rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Figure 3. Distribution of Bank Deposits in Zambia in March 2006</u>**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>above $US 1,000<br>70%<br>between $US$500.01 and $1,000<br>60% dollars<br>between US$200.01 and $500<br>50%<br>dollars<br>40% between US$100.01 and $200<br>dollars<br>30% below US$100 dollars<br>20%<br>10%<br>0%<br>By number of accounts By volume<br><!-- End of picture text --> Source: Data from commercial banks in Zambia Several studies indicate that the account holders are usually people living in urban areas and with a regular employment in the public sector or a large private firm. One additional characteristic is that the real value of small savings and deposit accounts declines over time, as no interest is usually paid on small savings accounts denominated in Kwacha. Only large firms receive positive interest rates on their deposits. In practice, for most households in Zambia bank savings and deposits are just a way to safeguard coins and notes, not a practical instrument to save money over time. As illustrated in the following figure, a 320,000 kwacha deposit (equivalent to $100 dollars) today would lose two thirds of its real value after 6 years if during this period the annual inflation rate remains at its 2004 level (18%) and no nominal interest is paid, as is currently the case for small saving deposits in Kwacha.

**Figure 4. Real Value of a 320,000 Kwacha Deposit***

<!-- Start of picture text --> 400,000<br>300,000<br>200,000<br>100,000<br>0<br>1 2 3 4 5 6 7 8 9 10 11<br>Years<br>Kwacha<br><!-- End of picture text -->

*It assumes an annual inflation rate of 18% and no interest payments.

12
Table A5: Heterogeneous effects of payment delays on food spending

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Panel A:|Male<br>HH head|Any previous<br>bank use|Close to<br>branch|Assets|Above median:<br>Patience|
|1-day delay|-405.642<br>(591.647)|69.188<br>(832.794)|-592.747<br>(654.067)|269.010<br>(766.502)|-223.956<br>(1017.129)|
|8-day delay|-536.167<br>(646.155)|1116.841<br>(1060.085)|-148.727<br>(742.053)|390.424<br>(839.486)|-1593.299<br>(967.504)|
|Observations|310|152|233|226|190|
|Mean of dependent variable in Immediate group|5737.41|4722.45|5600.39|5575.52|5866.08|
|R-squared|0.340|0.433|0.343|0.275|0.265|
|P-value: 1-day=8-day|0.841|0.270|0.545|0.895|0.212|
|Panel B:|Female<br>HH head|No previous<br>bank use|Far from<br>branch|Assets|Below median:<br>Patience|
|1-day delay|-281.353<br>(928.737)|-9.718<br>(691.840)|-181.439<br>(811.692)|-508.851<br>(683.831)|-278.239<br>(545.759)|
|8-day delay|883.401<br>(983.251)|-597.075<br>(701.526)|-488.375<br>(847.991)|-791.491<br>(758.031)|616.483<br>(659.203)|
|Observations|162|320|239|246|282|
|Mean of dependent variable in Immediate group|4489.60|5619.11|5081.20|5105.44|4860.43|
|R-squared|0.153|0.196|0.192|0.205|0.253|
|P-value: 1-day=8-day|0.239|0.404|0.710|0.689|0.154|
|P-value: equal efect of 1-day vs 0-day|0.905|0.939|0.683|0.430|0.961|
|P-value: equal efect of 8-day vs 0-day|0.204|0.155|0.755|0.276|0.048|
|P-value: equal efect of 1-day vs 8-day|0.248|0.144|0.481|0.716|0.059|
|OLS regressions. All specifcations include village <br>female-headed households and those with above-m<br>the transfer, those who live above the median dis<br>index of asset ownership, and those with above-m<br>transfer in one month instead of immediately. * p|and week-of<br>edian value<br>tance from t<br>edian patien<br>_<_0.10, ** p|-survey fxed ef<br>s for number of <br>he bank branch, <br>ce, as measured <br>_<_0.05, *** p_<_0.|ects. The to<br>transactions<br> those with <br> at baseline <br>01|p panel inc<br> at NBS in <br> above-medi<br> by amount|ludes respondents from<br> the 90 days preceding<br>an values of a principle-<br> required to prefer a|

47
# country.

