rafmacalaba/datause-encoder-data huggingface dataset:
unobserved dimensions) in ways that make them more likely to repay, to devote land to paprika, and to use fertilizer on paprika.

Analyses of the full sample of farmers, without restricting the sample only to borrowers, can help address such concerns about selection bias. Estimated effects of treatment (and interactions with predicted repayment) would then represent effects of being fingerprinted on average across treated individuals, whether or not the individual took out a loan. While such an analysis makes little sense for outcomes specific to loans such as repayment (as in the outcomes of Table 4), we carry out this analysis for the other examined variables from the August 2008 follow-up survey, namely land use, input use, and profits (the outcomes in Tables 5, 6, and 7).

As it turns out, full-sample regression results are very similar to those from the borroweronly regressions. The general pattern is for coefficients that were significant before to remain statistically significant, but to be only around half the magnitude of the coefficients in the borrowing sample regressions. This reduction in coefficient magnitude is consistent with effect sizes in the full sample representing a weighted average of no effects for nonborrowers and nonzero effects for borrowers (slightly less than half of individuals in the full sample are borrowers). All in all, we conclude that selection into borrowing is not driving the treatment effect estimates of Tables 5, 6, and 7.

# _<u>Results with “simple” predicted repayment regression</u>_

Results discussed so far examining heterogeneity in treatment effects construct the predicted repayment variable using the regression in column 3 of Online Appendix Table 1. The righthand-side of this regression contains farmer-level characteristics as well as all interactions between locality and week of initial loan offer fixed effects.

Because the baseline farmer-level characteristics listed in Online Appendix Table 1 are the most readily interpretable, we check the robustness of the results to constructing predicted repayment using only baseline farmer-level characteristics. The alternative predicted repayment regression is that of column 3 of Online Appendix Table 1, except that (locality)*(week of initial loan offer) fixed effects are dropped. This regression is then used to predict repayment for the full sample, and the predicted repayment variable is interacted with treatment to examine heterogeneity in the treatment effect.

23
data, such as the entry and exit of PFAs and the importance of passive portfolio changes, see Appendix 4. Despite further corrections, the results there are broadly consistent with the results reported here.

Our main results for the presence of momentum and contrarian trading based on the measures described above are reported in Table 13. Each entry in the table reports the average momentum statistic across PFAs, its standard error, the level of significance of the one-tailed test that each average is greater or lower than zero, depending on its sign, and the fraction of PFAs for which the null hypothesis of momentum and contrarian trading cannot be rejected at the ten-percent level. When testing whether a specific fund is a momentum trader the inference is performed across time, but when testing for the overall presence of momentum strategies the inference is conducted across funds only. Columns (1) to (3) show the three statistics based on lagged (previous month’s) returns.

For the overall group of assets no statistic can reject the null hypothesis that there is momentum trading across PFAs, and at the individual fund level the hypothesis cannot be rejected for a fraction of funds that vary between 38 and 54 percent. As usual, there is important variation across asset classes. Only domestic equities and quotas of foreign investment and mutual funds display robust evidence of momentum trading regardless of the specific measure used, while for government bonds, foreign fixed-income, and foreign equities the hypothesis cannot be rejected in two of the three measures. The evidence is mixed for the other asset classes.

Asset classes for which the hypothesis of momentum trading cannot be rejected are also typically those with the highest fraction of individual PFAs for which this hypothesis cannot be rejected. For instance, the hypothesis of momentum trading in domestic equities cannot be rejected for 30 percent of the PFAs in operation during the period of our analysis. In the case of quotas of foreign investment and mutual funds this fraction is 50 percent.

Interestingly, there is little robust evidence of contrarian trading across asset classes. In some classes, such as mortgage bonds, the hypothesis of contrarian trading cannot be rejected for two of the measures but the third measure indicates momentum trading. The best evidence for the presence of contrarian trading comes from quotas of domestic investment and mutual funds, for which the hypothesis cannot be rejected 31
creditors, and equity providers who end up bearing the interest rate risk. There are numerous other risks that do not necessarily end up being borne by the party best able to manage it. More often, it is the best and most experienced negotiator that ends up bearing the least amount of risk.

