rafmacalaba/datause-encoder-data huggingface dataset:
capita, population, land area) and project characteristics (project duration, funding amount, loan percentage allocated to public sector reform), and the full model adds IEG quality at entry and initial bureaucratic quality to the base model. All tables report odds ratios, the ratio of the odds that a project earns a successful rating to the odds that it earns an unsuccessful rating, adjusted for the simultaneous effect of the other explanatory variables.<sup>13</sup> Odds ratios greater than one imply that increases in the independent variable raise the probability of success; less than one, reduce it.

**Table 2:   Programmatic Parties and Project Ratings for Public Sector Reform**

||Base Model<br>Full Model|Base Model|Full Model|
|---|---|---|---|
||Inst. Dev. Impact Rating|Overall Out|come Rating|
|Programmatic Parties|3.874<sup>**</sup><br>6.061<sup>**</sup>|2.358<sup>*</sup>|2.616<sup>+</sup>|
||(0.001)<br>(0.004)|(0.020)|(0.083)|
|Log GDP per capita|1.165<br>1.133|1.079|1.108|
||(0.315)<br>(0.385)|(0.474)|(0.424)|
|Log Population|1.570<sup>+</sup><br>1.874<sup>+</sup>|1.275|1.402|
||(0.066)<br>(0.078)|(0.131)|(0.180)|
|Log Land Area|0.832<br>0.768|0.848|0.814|
||(0.350)<br>(0.286)|(0.235)|(0.300)|
|Funding Amount|0.998<sup>+</sup><br>0.998<sup>*</sup>|0.998<sup>*</sup>|0.998<sup>*</sup>|
||(0.092)<br>(0.046)|(0.042)|(0.026)|
|Project Duration|0.968<br>1.066|0.874<sup>+</sup>|1.013|
||(0.618)<br>(0.350)|(0.051)|(0.852)|
|Loan % Allocated to|1.005<br>1.008|1.007|1.011|
|PSR|(0.411)<br>(0.313)|(0.290)|(0.232)|
|IEG Quality at Entry|1.990<sup>***</sup>||3.505<sup>***</sup>|
||(0.000)||(0.000)|
|ICRG Bureaucratic|0.659<sup>+</sup>||0.598<sup>*</sup>|

13 For all countries in the sample, the odds ratio (odds of success relative to the odds of failure) is .653 for institutional development impact and .797 for overall outcome: failure is more likely than success. The corresponding probability that a project will earn a successful rating is .395 for institutional development impact and .445 for overall outcome.

29
# **Adjusting the Labor Supply to Mitigate Violent Shocks:**

# **Evidence from Rural Colombia**<sup>*</sup>

Manuel Fernández Ana María Ibáñez Ximena Peña World Bank Universidad de Los Andes Universidad de Los Andes Key Words: Conflict, labor markets, developing economies, instrumental variables JEL Codes: J13, J16, J22, J40

> * The authors are a consultant at the World Bank in Washington, D.C., and an associate professor and assistant professor at Universidad de Los Andes in Colombia, respectively. We gratefully acknowledge funding from the World Bank, with generous funding from the Government of Norway. Two anonymous referees, Kristin Bergtora Sandvick, Patricia Justino, Nidhiya Menon, Eleonora Nillesen, Yana Rodgers, PhilipVerwimp and participants in the PRIO and World Bank workshops provided valuable comments in the improvement of this paper. Any remaining mistakes in the paper are our own.
11 _to)_ **_Wwf Bak Dsw*p tma R.p^ 199._** p. 19.

|R<br>_anequtioe|spl<br>growthIn|G|DP ame**the paod**|**196W09.**||||
|---|---|---|---|---|---|---|---|
|ZIC|ZL||ZH|DE03|DE39|E4O|R '|
|_F_<br>_038_|0.4||0.04|ONO|0.04|13|023,|
|(17-7)|_(3A6)_||_(13)_<br>,,|(2.5,|(1-9)|(1.5)|'|
|ZK<br>cd<br>In|**log**of utRized cap|ita|l**196047**|||||
|_<br><br>ZH<br>chanp In|<br> **log**agulturl<br>l4|<br>a|<br>196047|||||
|ZL<br>chnp in|lbor force, 1960|47||||||
|<br>DE03<br>increue In|<br>aveg annual y|<br>em|<br>of education U lv|el lne<br>from 0 to 3||||
|<br>DE39<br>iname In|<br>wmg annual y|<br>oa|<br>of edution I le|<br>wM ag<br>ftm 3 to 9||||
|<br>960<br>averag n|<br>umbe of yea of e|<br>d|<br>ucatn of popati|<br>**g**<br>15-64 in 1960||||

v) MaNw. Romer and Weil (1992 p. 429) Regradon equation Wlats. growth in ra ODP per pewo of woekg age 196045

|**Co4n1t**|**In Y60**|**|**<br>b(WDP)|**In(n+g+d)**|In (School)|I<br>R|
|---|---|---|---|---|---|
|_309_|.0372|0.506|.772|0.266|0.44|
|**_(5.8)_**|(5.)|**_(5.3)_**|_<br>()|(33)||

