rafmacalaba/datause-encoder-data huggingface dataset:
# FIGURE 9 Effects of banking crises and epidemics on potential growth

A. Response of potential output growth after banking crises <!-- Start of picture text --> Percentage points<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>-2.0<br>-2.5<br>Year 1 Year 3 Year 5<br>C. Response of potential output growth in<br>EMDEs 5 years later<br>Percentage points<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>Recessions Banking crises Epidemics<br><!-- End of picture text --> <!-- Start of picture text --> B. Response of potential output growth in<br>advanced economies 5 years later<br><!-- End of picture text --> <!-- Start of picture text --> Percentage points<br>0<br>-1<br>-2<br>-3<br>Recessions Banking crises Epidemics<br>D. Response of potential output growth after<br>Epidemics<br>Percentage points<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>-2.0<br>-2.5<br>Year 1 Year 3 Year 5<br><!-- End of picture text --> Source: World Bank.

Note: Blue bars are coefficient estimates from local projections model. Orange whiskers indicate 90 percent confidence interval. Methodological details can be found in annex E. Recessions are defined as the period from the peak preceding a business cycle trough to the trough, with the troughs defined as years in which output growth is both negative and one standard deviation below the long-term average. Banking crises are identified as in Laeven and Valencia (2012, 2018, 2020). Epidemics include SARS (2003), swine flu (2009), MERS (2012), Ebola (2014), and Zika (2016). Sample includes unbalanced panel of 32 advanced economies 97 EMDEs for 1981-2020.

29
# 1 Introduction

Exchanging sex for money, goods, or services is a way of life for many poor women in developing countries, yet little is understood about the way that the commercial or transactional sex market functions. While commercial sex workers (CSWs) have long been identi...ed as critical in a¤ecting the spread of the HIV/AIDS epidemic (UNAIDS, 2002; Hawken et al., 2002; Hudson, 1996; Plummer et al., 1991), comparatively little work has gone beyond characterizing sex workers as a high-risk subpopulation.

Even estimating the number of women engaged in transactional sex is notoriously di¢ cult. This is particularly true in Sub-Saharan Africa, where transactional sex is present within many types of sexual relationships, including long-term partnerships and even marriage (Swidler and Watkins, 2007; Caldwell et al., 1989; Luke, 2006; Schoepf, 2004; Hunter, 2002; Wojcicki, 2002a). In Kenya, 14.5% of men and 5.5% of women aged 15-49 report having ever engaged in transactional sex, a ...gure which is likely an underestimate given the sensitive nature of the question (Central Bureau of Statistics, 2004). In this context, commercial sex work might be thought of as one extreme along a continuum of sexual relationships that feature a transactional component, with either "dating" or monogamous marriage at the other extreme. In this study, we identi...ed 1,205 formal and informal sex workers in Busia, Kenya, a peri-urban town in Western Kenya. This amounts to roughly 12.5% of the population of Busia women aged 15-49.

This paper utilizes a unique panel dataset constructed from 192 daily sex worker diaries to analyze how sex workers decide whether to engage in unprotected sex with clients. The diaries included questions on income, expenditures, transfers given and received, and, most importantly, the speci...c sexual services provided to each client, the amounts paid for these services, and whether a condom was used for each sex act. In total, the dataset includes information on 19,041 transactions over 12,526 sex worker days.

