rafmacalaba/datause-encoder-data huggingface dataset:
Savings: A General Equilibrium Analysis using Micro Data. Technical report, National Bureau of Economic Research.

- Baranov, V. and Kohler, H.-P. (2015). The Impact of AIDS Treatment on Savings and Human Capital Investment in Malawi. Working Paper, University of Melbourne and University of Pennsylvania.

- Beegle, K., De Weerdt, J., Friedman, J., and Gibson, J. (2012). Methods of Household Consumption Measurement Through Surveys: Experimental results from Tanzania. _Journal of Development Economics_ , 98(1):3–18.

- Beegle, K., Dehejia, R. H., and Gatti, R. (2006). Child Labor and Agricultural Shocks. _Journal of Development Economics_ , 81(1):80–96.

- Bils, M. and Klenow, P. J. (2000). Does Schooling Cause Growth? _American Economic Review_ , 90(5):1160–1183.

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- Boldrin, M. and Jones, L. E. (2002). Mortality, Fertility, and Saving in a Malthusian Economy. _Review of Economic Dynamics_ , 5(4):775–814.

- Buera, F. J., Kaboski, J. P., and Shin, Y. (2011). Finance and Development: A Tale of Two Sectors. _American Economic Review_ , 101(5):1964–2002.

- De Magalh ̃aes, L., Koh, D., and Santaeul`alia-Llopis, R. (2015). Consumption and Expenditures in Poor Countries: A Life With No Humps and Rural-Urban Insurance Traps to Growth. Working Paper, University of Bristol and Washington University in St. Louis.

- Deaton, A. (1997). _The Analysis of Household Surveys. A Microeconometric Approach to Development Policy_ . Johns Hopkins University Press.

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- Duflo, E., Dupas, P., and Kremer, M. (2015). Education, HIV, and Early Fertility: Experimental Evidence from Kenya. CEPR Discussion Papers - 10338.

- Dupas, P. (2011). Health Behavior in Developing Countries. _Annual Review of Economics_ , 3.

42
# **Table 14: Receiving SMS vs. Reporting Buying Food Reserves**

|||Buy food<br>|reserve=1;<br>|Do not=0<br>||||Buy food<br>|reserve=1;<br>|Do not=0<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)||(6)|(7)|(8)|(9)|(10)|
|SMS primary||||||Gov. SMS past||||||
|source|0.125***|0.104***|0.102***|-0.003|-0.003|week|-0.290***|-0.265***|-0.270***|-0.035**|-0.034**|
||(0.013)|(0.012)|(0.012)|(0.004)|(0.004)||(0.020)|(0.023)|(0.024)|(0.014)|(0.014)|
|Urban||0.107***|0.107***|||Urban||0.109***|0.109***|||
|||(0.012)|(0.012)|||||(0.012)|(0.012)|||
|Ln(income pc)||0.054***|0.053***||0.002**|Ln(income pc)||0.054***|0.054***||0.001**|
|||(0.002)|(0.002)||(0.001)|||(0.002)|(0.002)||(0.001)|
|Constant|0.315***|0.128***|-0.010|0.335***|0.330***|Constant|0.345***|0.150***|0.015|0.335***|0.331***|
||(0.006)|(0.008)|(0.027)|(0.001)|(0.002)||(0.005)|(0.008)|(0.027)|(0.000)|(0.002)|
|Month control|No|No|Yes|Ind. FE|Ind. FE|Month control|No|No|Yes|Ind. FE|Ind. FE|
|Observations|10,963|10,963|10,963|10,963|10,963|Observations|10,963|10,963|10,963|10,963|10,963|
|R-squared|0.011|0.082|0.084|0.000|0.001|R-squared|0.005|0.078|0.081|0.001|0.002|
|Unique individuals|2,153|2,153|2,153|2,153|2,153|Unique individuals|2,153|2,153|2,153|2,153|2,153|

Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 20
transactions. LLSV (1998) find that a country’s legal origin is important in explaining the country’s laws on creditor rights, shareholder rights, and private property rights and also a country’s level of market development. They argue that legal origin matters for financial development because some legal traditions are able to adapt more efficiently to evolving economic conditions. To the extent that legal origin might serve as a proxy for capital market development it would also affect a fund manager’s decision to invest in the ADR versus the domestic stock. Therefore, we use more direct measures of market development and transaction costs in addition to legal origin as explanatory variables. Specifically, we estimate the following model: _ADR Differential_ = β 0 + β 1( _GDP Per Capita_ ) + β 2 ( _MarketCap GDP_ ) + β 3 ( _Turnover Country_ ) + β 4 ( _Transaction Efficiency_ ) + β 5 ( _Settlement Efficiency_ ) + β 6 ( _FX Volatility_ ) + β 7 ( _French_ ) + β 8 ( _German_ )+ β 9 ( _Transition_ ) (4) Equation 4 is an expanded version of equation 3. We use direct measures of market development and transaction costs in addition to legal origin as explanatory variables. We include the ratio of market capitalization to GDP ( _Market Cap/GDP)_ as a measure of the overall size of the equity market relative to the size of the economy. Liquidity of an exchange is critical for institutional investors and we estimate the average daily turnover ( _Turnover Country)_ for the domestic stock exchange and include it as an explanatory variable _._ We also include a direct measure of transaction costs ( _Transaction Efficiency_ ). The score on this variable ranges from one to three and is obtained from Wilshire Consulting Group’s report for California Public Employees' Retirement System (CALPERS). A higher score implies lower transaction costs and therefore higher efficiency. Transaction costs are associated with trading in a particular market and include items such as stamp taxes and duties, amount of dividends and income taxed, and capital gains taxes. Countries such as Brazil and Mexico 25
_Table_ So Short term assets **(as a percentage** of **assets)**

||1970|1975|1980|1985|1990*|
|---|---|---|---|---|---|
|UK|4|8|5|4|7|
|Us|1|7|8|10|9|
|Germany|3|3|2|1|2|
|Japan|2|1|2|4|3|
|Canada|5|6|9|10|11|
|Netherlands|3|3|2|2|3|
|Sweden|0|0|0|1|3|
|Switxerland|7|6|6|7|12|
|Denmark|3|3|2|1|1|

* 1989 for **Canada** 1987 for Denmark

**fable 9:** Market **paper (am a percentage** of assets)

||1970|1975|<br>1980|1985|1990*|
|---|---|---|---|---|---|
|UK|2|5|3|1|1|
|uS|0|3|3|2|3|
|Germany|-|-|-|-|-|
|Japan|-|-|-|_|_|
|Canada|2|2|5|6|10|
|Netherlands|2|1|1|1|1|
|**Sweden**|0|0|0|1|3|
|Switzerland|3|2|4|6|10|
|Denmark|-||-|-|-|

* 1989 for Canada 1987 for Denmark

2 _pb_
The results reported by NQ have also been disputed by USAID (2014), who report on robustness of the NQ strategy to controlling for other forms of non-food aid external support for actors in civil conflicts including use of external military bases and economic support for rebels. USAID suggests that when these variables are included as controls, the statistical significance of food aid disappears. Unfortunately, the USAID results are not directly comparable to the NQ strategy for two reasons. First, external support to combatants only occurs by definition when a conflict exists. Food aid, on the other hand is sent to both countries that are experiencing conflict and those that are not, so that the NQ dataset can leverage information from countries that are not actively experiencing conflict. Second, the external aid variable is not available for the earliest years of NQ’s dataset. If NQ identify a causal effect that is strongest in the early period, the USAID strategy would miss the effect from those years. USAID argues that the NQ results are fragile with regard to these robustness checks, but they are not able to fully explain why the NQ strategy identifies an effect of aid on conflict. The threat to identification we outline can explain both why NQ found an effect and why USAID did not find an effect in their robustness checks. In addition to explaining why the effects appear in the NQ data, our approach provides a template of checks that can be used to assess the validity of similar approaches.

