rafmacalaba/datause-encoder-data huggingface dataset:
3 from time use surveys, estimates reported in the same study range from 30 percent time contribution by women to agricultural activities in The Gambia, to 60 to 80 percent in different parts of Cameroon. In addition to the wide variation across countries (and at times within countries), the time use surveys reveal important differences in time allocation across crops, agricultural activities and technology. FAO (2011) concludes with a call for more systematic evidence on women’s labor contribution to agricultural production in SSA.

The Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMSISA) initiative provides a unique opportunity to start filling this void.<sup>5</sup> Under the LSMS-ISA initiative, nationally-representative household surveys were fielded during the 2009-2011 period (and at least once thereafter) in six African countries (Ethiopia, Malawi, Niger, Nigeria, Tanzania and Uganda). Together, these countries cover a wide array of agro-ecological zones and farming systems, and make up approximately 40 percent of the region’s population. Detailed labor input data was collected at the plot-level for each household member and across activity domains, enabling systematic estimation of women’s time contribution to agricultural (crop) production as well as a systematic comparison across settings, crops and activities.

The primary objective of the paper is to provide detailed, systematic and nationally representative evidence on female labor input into agricultural activities for a series of countries in SSA. By putting the premise of the reasoning advanced above on more solid empirical footing, it helps assess its validity, while informing the policy dialogue on gender and agriculture more broadly. The focus is on time allocation to crop production. Crop production continues to make up

> 5 The LSMS-ISA is a household survey program established with a grant from the Bill and Melinda Gates Foundation to provide financial and technical support to governments in SSA in the design and implementation of multi-topic, national, panel household surveys with a strong focus on agriculture. The program is implemented by the Living Standards Measurement Study (LSMS) in the Development Research Group of the World Bank (www.worldbank.org/lsms).
trative boundaries, we use the Database of Global Administrative Areas version 2.8.

# **4 Summary Statistics and Stylized Facts**

## **4.1 Summary Statistics**

Our main analysis focuses on a merged data set of nighttime light growth and GDP growth at quarterly frequency. It covers 117 countries from 2013Q2 to 2020Q4, including 33 advanced economies and 84 EMDEs. Table 1 presents the summary statistics. Across all countries, nighttime lights grew faster than GDP and both nighttime lights and GDP grew on average slower in AEs than in EMDEs. Nighttime light growth is an order of magnitude more volatile than GDP growth, as is clear from the much wider interquartile range of nighttime light growth rates. As can be seen from the 25th percentile, nighttime light growth can be negative even when GDP growth is moderately positive. This is likely a result of the remaining noise in nighttime lights dwarfing the positive contribution from GDP growth to nighttime light growth.

Table 1: Nighttime Lights and GDP Summary Statistics at Quarterly Frequency

||Mean|p10|p25|p50|p75|p90|
|---|---|---|---|---|---|---|
|AEs (33 countries)|||||||
|Night light growth|3.0|-15.6|-6.2|2.1|11.3|23.7|
|GDP growth|1.6|-1.9|0.8|2.1|3.3|4.5|
|Avg. nightly obs.|25.6|5.9|15.7|27.5|35.1|40.8|
|EMs (61 countries)|||||||
|Night light growth|6.7|-15.7|-4.1|5.0|16.9|33.6|
|GDP growth|1.9|-3.2|0.7|2.8|4.5|6.1|
|Avg. nightly obs.|28.5|9.5|17.2|29.6|39.0|46.3|
|LIDCs (23 countries)|||||||
|Night light growth|5.2|-27.3|-13.4|4.3|23.1|40.6|
|GDP growth|3.7|-1.3|2.5|4.6|6.2|7.4|
|Avg. nightly obs.|24.9|12.0|16.4|24.6|32.8|38.8|
|Total (117 countries)|||||||
|Night light growth|5.5|-18.3|-6.0|4.1|16.2|32.1|
|GDP growth|2.1|-2.7|1.0|2.8|4.5|6.2|
|Avg. nightly obs.|27.2|9.4|16.6|27.9|36.7|44.0|

_Notes_ . This table presents summary statistics of year-on-year night light growth and GDP growth between 2013Q2-2020Q4 for different country groups. The mutually exclusive country groups are advanced economies (AEs), emerging markets and middle-income countries (EMs), and low-income developing countries (LIDCs).

