rafmacalaba/datause-encoder-data huggingface dataset:
whose treatment allocation was not changed<sup>7</sup> . Columns 2 and 3 report the same difference, but restricting the sample to the treatment group and the control group respectively<sup>8</sup> . In Column 2 we compare villages that were originally allocated to treatment group with the villages that were originally in the control group, but were in fact allocated to treatment. Similarly, in Column 3 we compare villages that were originally allocated to control group with the villages that were originally in the treatment group but were mistakenly allocated to control group. We would be particularly worried of opposite signs in Columns 2 and 3, which would suggest that some variables were used as grounds for preferential treatment allocation. We find evidence suggesting that Tigre villages were reallocated into treatment and away from the control group, which could possibly explain the imbalance in Tigre presence across treatment groups. The differences estimated along other dimensions are quite similar in Columns 2 and 3, suggesting that treatment allocation was not altered based on those characteristics.

# **B Additional data**

## **B.1 Normalised Difference Vegetation Index (NDVI)**

We complement our dataset with sub-zone level panel data on a vegetation index called Normalised Difference Vegetation Index (NDVI), which is obtained from the analysis of the colour spectrum of satellite imagery and it ranges between -1 and 1. In the absence of water surfaces or snow, it ranges between 0 and 1, where 1 means most vegetation and 0 stands for least vegetation. This index has been shown to be very highly correlated with the species of malaria called _Plasmodium falciparum_ , which accounts for more than 80% of malaria infections in Eritrea (Shililu et al., 2004), and generally measures the overall propensity of an area to harbour mosquito populations<sup>9</sup> .

> 7We conduct the same randomisation checks used to compare treatment and control villages, but this time to compare villages with altered treatment status to those with unaltered treatment status.

> 8Altered villages in Column 2 were moved from the control to the treatment group. Altered villages in Column 3 were moved from the treatment to the control group.

> 9For Sudan, Gaudart et al. (2009) find that the seasonal pattern of _P. falciparum_ incidence is significantly explained by NDVI from 1981-2006 and they identify a threshold NDVI value of 0.361, above which an increase in the incidence of parasitemia is predicted. Similarly, for Indochina Peninsula, Nihei et al. (2002)

6
# **6. References:**

- Baer, W. (1996) “Prepared Remarks” _Corporate Counsel Institute, Northwestern University School of Law._

- Choe, C. and C. Shekhar (2005) “Compulsory or Voluntary Pre-Merger Notification? Theory and Some Evidence” Working Paper

- Coate, M. R. Higgins and F. McChesney (1990) “Bureaucracy and Politics in FTC Merger Challenges” _Journal of Law and Economics_ , Vol 33, 463-82

- Coate, M and S. Ulrick (2006) “Transparency at the Federal Trade Commission: the Horizontal Merger Review Process 1996-2003” Antitrust Law Journal Vol 73,2 53165.

- Elzinga, K. (1969) “The Antimerger Law: Pyrrhic Victories” Journal of Law and Economics. 12, 43 -78

-

- Hovenkamp, H. (1999) “Federal Antitrust Policy” West Group.

- Johnson, R and A. Parkman (1991) “Premerger Notification and the Incentive to Merge and Litigate” _The Journal of Law Economics and Organization_ , Vol 7, No1 145-162.

-

- Lexmundi Pre-Merger Notification Survey

- Shekhar, C. and P. Williams (2004) “Should the Pre-Notification of Mergers Be Compulsory in Australia” _The Australian Economic Review_ Vol 37, No4 383-90

- Storng, N., A. Bollard and M. Pickford (2000) “Defining Market Dominance: A Study of Antitrust Decisions on Business Acquisitions in New Zealand” _Review of Industrial Organization_ 17: 209-27.

- Sullivan, L. and W. Grimes (2000) “The Law of Antitrust: An Integrated Handbook” West Group.