Vn are 75 developing countdes in the re_0Duon equtn.

**Y60 ODP** per pawn of workg age in 1960 IWDP avee ntio of Invesment to GDP. 196045 * sg4d poputit growth rate phis technil proge ad deprestion ate. (the ltter two ae asumed to add to _0OAS)_ School fmncti of 12-17 year olds **attending** b **ady** scbool smtiplied by faction of **workig-age** popubtion **aged 15-19** ise rgreflOn is resticted so that the coefdens on the vesdment ratio, SCHOOL ad on the sm of grwth of workgage popultin growth ad deprcitiDon add to zeo. w restriction is acpted at a 42% confidence L

It is difficult to interpret the results. Baumol, Blackmann and Wolff suggest that higher education makes less of a contribution to economic growth than does secondary education simply because the significance level associated with the variable is lower. They do not discuss the fact that the coefficient itself is higher. The most natural way to compare the effects of the three types of education would be to place aU three variables in a single regression equation. This would allow one to test and perhaps then impose the hypothesis that aU three types of education have the same effect on growth. One cannot, from their results, anticipate the outcome of this test.

The second study is provided by Barro (1991). He presents a number of regression explaining growth in GDP _per capita_ of a wide range of countries by a number of variables describing education and political and social stability. The equation in table 3iii suggests that an increase of 10% points in the fraction of the school-age population attending secondary school raises the _per capita_ growth rate of GDP by 0.2% p.a. A similar increase in the growth rate is achieved by a 10% point increase in the fraction of the school-age population attending primary
INTRODUCnTON lvii