We study sex workers’decisions in an intertemporal framework, and estimate how the supply of unprotected sex is a¤ected by health shocks. We ...nd compelling evidence that women increase their supply of risky, better compensated sex in response to short-term health shocks at home. Women are 3.1% more likely to see a client, 21.2% more likely to have anal sex, and 19.1% more likely to engage in unprotected sex on days in which another household member (typically 1
|Mindanao Rural Development Project - Phase 2|Carolina V. Figueroa-Geron|
|---|---|
|Mindanao Rural Development Project - Phase 2|Carolina V. Figueroa-Geron|
|Mindanao Rural Development Project - Phase 2|Carolina V. Figueroa-Geron|
|Mindanao Rural Development Project - Phase 2|Carolina V. Figueroa-Geron|
|Mindanao Rural Development Project - Phase 2|Carolina V. Figueroa-Geron|
|Health & Social Protection Project|Susanna Hayrapetyan|
|Health & Social Protection Project|Susanna Hayrapetyan|
|Health & Social Protection Project|Susanna Hayrapetyan|
|Health & Social Protection Project|Susanna Hayrapetyan|
|Health & Social Protection Project|Susanna Hayrapetyan|
|Health & Social Protection Project|Susanna Hayrapetyan|
|Health & Social Protection Project|Susanna Hayrapetyan|
|Health & Social Protection Project|Susanna Hayrapetyan|
|Health & Social Protection Project|Susanna Hayrapetyan|
|Health & Social Protection Project|Susanna Hayrapetyan|
|Health & Social Protection Project|Susanna Hayrapetyan|
|Health & Social Protection Project|Susanna Hayrapetyan|
|Health & Social Protection Project|Susanna Hayrapetyan|
|Health & Social Protection Project|Susanna Hayrapetyan|
|Health & Social Protection Project|Susanna Hayrapetyan|
|Health & Social Protection Project|Susanna Hayrapetyan|
|Ghana - Micro,Small and Medium Enterprise Project|Kofi-BoatengAgyen|
|Ghana - Micro,Small and Medium Enterprise Project|Kofi-BoatengAgyen|
|Ghana - Micro,Small and Medium Enterprise Project|Kofi-BoatengAgyen|
|Ghana - Micro,Small and Medium Enterprise Project|Kofi-BoatengAgyen|
|Ghana - Micro,Small and Medium Enterprise Project|Kofi-BoatengAgyen|
|Ghana - Micro,Small and Medium Enterprise Project|Kofi-BoatengAgyen|
|Ghana - Micro,Small and Medium Enterprise Project|Kofi-BoatengAgyen|
|Ghana - Micro,Small and Medium Enterprise Project|Kofi-BoatengAgyen|
|Ghana - Micro,Small and Medium Enterprise Project|Kofi-BoatengAgyen|
|Ghana - Micro,Small and Medium Enterprise Project|Kofi-BoatengAgyen|
|Ghana - Micro,Small and Medium Enterprise Project|Kofi-BoatengAgyen|
|Ghana - Micro,Small and Medium Enterprise Project|Kofi-BoatengAgyen|
|Ghana - Micro,Small and Medium Enterprise Project|Kofi-BoatengAgyen|
|Ghana - Micro,Small and Medium Enterprise Project|Kofi-BoatengAgyen|
|Ghana - Micro,Small and Medium Enterprise Project|Kofi-BoatengAgyen|
|Ghana - Micro,Small and Medium Enterprise Project|Kofi-BoatengAgyen|
|Micro,Small,and Medium Enterprise Competitiveness Project|Yira J. Mascaro|
|Micro,Small,and Medium Enterprise Competitiveness Project|Yira J. Mascaro|
|Micro,Small,and Medium Enterprise Competitiveness Project|Yira J. Mascaro|
|Micro,Small,and Medium Enterprise Competitiveness Project|Yira J. Mascaro|
|Micro,Small,and Medium Enterprise Competitiveness Project|Yira J. Mascaro|
|Micro,Small,and Medium Enterprise Competitiveness Project|Yira J. Mascaro|
|Micro,Small,and Medium Enterprise Competitiveness Project|Yira J. Mascaro|
|Micro,Small,and Medium Enterprise Competitiveness Project|Yira J. Mascaro|
|Micro,Small,and Medium Enterprise Competitiveness Project|Yira J. Mascaro|
|Micro,Small,and Medium Enterprise Competitiveness Project|Yira J. Mascaro|
22

# **Appendix: data**

The basic source are the Central Statistical Office's National Accounts, except for credit to the private sector, which was taken from the Report on Currency and Finance published by the Reserve Bank of India. The details are as follows.

_Private capital stock:_ constructed on the basis of real net fixed investment by the private sector, making use of the March 1981 capital stock figures reported by Central Statistical Office (1992). While the 1994 National Accounts report capital stock estimates for the private sector for 1980/81-91/92, the figures are not consistent with the net investment data (the inconsistency corresponds to the household subsector), and therefore were not used.

