rafmacalaba/datause-encoder-data huggingface dataset:
incentives programs. For example, the administrative costs of a lottery program are potentially lower compared to a traditional CCT program as only winners need to be paid. While the research and ethical protocol for the study required that all project participants were offered testing, the incentive for behavioral change will, under certain conditions, remain the same if only lottery winners are tested or if STI screening also is subject to a lottery, thus further reducing costs.

Our study has some limitations. The recruitment was based on voluntary enrollment after providing information about the study and the lottery program at the village level. However, by comparing baseline HIV prevalence rates in our sample with HIV prevalence rates from the most recent DHS for Lesotho (Ministry of Health and Social Welfare and ICF Macro, 2010), we found that our sample was not too different from the general population of Lesotho and that, if anything, HIV prevalence was slightly lower at baseline in our study sample than overall in Lesotho, probably because our sample did not include large urban centers such as Maseru, the capital city.<sup>25</sup> The lottery intervention may have influenced outcomes through non-pecuniary channels. For example, a public lottery event every four months could have highlighted the role of prevention in the community. This channel, though, presumably influenced all study participants, independent of assignment arm. Participation in a lottery may also have yielded direct non-pecuniary rewards, for example by offering participants an element of entertainment or fun. We view this as an additional benefit of using lotteries to incentivize safer sexual behavior.

Identifying ways to increase demand for prevention remains a priority. Our results provide evidence of one complementary tool – lotteries – that may be successfully implemented in various other prevention programs to enhance the demand for safer sexual behavior. The evidence we have presented further suggests that lotteries are particularly able to influence the behavior of individuals with a relatively high ex ante risk of getting infected by HIV.

> 25HIV prevalence according to DHS by age group was: age 15-19: 4.1% for women (2.9% for men); age 20-24: 24.1% (5.9%); age 25-29: 35.4% (18.4%). HIV prevalence at baseline in our sample was: age 18-19: 5.4% for women (1.2% for men); age 20-24: 17.6% (4.2%); age 25-29: 29.1% (15.2%).

21
program, provinces that privatized their banks should have little incentive to bail out troubled institutions.30 However, owners of privatized provincial banks are not in exactly the same situation as other private bankers -- most received a contract to continue providing banking services to the province as part of their sales agreement. As banker to the provincial government, privatized provincial banks maintain a privileged position within the banking industry whicil may distort incentives. In this section, we compare privatized provincial banks with well-established private ones to determine whether they operate similarly. Available post-privatization data strongly suggest that, despite their service contracts with the provinces, privatized provincial banks did change their operations substantially. Their similarities withi private banks are now far more pronounced than their similarities with the remaining public provincial banks. Importantly, although we cannot quantify the social welfare benefits, the data indicate that credit allocation is improving. While the focus of this paper is on the fiscal benefits to the provinces -- which appear to be quite substantial -- it is quite likely that the most important benefit of privatization is the economic growth that should follow from improved credit allocation. Indeed, these benefits could make it wise to privatize even if the fiscal calculus did not favor privatization.

**IV.1 Operating Income and Costs**

The pubic provincial banks' low ratio of operating income to administrative costs is one indicationi of their inefficiency. Between 1993 and 1996 the typical public provincial bank generated only .77 pesos in total income (net financial income and income from services) for every peso spent on administrative costs (Table 7). The typical private bank, in contrast, generated 1.44 pesos in income for every peso incurred in costs and the typical privatized

> 30 We recognize, however, that systemic bank crises do occur, and that governments often intervene to prevent financial system collapse. See Gerard Caprio, Jr. and Daniela Klingebiel, "Bank Insolvencies: Cross Country Experience," World Bank, April, 1996. However, it should also be noted that, in the event of systemic crisis, the bail-out responsibility likely devolves to the federal rather the provincial governments.

16
Figure A2: Tariffs on digital products in Africa and rest of the world, by type of good, 2018 <!-- Start of picture text --> 10 Computer and electronic products<br>Electrical equipment<br>Fabricated metal products<br>Machinery<br>Motor vehicles, bodies and trailers, and parts<br>8 Printing and related support activities<br>6<br>4<br>2<br>0<br>Africa Rest of the world<br>Tariffs on digital goods (%)<br><!-- End of picture text --> Notes: Tariff is a simple average by country and product. Authors’ calculations based on TRAINS.