**Table 14** Annual Population Increase and Growth **Rate by World** Bank Region and Country Department, **1985-2100**

||Annuat po|pulation|increas|e (miltion|s)|An|nual grow|th rate|(percent|)|
|---|---|---|---|---|---|---|---|---|---|---|
|Region and||||||||_|||
|country<br>department|1985-90<br>2|<br>000-05<br>2|<br>025-30<br>2|<br>050-75<br>2|<br>100-25|1985-90<br>2|<br>000-05<br>2|<br>025-30<br>2|<br>050-75<br>|<br>2100-25|
|World|88.2|91.7|78.5|37.2|4.9|<br>1.74|1.43|0.91|0.35|0.04|
|Borrowers and non-borrowers|||||||||||
|Sub-Saharan Africa|15.4|22.6|27.2|18.2|2.2|3.11|2.92|1.91|0.82|0.08|
|Asia and Oceania|46.7|42.4|28.7|8.5|1.5|1.74|1.26|0.68|0.18|0.03|
|Europe,<br>Middte<br>East,<br>and North<br>Africa|15.2|17.3|16.6|<br>9.1|1.0|1.29|1.21|0.88|<br>0.40|0.04|
|America|10.9|9.4|<br>6.0|<br>1.4|0.2|<br>1.57|1.10|0.57|<br>0.12|0.02|
|Borrowers only|||||||||||
|Africa<br>Region|14.2|21.2|25.8|<br>17.4|<br>2.1|<br>3.17|2.98|1.94|0.84|0.08|
|1 Occidental<br>and Centrat|<br>1.4|<br>2.1|<br>2.6|<br>1.8|0.2|<br>3.32|3.03|2.03|0.89|0.08|
|2 Eastern|3.6|<br>5.5|<br>6.9|<br>4.7|0.5|<br>3.18|3.00|2.01|0.86|0.08|
|3 South-Centrat and|||||||||||
|Indian Ocean|1.8|2.8|3.5|2.4|0.3|3.13|3.06|2.04|0.89|0.08|
|4 Western|4.1|<br>5.8|<br>5.8|<br>3.5|0.4|<br>3.15|2.86|1.63|0.69|0.07|
|5 Sahetian|1.1|<br>1.8|<br>2.7|<br>2.0|0.3|<br>2.79|3.00|2.27|1.02|0.12|
|6Southern|2.2|<br>3.3|<br>4.3|<br>2.9|0.3|<br>3.33|3.05|2.06|0.88|0.08|
|Asia Region|45.0|41.1|28.6|<br>8.6|1.5|<br>1.80|1.30|0.71|<br>0.19|0.03|
|<br>1 Other South|3.5|<br>4.0|<br>3.1|<br>1.5|0.3|<br>2.44|2.01|1.07|0.41|0.06|
|2Southeast|6.0|<br>5.5|<br>4.1|<br>1.3|0.2|<br>2.08|<br>1.46|0.83|0.22|0.03|
|3 China|15.4|12.5|<br>7.1|<br>1.3|0.2|<br>1.43|0.96|0.46|0.08|0.01|
|4 India|16.5|16.1|12.0|3.9|0.7|<br>2.05|1.53|0.86|0.24|0.04|
|5 Indonesia and Pacific|3.5|3.0|2.3|0.6|0.1|1.98|1.32|0.76|0.18|0.03|
|EN4 Region|12.3|15.6|16.2|<br>9.3|0.9|<br>2.44|2.17|1.43|0.59|0.05|
|1Pakistan and Turkey|4.8|5.7|5.8|3.2|0.3|3.01|2.40|1.50|0.59|0.05|
|2 North<br>Africa|1.6|<br>2.0|<br>1.8|<br>0.8|0.1|<br>2.76|2.29|1.31|0.46|0.04|
|3 Middle East|5.4|7.4|8.4|5.2|0.5|2.99|2.68|1.74|0.73|0.06|
|4 Europe|0.5|0.4|0.2|0.0|0.0|0.44|0.37|0.14|0.03|0.01|
|LAC Region|8.4|7.5|5.7|1.6|0.2|2.02|1.40|0.80|0.19|0.02|
|<br>1 Brazil|2.9|<br>2.4|<br>1.7|<br>0.4|0.1|<br>2.05|<br>1.28|0.71|0.14|0.02|
|2 Central America|2.5|2.4|1.9|0.6|0.1|2.25|1.61|0.96|0.23|0.02|
|3 Caribbean and Other|1.5|1.5|1.1|0.4|0.1|2.08|1.49|0.87|0.23|0.03|
|4 Temerate and Tropical|1.4|1.3|1.0|0.3|0.0|1.77|1.28|0.73|0.17|0.02|

Note: World Bank regions and country departments are defined in Exhibit 2.
from the tobacco module of the Malawi Integrated Household Survey, IHS, of 2004/2005. In contrast to the TCS, which was especially deployed for the present project, the IHS was carried out by the Malawi Statistical Office.<sup>10</sup> Table 2 summarizes the evidence.

The services provided by the clubs are derived from four main prerogatives that they are entitled to offer: institutional access, collective actions, economies of scale, and supporting network.

Institutional access has traditionally represented the major incentive for smallholder membership. For many years, tobacco clubs constituted almost the only way to access the auction floors and fetch higher prices. Upon registration with the Ministry of Agriculture, the club was allocated a burley quota and a license to sell the tobacco at the auction floor (Orr, 2000). Instead, non-members had to sell their tobacco either to intermediate buyers or across the borders, in Zambia or Mozambique, at a lower price. Although quotas have practically been _de facto_ abolished, tobacco clubs are still an efficient vehicle to reach output markets. The IHS and the TCS provide different complementary evidence, reported in panel A of Table 2, on institutional access. On the one hand, the IHS reveals that 73 percent of the clubs acknowledge easier access to the auction floor. On the other hand, according to the TCS, only 33 percent of the clubs actually get better prices at the floors. Both the IHS and the TCS agree instead that tobacco clubs provide preferential institutional access to official extension support. In Table 2, 90 and 69 percent of the clubs surveyed in the TCS and the IHS, respectively, claim to have received better training and technical advisory services from extension workers.

Burley clubs have a major role as facilitators of collective actions. One example is debt repayment monitoring. In the absent of collateral, credit institutions use peer monitoring as a way to bridge the asymmetric information gap between lenders and borrowers in small-scale credit transactions (Pitt and Khandker, 1998; Besley and Coate, 1995; Morduch, 1999). In Malawi, tobacco clubs act as a group-lending institution, and all members are made jointly liable for repayment.<sup>11</sup> In turn, the clubs have the right incentives to honor their debts in

> 10See Appendix 1 for a description of the Integrated Household Survey. The IHS was designed to make a thorough assessment of poverty in Malawi. Details of the survey and lots of analysis on poverty determinants, poverty profiles, coping mechanisms, and poverty-reducing policies can be found in World Bank (2006).