- Williamson, O.E. (1968), “Economies as an Antitrust Defense: The Welfare Tradeoffs,” American Economic

25
(mountainous terrain or forests) to political violence, relatively limited robust association was found in the data.<sup>19</sup> Identifying the true effect of political violence on economic growth requires controlling for the feedback effects from growth to political violence and vice versa as well as accounting for the common factors jointly determining these two vital development outcomes. Such factors must include economic and political institutions directly affecting the two outcomes as well as the social and geography characteristics that might be driving institutional outcomes. The societal and geography characteristics should not be treated as excluded (external) instruments because, despite the limited empirical evidence, there are also strong theoretical arguments for their having direct effects on growth and political violence. To fix the issues, we develop our empirical estimations around the following structural model: Where, p is the probability of the onset of political violence, which is a non-linear function of economic growth: g; income per capita: y; political institutions: Polity; and the timeinvariant social characteristics (Social) and the warfare technology (WarefareTech), which accounts for the land area of mountainous terrain. Economic growth, in turn, linearly depends on the probability of political violence; convergence effect (lagged per capita income); a range of economic and policy institutions; and similar sets of social and political variables to those of the violence equation. Except for the time-invariant Social and WarfareTech, all other regressors in equations 16 and 17 are presumed to be endogenous, most notably p and g. Our estimation strategy is a sequential one, which allows us to first estimate the risk of political violence, given initial growth and per capita income; then the growth equation can focus on analyzing the direct effects on growth of essentially all the controls that are likely to jointly influence conflicts and growth as well as their indirect effect on growth channeled through the predicted hazard of political violence. However, we

> 19 For example, two of the most widely quoted studies on the causes of civil war (Collier and Hoeffler, 2004a; and Fearon and Laitin, 2003) fail to find robust association between civil wars and social fractionalization. However, Sambanis (2004), who develops and uses a more comprehensive and better validated civil war onset dataset, finds that social fractionalization are more robustly associated with civil wars.  Moreover, Bodea and Elbadawi (2006) estimate a multinomial model of violence (civil wars, riots/uprisings, coups) and find that social fractionalization has had a robust negative and monotonic effect on civil war; and is positively but nonmonotonically associated with coups.

16
<!-- Start of picture text --> Deviation (% of GDP)<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>PFSsSo @ FHSCP© S2 HPSeWM LWLY HHWMSF BWCFPHVRKF KFYO FHSLAN Oo HF_PF_vwFKLLL oOHFSKA XKVW PFKM FHFKFA SKFECFFSFLA oO SF.@LK EFSPPD LKSG FEKX&SFLKS@ EESXWL LYS<br>g S we S oe VW ow Ve & SK KOM SL LS KS &<br>~O osos Sf ROSS iS) Yof ey<br>(O7 &<br><!-- End of picture text -->
**Table 2: Differences in Characteristics of Female- and Male-Owned Firms**

||Dif|ference in Avera|ges(Female - M|ale)<br>Eti|
|---|---|---|---|---|
||Mexico|Bolivia|Peru|nerprse<br>Surveys|
|**Panel A: Firm characteristics**|||||
|Ln firm age|-0.538***|0.017|-0.227***|-0.025|
||(0.031)|(0.098)|(0.074)|(0.054)|
|Ln employees|-0.094***<br>(0.009)|-0.361***<br>(0.105)|-0.029<br>(0.076)|-0.319***<br>(0.063)|
|Ln property value|-0.609***|-0.794**||-0.545***|
||(0.075)|(0.311)||(0.124)|
|Ln machinery value|-1.213***<br>(0.055)|-2.273***<br>(0.229)||-0.748***<br>(0.122)|
|Ln sales (last month)|-0.226***|-1.037***|-0.260**|-0.597***|
||(0.032)|(0.170)|(0.128)|(0.097)|
|Ln operating costs (last month)||-1.270***<br>(0.189)|-0.247*<br>(0.143)|-0.613***<br>(0.097)|
|Profits (last month, in thousands)|-0.061***<br>(0.009)|-0.191<br>(0.120)|-1.035**<br>(0.514)|-191.47***<br>(67.15)|
|Profits (last month, in thousands)|-0.062***|-0.160***|-0.109|-13.34***|
|- median regression|(0.000)|(0.035)|(0.083)|(5.65)|
|Firm is registered with the authorities|-0.067***<br>(0.011)|-0.174***<br>(0.061)|-0.008<br>(0.043)||
|Adopted new technology during past 3 years||||-0.058**<br>(0.030)|
|**Panel B: Owner characteristics**|||||
|Ln hours worked|-0.265***|-0.227***|-0.037||
||(0.020)|(0.086)|(0.052)||
|Owner's years of schooling|-0.740***|-1.806***|-0.323||
||(0.119)|(0.548)|(0.334)||
|Owner participated in business training||-0.141***<br>(0.051)|||
|Owner's mother has no schooling||0.087<br>(0.060)|||
|Numberof firms|8,293|265|582|2,175|