Also, the level and type of risk encountered may change over time. The 1998 Asian crisis increased perceived risk levels enough to increase the required rate of return to levels unachievable for most projects. On the other hand, governments may fall prey to a “feargreed cycle”, in which governments become afraid of program failure and thus offer increasingly better terms. Alternatively, prospective concessionaires who worry that they will get left out bid unrealistically. Subsequently, the element of greed takes over in which governments may fail to live up to commitments and the private sector seeks ways to privatize gains and socialize the project’s risks.

Successful PPPs have been characterized by a broad level of risk-sharing between the public and private sectors. Generally, the private sector is better at managing commercial risks and responsibilities such as those associated with construction, operation, and financing. In contrast, transport projects most likely depend on public participation in areas such as acquisition of right-of-way, political risk, and in some cases, traffic and revenue risk. PPPs has worked best when experienced, well-capitalized firms have enough discretion over design and confidence in pricing policy to accept construction and some degree of traffic risk, while the government assumes the risks that it controls and gives consideration to financial support or guarantees if traffic levels in the early years are insufficient.

Ultimatelly, the market seems to be adjusting in the kind of contracts it is writing. Athias and Saussier (2007) highlight the fact that the contracting parties try to sign not only complete rigid contracts in order to avoid renegotiations but also flexible contracts in order to adapt contractual framework to unanticipated contingencies and to create incentives for cooperative behavior. In the case of toll roads, this gives rise to multiple toll adjustment provisions and to a tradeoff between rigid and flexible contracts at the design stage. In an econometric assessment of 71 contracts, they find that the standard view that a rigid authorities, appear to be more likely rigid. This seems to corroborate the conjecture that private 17
projects were said to be too great and their profits too uncertain to attct adeqate private capital.' Only thie early North-South railways and the Pennsylvania coal roads were paid for largely by bonds of private coxporations. These lines were shorter and cheaper to build and more certain of regular traffic.

Subsequent investigations are consistent with the view that railways trw off positive extenalities. Fogel's (1960) study of the Union Pacific Rairoad in the United States, for example, estimated that the social return averaged 30 percent per annum, two and a half times the private return.' Yet it was far from clear that pnvate entrepreneurs were unable to capture thcse exteralties. Adjoining lands whose productivity and value were enhanced by investment in a tunpike, canal, or railway could be and often were purchased by the pmmoter of the ifrstructure project Textile mills and mercantile enterpises whose profits were boosted by infrastructure investments that provded a steady supply of raw materials to the factory and finished products to the market could be and oftn were owned by those who organized infrastructure projects. This ability to internaliz **e** xternalities clearly weakeed the case for subsidization.

A second justfication for government intervention was to offset information asymmetries and capital market imperfctions. Even if social retums could in principle be captured by investors, incomplete information that resulted in credit rationing might prevent them from doing so. Though this rationale receives less attention in the literature, it provides an explanation for the form taken by many subsidies: interest guarantees on 5 Chandler (1954, p.249).

**56** Fogel (1960, p.106). I return to this issue of social retuns in the section an social impact

24
The results in Table 3 report Poisson estimates of the regression discontinuity (RD) described by equation (2). The coefficient associated with incumbent victories represents the discontinuous increase in casualties across the incumbent’s victory threshold. The results in columns (1) and (2) suggest that municipalities experienced between 0.89 and 1.22 additional casualties in the 12 months after a narrow incumbent victory as compared to a narrow loss. These estimates are quite large as compared to the average of 0.50 casualties in the 12 months after the election and are statistically significant at the 1 percent level. Similarly, the results in columns (3) and (4) indicate that in the 12 months after a narrow incumbent victory, municipalities experienced between 0.31 and 0.40 additional violent incidents. The results in columns (5) - (8) provide no evidence of a statistically significant relationship between narrow incumbent victories and pre-election casualties or violent incidents. Appendix Table A.2 shows that the results are robust to limiting the bandwidth to 3 or 4 percentage points around the threshold.<sup>18</sup> One potential concern with the results reported in Table 3 is that the estimated effect of incumbent victories decreases when controls for observable municipal characteristics are added in columns (2) and (4). This raises the possibility that the results are driven by unobserved differences between municipalities in which incumbents won narrowly and municipalities in which they did not. In an effort to explore this hypothesis, the paper estimates a panel regression model that explicitly controls for systematic differences in unobserved characteristics. Specifically, the following equation is estimated based on observations from the 12 months before and the 12 months after the 2007 election:

> 18The paper also estimates a Probit model based on equation (2) where the dependent variable is an indicator for having experienced 1 or more casualties, 3 or more casualties, and 5 or more casualties. The results, reported in Appendix Table A.9, show that a narrow incumbent victory increases the likelihood of having 1 or more casualties and 3 or more casualties.