9
37. Stark,Oded and Robert E. Lucas, “Migration, Remittances, and the Family,” Economic Development and Cultural Change, 36, no.3, 465-481, Apr. 1988 38. Stark, Oded and J. Edward Taylor, ”Migration Incentives, Migration Types: The Role of Relative Deprivation,” Economic Journal, 101(408), 1163-78, September 1991 39. Roy, Andrew D., “Some Thoughts on the Distribution of Earnings,” Oxford Economic Papers, 3: 135-46, 1951 40. Timmins, Christopher, “Estimating Spatial Di ff erences in the Brazilian Cost of Living with Household Location Choices,” Journal of Development Economics, 80(1): 59-83, June 2006 41. Train, Kenneth E., Discrete Choice Methods with Simulation, Cambridge University Press, Cambridge, 2003 42. Uhlig, Harald, “Regional Labor Markets, Network Externalities and Migration: The Case of German Reuni fi cation,” American Economic Review, 96(2), 383-87, May 2006.

43. Winters, Paul, Alain de Janvry and Elisabeth Sadoulet, “Family and Community Networks

- in Mexico-U.S. Migration,” Journal of Human Resources, 36(1), 159-84, Winter 2001

44. Woodru ff , Christopher, “Remittances and Microenterprises in Mexico,” Graduate School of International Relations and Paci fi c Studies, UCSD, La Jolla, August 2001 (mimeograph) 45. Wooldridge, Je ff rey M., Econometric Analysis of Cross-Section and Panel Data, MIT Press, Boston, 2002 35
The Pitt and Khandker (PK, 1998) study provides estimates of the impact of credit provided to men and women on their labor supply, their children‟s schooling, household expenditure and nonland assets held by women. They find that it mattered whether credit was provided to the man or woman within the household. Program credit provided to poor households had a larger effect when women were the participants, for example in increasing household expenditure, increasing consumption, and increasing women‟s labor supply in cash income earning activities.

Their findings concerning the substantial benefits of micro-credit programs have not been without controversy. Roodman and Morduch (2009) attempt to replicate the PK study, but find negative estimated impacts on credit given to females relative to males on the same outcomes. They also perform overidentifying tests for the validity of some of the instruments used in PK‟s study and reject their validity. Lastly, they apply an alternative regression-discontinuity estimator to the same data, using the discontinuity in program eligibility that occurs when individuals have just under and just over one-half acre of land. The RD results point to little effect of program participation on school enrollment of girls or boys. The RD estimates indicate a negative effect of male borrowing on female labor supply with no association with male labor supply. The results based on the RD analysis are quite different from those based on the more structural analysis presented in PK. They are not necessarily inconsistent, though, as the RD analysis recovers the treatment effects locally, near the point of discontinuity and PK‟s analysis aims to uncover an average of treatment effects for all individuals participating in the program, including those not at the margin of eligibility by virtue of the amount of land they own. Nevertheless, Roodman and Morduch conclude from their analysis, “We come away from the PK study with doubts about the magnitude, sign and direction of the reported effects of microcredit.”<sup>14</sup> Khandker, Samad and Khan (1998) analyze the village level impacts of three micro-credit programs in Bangladesh (the Grameen Bank, Bangladesh Rural Advancement Committee (BRAC), and Bangladesh Rural Development Board‟s (BRDB) RD-12 project), allowing program effects to differ for the three programs. All three are group-based lending programs, with somewhat different features. For example, the BRAC program also provided literacy and training services in addition to credit. The Grameen Bank covered 40% of Bangladeshi villages, while BRAC and RD-12 covered roughly 20%. According to Khandker et al. (1998), 60% of rural households met the eligibility criteria for the programs but only about 45% participated,

> 14 For an analysis on how microcredit programs affect consumption variability, see Morduch (1994, 1995).

30
outcome, a 0 value was assigned. Because IPV frequently occurs in the home, cases were assigned to the municipalities of residence of the victims, which are not necessarily the same as the municipalities in which the health care facilities recording the outcomes are located. For the regression analysis, the study used the natural log of the count (adding 0.01 to each value as an adjustment for 0s). The observations were Winsorized in the 99th percentile by replacing the observation with the value of the cutoff for the 99th percentile.