34
Table F.8: Productivity and Technology Sophistication, GBF and SSBF EXT, Disaggregated Agriculture Sector

||(1)|(2)|(3)|
|---|---|---|---|
|VARIABLES|ln(VAPW)|ln(VAPW)|ln(VAPW)|
|GBF EXT|1.72***|||
||(0.32)|||
|GBF EXT<sup>2</sup>|-0.20***<br>(0.05)|||
|GBF MOST||1.93***<br>(0.45)||
|GBF MOST<sup>2</sup>||-0.28***<br>(0.09)||
|GBF GAP||0.38***||
|||(0.10)||
|GBF EXT*AGRI|||0.46**<br>(0.23)|
|GBF EXT*MANF|||0.61***<br>(0.08)|
|GBF EXT*SVC|||0.49***<br>(0.09)|
|SSBF EXT*Agriculture|0.34*|0.44**|0.46*|
||(0.21)|(0.20)|(0.26)|
|SSBF EXT*Livestock|0.30|0.37|0.36|
||(0.48)|(0.47)|(0.47)|
|SSBF EXT*Food Processing|0.30*|0.32*|0.28|
||(0.18)|(0.18)|(0.18)|
|SSBF EXT*Apparel|0.44***|0.40***|0.36***|
||(0.11)|(0.10)|(0.11)|
|SSBF EXT*Retail and Wholesale|-0.02|-0.07|-0.05|
||(0.11)|(0.11)|(0.12)|
|SSBF EXT*Other Manufacturing|0.07|0.05|0.02|
||(0.05)|(0.05)|(0.05)|
|SSBF EXT*Other Services|0.55**|0.58**|0.61***|
||(0.23)|(0.23)|(0.23)|
|Var(ABF EXT)|0.29***|0.36***|0.39***|
||(0.10)|(0.10)|(0.10)|
|Firm Characteristics|_~~√~~_|_~~√~~_|_~~√~~_|
|Observations|2,746|2,746|2,746|
|R-squared|0.50|0.50|0.50|

Note: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Robust standard errors in parentheses. The regressions controlled for observable firm characteristics including size (i.e., small, medium, and large), sector (i.e., agriculture crops, livestock, food processing, wearing apparel, pharmaceutical, moto-vehicle, wholesale and retail, finance, land transport, hospital, other manufacturing, and other services), age (i.e., 0-5, 6-10, 11-15, and 16 or more), export, and multinational status.

101
POLICY RESEARCH WORKING PAPER 2178

# Summary findings

Adams uses household-level data from a nationally representative survey to analyze the impact of nonfarm income on income inequality in rural Egypt. After pinpointing the importance of nonfarm income to the rural poor, Adams decomposes total rural income among five sources: nonfarm, agricultural, livestock, rental, and transfer.

He shows that while nonfarm income represents the most important inequality-reducing source of income, agricultural income represents the most important inequality-increasing source.

A 1 percent marginal increase in nonfarm income will cause the Gini coefficient of overall income to fall by 12.8 percent. But a 1 percent marginal increase in agricultural income will cause the Gini coefficient to rise by 15.8 percent.

The reason for this difference has to do with land, which is distributed very unevenly in this study.

Regression analysis of the determinants of income shows that land ownership is positively and statistically related to the receipt of agricultural income but has no statistical relationship to the receipt of nonfarm income. This leads Adams to three conclusions:

* If policymakers are interested in reducing poverty and improving income distribution in rural Egypt, they should focus on nonfarm income - which not only accounts for almost 60 percent of total income for the rural poor but also favorably affects income distribution.

* Nonfarm income is an inequality-reducing source of income in a land-scarce setting such as rural Egypt because inadequate land "pushes" poorer households out of agriculture and into the nonfarm sector.

Agricultural income contributes most to rural income inequality because it is highly correlated with land ownership and with total rural income.

- - This paper a product of the Human Development Sector Group, Middle East and North Africa Region is part of a larger effort in the region to identify the sources of income for the rural poor. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Moira Coleridge-Taylor, room MC3-797, telephone 202-473-3704, fax 202-522-3283, Internetaddressmcoleridgetaylor@worldbank.org. Policy ResearchWorking Papers are also posted on the Web at http://www.worldbank.org/html/dec/Publications/Workpapers/home.html. The author may be contacted at radams@worldbank.org. September 1999. (37 pages)

