rafmacalaba/datause-encoder-data huggingface dataset:
# **1 - Introduction**

This paper examines at the micro level two issues related to aid effectiveness which have been previously and separately considered at the macro level: whether aid effectiveness increases when receiving countries face exogenous shocks and decreases when the total amount of aid increases. The micro economic data on which the analysis relies are the success of the projects funded by the World Bank.

In the last ten years, a large debate on aid effectiveness has followed the Burnside and Dollar paper (2000) supporting the view that aid has a positive impact on growth on the condition that the country has sound policies and institutions. These results, criticized by many authors, are still the matter of keen debates (Hansen and Tarp, 2001). It has been argued that other features of recipient countries may have a potential impact on aid effectiveness, even more significant than policy and institutions. In particular macro economic studies (Guillaumont and Chauvet, 2001; Chauvet and Guillaumont, 2004) evidence that aid effectiveness, measured by the impact of aid on growth, is higher in vulnerable countries. Following these results, one would expect to find the same evidence at the micro economic level. However most practicians would agree that projects are harder to carry out in an unstable environment which at first sight seems to be in contradiction with the previous findings.

The apparent paradox results from the fact that the stabilizing effect of aid is hardly perceived at a micro economic level. We can consider this effect as a positive externality since each project evaluation does not include the stabilization brought by the project itself, but is affected by the stabilization resulting from the whole amount of aid received by the country. According to the quoted macro economic finding, instability (vulnerability) as such is a negative factor on growth, while aid dampens this effect. Correspondingly, we expect instability to lower the average return to the projects, but all the less that aid is higher.

The results presented in this paper show that the instability of exports harms project outcomes, as it harms the rest of the economy, while the success of the projects decreases with the total amount of aid received, due to absorptive capacity limitations. But we also find that this decrease is slower when instability is higher. For extremely high levels of instability it can even be reversed, leading to an increasing return to aid. Thus the present paper also meets the burning 2
Current health-related research is either in the industrialized countries or a small number of industrializing ones such as China, India, and Brazil. Only 4 percent of this research is on diseases widespread in low income countries (Weatherall and others 2006). To expand public research and provide incentives for the private sector to conduct research demands major long term investments in physical infrastructure for research and in human capital. As Singapore and Korea are finding, the outlay on even a modest research program (on biotechnology) can be sizable and requires support from governments which can draw upon an adequately broad and elastic revenue base. This impression is buttressed by data on the R&D expenditures of the major pharmaceutical and biotech companies. All of them maintain several laboratories and their annual expenditure is in the billions of dollars. Not every developing country needs to be engaged in cutting edge medical research—but more need to embark on this effort as this is a fruitful area in terms of new innovations, e.g. the developing of effective vaccines against retrovirus, and shigella and as a growth industry to produce low cost antimicrobials which are in high demand.<sup>74</sup> Encouraging evidence suggests that some of the most promising new findings in the biotechnology and bioengineering industries are by small new firms that have pursued fresh and promising research angles. Even low income countries can build up the capacity to adapt medications for local circumstance and variant pathogens, aside from finding solutions for their own specific problems.<sup>75</sup> Recent history suggests that such efforts are extremely unlikely to take off and acquire organizational momentum as well as a critical mass of researchers in the absence of rapid and substantial growth of the overall economy which brings together the capital and human resources and the public as well as corporate research capabilities as is now occurring in China, India and Chile.<sup>76</sup> This is not to say that isolated cases of good research in the absence of such growth cannot be identified, only that these are rare.

> 74 The Commission on Health and Research for Development has recommended that at least 2 percent of the national health budget should be devoted to research (Volmink and Dare 2005). 75 India, for example, has made some progress in controlling costs of drugs through the development of its pharmaceutical industry.  It has also brought down the costs of such key items as sutures for medical procedures and interocular lenses.  China, India, and other countries should also soon be able to produce diagnostic equipment such as CT scanners helping to further reduce costs (Prahalad 2006).

> 76 Several countries have shown considerable organizational ingenuity in controlling measles (Southern Africa), eliminating polio (Latin America), reducing the incidence of trachoma (Morocco), and checking goiter and TB (China), AIDS (Thailand), etc (Medlin and others 2006).