_Public infrastructure capital stock:_ sum of the real fixed capital stocks of the public sector in power, water and sewage, and transport and communications. From 1980/81 on, the figures are taken directly from the National Accounts. Prior to that year, public capital stock data are not available. However, the older National Accounts (base 1970/71, extended back to 1960/61) report real investment data at a comparable level of sectoral disaggregation. Thus, the disaggregated public capital stock series were extended applying to this sectoral investment data the same average depreciation rates of the more recent period; average depreciation rates were employed for each sector of use because the detailed asset structure of sectoral investment is not available.

_Public non-infrastructure capital stock:_ defined as the real fixed capital stock of the public sector, taken directly from the National Accounts, minus the public infrastructure capital stock defined above.

_Relative price of capital goods:_ defined as the deflator of real fixed investment of the private sector divided by the deflator of real private GDP, both taken from the National Accounts.

_Real credit to the private sector:_ defined as the stock of credit from the banking system to the commercial sector, divided by the deflator of real private GDP.
lim migrants may be smaller than differences between Hindus and Muslims in the general population.<sup>19</sup> This analysis uses the NFHS-3 data that report migrant status and latrine use along with other household characteristics.<sup>20</sup> These migrants are moving from rural to urban areas, which could be within the same state or across state lines.

Table 4 reports that latrine use is similar among migrant Hindu and Muslim households, both unconditionally (column 1) and conditional on household characteristics (column 2). As a comparison, column 3 and column 4 report estimated differences in the NFHS-3 data using all households, which are similar to the estimated differences in Table 2 and Table 3. The sample size is substantially smaller when analyzing migrants only, and so there is less precision in estimating the difference in likelihood of latrine use between Hindu migrants and – Muslim migrants in the same locations (95% confidence interval: 12.2pp less likely 7.0pp more likely). This difference is not statistically different than the within-location difference for all households (column 4), but is statistically smaller than the unconditional difference between Hindus and Muslims (column 3).

# **V Conclusion**

Improvements in sanitation have the potential to dramatically improve child health and adult health (UNDP, 2006).<sup>21</sup> Substantial efforts have been made to improve sanitation infrastructure in India and reduce open defecation, where there are the largest number of children and adults exposed to uncontained fecal material, but there remain concerns that deeply ingrained religious beliefs and associated traditional practices will limit the improvements in sanitation practices following investments in sanitation infrastructure.

The estimates in this study suggest that the unconditional differences in sanitation prac-

> 19From a bias perspective, differences between migrants and non-migrants would bias the estimated differences between Hindu migrants and Muslim migrants if selection of migrants is differential by religion.

> 20The NFHS-3 survey asked about the location of migrants’ prior residence and current residence. The sample is limited to respondents who moved from the “countryside” to a “city,” “large city,” or “capital.”

> 21For evidence on the historical importance of water and sewerage infrastructure for health in the United States, see work by Alsan and Goldin (2019).

13
In summary, the parallel system represented a separate education system for the KosovarAlbanian population, which directly reduced the access, resources and utilization of education services, particularly at the secondary school level.

# **_Identifying Those Affected by the Parallel System_**

Children in Kosovo enter formal education at the age of 6 or 7 years and spend 8 years in primary school. At age 14 or 15 years, they choose between enrolling into a general secondary school -- high school -- or a technical school. Successful completion of three to four years in a general secondary school allows access to university education while students of technical schools graduate after a period of two to three years. In order to identify the effects on the parallel system in secondary education, this paper distinguishes those individuals that -- because of their age -- partially or fully faced a parallel system in secondary education, from those that were not exposed to the system.

**Kosovo's Education System**

|**Agegroup**|**Grades**|**Level**|
|---|---|---|
|0-5||Preschool|
|6-14|1-8|Primary|
|15-18<br>|9-12/13<br>|Highschool<br>|
|15-17|9-10/11|Technical|
||4years|Professional|

Source: World Bank (2001c) Kosovo Poverty Assessment Report. The World Bank.

