rafmacalaba/data-use-mentions-extended huggingface dataset:
monetary policy and to guide monetary policy decisions. Credit bureau information can also be used by the tax authorities to improve fiscal control and to analyze the effect of different tax policies on credit flows.

# **_4. Competition_**

A further set of objectives relates to competition and performance in the credit market. The effects of information on market structure and market conduct may be somewhat complex, however, more information regarding credit risk is likely to make a banking system more competitive and indeed as more credit information is made more publicly available, competition between banks and non-banks should also be heightened.

Arguably the very raison d’être of banks is to establish profitable lending relationships with clients where information is costly to acquire. Given that relationships tie borrowers in to particular lending institutions, that institution may then acquire a type of local monopoly power. However, relationship lending may still be preferable to issuing a security (non relationship lending), as the return that would have to be offered to the market (given the assumed poor information) may still be more than the price that the local monopolist bank would offer having invested in the relationship. Rajan (1992) computes an equilibrium in which firms may borrow from both banks and in the open (bond) market such that issuing in the market controls the local monopoly power of the bank. In a model that allows entry into the banking sector, if information is very poor and local monopoly rents very high, then we may expect a highly fragmented banking system with many banks, low economies of scale and a high cost of credit.

Typically, theoretical models assume some type of asymmetric information that a bank may overcome by investing (a fixed cost) in a relationship. However, as discussed above, a PCR may force banks to share this information. This may reduce the rents available to the bank and may lead to multiple and quite subtle effects. One concern is that banks will lose the incentives to search for clients thus potentially even having a negative effect on
the occupation of the main wage earner. Headen and Lee (1974) show that the demand for life insurance depends on savings and interest rates. Burnett and Palmer (1984) show that education, income and religion are key determinants of the demand for life insurance. Babbel (1985) shows that prices have a negative effect on the demand for life insurance. Beenstock, Dickinson, and Khajuria (1986) provided one of the first empirical studies exploring cross-country data. Using a dataset of 10 developed countries, the authors conclude that income, life expectancy, and the 3
Policy Research Working Paper 10888

# **Abstract**

This paper uses microdata from the Labor Force and Household Surveys conducted in Afghanistan to show the wage premium differences for education between men and women, documenting a significantly larger premium for women. This sharp distinction is causal as demonstrated by analysis of the compulsory schooling law. Recent bans on women’s education and employment are projected to have significant negative impacts on women’s future schooling, wage growth, and national income growth.

This paper is a product of the Education Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at rbkattan@worldbank.org.

> _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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._

Produced by the Research Support Team
_Io Conclusions and Implications_ Like the notion of poverty itself, the concept of "vulnerability" to poverty is complex and multifaceted and will never be adequately summarized in a single measure. However, we think the definitions and measures proposed here provide a step forward in the dialogue on vulnerability to poverty as it allows application of household survey data to explore the notion of vulnerability quantitatively. The strong assumptions we make allow just one additional parameter, the standard deviation of inter-temporal changes in expenditures, to open up an entire line of analysis of vulnerability. This also means that one can estimate "coefficients of variability" by group with one data set and then apply these to subsequent cross sections to do vulnerability analysis, even without a panel.

We find that vulnerability is important quantitatively. In a sample in which the headcount poverty rate is set to be 20 percent of the population, an additional 10 to 30 percent of households are "vuilnerable" to poverty (that is, at even odds of at least one episode of poverty in 3 years), and hence 30 to 50 percent of the population is "vulnerable" to poverty. We also find significant differences across groups that would have been missed by the static measures of current consumption expenditures deficit (CCED) poverty.