36
Assessing the International Comovement of Equity Returns<sup>_∗_</sup> Girum Dagnachew Abate and Luis Serv ́en<sup>_†_</sup> The World Bank Group

**Keywords:** Equity returns, comovement, common factors, spatial dependence **JEL classification:** C23, C58, E44

> _∗_ We thank Geert Bekaert, Kristin Forbes, Wei Shi, and Sergio Schmukler for helpful comments. However, they are not responsible for any errors. We are especially indebted to Wei Shi for generously sharing his computer code. Hossein Asgharian, Luis-Diego Barrot, Ruth Llovet Montanes, and Nancy Xu kindly provided us some of the data used in this paper. The views expressed here are only ours and do not necessarily reflect those of the World Bank, its executive directors, or the countries they represent.

> _†_ The World Bank, email: gabate@worldbank.org, lserven@worldbank.org.
<!-- Start of picture text --> Index Value<br>| Average Diversity [oo <1.25<br>1989 - 2010 GH 1.25 -1.50<br>MB 1.51-1.75<br>f<br>wae ce HM 1.76<br>Hl > 2.00<br>Ss - 2.00<br>Pg y ti2 TE Nop<br>;<br>: Std Ap ri’ ov<br>yetee eewt ogee a.<br>~ he UNigese .<br>3 |<br>Banga orev, § ee) Chennai ae<br>2<br>i apy By<br>ohare & :<br>A 4 Oo 125250 500 750 1,000 ‘<br><!-- End of picture text -->
interest rates, both domestic and world have expected contractionary effects in large developed countries, but tend to lead to GDP expansion in small developed economies. We should note that in this group of counties, the effect of US interest rate shock is not statistically significant, while the effect of the domestic interest rate shock is very short-lived and dissipates within just 2 quarters. Overall, we interpret these results as suggestive that country size matters for business cycle dynamics and sources of volatility in developing countries, but does not seem to play an important role in developed economies.

Figure **6** . Impulse Responses of GDP to Various Shocks in Developed Economies: Small vs <u>Large Countries</u> <!-- Start of picture text --> reer tby rrate us<br>.0006 .003 .0008<br>.0006<br>.0004 .002 .0004<br>.0002<br>.0002 .001<br>0<br>0 0 −.0002<br>0 2 4 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10<br>gdp govexp rrate<br>.008 .004 .001<br>.003 .0005<br>.006 .002.001 −.0005−.0010<br>.004 0 −.0015<br>.002 0 2 4 6 8 10 0 2 4 6 8 10<br>quarter quarter<br>0<br>0 2 4 6 8 10 small countri es large countries small countri es large countries<br>gdp us<br>.004<br>.003<br>.002<br>.001<br>0<br>0 2 4 6 8 10<br>quarter Orthogonalized IRF<br>small countri es large countries<br><!-- End of picture text -->

# **4.5 Individual and Panel VAR Results by Fiscal Rules**

An important aspect of our analysis is to understand whether the adoption of fiscal rule(s) by a country has an effect on the sources of its GDP volatility. To study this question, we split all countries in our data set into a group that have adopted one or more fiscal rules since 1985, and a group that did not adopt any such rules. Note that the fiscal rules data set only identifies the presence of fiscal rules in various countries starting from 1985. Our data often start before that year. To maximize the time coverage, we consider a country to be a fiscal rule(s) adopter if it had a fiscal rule(s) at any point in time since 1985. Based to this classification, our sample of fiscal rule(s) adopters consists of 40 countries, while the sample of non-adopters contains 8 countries. The non-adopters are Bolivia, Macedonia, FYR, Paraguay, Philippines, South Africa, Thailand, Turkey, and Ukraine. The rest of the countries are classified as adopters.

21
Table **8.** Estimate of the Elasticity of Revenue with Respect to Volume for an Expansion of Cotton Exports by SSA.

# Soun:e IECIT, World Bank.

I/ Pnc Eblasticity of Demand = - 0.2; huxn Elasticity of Demand = 0.5; Growth Rate in Real GDP =2% p.t. 2/ Price 131ticit of Demand = - 0.01; Odr Paramrs as in Base Sim0ulation.

3/ ricme EBlasticity of Demand = 0.25; ORat Paramnees as in Base SimuDtion.

4/ Income Growth Rate = 1%; Other Paramters as in Baso Simulation.