The first group is comprised of individuals aged 17 years and over in 1990 (27 years and over in 2000). This cohort of individuals had already enrolled into secondary education before 1990 and consequently was not exposed to the parallel regime. A student enrolled in secondary education at age 17 would have typically already made the decision to enroll into secondary 7
**Figure 2. Predicted Poverty Rates Based on Imputation, from NPS 2019/20 and NPS 2020/21 to TAs, Normal Linear Regression Model, Tanzania**

<!-- Start of picture text --> TA 1 TA 2<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m8 m9<br>Imputation model Imputation model<br>TA 3<br>NPS 2019/20<br>NPS 2020/21<br>m1 m2 m3 m4 m8 m9<br>Imputation model<br>30 30<br>25 25<br>20 20<br>15 15<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>10 10<br>30<br>25<br>20<br>15<br>Estimated poverty rate (%)<br>10<br><!-- End of picture text -->

**Note:** 1000 simulations are implemented. Model 3 in TA 2 and Models 3 and 4 in TA 3 use standardized variables. The dashed lines represent the true poverty rates for TA 1 using the 2019/20 and 2020/21 poverty lines. The dotted lines represent confidence intervals of the true poverty rates.

39
village fixed effects. SD results are presented for a range of outcomes: housing, durable goods, livestock, household enterprises (HE) and agricultural activities.

# _A. Impacts on Livestock_

DID estimates show a large significant program impact on livestock ownership, which differentially increased by 0.418 Tropical Livestock Units (TLU) for beneficiary households (more than half a cow, or four goats, or forty chickens) (Table 4, Panel A). The magnitude of the impact is equivalent to more than half of the sample average baseline TLU. At 18 months after the termination of the cash transfer, the increase in livestock amounts to an increase of 87,812 FCFA in livestock value, which represents almost half of the transfers received over the 18 month program (180,000 FCFA). The significance and magnitude of the results are robust when restricting the sample to households within the PMT range with common support (Table 4, Panel B).<sup>20</sup> The SD estimates of impacts on livestock are also highly consistent with the DID estimates, significant and of large magnitude (Table 5, Panel A). With village fixed-effects (FE), an impact of 0.435 TLU or 86,095 FCFA is found.

The additional variables available in the follow-up survey further show that consumption from own livestock increases significantly among beneficiaries. However, increases in livestock sales for beneficiaries are found to be rather small and are only significant in the village fixed-effect specification.

The follow-up survey also asks retrospectively about livestock ownership in 2012 (6 months after the end of the program, and 12 months prior the follow-up survey). Impacts on the difference between livestock assets in 2013 and 2012 are not significantly different between beneficiaries and non-beneficiaries, suggesting that beneficiary households are not depleting livestock assets at a greater rate than non-beneficiaries since the end of the project.

> 20 Panel B provides more conservative estimates, using the preferred (common support) sample in the SD strategy. Note that the impact on investments found 18 months after program termination includes return on investments: for livestock it includes birth and death of the animals acquired. This may partly explain the high impact on livestock value.

15
**8**

Recall that if we do not make use of the approximation regarding the natural logarithm of a number close to one: Similarly, the social rate of return is, _r*_ **_=(e b_** 1) _K*_ Hence the typical assumption that the coefficient of schooling in the HCEF is the rate of return from schooling is not correct in principle, and is approximately correct only if K = 1 and the regression coefficient is a small number,

# **IV. Effects of other Explanatory Variables**

The HCEF is highly adaptable, and a variety of variables have been added to the righthand side. The most important and imaginative of these has been post-school labor market experience in Mincer's classic study (1974). Mincer shows that the coefficient of schooling
- [37] De Mello, L., 1997, Foreign Direct Investment in Developing Countries and Growth: A Selective Survey, Journal of Development Studies, 34(1), 1-34.

- [38] Denison, E.F., 1962, Sources of Growth in the United States and the Alternatives Before Us, Supplement Paper 13, New York, Committee for Economic Development.

- [39] Denison, E.F., 1967, Why Growth Rates Differ, Washington D.C.: The Brookings Institution.

- [40] Denison, E.F., 1974, Accounting for United States Economic Growth, Washington D.C.: The Brookings Institution.

- [41] Department of Transport, 1999, Moving South Africa.

