rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

Much of the extensive literature on the ‘resource curse’ phenomenon suggests a negative correlation between national resource endowments and economic growth (Sachs and Warner 1995, 2001), although some studies have attributed this relationship to endogeneity (Brunnschweiler 2007; Brunnschweiler and Bulte 2008), or have obtained different results by expanding Sachs and Warner’s regression. The literature subsequent to Sachs and Warner has concentrated on identifying the mechanisms through which natural resource wealth impacts on growth. By this line of reasoning, natural resource abundance leads to some phenomenon that in turn affects growth (Alexeev and Conrad 2009). The channels through which the so-called curse is transmitted include: real exchange rate appreciation, which leads to a contraction of the nonresource export sector or ‘Dutch disease’ (Corden and Neary 1982); rent-seeking and corruption, which drag down growth (Ades and Di Tella 1999; Mehlum et al. 2006a, 2006b); large swings in commodity prices that result in macroeconomic instability (van der Ploeg and Poelhekke 2010); the interaction of resource rents and government consumption (Atkinson and Hamilton 2003); volatility of discretionary government expenditure (Bleaney and Halland 2014); armed conflict to control resources (Collier and Hoeffler 2004); volatility of commodity prices (Blattman et al. 2007); and anticipation of better times (van der Ploeg 2011).<sup>1</sup> Given the availability of disaggregated fiscal and monetary data, Ghana provides a unique opportunity to consider the resource curse hypothesis from a case study perspective. The case of Ghana provides limited support for some versions of this hypothesis, but ultimately tells a simpler story of insufficient fiscal and monetary discipline resulting from challenges to policymaking. Crucially—and contrary to, for example, Botswana—Ghana was not able to manage expectations sufficiently after oil was discovered. Its experience is in that sense consistent with van der Ploeg’s (2011) ‘anticipation of better times’ hypothesis. It is also consistent with Atkinson and Hamilton (2003), who conclude that the combination of natural resource rents and high government consumption provides an explanation for the curse, and with Collier and Hoeffler (2009), who argue that the combination of rents from the extractive industries and open democratic systems is associated with slower growth unless there are enough checks and balances. Ghana’s large increase in sovereign debt after the discovery of oil reflects the concerns of Mansoorian (1991), who

> 1 Excellent surveys of the resource curse literature include van der Ploeg (2011) and Frankel (2010).

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Curtis V and Cairncross S (2003) Effect of washing hands with soap on diarrhea risk in the community: a systematic review. Lancet Infect Dis 3(5): 275–81.

Do, Q. T., & Chase, C. (2012). Handwashing behavior change at scale: evidence from a randomized evaluation in Vietnam.

DFID (2011) “Evidence Paper. Water, Sanitation and Hygiene.” DFID Literature Review. September 9, 2011.

DFID (2013) “Evidence Paper. Water, Sanitation and Hygiene.” May, 2013.

Dzwairo, B., Hoko, Z., Love, D., & Guzha, E. (2006). Assessment of the impacts of pit latrines on groundwater quality in rural areas: A case study from Marondera district, Zimbabwe. _Physics and Chemistry of the Earth, Parts A/B/C_ , _31_ (15), 779-788.

Ejemot-Nwadiaro RI, Ehiri JE, Meremikwu MM, Critchley JA. (2008) Hand washing for preventing diarrhoea. Cochrane Database of Systematic Reviews 2008, Issue 1. Art. No.: CD004265. DOI: 10.1002/14651858.CD004265.pub2.

Fewtrell, L., R. B. Kaufmann, D. Kay, W. Enanoria, L. Haller, and J.M.C. and Colford (2005) “Water, sanitation, and hygiene interventions to reduce diarrhea in less developed countries: a systematic review and meta analysis.” Lancet Infectious Diseases; Vol. 5. No. 1: 42-52.

Filmer, D., & Pritchett, L. H. (2001). Estimating wealth effects without expenditure Data—Or tears: An application to educational enrollments in states of india*. Demography, 38(1), 115-132.

Galiani, S., Gertler, P., & Orsola-Vidal, A. (2012). _Promoting handwashing behavior in Peru: The effect of large-scale mass-media and community level interventions_ (No. 6257). The World Bank.

Greene, L. E., Freeman, M. C., Akoko, D., Saboori, S., Moe, C., & Rheingans, R. (2012). Impact of a school-based hygiene promotion and sanitation intervention on pupil hand contamination in Western Kenya: a cluster randomized trial. _The American journal of tropical medicine and hygiene_ , _87_ (3), 385.

