rafmacalaba/datause-encoder-data huggingface dataset:
These per capita income rankings are correlated with the rankings obtained from the World Bank’s 2007 Doing Business Report (DBR), presented in panel C of figure 1.2. In 2007 Mauritius, Swaziland, South Africa, Botswana, and Namibia rank 32nd, 76th, 29th, 48th, and 42nd in the world based on the ease-of-doing-business indicators. This index considers questions such as the number of days required to start a business and the ease of dealing with licenses, registering a property, trading across borders, employing workers, and so on. Other 2007 rankings include 83rd for Kenya, 97th for Ethiopia, 165th for Egypt, and 170th for Eritrea.

To better understand the convergence or divergence of trends, we plotted the per capita income of each African country relative to the per capita income of the United States (see panel A of Figure 1.3). Convergence is observed only in Mauritius, Swaziland, and Botswana. For all other study countries, including South Africa, per capita income is diverging from the United States, while in a few (Egypt, Morocco, and Cape Verde) the ratio has remained stable. While persistently positive GDP growth allowed Mauritius’s per capita income to reach 45 percent of the United States in 2003, this is clearly the exception (together with Swaziland and Botswana). For the rest of the countries, including South Africa, relative per capita income was much lower in 2003 than in 1960 (indicating divergence). In fact, the 2003 per capita income of several countries was no larger than 5 percent of the per capita income of the United States. As expected, labor productivity is the main factor explaining this divergence in per capita income in Africa (panel B of Figure 1.3), given that labor force participation has a steady influence (panel C of Figure 1.3).<sup>5</sup> Since TFP is usually a key factor explaining the evolution of labor productivity, in this paper we seek to use investment climate surveys (ICSs) to identify the main infrastructure-related TFP bottlenecks in Africa.

> 5 The _per capita income_ of country J (YJ/PJ) is decomposed into labor productivity (YJ/LJ) and the employment-population rate (L<sup>J</sup> /P<sup>J</sup> ) by following the expression: (Y<sup>J</sup> /P<sup>J</sup> ) = (Y<sup>J</sup> /L<sup>J</sup> )*(L<sup>J</sup> /P<sup>J</sup> ), where Y is GDP, L is total labor force, and P is total population.

6
**_Table 1: The Global Carbon Reduction Incentive – payments and receipts for the top 10 countries_**

|**Country**|**Production CO2 (mil**<br>**tons)**|**Population**<br>**2019**<br>**(millions)**|**Per capita CO2**<br>**(production)**|**Deviation from global**<br>**average (per capita CO2 –**<br>**4.61 tons/person)**|**Tax or incentive USD**<br>**Billions (valuing**<br>**'excess' emissions at**<br>**USD 10per ton)**|
|---|---|---|---|---|---|
|United States|5291.77|325.03|16.28|11.67|37.92|
|China|10053.86|1420.67|7.08|2.46|34.96|
|Russian Federation|1651.85|145.48|11.35|6.74|9.80|
|Japan|1172.92|127.46|9.20|4.59|5.85|
|Saudi Arabia|639.54|33.05|19.35|3.65|4.87|
|Korea, Rep.|649.98|51.06|12.73|3.11|4.14|
|Canada|572.77|36.73|15.60|3.02|4.03|
|Germany|769.54|82.66|9.31|2.91|3.88|
|Iran, Islamic Rep.|683.75|80.69|8.47|2.33|3.11|
|Australia|412.90|24.58|16.80|2.25|2.99|
|Kazakhstan|299.67|18.07|16.58|1.62|2.16|
|Tanzania|11.35|54.70|0.21|-1.81|-2.41|
|Philippines|131.31|105.14|1.25|-2.66|-3.54|
|Congo, Dem. Rep.|2.55|81.46|0.03|-2.80|-3.73|
|Brazil|498.47|207.80|2.40|-3.46|-4.61|
|Ethiopia|14.96|106.43|0.14|-3.57|-4.76|
|Indonesia|592.60|264.58|2.24|-4.71|-6.29|
|Bangladesh|81.50|159.67|0.51|-4.92|-6.55|
|Pakistan|216.57|207.95|1.04|-5.57|-7.43|
|Nigeria|120.27|190.96|0.63|-5.71|-7.61|
|India<br>Source: Author cal|2462.08<br>culations based on ‘|1338.48<br>Our World in Da|1.84<br>ta‘(https://ourw|-27.87<br>orldindata.org/).|-37.16|

Source: Author calculations based on ‘Our World in Data‘ (https://ourworldindata.org/).