While household and national food self-sufficiency goals have been at the core of rural development strategies in the country, agricultural commercialization, particularly tobacco production, has been deemed essential for Malawian growth (Peters, 1996; World Bank, 1989, 2009; United Nations Development Programme, 2009; Republic of Malawi, 2000; Tobin and Knausenberger, 1998). Tobacco has played a prominent role in Malawi’s historical economic development. The production of the crop initially centered on large scale estates but, with international encouragement, Malawi began liberalizing their tobacco industry in the early 1990s.<sup>8</sup> Smallholders first sold tobacco on the Malawian auction floor, under a quota, in 1991 (Tobin and Knausenberger, 1998). In 1994, Malawi repealed the Special Crops Acts that favored estates and smallholders quickly rushed into tobacco production (Tsonga, 2004). Eventually smallholder tobacco restrictions, in the forms of quota systems, control boards and grower’s clubs, were abolished. By 1998, almost 20 percent of Malawian households, including over 400,000 smallholders, produced tobacco (Kadzandira, Phiri, and Zakeyo, 2004). Almost all of the new households who entered the market grew burley tobacco, with smallholders currently accounting for 70 percent of Malawi’s total production (Lea and Hanmer, 2009).

Although production levels have fluctuated over time, tobacco reigns not only as Malawi’s most important cash crop but also accounts for the majority of all Malawian exports (Orr, 2000; Jaffee, 2003). Malawi is well-suited for growing burley tobacco, much of which is used as a low-cost filler supplied to international cigarette producers. Tobacco production, with its delicate cultivation and particular harvesting requirements, exhibits few economies of scale (Jaffee, 2003). Additionally, burley tobacco’s air-curing process is not capital intensive, further lending itself to small-scale Malawian production (Takane, 2008).

All legally sold tobacco in Malawi goes through regional auction floors depicted in figure 1.<sup>9</sup> While tobacco production originally concentrated itself in the South, growth quickly spread throughout the country. Most smallholders continue to sell their crop through tobacco clubs, typically including 10 to 20 farmers, but recent legal changes allow them to sell directly to the

> 8See Chirwa et al. (2008) for additional information on the history of tobacco in Malawi. Zeller, Diagne, and Mataya (1998) provide insights into smallholder tobacco adoption decisions, but subsequent lifting of the governmental entry restrictions may have altered production choices.

> 9The original Limbe floor is in Southern Malawi, but a floor opened in the Central region in 1979 and in the Northern region in 1993.

6
- Bastos, P., J. Silva, and R. Proenca. 2016. “Exports and Job Training.” _Review of International Economics_ 24(4): 737–56.

- Beccaria, L., R. Maurizio, and G. V ́azquez. 2015. “Desigualdad e Informalidad en Am ́erica Latina: El Caso de la Argentina.” In V. Amarante, and R. Arim, editor. _Desigualdad e Informalidad: Un An ́alisis de Cinco Experiencias Latinoamericanas_ . Santiago, Chile: Comisi ́on Econ ́omica para Am ́erica Latina y el Caribe/Ministerio de Asuntos Exteriores de Noruega.

- Benguria, F., F. Saffie, and S. Urz ́ua. 2018. “The Transmission of Commodity Price SuperCycles.” NBER Working Paper 24560. Cambridge, United States: National Bureau of Economic Research.

- Bernard, A. B., and J. B. Jensen, 1999. “Exceptional Exporter Performance: Cause, Effect, or Both?” _Journal of International Economics_ 47(1): 1–25.

- Bosch, M., and J. Esteban-Pretel. 2015. “The Labor Market Effects of Introducing Unemployment Benefits in an Economy with High Informality.” _European Economic Review_ 75(C): 1–17.

- Bosch, M., and W. Maloney. 2010. “Comparative Analysis of Labor Market Dynamics Using Markov Processes: An Application to Informality.” _Labour Economics_ 17(4): 621–31.

- Bown, C., D. Lederman, S. Pienknagura, and R. Robertson. 2017. _Better Neighbors: Toward a Renewal of Economic Integration in Latin America._ Washington, DC, United States: World Bank.

- Brambilla, I., D. Lederman, and G. Porto. 2012. “Exports, Export Destinations, and Skills.” _American Economic Review_ 102(7): 3406–38.

- Bustos, P. 2011. “Trade Liberalization, Exports, and Technology Upgrading: Evidence on the Impact of MERCOSUR on Argentinian Firms.” _American Economic Review_ 101(1): 304–40.