Hammer, J., & Spears, D. (2013). Village sanitation and children's human capital: evidence from a randomized experiment by the Maharashtra government.

Humphrey, J. H. (2009). Child undernutrition, tropical enteropathy, toilets, and handwashing. _The Lancet_ , _374_ (9694), 1032-1035.

International Initiative for Impact Evaluation (2009). “Running Water, working toilets and safe hygiene practices: Essentials services to save lives.” 3ie Enduring Questions Brief No. 10. August, 2009.

Independent Evaluation Group (2008). “What Works in Water Supply and Sanitation? Lessons from Impact Evaluations.” IEG Word Bank Fast Track Brief. July 3, 2008 Independent Evaluation Group (2008). “What Works in Water Supply and Sanitation: Lessons from Impact Evaluation.” World Bank, Washington D.C.

Kremer M. and A. P. Zwane (2007). “What Works in Fighting Diarrheal Diseases in Developing Countries? A Critical Review.” World Bank Research Observer; 22(1): 1 - 24.

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# 4. How Can the Distribution of Public Spending be Improved?

A good benefit-incidence study can directly inform proposals for spending policy reforms aimed at improving the distribution of benefits. The preceding discussion has emphasized our still limited knowledge and continuing uncertainty about how to measure benefits and hence, judge the distributional impact of public spending. A number of areas where further investigation may pay off in this area were identified. Still, specific country case studies (including those specifically reviewed here) do contain some clear and consonant implications for improving the distribution of public expenditures. First, governments should invest and reallocate budgets towards basic services. The provision of such services often fails to attract private sector interest and thus accords with the principle that governments should be responsible for valuable goods which would otherwise be underprovided. But, above all, services such as primary education and basic health care are found to be among the best ways to reach the poor. Of course, care should always be taken that marginal spending increases are not being squandered on better quality for existing services consumed by the better off. Among other categories of public spending-including food subsidy schemes, social security and cash transfers-there have been both successes and failures. Many programs which claim to reduce poverty don't.

The most commonly heard proposal for achieving a more propoor benefit distribution is "improved targeting." That will be the main focus of this section. As I will show, a number of the studies in PSP throw light on this policy issue, both at the level of methodology and in substantive policy conclusions. Reform proposals also often raise new issues, such as administrative feasibility and political economy considerations. In many developing economies, it is the middle classes who are currently the primary beneficiaries of public social spending. Many of the poor are left out, while the rich have alternatives-namely the private sector at home or abroad. Redirecting or 'targeting' spending towards the poor will hurt the middle classes who, in the worst case scenario, may no 28
# **Table 9. Blinder-Oaxaca Decomposition of Per Adult Equivalent Income, Top Performers versus Constrained Gazelles**

|Per adult equivalent consumption, Log|Coefficients|Std. Err.|P>z|
|---|---|---|---|
|Differential||||
|Mean prediction (top-performers)|13.18|0.044|0.000|
|Mean prediction (gazelles)|13.05|0.019|0.000|
|Difference|0.12|0.048|0.009|
|Explained||||
|Value added per hour worked, Log|0.065|0 .017|0.000|
|Other factors|0.026|0 .018|0.154|
|Total|0.091|0.024|0.000|
|Unexplained|0.033|0 .046|0.468|
|Observations= 1,427||||

_Source:_ Calculations using 1-2-3 Survey data, 2012.

# **Table 10. Blinder-Oaxaca Decomposition of Per Adult Equivalent Income, Top Performers versus Survivalists**

|Per adult equivalent consumption, Log|Coefficients|Std. Err.|P>z|
|---|---|---|---|
|Differential||||
|Mean prediction (top-performers)|13.18|0.044|0.000|
|Mean prediction (survivalists)|12.89|0.014|0.000|
|Difference|0.29|0.046|0.000|
|Explained||||
|Value added per hour worked, Log|0.039|0.016|0.018|
|Other factors|0.117|0.026|0.000|
|Total|0.157|0.029|0.000|
|Unexplained|0.133|0.049|0.007|
|Observations= 1,889||||

_Source:_ Calculations using 1-2-3 Survey data, 2012.

Overall, the performance of firms is an important factor in explaining income differences between top performers and constrained gazelles while it explains less than 15 percent of income differences between top performers and survivalists. The results of the Blinder-Oaxaca decomposition are in line with the previous findings that well performing informal firms (the top performers) are less poor and exhibit less income inequality relative to constrained gazelles and survivalists.