# _Modification 1: Consumption versus Production_

An important adjustment to the GCRI is to focus on consumption rather than production – a country should not avoid responsibility for the carbon it consumes by outsourcing production to another country. So, the proposal adds the carbon embedded in imported goods and subtracts the carbon embedded in exported goods from the carbon produced by a country. For a country like China, CO2 emissions based on production are 10 billion tons and fall to 9 billion tons when a consumption-based measure is used. In the United States, 5.2 billion tons of CO2 are “produced” and 5.6 billion tons are “consumed”. This exercise is carried out for all countries where these data 8
# **Scaling Up** **_Oportunidades_ and Its Impact on Child Nutrition**

Gabriela Farfán<sup>†</sup> World Bank María Eugenia Genoni World Bank Luis Rubalcava CAMBS and Spectron Graciela Teruel Universidad Iberoamericana Duncan Thomas Duke University JEL: I1, I3, I38, I18 Keywords: Cash transfers, Nutrition programs, Nutrition policy and programming

> † Financial support from the Hewlett Foundation and Institute of International Education (IIE) is gratefully acknowledged. The authors are grateful for comments provided by María Laura Alzúa, Peter Arcidiacono, Federico Bugni, Guillermo Cruces, Erica Field, Susan Parker, Alessandro Tarozzi, Rebeca Thornton, and participants in the Duke Development Reading Group, LACEA 2012, Impact Evaluation Network 2012, NEUDC 2011, PAA 2011, and X Reunión Nacional de Investigación Demográfica, Mexico DF 2010. 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.
(GE) and an excess of rules, and prefer the rule of law to the rule of bandits (RL). When government transitions are decided by well-defined and long-lived rules, rather than by violent overthrow or perennial coups, government officials are more likely to have a longer time horizon, and to seek investment for growth (and political survival) rather than corrupt transfers. The PS index is intended to measure the expected orderliness of political transitions according to established rules. Thus, PS is related to CC, RL and GE either causally or by definition.

Control of corruption (CC). Controlling corruption implies a reduction in the use of public resources for private gain. Corruption is a costly, hidden (absence of VA) and usually illegal (absence of RL) transfer of revenues. Government officials often collect bribes as an ex-officio tax, fee or “gift” in exchange for a license or service (e.g. utility connection), or for exemptions to rules or taxes (implicating RQ and GE).

Regulatory quality (RQ). When governments establish numerous barriers to conducting business, it creates opportunities for public officials to collect bribes before delivering a service (CC). By definition, corrupt governments set up entry barriers so that public officials can act as gate-keepers, and collect (hidden) bribes and pocket the transfer before opening the gate to the briber-client (absence of VA). (There can also be too few rules: higher-quality regulation implies there are not excessive rules, and that rules are efficiency enhancing.) The RQ index is intended to measure the extent to which the formal (and informal) regulations that govern the relation between the public and the private sector (RL) foster growth rather than costly transfers (from the private client to the public regulator, or the other way). Thus RQ implicates (by definition or cause) CC, RL, RQ and VA.

In operationalizing these concepts empirically, there are additional obstacles preventing the WGI indexes from successfully representing six distinct concepts. First, in collapsing the large number of "governance" indicators they collected into a small number of groups, Kaufmann and his colleagues may have selected the wrong six concepts, or they may have chosen the wrong number of them. Arguably, they should have supplemented their intuition with some exploratory empirical work in identifying dimensions, but they do not report having done this analysis. The breadth of some concepts is potentially problematic, with "government effectiveness" the most obvious 6
While this survey is a milestone in large-scale LGBTI data collection, it illustrates a number of challenges that should be taken into consideration when developing future surveys.

# **India**

In India, the World Bank developed a survey with the goal to explore the interconnection between sexual orientation, gender identity and development outcomes, specifically the relationship between discrimination and socioeconomic status.<sup>42</sup> The implementing partner, Amaltas, used a similar community-based approach as employed in Nepal to recruit respondents. Due to a lack of organizations with a rural membership and the overall difficulties of data collection in remote areas, the survey was carried out in urban areas only. Within the L, G, B and T communities<sup>43</sup> the survey administrators found it particularly difficult to reach bisexual and lesbian respondents. In India, most bisexuals tend to hide their sexual orientation, especially since many of them are married with families. They live out their bisexuality mainly through same-sex behavior, rather than identification. Lesbians, on the other hand, were hard to reach because of a paucity of lesbian-focused community-based organizations that could help carry out the survey.