_Note_ : Property and machinery values in the Mexican and Bolivian data are conditional on having property or machinery, respectively. All monetary values are in US Dollars. All regressions include city, region, or country fixed effects. Robust standard errors in parentheses. Significance levels: *** 1 percent, ** 5 percent, * 10 percent. _Source_ : Encuesta Nacional de Micronegocios (ENAMIN), 2002, Bolivian World Bank Micro Enterprises Survey, 2007, Peruvian World Bank Micro Enterprises Survey, 2008, World Bank Enterprise Surveys for Brazil, Ecuador, El Salvador, Guatemala, Honduras, and Nicaragua, 2003.

10
# **1. Introduction**

In 2001, the government of Thailand launched the Thailand Village and Urban Revolving Fund (VRF) program, which aimed to provide a million baht (about $22,500) to every village and urban community in Thailand as working capital for locally-run rotating credit associations.<sup>1</sup> Thailand has almost 74,000 villages and over 4,500 urban (including military) communities, so the total injection of capital into the economy envisaged by the “million baht fund” amounted to 78 billion baht, equivalent to about $1.75 billion, making it the most ambitious of the estimated 120,000 microfinance initiatives anywhere in the world.2 The program was put into place rapidly. By the end of May 2005 the VRF committees had lent a total of 259 billion baht ($8.3 billion at the July 2007 exchange rate of Baht 31.23/$) to 17.8 million borrowers (some of whom borrowed more than once). This represents an average loan of $466. The total repayment of principal amounted to 168 billion baht, leaving outstanding principal of 91 billion baht.

In this paper we ask a narrowly focused question: Has the VRF had an impact on household incomes, spending, and asset accumulation, and, if so, how large are these effects? An answer to this question is necessary, but not sufficient, to help the Government of Thailand determine whether the program should be expanded or revised, and to help governments of other countries determine whether they should introduce or expand similar microcredit schemes. In order fully to address these policy issues, one would also need information on the costs of the program. A complete cost-benefit analysis of the Thailand Village Fund would be highly desirable, but goes beyond the scope of this paper.

The VRF represents a policy experiment on a grand scale, but it is not the only major source of household credit, even in rural areas. The Bank for Agriculture and Agricultural Cooperatives (BAAC) has an extensive network of rural lending. So it is appropriate to ask what additional role the VRF has played, an issue that we also address in this paper.

We summarize the relevant details of the VRF program in section 2, set out our general approach in section 3, describe the data employed in the impact evaluation in section 4, and in the subsequent sections explain the methodology and report the results of the impact evaluation using propensity score matching (section 5), instrumental variables (section 6), and panel data methods (section 7). The paper ends with a short set of conclusions in section 8.

> 1 The average exchange rate during 2001 was Bht44.51/$, which implies that a million baht are equivalent to $22,468. The exchange rate as of mid-July 2007 was Bht31.23/$, which would value a million baht at $32,020; this is the exchange rate that we use throughout the rest of the paper.

> 2 Estimated number of microfinance initiatives is from Kaboski and Townsend (2009), p.10.