_Homicides._ Vital statistics data on homicides come from Brazil’s national mortality database, the Mortality Information System (Sistema de Informações de Mortalidade. SIM), a registry of every death in the nation.<sup>5</sup> The study defines a death as a homicide if it is classified as one in the SIM or if the cause of death is coded as an assault (codes ICD-10 X85-Y09 of the International Statistical Classification of Related Diseases and Health Problems). The analysis complements the homicide variable with clinical cause of death information because the variable is associated with missing values. (The discrepancy between these definitions of homicide is 6 percent if both data fields are available.) _Hospitalizations for assault._ The Hospital Information System (Sistema de Informação de Hospitalares, SIH) includes information from public and private hospitals.<sup>6</sup> Hospitalizations are recorded when the physician fills out an authorization for hospitalization (Autorização de Internação Hospitalar, AIH), which is used to reimburse the hospital for the procedures performed on patients. These data only capture the most severe incidents because, according to the Ministry of Health, the form is only filled out if patients are admitted overnight. Hospitalizations that involve deaths are excluded because, otherwise, this would duplicate entries in the SIM death registry. Information on the reason for hospital admittance is provided in the ICD-10, which the study used to identify the hospitalizations for assault based on the same codes as in the homicide data. Though the primary cause typically indicates the location of the injury (for example, trauma in the knee or leg), the secondary cause often clarifies the incident associated with the injury (such as accident or assault).

The study confirmed that all municipalities in the sample had at least one hospitalization of any type, which indicates that all cities in the comparable sample have access to hospitals. The lowest number of hospitalizations in any municipality in any year

> 5 See “Sistema de Informações de Mortalidade,” DATASUS, Ministerio da Saude, Brasília, http://tabnet.datasus.gov.br/cgi/deftohtm.exe?sim/cnv/obt10br.def.

> 6 See “Sistema de Comunicação de Informação de Hospitalar e Ambulatorial,” DATASUS, Ministerio da Saude, Brasília, http://www2.datasus.gov.br/DATASUS/index.php?area=0203&id=6926.

8
**Table 7: Cambodia, Reconstruction of the school system**

|Number of teachers|1979-80|1980-81|1981-82|
|---|---|---|---|
|Level 1|21,605|30,316|37,000|
|(4 grades)||||
|Level 2|206|671|1600|
|(3 grades)||||
|Level 3<br>(3 grades)<br>**_Source_: **Adapted from Kilj|20<br>unen (1984).|28|78|

42
9 Table **1.7** Proportion of Expenditure Met Through Credit by Asset Quintiles and Use of Credit

|**Asset quintile**|Proportion<br>Input|of Expenditure<br>Capital<br>Co|Met by Credit<br>ns. Inc Food|**(°/.)**<br>Total|Proportion of<br>Credit-Using<br>Households to<br>Total Households|
|---|---|---|---|---|---|
|Lowest|47.65|37.33|31.86|32.89|58.67|
||(39.89)|(4.53)|(54.96)|(100.00)||
|Second|30.90|65.21|30.09|27.88|54.19|
||(22.75)|(26..65)|_(50.59)_|(100.00)||
|Third|17.16|44.90|24.37|21.05|58.25|
||(13.86)|(27.53)|(53.98)|(95.37)||
|Fourth|12.56|_10.56_|21.17|12.65|58.80|
||(18.37)|(13.33)|(68.30)|(100.00)||
|Highest|17.61|14.30|14.88|10.74|55.52|
||(37.41)|(23.39)|(39.20)|(100.00)||
|All households|22.15|23.34|22.70|17.02|57.05(g)|
||(27.67)|(19.78)|(51.57)|(100.00)||

Note: Figures in parentheses are percentage shares of credit used for each category out of the total credit. (a) This sample of households differs from the households covered in the 1985 Rural Credit Survey. Somrce: IFPRJT, Pakistan Panel Survey, Round 13, 1990, cited in Von Braun, Maik and Zeller (1993).