The survey reached 943 respondents in eight Indian cities. The majority of the respondents were ‘men who have sex with men’ (MSM). In India, the term is often used as an umbrella term to describe gay identities, but also includes people who engage in same-sex behavior yet would not identify as gay men.<sup>44</sup> The survey also reached a high number of hijras<sup>45</sup> and a much smaller number of ‘female-born’, one local term for lesbians.

The results show high levels of discrimination in the daily lives of the respondents. They are excluded from key services such as banking, housing, and health care. Around one-third of the respondents did not have a bank account and thus could not access any financial services. Similarly, many reported discrimination in the housing market, 13% said they were unfairly denied accommodation and 15% claimed they were evicted from their homes because of their sexual orientation, gender identity and/or expression. When attempting to access health care facilities, 15% of the respondents were denied access and another 15%

> 42 Singh, Suneeta; Krishan, Anjali; Mishra, Chittaranjan; Ghai, Kavya. Experienced discrimination and its relationship with life chances and socio-economic status of sexual minorities in India. 43 Intersex people were not part of the data collection.

> 44 Singh, Suneeta; Krishan, Anjali; Mishra, Chittaranjan; Ghai, Kavya. Experienced discrimination and its relationship with life chances and socio-economic status of sexual minorities in India.

> 45 Hijra is one South Asian term for male to female transgender identities; it does not cover all trans identities.

14
by the local mayor’s office or directly by FMSD.<sup>4</sup> The remaining pool of potential clients, about 20%, were called up by credit officers directly or visited the branch.

Once potential clients were registered, credit officers followed-up with a visit to assess eligibility, and to make randomized offer. All prospective clients also received a leaflet with information about the loan (see Appendix B for a sample of the flex product flyer in Spanish). Loan applications were collected by credit officers and reviewed by the credit committee. Clients with approved loans received additional explanations from a dedicated staff when the loan was disbursed either during the branch visit or over the phone.

Recruitment into the study took place continuously over 18 months. Overall, 22.4% of potential clients were assigned to a flexible offer (see Appendix A for further details). Panel A of Appendix Table 3 confirms that the randomized assignment of offer types was balanced overall with respect to the recruitment process and branch location (the p-value of a joint test of equality of means is 0.23).

# **Randomization of second stage (switch to flexible loans)**

Approved standard loans were randomly switched to flexible loans at disbursement, with a target probability of 50%, based on the observed distribution of the last three digits of the national identification document using the loan data set of our partner microcredit institution. In total, 1,893 standard loan offers were accepted and 971 (51%) of them were converted to flexible contracts as part of the second stage randomization.

Clients learned about the switch when their credit officer called them about the approval of their application and gave a short explanation of the new flexible loan. All clients in the Std→Flex group accepted the switch to the flexible loan.

We test for balance in the second stage randomization by looking at the sample of new clients that initially received a standard offer. Using a combination of data from the recruitment process, data collected by credit officers during the application process as well as the bank’s administrative data, we compare those who received a standard loan with those who were switched to a flexible loan. Appendix Table 4 shows means and standard deviations for the two groups and p-values of the tests of equal means. Out of the 18 variables including loan characteristics (Panel A), socioeconomic characteristics of clients (Panel B) and business characteristics (Panel C), only one difference is significant at the 10% level. The p-value of a joint test of differences across all variables is 0.90.

> 4 Sixty-two percent of the recruits from financial events came from those organized by the mayor’s office, which partnered with private partners to visit different neighborhoods to advertise the availability of existing services such health and education programs, conditional transfers, and microfinance. At an event, prospective borrowers received a "financial inclusion” briefing that included eligibility criteria to apply for a loan.
# **<u>Table 6: Effect of Program Availability on Mother’s Time Spent on Paid Work</u>**