- Campos-V ́azquez, R. M., L. F. Lopez-Calva, and N. Lustig. 2016. “Declining Wages for College-Educated Workers in Mexico: Are Younger or Older Cohorts Hurt the Most?” Policy Research Working Paper 7546. Washington, DC, United States: World Bank.

- Card, D., J. Heining, and P. Kline. 2013. “Workplace Heterogeneity and the Rise of West German Wage Inequality.” _Quarterly Journal of Economics_ 128(3): 967–1015.

- Card, D., and T. Lemieux. 2001. “Can Falling Supply Explain the Rising Return to College for Younger Men?” _Quarterly Journal of Economics_ 116: 705–46.

- Cardoso, A. R. 1999. “Firms’ Wage Policies and the Rise in Labor Market Inequality: The Case of Portugal.” _ILR Review_ 53(1): 87–102.

- Cerutti, P. M., E. Crivellaro, G. Reyes, and L. Sousa. 2019. “Hit and Run? Income Shocks and School Dropouts in Latin America.” Forthcoming in _Review of Labour Economics and Industrial Relations_ . (forthcoming).

38
6

- Related to this point, the fact that IDAAN enjoys such a high degree of discretion in determining the identity of the beneficiaries raises the risk that subsidies may be misdirected for political ends.

- The tendency to use non-payment of bills as an eligibility criterion for the subsidy scheme introduces a perverse incentive for households not to pay their bills.

- The subsidization of the full value of bills for the 'social cases' undermines any incentives for these households to make efficient use of water resources, and, moreover, means that they are losing the habit of paying for these services.

This analysis suggests that the existing subsidy apparatus could not continue in its present form.

# **The Incidence of Poverty**

A fundamental starting point in assessing the need for a new subsidy programme is to characterize the incidence of poverty among the customers of IDAAN. In its official definitions, the government of Panama distinguishes between two levels of poverty.

- _The poverty_ line-defined as the level of expenditure needed to pay for a basket of basic services comprising nutrition, housing, transport, health and education. In 1997, it was set at US$726 per capita per year.

- _The extreme poverty_ line-defined as the level of expenditure needed to pay for a basket of basic nutrition alone. In 1997, it was set at US$470 per capita per year.

On this basis, Table 3.1 summarizes the geographical distribution of poverty in Panama using data from the national Living Standards Measurement Survey conducted in 1997. The data clearly indicate that poverty in Panama is predominantly a rural phenomenon. This is an important finding, given that IDAAN only services the urban areas of the country. Indeed, only 11% of the urban population live in conditions of poverty, while less than _5%_ live in conditions of extreme poverty.

**Table 3.1: Distribution of poverty across rural and urban areas in Panama**

||**Extre**|**me poor**|**Po**|**or**|**Non**|**-poor**|**T**|**otal**|
|---|---|---|---|---|---|---|---|---|
||**m**|**%**|**m**|**%**|**M**|**%**|**m**|**%**|
|**Rural**|0.52|88.1|0.25|59.5|0.44|25.7|1.21-|<br>44.5|
|Urban|0.07|11.9|0.17|40.5|1.27|74.3|1.51|55.5|
|Total|0.59|100.0|0.42|100.0|1.71|100.0|2.72|100.0|

_Source:_ **Encuesta de Nivel de Vida, Panama, 1997.**
**Figure 3-1. BCG vaccination coverage in selected Southern African Countries**

<!-- Start of picture text --> 110<br>100 _ -_--<br>90 - - - -Botswana<br>o 80 . . . . . .Lesotho<br>o 70 - - - - Zambia<br>X 60 --- 60 ' N . - Zimbabwe<br>6Z 50 l " * - ' ' " - --- Swaziland<br>50 -<br>a- -e-4- -SouthAfrica<br>* 40<br>30 - l<br>1990 1992 1994 1996 1998 2000<br>Year<br><!-- End of picture text --> Not only does the figure display the very different levels of coverage, but also different trends. Coverage in Lesotho was declining until 1996, while now it is apparently on the increase, although the rise is very slow. South Africa, on the other hand, has been increasing its coverage, with a very dramatic rise since the 1994 elections.

> What can be surmised from the above is that governments in the region are willing to use vaccination programs as a major health intervention, but that their commitment towards them is uneven. Zambia and South Africa, and Botswana to some extent have shown very positive attitudes towards childhood vaccines with either high or rapidly increasing levels of coverage.