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Research Dissemination Center
21 <u>Table 4: Survey ‘families’ used in health equity side of HEFPI database</u>

|Survey<br>|Title in full<br>|<br>Core topics<br>|<br>No. of countries<br>|<br>History<br>|<br>Data collection<br>method<br>|Sample size<br>|Further<br>information on<br>data and access<br>|
|---|---|---|---|---|---|---|---|
|DHS|Demographic<br>and Health<br>Survey|Population, health, and<br>nutrition, with a focus on<br>reproductive, maternal and<br>child health|91 low and middle-income<br>countries (LMIC), 74 in<br>HEFPI (data sets with<br>restricted access and those<br>collected before 1990<br>excluded, several collected<br>from 2016 to be added)|Ongoing, dating<br>back to 1984.<br>First HEFPI<br>data from 1990<br>(Phase 2)|Face-to-face<br>interviews,<br>physical<br>measurements,<br>biochemical<br>measurements|Typically<br>4,000-<br>15,000<br>households|https://dhsprogra<br>m.com/|
|ECHP|European<br>Community<br>Household Panel|Multipurpose panel survey with<br>adult health module|15 European high-income<br>countries, 14 in HEFPI<br>(Germany micro-data not<br>available)|1994-2001|Face-to-face<br>interviews|Typically<br>4,000-<br>12,000<br>adults|http://ec.europa.e<br>u/eurostat/web/m<br>icrodata/europea<br>n-community-<br>household-panel|
|EHIS|European Health<br>Interview Survey|Adult self-perceived health,<br>chronic conditions, disease<br>specific morbidity, physical and<br>sensory functional limitations,<br>hospitalization, consultations,<br>unmet needs, use of medicines,<br>preventive actions, height and<br>weight, health behaviors|31 middle and high-income<br>countries (European Union,<br>Iceland, Norway, and<br>Turkey), 20 in HEFPI (data<br>sets with restricted access<br>excluded)|Ongoing, dating<br>back to 2006|Face-to-face<br>interviews|Typically<br>1,000-<br>10,000<br>adults|http://ec.europa.e<br>u/eurostat/web/m<br>icrodata/europea<br>n-health-<br>interview-survey|
|Eurobaro<br>meter|Eurobarometer|Multipurpose survey with<br>changing focus, adult health<br>module in 2003 and 2006|28 European high and<br>middle-income countries, all<br>in HEFPI|Ongoing, since<br>1974, health<br>modules in 2003<br>and 2006|Face-to-face<br>interviews|Typically<br>1,000<br>adults|http://ec.europa.e<br>u/commfrontoffic<br>e/publicopinion/in<br>dex.cfm/General/i<br>ndex|
|ISSP|International<br>Social Survey<br>Program|Multipurpose survey with<br>changing focus, adult health<br>module in 2011|32 middle and high-income<br>countries in 2011 wave, all in<br>HEFPI|Ongoing since<br>1985, health<br>module in 2011|Face-to-face<br>interviews,<br>telephone<br>interviews,<br>postal and web<br>surveys|Typically<br>1,000-2,500<br>adults|https://www.gesis<br>.org/issp/modules/<br>issp-modules-by-<br>topic/health-and-<br>health-care/|
60 tion, because analysis of labor productivity relies on analyses of only wage earners, the private rate of return to primary education is 5 percent per year for women, 34 percent to attend secondary school, and 2 percent to university education. For men the OLS primary school returns are 16 percent, secondary school 24 percent, and university education 5 percent. The statistical correction for **the** two **sample selection** processes that **might** render these estimates biased modifies the **estimates** markedly in four out of the **six** cases. Three out of four selection terms **are** statistically highly significant (Appendix Tables A-7 and A-8). The returns to primary education for women doubles to 10 percent, whereas **the** returns to secondary school declinte for both women (to 25 percent) and men (to 13 percent). Higher education private returns **increase** to 11 **percent** for women and decline for men to 4 percent. Although much more work is **needed** to assess **the** effect of alternative methods to deal with **sample** selection bia as it impacts on the returns to education of women compared to men, these data from Thailand suggests the levels of returns to schooling may be sensitive to this source of bias. In this instance, returns to women's schooling, increaso compared to the returns to men's schooling.