- [42] Dessus, S., and Herrera, R., 1999, Public Capital and Growth: An Econometric Study on Panel Data, Revue Economique, 50(1), 113-26.

- [43] Destefanis, S., and Sena, V., 2005, Public Capital and Total Factor Productivity: New Evidence from the Italian Regions, Regional Studies, 39(5), 603-17.

- [44] Demetriades, P.O., and Mamuneas, T.P., 2000, Intertemporal Output and Employment Effects of Public Infrasructure Capital: Evidence from12 OECD Economies, Economic Journal, 110(465), 687-712.

- [45] Devarajan, S., Xie, D., and Zou, H., 1998, Should Public Capital be Subsidized or Provided? Journal of Monetary Economics, 41(2), 319-31.

- [46] Doms, M.E., Dunne, T., and Troske, K., 1997, Workers, Wages and Technology, Quarterly Journal of Economics, 112(1), 253-90.

- [47] Doms, M.E., and Jensen, J.B., 1998, Comparing Wages, Skills and Productivity Between Domestic and Foreign Owned Manufacturing Establishments in the United States, in R.Baldwin, R.Lipsey, and J.Richardson (eds.), Geography and Ownership as Bases for Economic Accounting, Chicago: University of Chicago Press.

- [48] Easterly, W and Rebelo, S., 1993. Fiscal policy and economic growth, Journal of Monetary Economics, 32, 417—458.

- [49] Easterly, W. and Servén, L., 2002, The Limits of Stabilization: Infrastructure, Public Deficits and Growth in Latin America, Stanford University Press and the World Bank.

- [50] Entorf, H., and Kramarz, F., 1998, New Technologies, Wages and Worker Selection, Economics of Innovation and New Technology, 5(2-4), 165-97.

- [51] Esfahani, H.S., and Ramirez, M.T., 2003, Institutions, infrastructure and economic growth, Journal of Development Economics, 70, 443-477.

- [52] Estache, A., 2005, What Do We Know About Sub-Saharan Africa’s Infrastructure and the Impact of the 1990s Reforms?, Mimeo, World Bank, Washington DC.

- [53] Estache, A. Foster, V., and Wodon, Q., 2002, Accounting for Poverty in Infrastructure Reform: Learning from Latin America’s Experience, WBI Development Studies, Washington DC: The World Bank.

- [54] Estache, A. and Goicoechea, A., 2005,.A ‘Research’ Database on Infrastructure Economic Performance, Policy Research Paper No. 3643, The World Bank, Infrastructure Network, Office of the Vice President (June), Washington, D.C.

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# **TABLE 5. Estimation results and income elasticities**

|**Commodity**|**Log per**<br>**capita**<br>**income**|**Squared**<br>**log per**<br>**capita**<br>**income**|**Income**<br>**elasticity**<br>**at 2017**<br>**median**<br>**income**|
|---|---|---|---|
|**Aluminum**|3.5|- 0.15|0.8|
|**Zinc**|2.6|- 0.12|0.3|
|**Copper**|2.95|- 0.12|0.7|
|**Crude oil**|2.31|- 0.1|0.5|
|**Coal**|6.04|- 0.31|0.6|
|**Natural gas 1**|0.38|...|0.4|
|**Rice**|1.39|- 0.09|- 0.3|
|**Wheat**|1.05|- 0.04|0.3|
|**Maize 1**|0.85|...|0.8|
|**Soybeans 1**|0.84|...|0.8|

Note: Results shown are a sub-set of the estimations obtained using the pooled mean group model (see Annex SF1.1). Values for log and log squared per capita income are the coefficients for these variables as estimated by the model. Income elasticities are calculated using these coefficients, together with median global per capita income in 2017. Annex Table SF1.5 displays the full set of results from the estimation, including both short-run and long-run coefficients.

1 indicates linear regression results for commodities which do not appear to have a non-linear relationship with income.