In the next step, the drivers of the performance of firms are analyzed. The following regression explains the value added per hour worked by a number of characteristics of the entrepreneurs such as human capital including educational attainment, financial knowledge, a proxy for social networks and proxies for management skills. The equation<sup>9</sup> is as follows: `′ ′ ′ ′ ′` lnvahi = α0 + α1lnKi + α2nocapitali + Xi α3 + Hi α4 + Mi α5 + Si α6 + Ri α7 + εi (7)

> 9 The regressions on the drivers of firm performance rely on the Cobb-Douglas production function.

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- 21 -

# 1) Manual Cbarging via Tolates

22. The first mechanism to conider is the use of manual tollgates. This is an established and well-known technology, administatively st tforward, publicly accepted for toll roads, bridges and tunnels, as well s politically palatable. However, manual charging results in low throughput, with about 350-400 vehicles per hour for a toll lane (Harte, 1991; Ardekani, 1991). In contrast to supplementary licensing, in which systems costs are relatively low and uer costs are also low because of its nonstop naure, the setting up of tollgates requires significant capital investments in toll plazas and toll collection facilities. In orde to cater to the typical need for two to three approaching toll lanes to consvatively serve every expressway lane (as in the United States (Seila and Wilson, 1991)), toll plazas require large plots of open space, not often found in urban areas.'<sup>0</sup> The construction cost of toll lanes (and tollbooths) and land acquisition costs are all fairly high in major conurbations. The opating cost is nontrivial because it is heavily driven by toll opeators' salaries. Based on a sample of six mainlinetramp barrier systems, the toll collecdon cost per tanaction ranges from _US3.6_ cents to _US7.5_ cents in 1983 (Wustefeld, 1988, Table 5)." 23. The tollgate method of direct charging is akin to supplementary licensing if one is conpidering a simple zone, and is therefore able to tackle the problem of through traffic not captured by the instrument of parldng charges, for instance. However, manual charging imposes more extrnalities than it attempts to internalize by requiring that aU vehicles come to a full stop to elicit payment, creatng long queues and waiting times inevitably. The stop-and-go traffic results in increased travel time and prafting costs (in the form of fuel and vehicle wear and tear). Besides being particularly susceptibl to these costs, heavy vehicles also create severe road damages as a result of stopping and starting. In general, less attention has been paid to the

**10** Sml wonder the Smeod Repot Minisy of TMupor 1964) dimiso dos apohi to wrtail oiguot in a mute couple of **_amtezmu_**

Th lb... gures aoe boad an toll f_ihti whch hae la1 for bod mnual opatm and automaic coin _mnn._
these concerns, we add indicators of information acquisition and financial literacy in the main model. Our main results remain robust, indicating that neither information acquisition nor financial literacy drives the link between credit constraints and fraud victimization.

Further analyses on potential mechanisms suggest that the personal discount rate (impatience) and the need to expand the social network are important pathways through which credit constraints affect fraud victimization. Borrowing constraints can shape people’s preferences on current versus future consumption (Harrison et al., 2002; Ventura, 2003; Nakata and Sawada, 2015). Credit constraints may lead to a higher discount rate over the future, and thus make people more prone to believe well-disguised fraud schemes that promise an egregious return within a short period. In addition, to obtain social collateral, households with severe credit constraints would engage in activities to expand their social networks; more social interaction itself, along with the associated behavior changes such as emphasis on cooperative behavior, could make them more susceptible to become victims.

We contribute to the literature in several ways. First, we propose a new perspective to understand fraud victimization. To the best of our knowledge, while there is an economic literature on credit constraints and crime (Garmaise and Maskowitz, 2006; Cortés et al., 2016; Avenancio-León, 2019); Palmer et al., 2019), this is the first paper using nationally representative data on fraud to examine the impact of credit constraints on fraud victimization. Thanks to the richness of our data, we look at both people being approached for fraud and victimization. Second, we explore the mechanisms through which credit constraints affect fraud victimization. Third, we assess the effects of alternative policies to combat fraud (such as improving information acquisition and financial literacy) and find that improving financial access is more effective in reducing fraud victimization. Finally, we contribute to the literature on the impact of credit constraints. It is well established in the literature that credit constraints can shape household behaviors such as consumption (Garcia et al., 1997; Jappelli et al., 1998), entrepreneurship (Hurst and Lusardi, 2004), precautionary saving (Lee and Sawada, 2010), and labor supply (Rossi and Trucchi, 2016). We find that credit constraints have an additional consequence: fraud victimization.