- _3.2. Current spending_

> Estimating the level of government expenditure on HIV/AIDS is difficult, as much of it is too intertwined with other expenditures to be separated. For example, spending on medical care for AIDS patients is difficult to distinguish from other health care spending. It is easier to estimate the level of spending on HIV programs. This spending provides an indication of the level of government commitment to fighting HIV directly. When an HIV vaccine becomes available, government will probably draw on these types of directly allocated funds.

Unfortunately comparable data are not available for all the countries in the region. Figure 3-2 provides estimated levels of government expenditure on HIV related programs. It also shows the level of international donor support and what these funding levels mean in terms of funds per HIV positive person.

**_23_**
program, finding that even a relatively low-cost program focused on the provision of school uniforms and textbooks to each child caused sponsored children to advance a third of a grade farther in schooling completion.

In this paper we present results for the impacts of child sponsorship on the adult income<sup>4</sup> and wealth of children sponsored through one of the leading international child sponsorship organizations. An understanding of these impacts is important for the millions of individuals in wealthy countries involved in international child sponsorship, individuals who are likely to view their contributions as an investment in these overseas children that yields tangible economic returns in the future, when they are adults. But it is also important for governments in countries implementing similar programs that work directly with impoverished children, helping them to understand whether direct investments in child development are financially sustainable by virtue of the positive impacts on the future incomes of beneficiaries. Thus we ask the question: Does international child sponsorship pay off for children in adulthood?

# II. METHODOLOGY

Fieldwork for our six-country study took place from 2008 to 2010. We obtained initial enrollment lists from village projects that were rolled out from 1980 to 1992 by Compassion International, the world’s third largest sponsorship organization, which currently sponsors over 1.3 million children in 26 countries. Compassion’s child sponsorship program is a very intensive intervention in the lives of impoverished children. Children typically begin sponsorship at age 4–6 and continue into their mid-teens. Many sponsored children attend retreats together with program staff that focus on the nurture of their spiritual and moral values as well as their aspirations. Since, even in a typical week, children typically spend about 8–10 hours per week after school and on Saturdays participating in the program and because the average duration of sponsorship is 9.3 years, this means that during the course of their childhood, on average sponsored children spend slightly more than four thousand hours participating in Compassion programming. Average years of participation by country is given in table 1.

Across the countries in which it is implemented, the Compassion program contains many similar elements. In each country Compassion uses funding to provide tuition fees for children, several nutritious meals per week, basic healthcare, school uniforms, and an after-school tutoring program. The tutoring program not only helps sponsored children with homework and gives them additional academic instruction, but emphasizes spiritual and character formation and the 3
30

# Table 6: Establishment Cost as a Percentage ofTotal Deposits, Selected Years 1951-75

|Year|Syndicate Bank<br>Banking System United States- All Insured<br>Commercial Banks|
|---|---|
|1951|2.27<br>1.70|
|1961|2.12<br>1.80|
|1971|2.80<br>3.00|
|1975|3.02<br>3.06<br>2.90|
|Source:|For Syndicate Bank, the same as in Table I. For the Banking<br>System in India, Centre for Monitoring Indian Economy, Basic<br>Statistics Relating to the Indian Economy, vol. 1:AllIndia<br>(September 1977, Bombay), Table 19.1. For the United States,<br>Federal Reserve bulletin vol. 63,no. 7, (July 1977).|
|Direct|0.471|0.623|0.363|0.324|
|---|---|---|---|---|
|AGEB-level model|0.541|0.750|0.497|0.264|
|Transformed Area-level model|0.444|0.652|0.294|0.373|
|**Estimated Pearson correlation**|||||
|Direct|0.493|0.659|0.418|0.385|
|AGEB-level model|0.553|0.770|0.415|0.614|
|Transformed Area-level model|0.405|0.619|0.209|0.384|

Table A4 in the appendix also presents a comparison of an area-level model using the transformed target variable and an area-level model using a linear (untransformed) model. The transformed model generally performs better than the linear model, with the possible exception of some female LFP statistics (coverage rates, absolute deviation, and correlations). However, the AGEB-level model remains the best choice in this context.