> 11The loan is later repaid when tobacco is sold at the auction floor by deducting the amount of the loan,

11
# **4.3 Simulating a 10 percent increase in sugar prices**

First, we provide an estimate of how a 10 percent increase in the world price of sugar would affect wages of those currently employed.<sup>20</sup> We use the actual survey data to calculate the predicted change in wages for each person employed and aggregate the results for various groups. The results by sector and education level are presented in Table 8. Again, workers in sugar growing and sugar processing stand to gain the most. Even with imperfect transmission (recall that in many states a 10 percent increase in the world price would translate into less than 10 percent increase in the local price) this change would bring a 2 to 4 percent wage increase, depending on the level of education. Workers in the service sector that have low level of education would on average lose 0.5 percent in wages, while well-educated service workers would experience a small gain (0.8 percent).

The first column of Table 9 provides an estimate of the percentage change in household income brought by the change in expected wages of those currently employed (as in Table 8), by income quintile. The average individual in all quintiles benefits from the 10 percent increase in world sugar prices, but the wage increases are larger for individuals in the top income quintile. Conditional on being employed, the average individual in the top two income quintiles experiences an increase in income which is 17 percent higher than the one experienced by the average individual in the bottom two income

> 20 Note that all changes in income provided in this section are based on a somewhat arbitrary 10% increase in the international sugar price. Dividing the percentage income changes reported here by 10 would produce easily interpretable income elasticities with respect to the world price of sugar. In other words, the percentage change in income is linear in the percentage change in world prices and therefore to obtain the percentage change in income after an _x_ % change in world prices, one can simply multiply the percentage change income reported here by _x/_ 10.

22
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**In light of the inefficiencies observed in the insolvency systems across EMDEs, some may turn to out-of-court workouts (OCWs) as a more efficient alternative to court-supervised insolvency proceedings for the resolution of corporate distress.** Specifically, governments may need to develop a framework for encouraging negotiations between private debtors and their creditors, which has been a successful tool in past crises. As informal tools, OCWs can be put in place relatively quickly and work as complements or alternatives to the formal insolvency system.<sup>9</sup>

# 7. References

Banerjee, Ryan, Anamaria Illes, Enisse Kharroubi and José-Maria Serena (2020). Covid-19 and corporate sector liquidity. Bank for International Settlements Bulletin No.10, 2020.

Feyen, Erik, Norbert Fiess, Igor Zuccardi, Igor, Lara Pillonca (2017). Which emerging markets and developing economies face corporate balance sheet vulnerabilities?: a novel monitoring framework (English). Policy Research working paper; no. WPS 8198. Washington, D.C.: World Bank Group.

Gurhy, Bryan, Jing Zhao, Catiana Garcia-Kilroy, Ana Carvajal, Gonzalo Martinez Torres, Jose Antonio Gragnani and Tanya Konidaris (2020). COVID-19 Outbreak: Capital Markets

> 9 For a more detailed analysis of OCWs and their benefits, please see a forthcoming FCI Technical Note on Out of Court Workouts and “A Toolkit for Out-of-Court Workouts” WBG (2016).

16
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quarterly labor income in Connecticut (CT) to 6.4% of quarterly labor income in Mississippi (MS). Two factors are at play here: First, the level of per-capita labor income is much lower in MS than CT, and so xed dollar payments are mechanically more important. Second, much of the variation is driven by the low-income rebate (those paying no net taxes), which is focused toward poor states. Of the 4.1 percentage point gap in stimulus transfers in 2008Q2 between MS and CT, 3.4 percentage points are due to the cross-state allocation of the low-income component. Panel C of Figure 2 shows the same stimulus package and quarter but for quarterly growth in GDP per capita rather than labor income, which generates a steeper slope of 0.9, but one that is less precisely estimated (t-stat = 2.5). The nal scatter plot (Panel D) displays the relationship between the 2001 EGTRRA stimulus payments (as a share of labor income, x-axis) and contemporaneous per-capita labor income growth in 2001Q3 (y-axis). Again, states that received a larger payment (as a share of labor income) tended to grow faster contemporaneously, though here the multiplier is large at 1.25 (t-stat = 4). I do not put too much weight on these scatter plots quantitatively as they have no controls.