||**Did Mother**|**Participate**|**Minut**|**es per typical**|**school day sp**|**ent on paid w**|**ork**|
|---|---|---|---|---|---|---|---|
||**Pooled**<br>**OLS**|**Fixed**<br>**Effects**|**Pooled OLS**||**Fixed E**|**ffects**||
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Treatment District|0.061*||16.059|||||
||(0.037)||(13.814)|||||
|Round 3|-0.006|-0.002|-4.895|-2.395|-3.175|-12.992|-8.399|
||(0.014)|(0.014)|(5.805)|(5.577)|(7.853)|(13.276)|(14.426)|
|Round 4|-0.047***|-0.043***|-17.477***|-16.462***|-18.540*|-18.544*|-13.329|
||(0.015)|(0.014)|(6.419)|(6.284)|(9.775)|(10.171)|(11.574)|
|Treatment District * Round 3|<br>-0.112***|<br>-0.111***|<br>-31.651**|<br>-31.857**|<br>-29.504**|<br>-25.494**|<br>-24.431*|
||(0.036)|(0.036)|(13.014)|(12.679)|(12.652)|(12.623)|(12.671)|
|Treatment District * Round 4|<br>-0.080**|<br>-0.089**|<br>-24.987*|<br>-27.736*|<br>-25.601*|<br>-19.560|<br>-20.136|
||(0.040)|(0.041)|(14.334)|(14.663)|(15.153)|(16.053)|(16.385)|
|Constant|0.085***|0.097***|30.850***|33.728***|12.186|26.776|19.433|
||(0.015)|(0.008)|(6.355)|(3.240)|(63.482)|(61.741)|(63.435)|
|Number of observations|1,381|1,381|1,378|1,378|1,378|1,377|1,377|
|Number of mothers||482||481|481|481|481|
|R2 within||0.041||0.031|0.067|0.086|0.088|
|Controls|None|None|None|None|Basic|Extended|Full|

note: *** p<0.01, ** p<0.05, * p<0.1. The mean of the dependent variable for columns 1 and 2 is 0.1014. The mean of the dependent variable for columns 3 – 7 at baseline is 47 minutes/typical school day. Basic controls: a series of indicator variables that fully describe the composition of the household by age and gender. For example: an indicator for each number of adult men, women, teenage boys, teenage girls, pre-teen girls, pre-teen boys as well as infant boys and girls. Extended: In addition to basic specification includes age of mother and father as well as indicators for whether this information is missing. Includes an asset index measured as the first principal component of a list of 16 assets over all rounds of the data. Also includes a series of indicators for wealth status of household as measured by wealth relative to five years ago and harvest relative to last year. Full: In addition to extended controls a series of indicator variables for quarter in which household was interviewed. Round 3 is the first post-program year for which LEAPS data on this outcome is available. Heteroskedasticityrobust standard errors in parentheses. Standard errors adjust for clustering at the mother level.

36
The focus so far has been on the first moment of the test score distribution, but the effect of incentives on inequality is also of theoretical interest. There is a small overall increase in test score inequality in program schools relative to comparison schools in the two years of program, but differences are minimal and not generally statistically significant (Table 6). The slight (though insignificant) increase in test score inequality in program schools is inconsistent with one particular naïve model of cheating, one in which program school teachers simply pass out test answers to children in their class.<sup>19</sup> This would likely produce less inequality in program schools relative to comparison schools (note that the inequality patterns we find are not inconsistent with all models of cheating, however). The changes in inequality over time in program versus comparison schools are similarly minimal for boys (not shown).

# **<u>5.2 School Participation, Attitudes and Behaviors</u>**

It is useful to explore the channels causing estimated program effects, since some mechanisms, such as cheating or increased coaching, might raise test scores without improving underlying learning. The scholarship program had a significant positive impact on student attendance (measured during unannounced enumerator visits) in 2001 and 2002 in Busia district (Table 7, Panel A, regression 1): pooling cohorts 1 and 2 in the Busia restricted sample and measuring the effect of the scholarship on overall attendance yields a coefficient estimate of 0.05 percentage points (0.02 standard error), which is statistically significant at 95 percent confidence, and reduces student absenteeism by approximately 30 percent. These attendance gains suggest program school students are exerting extra effort in one important dimension. There are statistically insignificant gains in Teso district (regression 2), and the estimated student school attendance gain pooling Busia district and Teso district together for girls and boys is not significant at traditional confidence levels (0.02, standard error 0.02 – regression not shown).