**Table** 1.8 Source of Loans by Asset Quintile

||Institut|ional||Noninst|itutional||
|---|---|---|---|---|---|---|
|Asset Quintiles|Percentage of<br>Credit from<br>Formal Sources|No. of<br>Loans|% Credit from<br>Friends and<br>Relatives|No. of<br>Loans|% Credit from<br>other informal<br>Sources|No. of<br>Loans|
|......**..**..................<br>Lowest|.....................................<br>1.05|............................<br>3|**.............**<br>..........................<br>31.43|....................<br>201|....................................<br>67.51|........................<br>585|
|Second|4.83|13|49.53|249|45.63|396|
|Third|12.60|23|48.33|244|39.07|308|
|Fourth|29.62|52|35.44|174|34.95|285|
|Highest|58.36|116|20.10|139|21.54|263|
|AR households|32.16|207|32.39|1007|35.45|1837|

SoM : IFPR1 Pakistan Panel Survey, Round 13, 1990, cited in Von Braun> Malik, and Zeller (1993).
If we break FAFH into its different components, we note that adult consumption is the main driver of the expenditure effects observed. Accounting for adult consumption outside the home translates into a 2.9 percentage point decrease in extreme poverty and 9 percentage points in moderate poverty. In contrast, accounting for food away from home consumed by children or take-out translate into very small changes in the extreme poverty rate, 0.2 and 0.1 percentage points respectively, and accumulate to a change of about 1 percentage point in moderate poverty. As expected, these differences are explained by the relative small importance that each component has on the household budget.<sup>19</sup> Indeed, summary statistics reported in Table 2 show that adult consumption is by far the most important component, with an incidence of almost 90 percent, and a mean participation in the household food budget of about 16 percent.<sup>20</sup> By contrast, significantly fewer resources are assigned to child consumption or take-out. The incidence is much lower - about 40 percent for either, and the participation of these expenditures in the food household budget is at most 2.2 percent. Even if we restrict the analysis to households that report positive expenditures, these expenditures do not account for more than 5 percent of the food budget.

The same pattern follows when we look at the poverty gap and the severity of poverty. Accounting for FAFH reduces the distance between the mean expenditures among the poor and the extreme poverty line by S/0.87, and the moderate poverty line by almost S/.3.99 (Table 1). Furthermore, the reduction is relatively stronger for those further away from the poverty line, causing the severity of poverty to fall too. Again, these effects are dominated by adult consumption.

# **Poverty line effect**

As mentioned in the previous section - and in contrast with the expenditure effect - the direction of the poverty line effect is a priori unknown. Furthermore, the extreme and moderate poverty lines need not move in the same direction. Table 2 presents the simulated poverty lines under each scenario. We find that when all components of FAFH are accounted for, the extreme poverty line increases by S/.33 to S/.134.5 – a 32 percent change over the baseline poverty line, while the moderate poverty line only increases by S/.19 – an 8 percent increase over baseline. The increase in

> 19 Potentially, this may reflect a weakness in the design of these module to accurately collect this information in the survey. See Borlizzi and Cafiero (2014).

> 20 Since poverty rates are calculated at the individual level, all statistics in the Table 2 are at the individual level. An incidence of 90 percent means that 9 in 10 individuals live in households where at least one adult member consumed outside during the reference period. Similarly, on average individuals live in a household where 16% of the food budget is allocated to expenditure on food consumed away from home by adult members.

19
# **About the Author**

Emmanuel Pinto Moreira is Lead Economist in the World Bank. He has a PhD in Macroeconomics from University of Lorraine (France). Mr. Pinto Moreira joined the Bank in 1996. He has also served as a Senior Economist and Senior Advisor at the IMF. His publication record covers public economics, growth, poverty, natural resources management, public finance management, and general equilibrium modelling.

# **Bibliography**

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------, “Optimal Fiscal Management of Commodity Price Shocks,” _Journal of Development Economics_ , 122 (September 2016), 183-96.

Agénor, Pierre-Richard, and Peter J. Montiel, _Development Macroeconomics_ , 4th ed., forthcoming, Princeton University Press (Princeton, New Jersey: 2015).

Agénor, Pierre-Richard, and Kyriakos Neanidis, “Innovation, Public Capital, and Growth,” _Journal of Macroeconomics_ , 44 (June 2015), 252-75.

