in the pre-analysis plan: the first-tier randomization was carried out at the level of the subject rather than the subject-family. An example of a districtby-subject-family assigned to the Mixed treatment is Ngoma-TML. An individual living in Ngoma with a TML qualification could have applied for an advertised Ngoma post in English on a FW contract, or an advertised Ngoma post in Kinyarwanda on a P4P contract. In contrast, KireheTML is in the P4P only treatment. So someone in Kirehe with a TML qualification could have applied for either an English or Kinyarwanda post, but both would have been on a P4P contract.

> 11Details of the promotional materials used in this campaign are provided in Appendix E.

> 12Because schools could receive multiple recruits, for different teaching specializations, it was possible for enrolled schools to contain two recruits who had experienced distinct advertised treatments. Recruits hired under the mixed advertisement treatment, and the schools in which they were placed, also met our enrollment criteria. These were similarly re-randomized to either experienced P4P or experienced FW in the second-tier randomization.

9
# _4.3.2 Enablers: public intent data_

“Public intent data” refer to data collected for public purposes, regardless of the collection instrument or the entity that manages the data. Traditionally, public intent data are collected by censuses and household surveys, although newer sources of such data have become available and more prominent in recent years, such as data collected through digital identification and facial recognition through public cameras. Issues measured under the dimension of public intent data include: the existence of open data laws, the interoperability of government data exchange platforms, the existence of a data classification policy and its mandatory use for government data, the existence of access to information (ATI) legislation and related exceptions, and, finally, the adoption by governments of an open licensing regime.

Country performance on the abovementioned aspects vary significantly, except on ATI legislation (figure 8), which has been adopted by 70 percent of the countries surveyed, while about half the countries include exceptions regulating ATI rights.<sup>19</sup> ATI legislation provides citizens a legal instrument to obtain public sector disclosure. Access and circulation of information held by public actors is a cornerstone for freedom of information and expression. This is one of the SDGs as it increases accountability and transparency of government action.<sup>20</sup>

**Figure 8. Percentage of countries with good regulatory practices to facilitate the use of public intent data (by country income)**

<!-- Start of picture text --> High income Upper middle income Lower middle income Low income<br>Semantic interoperability mandated for<br>government entities<br>100%<br>Adoption of open licensing regime by 80%<br>60% Existence of Open Data Law<br>governments<br>40%<br>20%<br>0%<br>Existence of exceptions to the ATI right Existence of data classification policy<br>Existence of Access to Information (ATI) Mandatory usage of data classfication on<br>Legislation government data<br><!-- End of picture text --> _Source:_ Author’s calculation based on results from the diagnostic.

Compared with ATI legislation that provides an ex post channel for citizens to obtain information, open data laws establish an ex ante responsibility for the disclosure of public data. Korea’s Act on the Promotion

> 19 Countries score points if they regulate all the following exceptions to ATI rights: (1) sensitive information on national security, defense, or foreign policy grounds; (2) trade secrets or other commercial interests; (3) personal data; (4) law enforcement; (5) privileged information; and (6) public investigations and audits.

> 20 Goal 16.10.2, see: https://sdgs.un.org/goals/goal16.

21
22 were associated with a substantially bigger role of the transport and machinery sectors. Throughout the nineteenth and twentieth centuries, countries in North America, Western Europe and Asia were able to transform their economies from agrarian to industrial powers, which included a rapidly growing services sector fueled in large part by the multiplier effect of manufacturing. As a result, they built prosperous middle classes and raised their standards of living.

Recent empirical studies show that manufacturing has been a key factor to the prosperity of nations, with over 70 percent of the income variations of 128 nations explained by differences in manufactured product export data alone (Hausman et al. 2011). In fact, few countries have achieved high-income status without industrializing. Only in circumstances such as an extraordinary abundance of natural resources or land have countries been able to do so (Unido 2009).

Manufacturing has evolved and changed the dynamics of the world economy. Profound changes in geopolitical relations among world nations, the widespread growth of digital information, the decline of transportation costs and the development of physical and financial infrastructure, computerized manufacturing technologies, and the proliferation of bilateral and multilateral trade agreements have contributed to the globalization of manufacturing. These developments have permitted the decentralization of supply chains into independent but coherent global networks that allow transnational firms to locate various parts of their businesses in different places around the world. The creative design of products, the sourcing of materials and components, and the manufacturing of products can now be done more cheaply and more efficiently from virtually any region of the planet while final goods and services are customized and packaged to satisfy the needs of customers in faraway markets.

The globalization of manufacturing has thus allowed developed economies to benefit from lower-cost products driven by the lower wages used for production in developing countries such as China, India, Bangladesh, Costa Rica, Mexico, or Brazil while creating job and learning opportunities in these formally poor nations. The intensity of these exchanges has led to new forms of competition and co-dependency.