The rest of this paper is organized as follows. Section 2 introduces the institutional background, reviews the previous literature, and presents the conceptual framework. Section 3 describes the data. Section 4 describes the methods. Section 5 presents the 5
**Table 3: Swift Plus model estimates in urban and rural samples.**

|**PANEL A. URBAN MODEL**<br>**Variables**|**Coeff.**|**IELFS**<br>**Q3**<br>**Mean**|**AWMS**<br>**R3**<br>**Mean**|**PANEL A. RURAL MODEL**<br>**Variables**|**Coeff.**|**IELFS**<br>**Q3**<br>**Mean**|**AWMS**<br>**R3**<br>**Mean**|
|---|---|---|---|---|---|---|---|
|**Household Characteristics**||||**Household Characteristics**<br>||||
|Log Household Size<br>|-0.500|0.83|0.88|Log Household Size<br>Ratio of household|-0.410|0.86|0.93|
|Ratio of household<br>members (<15 & >65)||||<br>members (<15 & >65) over<br>ki l|-0.022|2.7|2.5|
|over working age males|-0.020|2.3|2.1|worng age maes<br>Region 2|0.141|14.3|13.7|
|Region 1|-0.103|63.8|55.9|Region 5|0.192|11.3|7.1|
|**Household Head Education**||||Region 6|-0.173|15.6|17.8|
|Lower Secondary|0.127|25.5|23.4|Region 7|-0.136|9.1|6.2|
|Upper Secondary or||||Region 8|-0.210|8.5|11.4|
|Above|0.158|16.8|16.9|**Household Head Education**||||
|**Asset Ownership**||||||||
|Car|0.224|12.6|8.3|Primary|-0.079|7.9|12.2|
|Refrigerator|0.066|44.4|39.9|**Household Head age**||||
|TV|0.062|71.9|68.7|Age 46-55<br>**Lb Mkt Ptiiti**|0.040|16.2|19.5|
|Vacuum Cleaner|0.109|25|19|**aor are arcpaon**<br>**(household head)**||||
|Washing Machine|0.158|40.1|36.8|Multiple Jobs|-0.105|23.6|8.7|
|Small Livestock|0.127|6.5|29|**Asset Ownership**||||
|**Consumption in the last 7 da**|**ys**|||Bicycle|0.117|8.8|19.3|
|Apples|0.156|10.3|13.9|Car<br>l|0.329<br>|7.9<br>|6.9<br>|
|||||Motorcyce|0.058|26.4|25.8|
|Chocolate|0.172|64.4|74.5|TV|0.088|25.6|32.1|
|Meat|0.194|57.3|60.1|**Consumption in the last 7 day**|**s**|||
|Milk|0.117|25.7|34.6|Apples|0.131|4.6|12|
|Fuel for Car|0.075|14.2|20.2|Eggs|0.112|32|60|
|**Dwelling Characteristics**||||Beans/Pulses|0.122|72.4|77.3|
|Wall material is Concrete|0.100|53.7|33.5|Chocolate|0.129|51.7|73.3|
|**Performance metrics**||||Meat|0.228|48.1|61.5|
|<br>||||Milk|0.095|42.6|49.8|
|R-squared<br>||0.669<br>||Fuel for Car|0.104|30.1|30.6|
|Constant||8.575||**Dwelling Characteristics**||||
|Observations||988||Wall material is Concrete|0.115|10|12.7|
|||||No Toilet<br>**Performance metrics**|-0.055|16|3.9|
|||||R-squared||0.582||
|||||Constant||8.091||
|||||Observations||2,986||

**Source** : World Bank estimations using 2019/20 IE-LFS and AWMS R3 surveys.

9
are therefore often unable to switch jobs and capitalize on available employment opportunities (see for instance Neri and Thomas (2000) for Brazil). A third argument is that indirect sources of income and public transfers may cecline during crises, because during such episodes the ability of relatives (or communities) to engage in income redistribution may be reduced and governments may be forced to adjust drastically their fiscal accounts with acrossthe-board cuts in expenditure.