3.2. Main Empirical Results. The main empirical results are presented in Panel A of Table 1, which pool across all transfer changes grouped by persistence and dependent variable. The rst row presents a parsimonious specication, with no controls beyond state and quarter FEs. The second row presents a benchmark specication, which includes controls for US GDP growth _×_ state FEs, and population growth, and is used for placebo tests (Figure 2) and used to compare with the theoretical model in Section 5. The 50 US GDP growth _×_ state FEs are to control for dierential state sensitivities to the aggregate business cycle potentially important as the 2001 and 2008 transfers were explicitly countercyclical at the aggregate level discussed further below. Population growth is important because sunbelt states with more retirees tend to have faster growing populations than other states, which might aect per capita income growth and also be correlated with transfer size (for example retirees claiming Social Security benets migrate to warmer states).

16
consumption, leading to underestimation of true impacts. A reduction in electricity consumption may occur, but it may not be detected (or muted) due to contamination of the comparison group.

3. _Aggregate effect:_ Energy efficient technologies could have an effect on aggregate electricity consumption within the local distribution system. The sign and magnitude of this aggregate effect will depend on the proportion of households that use CFLs, as well as on the sign and magnitude of the impacts they individually experience. If a large enough proportion of households within a transformer adopt CFLs and their individual impacts combine into aggregate electricity savings, the probability of an outage occurring due to a transformer overload would be reduced, thus increasing reliability of electricity services.

4. _Technological externality:_ The distribution of CFLs could generate a positive (negative) technological externality if the returns to a household from adopting such technology increase (decrease) in the fraction of the population utilizing the energy efficient technology. In the scenario of a positive technological externality, when consumers install energy efficient devices at a high saturation and aggregate demand decreases such that reliability improves, households are able to consume more hours of electricity services. This effect is experienced by all households within a transformer, regardless of the individual household’s own adoption. The returns from CFL adoption increase because the potential for electricity consumption increases and therefore the potential for energy savings through CFL use does as well.<sup>26</sup> This field experiment employs a randomized saturation design to address these potential impacts of energy efficiency.

# **3 Randomized experiment with energy efficiency**

## **3.1 Sampling process**

In order to identify transformers and households within the transformers, we used data from electricity utility records on over 40,000 residential customers in a district adjacent to

> 26If electricity is limited, the benefits from CFL use are positive but small–zero if there is no electricity. As electricity becomes more available the benefits increase, possibly in a non-linear way, at a decreasing rate and/or discontinuously.

10
SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE South Sudan’s infrastructure has restricted capacity in the area of fixed-line telephony. Unless there are changes in the licensing agreements in South Sudan, fixed-line telephones are not expected to have increased significantly.

Further, the fact that operators licensed to operate in South Sudan have not developed their network more fully indicates that there are noncommercial barriers to expansion. Non–South Sudanese operators report that they have had difficulties obtaining permission from the government to operate in South Sudan. The South Sudanese operators (NOW and Gemtel) also have not been issued with formal licenses recognized by the GNU. In the past this had made it difficult for them to raise finance to invest in networks.

South Sudan has yet to connect to the undersea cable, which has proven to decrease prices for ICT services in other countries considerably. Evidence shows that Sub-Saharan Africa’s access to the submarine cable has generally reduced costs of ICT services when international gateways are present (table 17). According to Sudatel, an optical fiber to South Sudan was planned as part of the nationwide backbone (figure 21) but its status is unclear. The existing backbone transmission is believed to be through microwave with eventual connectivity to fiber optic in the north and onward to Port Sudan for international connectivity through undersea cables. One possible option for landlocked South Sudan to gain access to the undersea fiber-optic network is through a backbone from Juba to the Kenyan border.

South Sudan needs to add around 643 km to connect to the fiber-optic cable and establish a strong ICT backbone. South Sudan is missing a link to connect to Uganda, which has largely achieved connectivity to the fiber-optic backbone. Adding these missing kilometers of cable would allow South Sudan to connect to the Ugandan border from Malakal via Juba. Meanwhile, Sudatel has financed the construction of fiber from Khartoum to Malakal.

**Table 17.  Prices of Internet and phone calls in Sub-Saharan Africa, with and without access to submarine cables**

|US$|Call within<br>region|Call to the<br>United States|Internet<br>dial-up|Internet ADSL|
|---|---|---|---|---|
|Without submarine cable|1.34|0.86|68|283|
|With submarine cable|0.57|0.48|47|111|
|Monopoly on international gateway|0.70|0.72|37|120|
|Competitive international gateway|0.48|0.23|37|98|

_Source:_ AICD calculations. _Note:_ ADSL = asymmetric digital subscriber line.

37