16
<!-- Start of picture text --> SESEREEE 9552853 ETHEL TTTHy<br><!-- End of picture text --> <!-- Start of picture text --> 3” 3” 3”<br>; d ; || ; i<br><!-- End of picture text -->

<!-- Start of picture text --> 358852889239285 25 gP225S8 22222588 223282856885<br><!-- End of picture text -->
**<u>Table A.4 Baseline Balance (Weighted) with Attrition</u>**

||**Control**<br>**(Mean)**|**Arm 1**<br>**(Mean)**|**Arm 2**<br>**(Mean)**|**Arm 3**<br>**(Mean)**||**No**|**rmalized Me**|**an Differe**|**nces**||
|---|---|---|---|---|---|---|---|---|---|---|
|Number of gram panchayats (GPs)<br>Number of households|77<br>1,605|58<br>1,208|58<br>1,219|58<br>1,243|**Control-**<br>**Arm 1**|**Control-**<br>**Arm 2**|**Control-**<br>**Arm 3**|**Arm 1-**<br>**Arm 2**|**Arm 1-**<br>**Arm 3**|**Arm 2-**<br>**Arm 3**|
|**A. Household characteristics**|||||||||||
|**Number of members in the household**|||||||||||
|All|5.38|5.326|5.345|5.21|.0187|.0121|.0602|-.0066|.0422|.0485|
|Children less than 5 years|.6546|.5978|.6923|.6761|.0529|-.0337|-.0195|-.0866|-.073|.0145|
|Children between 6–14 years|1.066|1.054|1.004|1.02|.0083|.0426|.0314|.0343|.023|-.0116|
|**Caste/social category of the household**|||||||||||
|General|.3427|.3032|.346|.2981|.0598|-.005|.0677|-.0648|.0079|.0726|
|Scheduled caste/scheduled tribe (SC/ST)|.4882|.5243|.446|.562|-.051|.06|-.1047|.1112|-.0535|-.1652|
|**Religion of the head of household**|||||||||||
|Hindu|.2342|.2314|.2206|.2546|.0047|.023|-.0336|.0183|-.0383|-.0566|
|Sikh|.7139|.715|.7256|.6891|-.0017|-.0185|.0384|-.0168|.04|.0569|
|**Economic category**|||||||||||
|Below poverty line (BPL)|.4938|.4854|.5502|.5288|.0119|-.08|-.0495|-.0919|-.0614|.0304|
|Above poverty line (APL)|.3516|.2844|.244|.2503|.1024|.1675|.1571|.0648|.0544|-.0103|
|**Landholding by category**|||||||||||
|Small and marginal farmer|.2384|.2283|.2969|.2432|.017|-.0936|-.008|-.1106|-.0249|.0856|
|No land|.6884|.6921|.6242|.6969|-.0056|.0958|-.013|.1014|-.0074|-.1088|
|**Asset ownership**|||||||||||
|Own house|.9871|.9893|.9814|.9774|-.0144|.0327|.0519|.0469|.0658|.0195|
|Radio|.03624|.03165|.04603|.03787|.0179|-.0349|-.0061|-.0527|-.024|.0288|
|Color television|.9158|.8622|.8949|.8816|.1211|.0506|.0802|-.0708|-.0412|.0297|
|Gas stove|.8942|.8369|.8588|.8714|.119|.0761|.0501|-.0431|-.0691|-.026|
|Refrigerator|.8564|.8051|.8463|.8194|.097|.0201|.071|-.0769|-.026|.0509|

45
Figure 5: Real Personal Incomes and Formal Education by Sectors <!-- Start of picture text --> Real Income<br>1990 1995 2000<br>Year<br>Manufacturing Service<br>Agricultural Informal<br>Education<br>1990 1995 2000<br>Year<br>Manufacturing Service<br>Agricultural Informal<br>Data source: ENIGH<br>5000<br>4000<br>3000<br>Personal incomes<br>2000<br>1000<br>9<br>8<br>7<br>6<br>Years of Schooling 5<br>4<br><!-- End of picture text --> A couple of important points arise from Figure 5. Firstly, average income in the informal and agricultural sectors (where poverty concentrates) were decreasing even before the peso crisis. As a matter of fact, during the peso crisis (1994-96) real incomes of heads of households working in the agricultural sector, the lowest remunerated sector, decreased proportionally less than the large income reduction experienced by heads of household working in the relatively better off manufacturing and service sectors. This helps explain the improvements in distribution after the 1994-96 peso crisis. Secondly, after the combination of the peso crisis and NAFTA (1994), in the middle of a recession that caused an increase in the rate of unemployment, the years of education of the representative laborer increased in all sectors, suggesting that the ones who kept their jobs were the relatively better educated workers.