More recently, it has also been argued that cyclical downturns and economic crises may have an _asymmetric effect_ on poverty. Recessions and/or sharp output contractions tend to increase poverty rates significantly (through some of the channels identified above), whereas expansions tend to have a more limited effect. Lustig (2000), for instance, argued that the fall in real income associated with economic downturns may have an irreversible impact on the human capital of the poor. Her argument is that children in poor families (particularly the very poor ones) are sometimes taken out of school and put to work in response to large adverse shocks-thereby mitigating the fall in the household's income-but do not return to school when the "good times" roll again. To the extent that adverse shocks to household income affect adversely investment in schooling (in addition to nutrition and health) and the longer-run ability of the poor to enhance their stock of human capital, they will hinder their ability to escape from poverty. Thus, large recessions create some sort of "asymmetric hysteresis" effect on poverty, in the sense of temporary negative shocks having persistent effects.<sup>2</sup> However, the evidence supporting this view is mixed; for instance, Neri and Thomas (2000) found that in Brazil, children are not more likely to drop out of school in recessions than during expansions.

> 2 There have been few attempts to test rigorously whether transitory shocks to poverty (induced or not by changes in output) may affect the trend (or permanent component) of poverty, as the "hysteresis" argument would imply. Lokshin and Ravallion (2000) used dynamic panel data in a study of household incomes in Hungary t o assess the persistence of shocks. A time-series based approach could dwell on the unobserved components technique used by Jaeger and Parkinson (1990) to test for hysteresis in unemployment.

3
# 1.1 Related Literature

Several studies analyzed the response of fertility in Sub-Saharan Africa to the HIV/AIDS epidemic and reached mixed conclusions. Young (2005) studies the eect of the HIV/AIDS epidemic on the welfare of future African generations. He concludes that thanks to a negative eect on fertility, the epidemic, on net, will increase future per capita consumption.<sup>8</sup> He attributes the reduction in fertility to an unwillingness to engage in unprotected sexual activity and increasing labor opportunities for women because of scarcity of labor. He shows empirically a negative relationship between fertility and HIV prevalence using retrospective fertility histories and seroprevalence in antenatal clinics in South Africa. Using time series cross-country data on fertility and HIV prevalence rates, Young (2007) nds a similar eect. Kalemli-Ozcan (2006), using similar types of data, shows that regressing total fertility rate on HIV/AIDS prevalence can yield both positive and negative eects with dierent estimation strategies and dierent measures of HIV prevalence.

Later studies use the cross-country data from the latest rounds of the Demographic and Health Surveys (DHS). These surveys are nationally representative and contain results of HIV testing of respondents. Fortson (2009) and Juhn et al. (2008) nd lower fertility rates of HIV-infected women than of HIV-uninfected women; however, they nd no eect of local prevalence rates on the fertility of uninfected women and an overall insignicant aggregate eect of HIV on fertility. Fink and Linnemayr (2009), linking historical data from World Fertility Surveys (WFS) with the DHS data, argue that while HIV does not have a signicant eect on aggregate fertility levels, it aects dierently women depending on their educational attainment. They nd that, in the presence of HIV, more educated women reduce fertility more than uneducated women. Analyzing the Malawi DHS surveys from 2000 and 2004, together with HIV rates obtained from antenatal clinics, Durevall and Lindskog (2011) conclude that although the HIV/AIDS epidemic has small impact on the number of

> 8According to Young (2005), the positive eect of reduces fertility dominates a negative eect of reduced human capital accumulation by orphans.

6
In sum, although both grants and ODA debt forgiveness to African countries have increased since the late 1980s, the allocation of these funds is not well explained by basic economic hierarchies such as relative poverty levels or foreign exchange scarcity. The relative level of indebtedness of the recipient countries seems to partially drive the allocation of ODA debt relief, but the large and increasing arrears for these highly indebted countries suggests that the incoming funds are not sufficient to solve their debt overhang problem. I IFurthermore, it suggests that countries receiving relatively more debt relief may not necessarily end up having more foreign exchange to spend on other uses, such as financing a larger import bill. We discuss this subject in the next section.

**Chart 2. Total arrears as a share of total debt for the three groups of countries, 1984-93**

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This section assesses the effect ODA debt forgiveness has had on recipient countries. To do so, we first propose a simple indicator to measure the benefits that accrue to recipient countries when granted debt relief. Then, using this indicator we analyze, whether countries receiving more debt relief are better off than those receiving less. The main conclusion here is that countries receiving more bilateral ODA debt relief are neither better off nor worse off than those receiving less. Finally, we attempt to explain these findings by exploring additional hypothesis, such as the presence of compensatory flows. Appendix 2 provides the analytical framework used in this section. The following main conclusions can be drawn from this section:

- The increase in ODA debt relief since 1989 has been neutral, on average and on an incremental basis, in the sense that it has not affected the import capacity _of_ the major recipient countries.

- "On this issue see also World Bank. 1996. chapter 2.

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