In concluding this section on measuring labor productivity and **the re-** turns to investing in the economic capabilities of **either women** or **men,** I should stress the need first to understand what underlies **the** individual's allocation of tim. This is required to correct analyses of **wage earners** from which one seeks to infer **the** effect of **investmnts** in schooling on the productivity of all workers. A potential bias is introduced by the process. This sample selection correction procedure depends on **the** relevant family unit and how family behavior is **modeled. The** bargaining model implies **a few,** pos-
Table 8: Estimation results: alternative specifications of cross-sectional dependence

||||All countries|||
|---|---|---|---|---|---|
|||||Spatial only||
||None|Factor only|Trade|FDI|Distance|
|_gt−_1|0.323|0.256|0.273|0.263|0.272|
||(15.106)|(15.506)|(16.806)|(16.061)|(16.671)|
|GDP growth|0.577<br>|0.343<br>|0.302<br>|0.261<br>|0.394<br>|
||(4.044)|(3.232)|(2.785)|(2.292)|(3.556)|
|∆Real interest rate|-0.917|-0.680|-0.752|-0.776|-0.786|
||(-11.104)|(-11.193)|(-11.997)|(-11.806)|(-12.274)|
|Real credit growth|0.173<br>|0.162<br>|0.143<br>|0.120<br>|0.117<br>|
||(4.009)|(4.443)|(4.357)|(3.469)|(3.487)|
|_Wgt_|||0.799|0.660|0.626|
||||(71.970)|(37.633)|(42.764)|
|_Wgt−_1|||-0.231|-0.125|-0.160|
||||(-9.765)|(-5.411)|(-7.561)|
|Pesaran CD statistic|126.681|4.867|11.745|27.812|26.02|
|(p-value)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Exponent of CSD|0.982|0.617|0.841|0.907|0.876|
|_R_<sup>2</sup>|0.144|0.576|0.512|0.462|0.495|
|Factor|No|Yes|No|No|No|
|Spatial|No|No|Yes|Yes|Yes|
|||Adv|anced countri|es||
|_gt−_1|0.344|0.283|0.305|0.305|0.292|
||(10.788)|(13.498)|(14.480)|(14.488)|(13.705)|
|GDP growth|1.876<br>|0.990<br>|1.027<br>|1.010<br>|1.074<br>|
||(6.725)|(5.305)|(5.578)|(5.490)|(5.701)|
|∆Real interest rate|-0.376|-0.368|-0.441|-0.353|-0.382|
||(-1.109)|(-1.506)|(-1.969)|(-1.577)|(-1.671)|
|Real credit growth|0.083<br>|-0.025<br>|0.003<br>|-0.008<br>|0.017<br>|
||(1.073)|(-0.392)|(0.054)|(-0.156)|(0.328)|
|_Wgt_|||0.832|0.784|0.730|
||||(80.199)|(54.610)|(48.889)|
|_Wgt−_1|||-0.243|-0.242|-0.187|
||||(-8.509)|(-8.976)|(-6.899)|
|Pesaran CD statistic|101.718|9.313|4.393|6.727|13.007|
|(p-value)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Exponent of CSD|1.002|0.799|0.924|0.846|0.897|
|_R_<sup>2</sup>|0.156|0.673|0.643|0.646|0.631|
|Factor|No|Yes|No|No|No|
|Spatial|No|No|Yes|Yes|Yes|

Notes: Bias-corrected QML estimates. The dependent variable is the real stock return computed as the first difference of the log real equity price. GDP growth is the first difference of the log of real GDP, ∆Real interest rate is the first difference of the real interest rate, and Real credit growth is the first difference of the log of the real credit stock. T-statistics in brackets. The sample period covers 1995:1-2016:3.

The second column of Table 8 adds a common factor but omits spatial effects. The 25
|**_Theme_ / Layer**|**Description**|**approximate grid**|**Source**|
|---|---|---|---|
|||**size**||
|_Population count and_||||
|_density_||||
|lpop12|Landscan (2012)|1km2|Bright et al.<br>(2013)|
|urban<br>~~l~~pop12|Urban population|1km2|Bright et al.<br>(2013); WDI<br>(2017); Authors’<br>calculations|
|ghspop|GHS-Pop (1975, 1990,<br>2000, 2014)|1km2|Freire et al.<br>(2016)|
|ghsbuiltup|GHS-Built up (1975, 1990,<br>2000, 2014)|300m|Freire et al.<br>(2016)|
|_Travel time and ac-_<br>_cessibility_||||
|dist2railway|Distance to railway|N/A|NSO and Global<br>Insights v6.1|
|tt50k2000|Mean Travel time to major<br>cities (circa 2000)|1km2|Uchida and Nel-<br>son (2008)|
|tt50k2010|Mean Travel time to major<br>cities (circa 2010)|1km2|Berg et al. (in<br>prep)|
|ai50k2000|Agglomeration Index (circa<br>2000)|1km2|Uchida and Nel-<br>son (2008)|
|ai50k2010|Agglomeration Index (circa|1km2|Roberts et al.|
||2010)||(2017)|