The program increased the average likelihood of school attendance by 6 percentage points among girls in cohort 1 in 2001, and by 10 percentage points among cohort 2 in 2001 (a pre-program effect), and estimated gains in 2002 are also positive but considerably smaller (Table 7, Panel B, regression 1). The

> 18 Source: authors’ field notes, July 15, 2002.

23
values: liability and indemnity. Liability is a measure of the insurer’s exposure to loss. It represents the total insured value of the crop, defined as Liability = acres planted x APH yield x selected coverage level x base price x price election.

The APH yield, based on the producer’s actual production history, represents the producer’s normal yield defined as the 4-10 year average yield. The coverage level represents the producer’s deductible, with a coverage level of 75% meaning that indemnities are paid only if the actual yield is less than 75% of the APH yield. Coverage levels are usually available between 50% and 85%, in 5% increments. The base price represents the price of the crop at the start of the growing season, as established by RMA. The producer may choose to insure production at any level (i.e., price election) from 60% to 100% of the base price of the crop.

Indemnity is the amount paid under the MPCI coverage for a producer suffering a covered loss. Indemnity is paid when the value of production is less than the liability purchased. The amount of indemnity is thus equal to the difference between the liability purchased and the value of production, i.e., Indemnity = acres planted x base price x price election x max[APH yield x selected coverage level - actual yield,0].

The premium paid by a producer is thus derived as Premium = liability x rate x adjustment factor.

Box 3 illustrates the steps in determining the liability, premium and loss.

**Box 3.  Multiple peril crop insurance: an illustration**

Step 1. Liability Acres planted 500 acres Farmer’s actual production history 140 bushels/acre Coverage level selection 75% Base price USD 3 Price election 100% Liability USD 157,500 = 500 x 140 x 75% x 3 x 100% Step 2. Premium Liability USD 157,500 Premium rate 3% Premium USD 4,725 = 157,500 x 3% Premium subsidy 40% Producer paid premium USD 2,835 = 4,725 x (1 – 40%) Step 3. Loss Actual yield 95 bushel/acre Value of production USD 142,500 = 95 x 500 x 3 x 100% Indemnity payment USD 15,000 = 157,500 – 142,500 The producer expects to harvest 70,000 bushels and insures 52,500 bushels. The actual production is 47,500 bushels. The indemnity is thus equal to 5,000 bushels evaluated at the base price.

Sources: Authors, from Schnapp et all (2000).

21
increases the likelihood that the households they head in adulthood fall below the poverty line (9 percentage points or around 18%). This evidence suggests that effects of uninsured weather shocks that occurred decades ago show strong persistence over time and are still felt by affected individuals and their families to this day.

# _Why negative effects?_

The burden placed by rainfall shocks on agricultural output is identified as the most plausible channel driving the effects on socioeconomic outcomes in the long run. Back in the 1970s, virtually all Mozambicans (nearly 98 percent) lived in rural areas. At the time, agriculture –even more so than today— represented the main source of their livelihoods. Arguably, agriculture is therefore the main mechanism at play behind the observed negative impacts. In fact, the shocks examined in this paper are expected to strongly affect the water balance of soil by translating into too much or too little water. Severe droughts or floods are expected to exert a negative impact on yields and, in turn, likely on household consumption, food security and income.

We use the Water Requirement Satisfaction Index (WRSI) to examine the relationship between water conditions and agricultural yields in Mozambique. The WRSI is a measure employed by the Famine Early Warning System project (FEWS NET) to predict harvest outcomes and to identify potential food security issues on a seasonal basis. The WRSI is defined as the ratio of seasonal actual crop evapotranspiration to the seasonal crop water requirement, and captures the expected impact of water deficits on harvest at different points in time over the growing season. The WRSI is crop-specific, spatially explicit and identifies the start of the season based on rainfall patterns. It ranges between 0 and 100, with values below 50 showing levels of soil water well below the minimum. The WRSI has some limitations such as not accounting for excess water (e.g. floods), and the coarse resolution of some of its static inputs (e.g.: soil water holding capacity).

Weather data and crop growth models reveal the high sensitivity of agricultural output to extreme weather in Mozambique. While data on agricultural yields at the province level is patchy, both spatially and across time, we look at the unconditional correlation between the WRSI for maize and their corresponding yields. The WRSI mean is calculated locally, at the grid cell level, then averaged over the province. The results provide suggestive evidence of a positive relation between water supply during the growing season and crop performance (Figure 3). More 10