10
|**Blue Line (introduction)**|0.194|0.157|0.067|0.064|
|---|---|---|---|---|
|**Blue Line (first extension)**|0.222|0.235|-0.161|0.116|
|**Blue Line (second extension)**|0.382**|0.147|0.624***|0.152|
|**Rainfall**|0.010|0.017|0.036***|0.013|
|**Relative Humidity**|0.010|0.015|0.009|0.016|
|**Temperature**|-0.084**|0.035|0.024|0.032|
|**Wind Speed**|-0.008**|0.004|-0.005|0.003|
|**Workday**|0.026|0.030|0.033*|0.018|
|**Summer**|-0.186*|0.095|0.142|0.088|
|**Winter**|0.205**|0.078|0.095|0.084|
|**Diwali**|0.025|0.085|0.113**|0.051|
|**Observations**|25260||25260||
|Source: Authors' analysis based o|n data described|in the text.|||
|The dependent variable is an indi<br>missing and =0 otherwise). The e<br>addition of season fixed effects.|cator of whether<br>xplanatory vari<br>Std. Errors are c|the observat<br>ables are the<br>lustered at on|ion is missing<br>same as in Tab<br>e week. * indi|(=1 if<br>le 4 with the<br>cates|
|significantly different from zero|at 10 percent le|vel, ** at 5 p|ercent level,||
|and *** at 1 percent level.|||||

|**Table S1.7 Predicting Missing PM2.**<br>**2004-2006**|**5 using CO measure, at I**|**TO: Entire Series,**|
|---|---|---|
||**PM2.5 at ITO**||
|**Share Missing in Entire Series**|42||
||**Coefficient**|**Std. Error**|
|**High_CO**|-0.037|0.032|

67
# **Commitments to Save: A Field Experiment in Rural Malawi**<sup>∗</sup>

Lasse Brune Department of Economics, University of Michigan

## Xavier Giné

Development Economics Research Group, World Bank and Bureau for Economic Analysis and Development (BREAD)

## Jessica Goldberg

Ford School of Public Policy and Department of Economics, University of Michigan Dean Yang Ford School of Public Policy and Department of Economics, University of Michigan, Bureau for Economic Analysis and Development (BREAD), and National Bureau of Economic Research (NBER)

## **Abstract**

This paper reports the results of a field experiment that randomly assigned smallholder cash crop farmers formal savings accounts. In collaboration with a microfinance institution in Malawi, the authors tested two primary treatments, offering either: 1) “ordinary” accounts, or 2) both ordinary and “commitment” accounts. Commitment accounts allowed customers to restrict access to their own funds until a future date of their choosing. A control group was not offered any account but was tracked alongside the treatment groups. Only the commitment treatment had statistically significant effects on subsequent outcomes. The effects were positive and large on deposits and withdrawals immediately prior to the next planting season, agricultural input use in that planting, crop sales from the subsequent harvest, and household expenditures in the period after harvest. Across the set of key outcomes, the commitment savings treatment had larger effects than the ordinary savings treatment. Additional evidence suggests that the positive impacts of commitment derive from keeping funds from being shared with one’s social network.

**Keywords** : savings, commitment, hyperbolic preferences, self-control, sharing norms **JEL codes** : D03, D91, O16, Q14