The best remedies for this prospective confounding are three. First, prudence dictates acknowledging that one can only confidently identify associations, not causal effects, as Peri (2012) does using this method. Second, try to identify credible instruments also for the endogenous exposure variable, following Dube and Vargas (2013). Third, carefully explore the patterns in the time series under study. For example, Hanna and Oliva (2015) use the timing of the closing of a refinery (which is plausibly exogenous but has limited variation) interacted with the (endogenous) location of worker residence relative to the facility to identify the effect of pollution exposure on labor supply. They present graphically the trends in 22
# **Table 14: Time to deal with public service providers**

||Time Getti<br>|ngElectricity (Log)<br>||
|---|---|---|---|
||Full Sample|Public Utilities only|Private Utilities only|
|Time to Start a Business (Log)|0.034<br>-0.030|0.021|-0.138*|
|Ln GNI per capita|(0.050)<br>(0.051)|(0.063)|(0.064)|
||-0.124***|-0.139***|-0.032|
|Constant|(0.034)|(0.039)|(0.076)|
||4.402***<br>5.622***|5.592***|5.172***|
||(0.157)<br>(0.374)|(0.447)|(0.681)|
|R-squared||||
|Observations|0.003<br>0.078|0.132|0.041|
|Standard errors in parentheses|*** p<0.01, ** p<0.05, * p<0.1|||

|||Time G<br>|ettingElectricity (Log)<br>||
|---|---|---|---|---|
|Time to Transfer Property (Log)|0.094*|Full Sample<br>0.035|Public Utilities only<br>0.056|Private Utilities only<br>0.001|
||(0.046)|(0.045)|(0.056)|(0.080)|
|Ln GNI per capita||-0.110**|-0.130***|-0.01|
|||(0.035)|(0.038)|(0.087)|
|Constant|4.174***|5.300***|5.383***|4.594***|
||(0.171)|(0.385)|(0.441)|(0.897)|
|R-squared|0.023|0.082|0.138|0|
|Observations|180|180|131|49|
|Standard errors in parentheses|*** p<0.01,|** p<0.05, * p<0.1|||

|||Time G<br>|ettingElectricity (Log)<br>||
|---|---|---|---|---|
||||||
|Time to Obtain a|0.228**|u ampe<br>0.166|uc es ony<br>0.173|rvae es ony<br>0.152|
|Construction Permit (Log)|(0.081)|(0.087)|(0.103)|(0.159)|
|Ln GNI per capita||-0.107**|-0.134***|0.013|
|||(0.035)|(0.039)|(0.076)|
|Constant|3.343***|4.554***|4.741***|3.611**|
||(0.418)|(0.625)|(0.744)|(1.118)|
|R-squared|0.042|0.102|0.162|0.018|
|Observations|182|182|132|50|
|Standard errors in parentheses|*** p<0.01,|** p<0.05, * p<0.1|||

45
We estimate that the TFR has decreased between 0.965 (without controls) and 0.988 (with controls) in the reform areas. All the estimates are highly significant at the 1 percent level. This effect is clearly driven by rural households, for which we estimate that the TFR has decreased between 1.224 and 1.202, depending on the specification. Again, the point estimates are highly significant at the 1 percent level. In turn, looking at the urban data, no significant effect for the TFR in the reform areas is found.

# [Insert Table 4 about here]

We repeat the regression set from Table 4, replacing the binary policy indicator with the proportion of peasant associations per district implementing the reform as a measure of intensity of the reform. Doing so, we account for the fact that not all peasant associations may have fully implemented the reform. Table 5 has the results. We find the same pattern of results, with point estimates fairly close to those estimated with the binary policy indicator. For the full data, we estimate a reduction in the TFR between 0.793 and 0.822. Again, this effect is driven by the rural data, for which we estimate a reduction in the TFR between 1.045 and 1.032. All of these point estimates are significant at conventional levels. For the urban data, we do not find any significant relationship.