# **5. Conclusion**

This paper considers the extent to which combining simulated sample data with publicly available geospatial data improves state and municipal estimates of male and female labor force participation and unemployment rates. Results are compared against the full 2020 census. The small area estimation procedure greatly improves the accuracy and precision of state level estimates for all four indicators, as well as municipal estimates of male and female labor force participation. This method can therefore be used to obtain more granular information on municipalities with low LFP, which can lead to a better understanding of the causes of low female LFP and potential policies to address it.

The method does not work nearly as well for estimating urban unemployment, because the values for unemployment rates are very low and zero for many clusters in the sample. In this setting, linear mixed models do not perform well. Although rank correlations and mean absolute deviations both improve, the simple correlation with the census value falls and mean relative bias is very high. In all cases except for one, estimates from a model specified at the AGEB level generate more accurate predictions than those obtained from an area-level model, with the lone exception being when considering the rank correlation for estimated female unemployment rates. Not surprisingly, using a hypothetical enumeration of all households in selected clusters dramatically improves the accuracy of the estimates. But in this case the small area estimates offer very minor improvements in accuracy and precision over direct estimates from the fully enumerated sample.

Non-linear models such as two-part models may be an appealing alternative for small area estimation of low-probability events in a sample. In the two-part model, the first part models the probability of a positive unemployment rate and the second part models the unemployment rate conditional on a positive rate. This is feasible to incorporate in a small area estimation framework, but significant effort would be required to implement it in existing software.

32
benefits that are not captured by the lending institution, such as increased income due to more intensive input application by fingerprinted farmers.<sup>26</sup>

# **8. Conclusion**

We conducted a field experiment where we randomly selected a subset of potential loan applicants to be fingerprinted, which improved the effectiveness of dynamic repayment incentives for these individuals. For all the recent empirical work on microcredit markets in developing countries, to our knowledge this is the first randomized field experiment of its kind, and the first to shed light (thanks to a detailed follow-up survey of borrowers) on the specific behaviors germane to the presence of asymmetric information problems.

Consistent with a simple model of asymmetric information in credit markets, we find heterogeneous effects of being fingerprinted, with the strongest effects among borrowers expected (ex ante) to have the worst repayment performance. Fingerprinting leads these “worst” borrowers to raise their repayment rates dramatically, partly as a result of voluntarily choosing lower loan sizes as well as devoting more agricultural inputs to the cash crop that the loan was intended to finance. The treatment-induced reduction in loan size represents a reduction in adverse selection, while the increased use of agricultural inputs on the cash crop represents a reduction in ex-ante moral hazard.

The short-term improvements in repayment estimated in this paper may indeed be smaller than the effects that would be found over a longer horizon. First of all, borrowers’ assessments of the effectiveness of the technology and the credibility of the threat to withhold credit would likely rise over time as they gained further exposure to the system, observed that their past credit performance was being correctly retrieved by the lender, and saw that credit history information was indeed being shared with other lenders. In addition, the lender should be able to selectively allocate credit to the pool of good-performing borrowers over time, further improving overall repayment performance of the borrowing pool. Finally, because there is less risk involved for the

> 26 Unfortunately, our estimates of the impact of fingerprinting on profits are too imprecise to say whether profits definitely increased due to this intervention.

28
|**Table 4: Does treatment statuspredict surviv**|**al or reportin**<br>(1)|**g profits in surv**<br>(2)|**ey**<br>(3)|(4)|
|---|---|---|---|---|
||In business <br>at R4|Report profits <br>in R2|Report profit <br>in R3|Report profits<br>in R4|
|Information and Reimbursement Treatment|0.000653|0.0556|0.0266|0.0531|
||(0.0408)|(0.0569)|(0.0543)|(0.0618)|
|10,000 Rs Treatment|0.00231|0.00747|-0.0600|-0.0719|
||(0.0428)|(0.0567)|(0.0592)|(0.0682)|
|20,000 Rs Treatment|0.0203|0.0190|0.0667|0.0571|
||(0.0399)|(0.0559)|(0.0517)|(0.0632)|
|40,000 Rs Treatment|0.00114|0.0425|-0.0462|-0.0140|
||(0.0428)|(0.0572)|(0.0591)|(0.0649)|
|Observations|483|520|520|520|
|F-testp-value thatjointlyzero|0.9849|0.8262|0.1049|0.2751|
|All regressions include controls for randomiz<br>Robust standard errors in parentheses.<br>*, **, and *** indicate significance at the 10,|ation strata.<br>5 and 1 perce|nt significance l|evels respectiv|ely.|

29