> ∗ Brune: <u>lfbrune@umich.edu. Giné: xgine@worldbank.org. Goldberg: jegoldbe@umich.edu. Yang: deanyang@umich.edu. We thank Niall Keleher, Lutamyo Mwamlima and the IPA staff in Malawi; Steve Mgwadira,</u> Mathews Kapelemera, and Webster Mbekeani of OIBM; and the OIBM management and staff of Kasungu, Mponela and Lilongwe branches. Matt Basilico and Britni Must provided excellent research assistance. We are grateful to Beatriz Armendariz, Orazio Attanasio, Oriana Bandiera, Abhijit Banerjee, Luc Behagel, Marcel Fafchamps, Maitreesh Ghatak, Marc Gurgand, Karla Hoff,  Sylvie Lambert, Kim Lehrer, Ethan Ligon, Rocco Macchiavello, Lou Maccini, Sharon Maccini, Marco Manacorda, Costas Meghir, Rohini Pande, Albert Park, Imran Rasul, Chris Woodruff, Andrew Zeitlin, and seminar participants at the FAI Microfinance Innovation Conference, Ohio State, London School of Economics, Warwick, Institute for Fiscal Studies, Paris School of Economics, and Oxford for helpful comments. We appreciate the support of David Rohrbach (World Bank) and Jake Kendall (Bill & Melinda Gates Foundation). We are grateful for research funding from the World Bank Research Committee and the Bill & Melinda Gates Foundation. The views expressed in this paper are those of the authors and should not be attributed to the World Bank, its executive directors, or the countries they represent.
# **Appendix B. Methodological Activities**

_Enumerator training and fieldwork._ Before commencing data collection, supervisors and interviewers underwent a two-week training session. All of the interviewers and supervisors had previous experience using Survey Solutions and conducting household surveys. Following the training, the interviewers carried out mock interviews in order to become more familiar with the questionnaire and interviewing techniques. After the mock interviews, interviewers participated in a 1-day pilot with non-participating households within the EAs included in the experiment. All pilot interviews were recorded and later audited by two trained monitors who provided each of the interviewers with written feedback. The field coordinators then met with each interviewer and discussed the feedback. After the individual meetings, the team led a debrief session with all of the interviewers to address any remaining questions and concerns.

Six teams comprised of one supervisor and three interviewers each conducted the survey between August and October 2022. The teams operated in a roving manner, interviewing all selected households in each EA before moving on to the next. All of the households in each treatment status were interviewed following the same protocol: upon arriving at a selected household, the interviewer introduced herself to the first working-age person who opened the door, read aloud the consent statement, and, after the working-age adult agreed to continue, began the interview. All households that completed the interview received an in-kind incentive valued at USD$5.00.

_Household substitution protocols._ We randomly selected 21 additional substitute households for each EA in case participating household members or proxies did not show for the interview. Interviewers were asked to visit all households up to 5 times to obtain personal or parental consent (for youth) from household members to participate in the survey. If enumerators were unable to obtain consent after 5 visits, then they had to inform their supervisors, who would try to obtain consent one additional time. If neither of these approaches worked, then the household was replaced. In total, 22.6% of the households were replaced by substitutes.<sup>21</sup>

> 21 The main reasons for substitution were as follows: household members refused to participate (18.6%); households could not be found (1.0%); households were not eligible to participate in the survey because they did not report having at least 2 household members aged 15 to 64 years (2.3%); and the interviewer was unable to interview the designated ESR household member (0.7%).

59
2013 but switch to group Y in 2014, we count them as members of group X in 2014 regardless of group X and Y’s treatment status. Our computation of total group size in 2014 is therefore not the equivalent of computing (size in the previous year + new members – dropouts), but instead (size in the previous year + new members + switchouts – dropouts – switchins), where switchouts are clients who left the group for another one, and switchins join the group from another one. In our group-level analysis, we follow the initial 2013 group composition and apply these definitions of dropouts and new members.

Second, we collect a multi-module household survey among OAF clients in our study sample in May–August 2013.<sup>20</sup> The instrument was designed to generate quantitative data on agricultural production, participation in agricultural extension services, perceptions of agricultural extension providers, and socioeconomic characteristics over the course of two visits.

# 3.2   Farmer characteristics

OAF clients in the sample areas took loans averaging $56 (RWF 38,641) in 2013. Female clients take significantly smaller loans, $48 on average for 2013, compared to $60 for men.

19 OAF FOs serve our study area. One FO is responsible for 12 farmer groups on average (minimum 2, maximum 18), a total of 134 clients (minimum 20, maximum 209), and are overseen by 5 FMs. FMs manage an average of 6 of the sampled FOs each (minimum 2, maximum 9).<sup>21</sup> Of the sampled households, 73% are male-headed. Household heads are on average 45 years old, and most (77%) have less than a primary school education. The household head is the OAF client in 86% of households. Compared to male OAF clients, female clients are

> 20 Fieldwork was implemented by an independent survey firm. 21

> Statistics come from OAF administrative records and refers to all OAF clients in LWH sites.

13