Table 3: Data source summary 20
# **5. Conclusion**

Evidence from the five SME finance studies suggests that competition, especially through innovators, can have a large effect on downscaling and frontier lending of banks. While competition and innovation cannot be introduced by directive, encouraging innovation by facilitating bank outreach through agency banking for instance is within the regulatory realm, as is a supportive regulatory environment for microfinance institutions to increase competition from the lower end of the market. Competition is required to move commercial banks out of their comfort zones in countries like Nigeria where high interest rates on Government securities provide a disincentive to engage in lending to SMEs. In countries with lower yields on Government securities, incentives are much higher for banks to expand lending to SMEs and, if knowledge is transferred in such circumstances, as was in the case of Rwanda, SME lending expands. While the interrelationship between yields on Government securities and SME lending cannot be determined conclusively with the data collected for this paper, additional research in this area could usefully shed light on this dependency.

Although there may be a role for Government to encourage lending to SMEs in markets where that development has not yet taken place, providing a conducive lending environment seems to be the most important aspect. Ensuring that an effective credit bureau, enabling access to comprehensive (positive as well as negative) and reliable information about borrowers, is in operation, and that the securitization and realization of (movable) collateral is efficient are fundamental challenges in a number of countries. Building on the experience in Kenya with hirepurchase and invoice-discounting, Governments could further support competition and innovation by introducing the legal/institutional framework for leasing and factoring, both of which offer considerable potential for SME financing.

As regards Government support programs the authorities need to ensure that they are adding value rather than distorting the market. A PCG or credit line, especially if combined with technical assistance for financial institutions to develop new SME lending technologies, can be useful, but international experience with the design of these schemes warns of common pitfalls and the need to test the design of such schemes with the private sector before launching them and checking in with market participants as the use of the schemes evolves.

17
linearly, while for labor productivity growth a quadratic specification fits the data better. Intuitively, on the one hand, we expect labor productivity to grow at the beginning, until a certain point, beyond which higher levels of pollution will start leading to declining values of labor productivity. On the other hand, the positive linear relationship between pollution and employment growth may indicate that firms will always try to hire more employees, even while pollution levels are rising. Since we find that productivity decreases while pollution increases (after a certain point) this positive relationship between pollution and employment growth is not surprising. Firms need to hire more employees to achieve similar production levels. When focusing on African firms, however, we find that a cubic specification of pollution fits better, for both dependent variables.

When looking at the effect of pollution on firm performance around the world, we find that results differ for employment growth versus labor productivity growth (Table 8). Because PM 2.5 and employment growth are linearly correlated, we only see constant effects. As expected, the effect is negative (-0.0002). An increase of PM 2.5 in 10 units, is associated with a reduction in employment growth of 0.2%. This effect appears to be constant over time, which implies considerably big effects for firms in cities experiencing high levels of pollution. For example, in China and India, firms in cities like Chengdu or Bihar are exposed to pollution levels above 90 μ . For these firms, employment growth could be approximately 1.4 percentage points compared to what it could be if pollution levels were below the WHO guideline (10 μ ). /mm<sup>3</sup> /mm<sup>3</sup>

**Table 8. Pollution Effect on Firm Performance (Global)**

||**L-grow**<br>|**th**|**LP-gro**<br>|**wth**|
|---|---|---|---|---|
||**coef**|**se**|**coef**|**se**|
|Security Cost|-0.165*|0.073|-0.027|0.141|
|Corruption obstacle|-0.028**|0.008|0.019|0.017|
|Overdraft facility|0.013*|0.007|0.033<sup>×</sup>|0.017|
|Trade credit|-0.010|0.009|0.049**|0.018|
|Skilled labor obstacle|0.001|0.007|-0.005|0.016|
|Labor regulation obstacle|0.013|0.008|-0.003|0.018|
|Tax rate obstacle|-0.004|0.008|0.008|0.018|
|Land access obstacle|-0.010|0.008|0.005|0.021|
|Outage|0.007|0.007|-0.019|0.015|
|Web Intensity|0.019**|0.007|0.034*|0.014|
|In Big City|0.004|0.003|-0.003|0.006|

18