_Village Fund, draft of June 29, 2009_ _Page 2 of 34_
terms-of-trade are responsible for growth, we include the Indonesian government’s Farmer Terms of Trade index.<sup>22</sup> Again, this has no statistically significant association with growth.

**Table 6: OLS regression of real per capita GDP growth on selected variables**

||**-1**|**-2**|**-3**|
|---|---|---|---|
||**1993-97**|**1999-2001**|**2001-05**|
|Ln per capita real GRDP, initial year|-0.002|-0.073|-0.005|
|Weighted average growth of neighboring districts|(0.48)<br>0.037|(4.91)**<br>0.048|(1.53)<br>0.011|
|during the period||||
||(1.80)|(0.93)|(0.60)|
|Share people ever/being in junior secondary school per|0.045|0.028|0.012|
|total population||||
||(1.03)|(0.16)|(0.32)|
|Share of population that is urban|0.019|0.019|0.031|
||(1.90)|(0.59)|(3.56)**|
|Labor force, initial year|0.003|0.006|0.005|
||(1.49)|(0.85)|(2.74)**|
|Share of non-oil and gas manufacturing to total GRDP,<br>|0.034|0.105|-0.019|
|initial year||||
||(2.85)**|(2.37)*|(1.77)|
|Oil and gas is the main sector|-0.034|0.053|-0.011|
||(2.37)*|(1.43)|(1.28)|
|Gini coefficient of sectoral employment|0.050|0.010|0.015|
||(1.73)|(0.12)|(0.70)|
|Farmer's terms of trade index|0.032|-0.029|0.002|
||(1.25)|(1.39)|(0.49)|
|Distance district to province's capital|0.000|-0.000|0.000|
||(0.76)|(1.83)|(1.04)|
|Have to get on a boat to get to province's capital|-0.005|-0.018|-0.009|
||(0.87)|(0.86)|(1.75)|
|Share of households with telephone connections|-0.051|0.248|-0.044|
||(0.59)|(2.09)*|(1.73)|
|Ethno-linguistic Fragmentation Index|0.003|0.037|-0.003|
||(0.59)|(1.85)|(0.64)|
|Java dummy|<br>0.001|<br>-0.047|<br>-0.008|
||(0.21)|(2.86)**|(1.98)*|
|Constant|-0.039|1.001|0.026|
||(0.52)|(4.01)**|(0.47)|
|Observations|128|167|219|
|R-squared|0.25|0.29|0.14|
|Note: Robust standard errors in parentheses.||||
|* significant at 5%; ** significant at 1%.||||

Note: all variables are for the base year of the regression shown unless otherwise indicated. 1993-97 and 1999-2001 growth rates are in constant 1993 prices; 2001-2005 growth is in constant 2000 prices.

+ Data for 1999-2001 is from Podes 2000.

> 22 This is an index of output prices to input prices and consumption prices.

20
orrelation with O_ itted Variable Negative Positive Standard deviation Standard deviation Standard deviation stimates based on Simulation 3, with treatm nt effects are independent of poten al outcomes, s_ the de onvolution assumptions are sati fied. is 0, the moments assumptions are atisfied. When or—1, here is an omitted variable which is positively or neg tively correlated with both treatment outc mes and tre tment effects, respectively.

In practice, we likely need to extend the simple model from above and adapt the methods to a specific setting. To provide some guidance for this, we next discuss the panel data setting from Arellano and Bonhomme (2012), which can easily be adapted if the assumptions above do not seem plausible or the available data set has a different structure. The conditional independence assumption required for deconvolution is more likely to be satisfied when the researcher can control for a rich set of individual characteristics. Panel data are a special As discussed previously, the central difficulty in studying the distribution of treatment panel, the same individual can often be observed in the treated and control states at different times. This allows one to model individual heterogeneity quite flexibly, but instead requires restrictions on how the treatment effect and the residual are allowed to vary over time, as we show below. Arellano and Bonhomme (2012) show how to estimate higher order moments of the treatment effect distribution and apply deconvolution methods to estimate the full distribution of treatment effects when panel data are available. Consider the panel er o e Oo wewor ghout this section: Outcomes are sh_ ed by the treatment effect in all p riods after an individual is treated o this model imp es that the treatment effect consta t over the time horizon of
- Guadarrama M, Molina I, Rao J (2018). “Small area estimation of general parameters under complex sampling designs.” Computational Statistics & Data Analysis, 121, 20–40.