Alhashel, Bader, “Sovereign Wealth Funds: A Literature Review,” _Journal of Economics and Business_ , forthcoming (March 2015).

Bems, Rudolfs, “Aggregate Investment Expenditures on Tradable and Nontradable Goods,” _Review of Economic Dynamics_ , 11 (October 2008), 852-83.

Berg, Andrew, Rafael Portillo, Shu-Chun S. Yang, and Luis-Felope Zanna, “Public Investment in Resource-Abundant Developing Countries,” _IMF Economic Review_ , 61 (March 2013), 92-129.

Bom, Pedro R., and Jenny E. Ligthart, “What Have we Learned from Three Decades of Research on the Productivity of Public Capital?,” _Journal of Economic Surveys_ , 28 (December 2014), 889-916.

Boyce, James K., and Léonce Ndikumana, “Capital Flight from Sub-Saharan African Countries: Updated Estimates, 1970-2010,” Research Report, University of Massachusetts Amherst (October 2012).

Central Bank of Congo, _Annual Report 2013_ , CBC Publications (Kinshasa: 2013).

Collier, Paul, Frederick van der Ploeg, Michael Spence, and Anthony J. Venables, “Managing Resource Revenues in Developing Economies,” _IMF Staff Papers_ , 51 (April 2010), 84-118.

37
Policy Research Working Paper 4953

# **Abstract**

Research suggests that a donor country’s decision to provide post-disaster assistance is not only driven by the severity of a disaster and the resulting humanitarian needs in the recipient country, but also by strategic considerations. The authors argue that the identification of the determinants of the _size_ of disaster assistance is a first step in the analysis of the donor’s behavior. Since all aid is not motivated by the same reasons, the evaluation of the donor country’s behavior requires a second step accounting for the _type_ and the _channel_ of aid provided. Using data on international disaster assistance between 2000 and 2007, the analysis examines both the donor countries' decision on the channel (bilateral versus multilateral) and the type of disaster relief (cash versus in-kind). The empirical results suggest that international disaster relief is not as much driven by the needs of the recipient country, but also by strategic interests (for example, oil or trade relationships) of the donor country. Bilateral and cash transfers are used as a vehicle to signal strategic interests, while multilateral and in-kind transfers are chosen to control for misuse in badly governed recipient countries.

This paper—a product of the Global Facility for Disaster Reduction and Recovery Unit, Sustainable Development Network Vice Presidency—is part of a larger effort in the department to disseminate the emerging findings of the forth coming joint World Bank-UN Assessment of the Economics of Disaster Risk Reduction. The team leader for the Assessment— iApurva Sanghi—ican be contacted at “asanghi@worldbank.org.” We would like to thank Jesus Crespo-Cuaresma, Howard Kunreuther, Harald Oberhofer, Michael Pfaffermayer, S. Ramachandran, Apurva Sanghi, Mark Schelker, Reimund Schwarze, Martina Tonizzo and Hannelore Weck-Hannemann as well as participants of a workshop in Zurich 2008 and a seminar at the World Bank in 2009 for their useful comments.

_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
Policy Research Working Paper 8541

# **Abstract**

Many innovative start-ups and small and medium-size enterprises have good ideas, but do not have these ideas fine-tuned to the stage where they can attract outside funding. Investment readiness programs attempt to help firms to become ready to attract and accept outside equity funding through a combination of training, mentoring, master classes, and networking. This study conducted a five-country randomized experiment in the Western Balkans that worked with 346 firms and delivered an investment readiness program to half of the firms, with the control group receiving an inexpensive online program instead. A pitch event was then held for these firms to pitch their ideas to independent judges. The investment readiness program resulted in a 0.3 standard deviation increase in the investment readiness score, with this increase occurring throughout the distribution. Two follow-up surveys show that the judges’ scores predicted investment readiness and investment outcomes over the subsequent two years. Treated firms attained significantly more media attention and were 5 percentage points more likely to have made a deal with an outside investor, although this increase is not statistically significant (95 confidence interval of −4.7 to +14.7 percentage points) .

This paper is a product of the Development Research Group, Development Economics. 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/research. The authors may be contacted at dmckenzie@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