**TABLE 6. Estimation results under generalized method of moments**

||**Aluminum**|**Zinc**|**Copper**|**Oil**|**Coal**|**Gas**|**Rice**|**Wheat**|**Maize**|**Soybeans**|
|---|---|---|---|---|---|---|---|---|---|---|
|Log per capita|3.99***|3.81***|2.57***|2.41***|4.19***|0.27***|1.49***|0.70***|0.47***|0.48***|
|income|(0.21)|(0.18)|(0.36)|(0.12)|(0.25)|(0.09)|(0.13)|(0.12)|(0.03)|(0.05)|
|Squared log<br>r it|- 0.17***|- 0.19***|- 0.06***|- 0.10***|- 0.19***||- 0.09***|- 0.04***|||
|pe capa<br>income|(0.02)|(0.01)|(0.02)|(0.01)|(0.02)||0.00|(0.01)|||
|L l i|- 0.45***|- 0.18***|0|- 0.05***|0.07|- 0.47***|- 0.33|- 0.04|- 0.48***|- 1.33***|
|og rea prce|(0.05)|(0.04)|(0.12)|(0.01)|(0.08)|(0.13)|(0.02)|(0.03)|(0.09)|(0.15)|
|C|- 19.51***|- 18.16***|- 17.67***|- 13.60***|- 23.64***|- 4.16***|- 1.83***|- 1.46***|6.29***|10.01***|
|onstant|(0.83)|(0.77)|(0.73)|(0.63)|(1.13)|(0.87)|(0.50)|(0.47)|(0.51)|(0.97)|
|Adj._R_<br>2|0.86|0.81|0.8|0.96|0.9|0.84|0.91|0.91|0.12|0.11|
|J- statistic|0|0|0|0|0|0|0|0|0|0|
|Observations|1,608|1,583|1,275|1,617|1,428|1,583|2,776|1,730|2,372|1,501|
|Number of<br>countries|33|33|25|33|28|33|55|35|47|32|

Note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors in parentheses. One lag of independent variables is used as instruments. The J-statistics confirm their validity.

21
Table 3: Effect of Self-interviewing (SI) on Placebo Questions

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Farm work|Market visit|Maize/Rice|Meat|
||(past year)|(past week)|(next week)|(next week)|
|**Panel A. Malawi**|||||
|SI _×_ Screen Pass (_β_)|-0.01|0.08***|0.13***|0.15***|
||(0.01)|(0.03)|(0.03)|(0.03)|
|SI _×_ Non-Pass (_γ_)|-0.03|0.09**|-0.00|0.07*|
||(0.02)|(0.04)|(0.04)|(0.04)|
|Screen Pass|0.02|0.05|0.03|-0.01|
||(0.02)|(0.03)|(0.04)|(0.03)|
|FTFI _×_ Non-Pass mean|0.93|0.43|0.52|0.28|
|_p_-value (_β_ =_γ_)|0.445|0.875|0.018|0.121|
|Observations|1,718|1,713|1,345|1,228|
|_Efect of SI for the average _|_respondent_||||
|Pooled SI efects|-0.02|0.09|0.08|0.11|
|_p_-value|0.132|0.000|0.003|0.000|
|**Panel B. Liberia**|||||
|SI _×_ Screen Pass (_β_)|0.01|0.07**|-0.03**|0.00|
||(0.03)|(0.03)|(0.01)|(0.03)|
|SI _×_ Non-Pass (_γ_)|0.02|0.12**|-0.06**|-0.03|
||(0.04)|(0.05)|(0.03)|(0.05)|
|Screen Pass|0.04|0.09**|0.03*|0.05|
||(0.04)|(0.04)|(0.02)|(0.04)|
|FTFI _×_ Non-Pass mean|0.77|0.61|0.95|0.65|
|_p_-value (_β_ =_γ_)|0.836|0.415|0.266|0.637|
|Observations|1,259|1,260|1,226|1,101|
|_Efect of SI for the average _|_respondent_||||
|Pooled SI efects|0.01|0.08|-0.04|-0.01|
|_p_-value|0.548|0.001|0.002|0.794|

Note: Regressions are at the respondent-question level. Regressions include individual controls (including all variables in Table 1). “Screen Pass” is defined by selecting “yes” to all questions in Table 2. Standard errors clustered at individual level in parentheses. ***, **, and * represent significance at 1%, 5%, and 10%, respectively.

21