- Hossain MJ, Das S, Chandra H, Islam MA (2020) Disaggregate level estimates and spatial mapping of food insecurity in Bangladesh by linking survey and census data. PLoS ONE 15(4): e0230906. <u>https://doi.org/10.1371/journal.pone.0230906</u>

- Hurles et al. (2005). “The dual origin of the Malagasy in Island Southeast Asia and East Africa: evidence from maternal and paternal lineages”. American Journal of Human Genetics 76(5);

- Institut National de la Statistique (INSTAT) et ICF. 2022. Enquête Démographique et de Santé à Madagascar, 2021. Antananarivo, Madagascar et Rockville, Maryland, USA : INSTAT et ICF

- Kreutzmann, A. K., Pannier, S., Rojas-Perilla, N., Schmid, T., Templ, M., & Tzavidis, N. (2019). The R package emdi for estimating and mapping regionally disaggregated indicators. Journal of Statistical Software, 91.

- <mark>Martorell R. 2017. Improved nutrition in the first 1000 days and adult human capital and health. Am J Hum Biol. 2017 Mar;29(2):10.1002/ajhb.22952. doi: 10.1002/ajhb.22952. Epub 2017 Jan 24. PMID: 28117514; PMCID: PMC5761352.</mark>

- Molina, I., & Rao, J. N. K. (2010). Small area estimation of poverty indicators. Canadian Journal of statistics, 38(3), 369-385.

- Pfeffermann D, Skinner CJ, Holmes DJ, Goldstein H, Rasbash J (1998). “Weighting for unequal selection probabilities in multilevel models.” Journal of the Royal Statistical Society: series B (statistical methodology), 60(1), 23–40.

- Razakamanana MV, Andrianatoandro VT, and Ramiandrisoa TO. 2023. Do public health expenditures affect maternal and child health in Madagascar? Health Economics Review 13:47 https://doi.org/10.1186/s13561-023-00462-7

- Saurav Guha and Hukum Chandra. 2021. Measuring disaggregate level food insecurity via multivariate small area modelling: evidence from rural districts of Uttar Pradesh, India. Food Security 13, 597-615

- Schiff, Maurice W. and Valdes, Alberto, The Link between Poverty and Malnutrition: A Household Theoretic Approach (November 30, 1990). World Bank Policy Research Working Paper No. 536

- Sen A. Poverty and Famines: An Essay on Entitlement and Deprivation. Oxford: Clarendon Press; 1981.

- Skarke F, Kreutzmann AK, Würz N (2023). Extensions to the ebp function in the R package emdi. R package Vignette, URL https://CRAN.R-project.org/package=emdi.

Sohnesen TP, Ambel AA, Fisker P, Andrews C, Khan Q. Small area estimation of child undernutrition in Ethiopian woredas. PLoS One. 2017 Apr 14;12(4):e0175445. doi: 10.1371/journal.pone.0175445. PMID: 28410435; PMCID: PMC5391934.

17 | P a g e
Campos, F. and Gassier, M. (2017) “Gender and Enterprise Development in Sub-Saharan Africa: A Review of Constraints and Effective Interventions” Policy Research Working Paper No. 8239. World Bank, Washington DC.

Chakravarty, S., Das, S. and Vaillant, J. (2017) “Gender and Youth Employment in Sub-Saharan Africa: A Review of Constraints and Effective Interventions” Policy Research Working Paper No. 8245. Washington DC.

Duflo, E. (2012) “Women Empowerment and Economic Development” _Journal of Economic Literature_ 50(4): 1051-1079.

EBRD (2016) _Strategy for the Promotion of Gender Equality 2016-2020_ , Document of the European Bank for Reconstruction and Development.