22
- [21] Keating J. 2013. "The Nudgy State". _Foreign Policy: The Global Magazine of News and Ideas_ , http://www.foreignpolicy.com/articles/2013/01/02/the_nudgy_state.

- [22] Kling, JR.,JB. Liebman, LF. Katz, and L. Sanbonmatsu. 2004. "Moving To Opportunity and Tranquility: Neighborhood Effects on Adult Economic Self-Sufficiency and Health from a Randomized Housing Voucher Experiment," KSG Working Paper No. RWP04-035.

- [23] Lammers, J., and S. van Wijnbergen. 2007. "HIV/AIDS, Risk Aversion and Intertemporal Choice". Tinbergen Institute Discussion paper, 2007-098/1.

- [24] Lesotho Labour Force Survey. 2008. _Lesotho Integrated Labour Force Survey_ , Ministry of Finance and Development Planning, Bureau of Statistics, Maseru, Lesotho

- [25] Ministry of Health and Social Welfare and ICF Macro. 2010. _Lesotho Demographic and Health Survey 2009_ , Maseru, Lesotho: MOHSW and ICF Macro

- [26] Napierala Mavedzenge S, Doyle A, Ross D. 2010. "HIV prevention in young people in sub-Saharan Africa: a systematic view" http://zunia.org/uploads/media/knowledge/SRGreview1268137879.pdf

- [27] Norton, E.C., M.M. Miller, L.C. Kleinman. 2013. Computing adjusted risk ratios and risk differences in Stata, _The Stata Journal_ 13(3): 492-509.

- [28] Oster E. 2012. "HIV and Sexual Behavior Change: Why Not Africa?", _Journal of Health Economics_ , Volume 31, Issue 1, January 2012, Pages 35–49

- [29] Padian, N.S., McCoy, S.I., Balkus, J.E., Wasserheit, J.N. 2010. "Weighing the gold in the gold standard: challenges in HIV prevention research" _AIDS_ 2010; 24: 621–635.

- [30] Pettifor A., MacPhail C., Nguyen N., Rosenberg M. 2012. "Can Money Prevent the Spread of HIV? A Review of Cash Payments for HIV Prevention". _AIDS Behavior_ 16(7):1729-38.

- [31] Powers KA, Poole C. Pettifor AE, Cohen MS **.** 2008 "Rethinking the Heterosexual Infectivity of HIV-1: A Systemic Review and Meta-analysis", Lancet Infect Dis 2008;8(9):553–63.

- [32] Strauss, J., and D. Thomas. 1998. "Health, Nutrition, and Economic Development", _Journal of Economic Literature,_ 36: 766-817.

24
24

# **Annex 2 Survey of Funding Sources for Select Dedicated TA Funds**

## **1. Multiregional and Global Funds**

### **a).  LeapFrog Investments**

- _Fund objective:_ Micro-insurance in Africa and Asia

- _Fund size:_ $125 million, €5 million dedicated TA Fund

- _Investors and funders include the following:_

- Accion International

- EIB

- FMO

- Omidyar Network

- Triodos-Doen Foundation

### **b).  SEAF (Small Enterprise Assistance Funds)**

- _Fund objective:_ MSME entrepreneurs in emerging markets

- _Fund size:_ More than $600 million in more 20 funds since inception

- _Investment size:_ Between $1 million and $5 million and other higher amounts

- _Investors and funders include the following:_

- BIO

- COFIDE (the Peruvian national development bank)

- European Bank for Reconstruction and Development

- Finnfund

- FMO

- Ford Foundation

- IFC

- KfW/DEG

- Omidyar Network

- OPIC

- Rockefeller Foundation

- Swedfund

- USAID

### **c).  Verde Ventures**

- _Fund objective:_ Invest in SMEs that contribute to healthy ecosystems in Africa, Latin America, and Asia