# [Insert Table 5 about here]

The regression results show that there was a pronounced decline in the TFR in the reform districts as compared to the non-reform districts. This effect is driven by the rural data. For the following regressions, we use the same difference-in-differences specification as before but disaggregate the TFR into the ASFR used to construct it. We ran eight different regressions for each ASFR for each age group of women in their childbearing years (in 5-year intervals) for the full, rural and urban sample, separately. All regressions include district and controls.

Table 6 has the results for the full sample. We find a significant reductions in the ASFRs for age groups between 20 and 24 years of age to 40 and 44 years of age. The strongest effect is found for women in the age groups 25—29 and 30—34. Again, the effect is clearly driven by the rural sample. Table 7 has the results. Across virtually all age groups we estimate a reduction in ASFRs for the reform districts. The effect is particularly pronounced for women in the most fertile age group (25—29), for which the ASFR is estimated to decrease by 64.97 per 1000 women, and for those 30—34, for which the ASFR is estimated to decrease by 59.41. Thus the reduction in the TFR for women in the reform districts shown above is driven by a reduction in the ASFRs in age groups where women are unlikely to be giving birth for the first time<sup>17</sup> , i.e., fertility seems to reduce at the intensive rather than the extensive margin. For the urban sample, we do not find a systematic relationship between the reform and the ASFRs across age groups. Table 8 shows the regressions from the urban sample.

> 17 According to the Demographic and Health Survey (2000), more than 60 percent of Ethiopian women gave birth for the first time before age 25.

15
# **Can We Measure the Power of the Grabbing Hand? A Comparative Analysis of Different Indicators of Corruption**<sup>**1**</sup>

Alexander Hamilton<sup>2</sup> with Craig Hammer<sup>3</sup> _JEL Codes: D72 D73 H11_ _Keywords_ : Governance Indicators; Anti‐Corruption; Public Sector Governance; Public Sector Performance Measurement; Sustainable Development Goals

> 1 This paper summarizes and adapts some of the key empirical findings emanating from Alexander Hamilton’s doctoral thesis undertaken at the University of Oxford (2008-2012). The completion of this work would not have been possible without the professional and personal support received from the following: my supervisors, Raymond Duch, Indridi Indridason, and Desmond King; my examiners Pablo Beramendi, David Soskice, and Jane Green as well as the funding provided by the ESRC. Invaluable feedback was also provided by Cristina Corduneanu-Huci, Varvara Lalioti, the participants of Nuffield College’s GRIPS Workshop and a seminar at the World Bank in Washington, DC.

> 2 Department for International Development, 22 Whitehall, London, SW1A 2EG. UK Comments and feedback on this paper are welcome:. <u>alexander-hamilton@dfid.gov.uk.</u>

> 3 The World Bank, 1818 H St NW, Washington, DC 20433, USA.
the labor market.

Future research on the questions discussed in this study could take several directions. We list a few below.

Benefits from reforms involving state-owned enterprises. First, many EMDEs host large state-owned enterprises or poorly regulated private monopolies. Reforms to these could trigger increases in productivity as capital and labor are reallocated towards more productive uses. A better understanding of the impact on potential growth for EMDEs (beyond individual case studies) as well as the identification of conducive preconditions and complementary reforms would be helpful.

Benefits from improvements of governance and business climates. Second, many EMDEs have weak governance and business climates. A fuller quantitative assessment of the effects of improvements in various dimensions of governance and business climates on potential growth, including through firm productivity and household decisions on labor force participation and informal employment, would be helpful.

Better understanding of longer-term impact of reforms. Third, the exercise conducted for this study rested on as wide a cross-country sample of data as possible, in order to be representative of the heterogeneity of EMDEs. Data constraints prohibited analysis of developments before 1990. However, for a smaller set of countries, earlier data should be obtainable which could allow analysis of the longer-term effects of the profound structural policy changes that occurred in the 1970s and 1980s. Analysis of a longer time period may also allow for a better assessment of the possible cleansing effects of adverse shocks at the macroeconomic level.<sup>20</sup> Additional analysis on climate-related infrastructure investment. Fourth, the climate change scenario explored in this study is based on regional estimates of infrastructure investment needs because of data limitations for a large number of individual EMDEs. Given the wide heterogeneity in climate challenges, these regional estimates should ideally be supplemented or replaced by country-specific estimates that can provide more precision. For some countries, country-specific infrastructure investment goals are available, including, for EU countries, in the national Recovery and Resilience plans funded by NextGeneration EU investments. For other regions, however, such countryspecific data are for now unavailable.