GFLEC (2016) S&P Global Finlit Survey. Global Financial Literacy Excellence Center, George Washington University. http://gflec.org///sp-global-finlit-survey-methodology/.

Guven, D. and Rodrik, D. (2016) _Turkey’s Growth Puzzle_ . Harvard University, Cambridge MA.

Guriev, S., Leipziger D., and Ostry, J. (2017) _Making Globalization More Inclusive: A Way Forward_ , VOXEu.org, October.

Guriev, S., Leipziger D., and Ostry, J. (2018) Inclusive Growth or Else, Project Syndicate, February.

Hausmann, R., Rodrik, D. and Velasco, A. (2005) Growth Diagnostics, mimeo.

Ianchovichina, E. (2018) _Eruptions of Popular Anger: The Economics of The Arab Spring and Its Aftermath_ , MENA Development Report, World Bank Group, Washington DC.

Ianchovichina, E. and Lundstrom, S. (2009) “Inclusive Growth Analytics: Framework and Application” Policy Research Working Paper No. 4851, World Bank, Washington DC.

Ianchovichina, E. and Lundstrom-Gable, S. (2012) “What is Inclusive Growth?” in _Commodity Price Volatility and Inclusive Growth in Low Income Countries_ , Arezki, Pattillo, Quintyn, and Zhi (eds), International Monetary Fund.

IMF (2016) Turkey: Article IV Consultation, Staff Report, International Monetary Fund, Washington DC.

Kanbur, R. and Stiglitz, J. (2016) “Dynastic Inequality, Mobility and Equality of Opportunity,” _Journal of Economic Inequality_ 14(4): 419-434.

17
18

**_B. Trends and Likelihood:_ a low probability of achieving MDG7** . Between 1990 and 2004, the proportion of households with access to an improved drinking water source increased from 37 to 40 percent (GIRM 2007b), a modest improvement (note that official data slightly differ from the estimates presented above). If the historic trend is maintained Mauritania will be unable to reach this MDG target in 2015 (Figure 14).

**_C. Financial Needs:_ 0.27 percent of GDP on average per year.** The financing gap to reach this goal is the third smallest of those considered in this paper, despite the scale of the problem.<sup>13</sup> The cost of achieving this target for the period 2008-2015 amounts to 1.1 percent of GDP on average per year, corresponding to US$220.8 million in total. Once the government allocation of expenditure is taken into account, the country needs an additional total financing of 0.27 percent of GDP on average per year (Figure 15), which translates into US$ 59.3 million in total (Table 10). For a detailed, yearly cost breakdown, see Annex 1.

**Figure 15. MDG 7: Financing Gap**

<!-- Start of picture text -->
-4 0.0<br>2008 2009 2010 2011 2012 2013 2014 2015<br>-6 -5.23 -5.60 -5.87 -0.1<br>-8 -0.14 -0.14 -0.13<br>-0.2<br>-10<br>-11.53 -11.42 -0.3<br>-12 -12.39 -12.59 -0.32<br>-0.37 -0.38 -0.4<br>-14<br>-0.42<br>-15.69<br>-0.5<br>-16<br>Millions of US$<br>-0.57 % of non-oil GDP (secondary axis)<br>-18 -0.6<br><!-- End of picture text -->

_Source:_ Authors’ calculations.

**Table 10. MDG 7: Cost, Budget and Financing Gap (2008-2015)**

|**Total Cost**|US$ 220.83 m<br>MRO 59,919.94 m|
|---|---|
|**Budget**|US$ 161.52 m|
|**Allocation**|MRO 43,783.77 m|
||US$ 59.31 m|
|**Financing**|MRO 16,136.2 m|
|**gap**|0.27% of non-oil GDP|
||US$2.81p.c.|

_Source:_ Authors’ calculations.

> 13 Funds have already been secured for the “ _Aftout Essaheli_ ” project, which will start in 2010 and provide drinking water from the Senegal River to the capital city Nouakchott.