- _Fund size:_ $17.5 million (first fund); currently raising a new fund with a goal of $50 million

- _Investment size:_ Between $30,000 and $5 million

- _Investors and funders include the following._<sup>_5_</sup>

- AFD

- Conservation International

- GBMF

- KfW/DEG

## **2.  Latin America-Focused Funds**

### **a).  Adobe Capital**

- _Fund objective:_ Invest in SMEs that positively contribute to society

- _Fund size:_ $20,000,000 with TA provided by new ventures

- _Investment size:_ Between $100,000 and $3 million

> 5 See Annex 3 for acronyms used in these lists of investors and funders.
-7 -

**Table 5**

**Turkey: External Debt**

|(i|n billions of|US Dollars<br>|; end of p<br>|eriod)||||
|---|---|---|---|---|---|---|---|
|||Ol|d Series|||New Se|ries|
||1992|1993|1994|1995|1996|1996|1997|
||||(By|borrower)||||
|**Medium-Long Term**|42.9|48.8|54.3|57.6|59.2|64.1|69.6_|
|Public Sector|39.8|42.8|48.2|50.0|48.8|51.1|49.2|
|of which:||||||||
|Consolidated EBudget|25.8|28.3|30.4|31.1|30.2|31.4|30.8|
|State Owned Enterprises|5.1|5.4|_5.5_|4.8|4.4|4.9|4.7|
|Central Bank|6.2|6.6|8.6|10.5|10.7|10.7|10.3|
|Private Sector|3.2|6.0|6.1|7.6|10.4|13.0|20.4|
|of which:||||||||
|Banks|-|-|-|-|-|2.7|_5.1_|
|Non-financial Companies|-|-|-|-|-|10.2|14.1|
|**Short Term**|12.7|18.5|11.3|15.7|20.5|20.5|22.6|
|Central Bank|0.6|0.7|0.8|1.0|1.0|1.0|0.9|
|Deposit Money Banks|7.2|11.1|4.7|6.6|8.5|8.4|8.5|
|Other Sectors|4.9|6.7|5.8|8.0|11.0|11.1|13.2|
||||(By ty|pe of credit)||||
|**Medium-Long Term**|42.9|48.8|54.3|57.6|59.2|64.1|69.6|
|of which:||||||||
|Project and Program Credits|21.8|21.8|25.2|23.6|22.1|-||
|Bond Issues|9.3|12.6|13.8|14.2|14.8|-||
|**Short**Term|12.7|18.5|11.3|15.7|20.5|20.5|22.6|
|Credits|10.1|15.4|8.0|11.2|15.0|-||
|Credits for Imports|2.6|4.8|3.8|5.4|8.3|-||
|Pre-Export Credits|0.9|1.1|1.4|1.6|1.6|-||
|FX Credits to BEnks|_5.1_|8.7|2.2|3.2|3.9|-||
|Other|1.5|0.8|0.6|1.0|1.2|-||
|Deposits|2.6|3.1|3.3|4.5|_5.5_|-||
|Source: Turkishn<br>Treasury.||||||||

# **11.2 Decomposition of the Capital Account**

The balance of payments identity allows decomposing the capital account surplus (i.e. net capital inflows) plus statistical discrepancies , into the current account deficit, and the accumulation of official foreign exchange reserves. The current account can further be
**Do returns to education depend on how and who you ask?**<sup>**12**</sup>

Pieter Serneels Kathleen Beegle Andrew Dillon University of East Anglia, IZA The World Bank, IZA Michigan State University Key words: returns to education, survey design, field experiment, development, Africa JEL codes: J24, J31,C83

> 1 This work was supported by the World Bank Research Support Budget and the World Bank's Gender Action Plan Trust Fund, and Norwegian‐Dutch World Bank Trust Fund for Mainstreaming Gender.

> 2 Pieter Serneels (corresponding author) is Associate Professor at the University of East Anglia and his email address is p.serneels@uea.ac.uk, Kathleen Beegle is Lead Economist at The World Bank with email kbeegle@worldbank.org; Andrew Dillon is Assistant Professor at Michigan State University, his email is dillona6@msu.edu.  The authors would like to thank Joachim DeWeerdt, Brian Dillon, Hans Hoogeveen, Andrew Kerr, Mans Soderbom, Helene Bie Lilleør and attendant of CSAE conference at University of Oxford, for useful comments. All errors remain ours.
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# _(A7)_

and indicating the cumulative proportion of the population up to the midpoint of each group interval. Typically, the first term will not equal one on grouped data.