> 20 Loayza and Pennings (2022) have developed tools to model long-term growth. These include applications such as <mark>how public investment affects growth, the determinants of TFP, and the evolution of growth in resource-rich economies.</mark>
# **<u>References</u>**

Acemoglu, D., and M. Jackson (2017): “Social Norms and the Enforcement of Laws”, Journal of the European Economic Association vol. 15(2), 245–295.

Adams, A., and A. Andrew (2019): “Preferences and beliefs in the marriage market for young brides”, IFS Working Papers, W19/05, Institute for Fiscal Studies.

Adoho, F., S. Chakravarty, D. Korkoyah Jr., M. Lundberg, and A. Tasneem (2014): “The Impact of an Adolescent Girls Employment Program: The EPAG Project in Liberia,” World Bank Policy Research Working Paper 6832.

Agarwal, S., _and others_ (2016). “Family Planning Counseling in Your Pocket: A Mobile Job Aid for Community Health Workers in Nigeria,” Global Health: Science and Practice 2016 | Volume 4 | Number 2

Alam, A., J. Baez, and X. Del Carpio (2011): “Does Cash for School Influence Young Women’s Behavior in the Longer Term? Evidence from Pakistan,” <mark>World Bank Policy Research Working Paper 5669.</mark> Akresh, R., D. Halim, and M. Kleemans (2021): “Long-term and Intergenerational Effects of Education: Evidence from School Construction in Indonesia,” World Bank Policy Research Working Paper 9559.

Akresh, Richard; de Walque, Damien; Kazianga, Harounan (2016): “Evidence from a Randomized Evaluation of the Household Welfare Impacts of Conditional and Unconditional Cash Transfers Given to Mothers or Fathers”, World Bank Policy Research Working Paper No. 7730.

<mark>Akyol, S., and N. Mocan (2020): “Education and Consanguineous Marriage”, NBER Working Paper 28212.</mark> Amin, S., J. Ahmed, J. Saha, I. Hossain, and E. Haque (2016): “Delaying child marriage through community-based skills-development programs for girls: Results from a randomized controlled study in rural Bangladesh,” New York and Dhaka: Population Council.

<mark>Amirapu, A, M N Asadullah, Z Wahhaj (2020), “Can child marriage laws change attitudes and behavior? Experimental evidence from an information intervention in Bangladesh”, Working Paper</mark> Andrabi, T., J. Das, and A. Khwaja (2017): “Report Cards: The Impact of Providing School and Child Test Scores on Educational Markets,” American Economic Review, vol. 107(6): 1535–1563.

Andrews, C., A. de Montesquiou, I. Arévalo Sánchez, P. Vasudeva Dutta, B. Varghese Paul, S. Samaranayake, J. Heisey, T. Clay, S., Chaudhary (2021): “The Potential to Scale,” The State of Economic Inclusion Report 2021, World Bank.

Angrist, J., D. Autor, and A. Pallais (2020): “Marginal Effects of Merit Aid for Low-Income Students,” <mark>NBER Working Paper No. 27834.</mark> 24
# 8. **References**

Holgate, S., Samet, J., Koren, H., Maynard, R. (ed.), 1999, _Air Pollution and Health,_ San Diego, California: Academic Press.

Pandey, K. D., Bolt, K., Deichmann, U., Hamilton, K., Ostro, B., Wheeler, D., 2003 (forthcoming), "The Human Cost of Air Pollution: New Estimates for Developing Countries," _World Bank Development Research Group Working Paper,_ Washington, DC.

Wang, L., Bolt, K., Hamilton, K., 2003 (forthcoming), "Estimating Potential Lives Saved from Improved Environmental Infrastructure," Environment Department, The World Bank, Washington, D.C.