_1s_
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Policy Research Working Paper 9679

# **Abstract**

This paper uses computable general equilibrium model-based simulations to assess the gender dimensions of the impact of COVID-19 on economic outcomes, that is, labor force participation, employment, wages, and earnings. It leverages the 2020 High-Frequency Phone Survey in Chad to assess the impact of COVID-19 on female-headed households, which comprise 23 percent of the country’s households. The findings show that the COVID-19 pandemic will have a disproportionately higher negative impact on women in urban areas. The simulation results suggest that more women than men working in paid jobs might lose their jobs. Although the paper focuses on the impact of COVID-19 in 2020, the findings can be generalized as the hysteresis effects might be deeper and more prolonged if the pandemic is more prolonged. The situation is potentially dire, especially in service sectors, where most women are employed in urban areas. Moreover, the High-Frequency Phone Survey shows that COVID-19 has notably impacted the households’ income from enterprises and suggests that this negative impact is more prevalent for female-headed households. Although male- and female-headed households are using common coping strategies during the pandemic, female-headed households in rural and urban areas have been more reliant on aid from family and friends and less reliant on savings, credit, or the sale of assets.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at kkabir@worldbank.org, hdudu@worldbank.org, and ftchanatchana@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
the average firm is able to price above marginal cost. About 20 percent of the firm-year observations in our sample are foreign-owned firms, while 2 percent are state-owned firms.<sup>8</sup> Also, the sector-year statistics show that the average manufacturing sector accounts for 44% of its output in foreign-owned firms and has an export share of turnover of around 27%. However, there is significant dispersion with standard deviations around 25% and 17% for foreign-owned firms’ share of output and the export share of turnover, respectively.

_Table 1. Summary statistics_

||**Variable**|**Obs.**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|---|
||log (Outputi,s,t)|120,029|5.15|1.86|-2.06|15.32|
||log (Employmenti,s,t)|120,029|4.05|1.35|2.30|11.35|
|**Firm**|log(Capitali,s,t)|120,029|5.32|1.66|-2.20|14.28|
|**characteristics**|log(Materialsi,s,t)|120,029|4.65|2.08|-6.13|15.12|
||MNEi,s,t|120,029|0.20|0.40|0.00|1.00|
||SOEi,s,t|120,029|0.02|0.15|0.00|1.00|
|**Markus**|Yi,s,t (Cobb-Douglas)|117,363|1.11|0.42|0.05|3.00|
|**p**|Yi,s,t (translog)|113,647|1.16|0.28|0.00|3.00|
|**Sector**|FDIs,t|168|0.44|0.25|0.01|0.99|
|**characteristics**|EXPs,t|144|0.27|0.17|0.00|0.72|

Over time, both foreign presence and markups increased over our sample period across most two-digit manufacturing sectors within Vietnam. Figure 1 displays the evolution of foreign-owned firms’ share of industry output between 2010 and 2016 by two-digit manufacturing sector. The repair and maintenance, machinery manufacturing, and textiles sectors saw the largest increases in foreign presence over the 2010-2016 period, while the motor vehicles subsector saw the largest—albeit still modest—decrease. Figure 2 displays the evolution of mean markups by two-digit manufacturing sector between 2010 and 2016. Average markups increased across most two-digit sectors between 2010 and 2016, especially for motor vehicles and apparel. In contrast, the tobacco subsector saw a decrease in average markups. The increase in markups over time does not necessarily imply that firms have more market power. For instance, an increase in fixed costs or new product varieties may increase markups without necessarily implying higher profits. Unfortunately, due to data restrictions associated with the firm's cost structure, we cannot perform further analysis of profits. Therefore, we limit our conclusions to the rise in markups without emphasizing the potential market power rises.

> 8 In the data, we define SOEs as firms which have >50 percent state ownership based on their legal form. This corresponds with the legal forms for “Central State”, “Local State”, and “Joint Stock Co. having state capital >50%”.

8