World Bank, 2003, _World Development Report 2003: Sustainable Development in a Dynamic World,_ Washington: World Bank/Oxford University Press.

World Health Organization (WHO), 2000, _Guidelines for Air Quality,_ WHO, Geneva. Available at <http://www.who.int/peh/air/Airqualitygd.htm>).

World Resources Institute (WRI), 1999, _World Resources 1998-1999: A Guide to the Global Environment: Environmental Change and Human Health,_ New York, NY: Oxford University Press.

16
# **Table A8.4: Comparison of AICD results and results from the present study**

|**Country**|**Date r**|**ange**|**T&D lo**|**sses, %**|**Bill collec**<br>|**tion rate,**<br>**%**|**QFD as % o**|**f revenue**|**Comment**|
|---|---|---|---|---|---|---|---|---|---|
||**AICD**|**Present**<br>**study**|**AICD**|**Present**<br>**study**|**AICD**|**Present**<br>**study**|**AICD**|**Present**<br>**study**||
|Benin|2009|2011–13|21|21|96|95|12|712|Total losses and the QFD remained relativelystable.|
|Botswana|n/a|2010–13|10|117|100|99|15|94<br>174|Total losses remained relatively stable. Steep increase in<br>QFD since AICD, and within the period covered by the<br>present study, driven in part by issues with Morupule coal<br>plant.|
|Burkina Faso|2007–08|2011–14|18, 16|16<br>17|91|83<br>98|46, 37|7148|T&D losses stable, bill collections improved. QFD increased<br>and returned to 2008 levels.|
|Cape Verde|2000–09|2011–12|17 →<br>26|30<br>32|91–92|93<br>88|129 → 45|2934|Total losses increased, yet improvements in 2001–09 seem<br>to have continued, driven bysubstantial tariff increases.|
|Central African<br>Republic|2004–09|2013–14|48|52<br>48|69|78|48117|7168|T&D losses remained high, and bill collection rates low, yet<br>QFD seems to have dropped back to 2004 levels.|
|Congo, Rep.|n/a|2012|47|46|91|80|86|76|T&D losses remained high, bill collection rates seem to have<br>regressed. QFD change within a 10% margin of error.|
|Côte d’Ivoire|2005–09|2013–14|18, 23<br>→ 17|22<br>21|88|80<br>82|63 → 137|6374|Despite increasing T&D losses and reduced bill collection<br>rates, QFD seems to have fallen.|
|Ethiopia|2005–09|2011–12|22|23<br>25|96.5|87|133|399<br>353|Substantial increase in QFD driven by major capital<br>expansionprogram.|
|Ghana|Mid to<br>late 2000s|2013|25.4<br>25.6|23|89.6<br>89.3|95|37118|22|QFD reduced partly driven by bill collection rate<br>improvement.|
|Kenya|2001–08|2012–15|18|17<br>18|98.7|99|550|3140|Losses stayed consistent. QFD seems to have returned to the<br>levels in the early2000s after departure from cost recovery.|
|Liberia|n/a|2012–14|25|23, 29,<br>25|93|82<br>94|158|4021<br>33|Losses relatively steady while QFD decreased substantially<br>due to increase in tariffs.|
|Malawi|n/a|2011-14|23|2124|59.8|9293|264|11582|QFD reduced in part driven by a significant increase in bill<br>collection rate.|
|Mali|1999–<br>2008|2011–14|25 →<br>22|29<br>23|92 →<br>96|101<br>99|95 → 131|5272<br>62|Total losses steady. QFD improved from AICD period.|
|Mozambique|2005–09|2011–13|25–27|20–22|98–100|97<br>98|41, 44, 57,<br>37, 44|5045|T&D losses improved. Bill collection remained high. QFD<br>increased but has come back down to 2005 levels.|
|Nigeria|2005,<br>2007–09|2014|30 →<br>20|39|63.5 →<br>88|66|548, 292,<br>220, 247|174|Losses remained high. QFD, while high, fell substantially,<br>driven in part by the privatization process.|

99