Expanding the analysis of poverty to vulnerability has several policy implications. First, the issue of targeting becomes more problematic. Are programs intending to reach only the "persistently poor"? If so, they will fail to capture a large swath of the population who, while they may not be "always poor," experience episodes of poverty. Without the ability to observe _current_ incomes or expenditures 25
(formal health insurance holders) and household heads not covered by any health insurance.<sup>10</sup> Green Card holding household heads record, on average, less educational attainment and a higher likelihood of employment in the informal sector than other groups (note that the latter is one of the beneficiary selection criteria as discussed above). In Table 5B, the same variables are presented for the sample of households in the poorest asset quintile only. Here, the difference across insurance types by educational attainment is not as pronounced. The proportion of household heads employed in agriculture and industry are also roughly similar between Green Card holding households heads and those without any insurance. The only difference remains in the proportion of household heads formally employed with 4.3 percent of Green Card beneficiaries holding formal jobs and 21.1 percent of household heads without any insurance. This difference is a function of the eligibility criteria for the card and derives from the targeting process described above.

# **6.1 Non-parametric Estimation**

We use three different estimation techniques with the TWMS to assess the protective impact of the Green Card program during the economic crisis in Turkey. We first employ the below non-parametric comparison between the group of households with access to the Green Card and those that have no health insurance coverage: where Gi is the treatment variable for having access to the Green Card in period t1; Hi is the variable for having access to other health insurance at the household level (through formal sector health insurance _SGK_ or private insurance); and x is log income per capita in the household. The outcome variable is a binary variable that represents the reduction in health utilization between October 2008 and May 2009 and takes on two different variable definitions: (i) reduction in utilization of curative care and (ii) reduction in utilization of preventive care. We consider the responses of households to the following two questions in the baseline data: _(i) “Have you had to reduce visits to the doctor between October
fees. Having said that, providers could opt to use the vouchers to prescribe simple treatment instead of severe because this strategy leads to an almost guaranteed purchase. In this case, providers might be revenue driven and motivated to oversell treatment, but they may not be “driving demand” (prescribing more treatment than patients would like) per our definition.

Fortunately we can test whether providers are revenue motivated independent of whether this leads doctors to drive demand for severe malaria treatment: prediction (6) in Appendix A shows that vouchers will lead to an increase in clinic revenue when doctors are purely revenue motivated, and prediction (7) says that revenue-maximizing doctors will use the informational advantage in the _DV_ treatment to increase clinic revenues relative to _PV_ .<sup>32</sup> In practice, we find no significant differences in (per-patient) clinic revenues across treatment arms (Appendix Table B15). Moreover, the average patient in both _PV_ and _DV_ pays roughly CFA 500 _less_ than in the Control group (significant at the 5 percent level for _DV_ only). Lastly, from the results we already saw, ACT prescriptions and purchases for low-risk patients, voucher use, and overall treatment rates in DV do not increase at the same rate as in PV. This is not consistent with providers who are simply trying to sell as much treatment as possible. All told, our results do not paint a picture of providers primarily motivated by revenue, at least on the margin.

# **4.4 Quantifying Contributions to Overtreatment**

What do doctors’ gatekeeping failures mean for overuse of antimalarials? Ideally, we would use the home-based RDTs to directly assess the match between prescriptions/purchases and the patient’s underlying malaria status. Unfortunately, patients who consented to the home-based test were significantly sicker and more likely to have malaria, based on our predicted risk metric, than patients who refused the test (Appendix Table B6). Since _PV_ - _DV_ differences in malaria prescriptions and purchases are concentrated among those
and leads to an overestimation of the degree of fungibility. A marginal effect that is smaller than 1 does not necessarily indicate that aid is fungible; such a value could simply indicate that some aid is not recorded on a recipient government’s budget. This bias in the assessment of the degree of fungibility is larger if the variance of off-budget health aid is larger than the variance of on-budget health aid.<sup>4</sup> Other studies have estimated fungibility for a single country using a time series of recipient-based aid data (e.g., Pack and Pack, 1990, 1993; Franco-Rodriguez, Morrissey, and McGillivray, 1998; Feeny, 2007). In this case, because a recipient government’s reports of aid, by definition, exclude off-budget aid, only the effect of on-budget aid on government expenditures is estimated: Hence, the first problem is that we cannot estimate the degree of fungibility of off-budget health aid. Moreover, because off-budget health aid acts as an omitted variable and off- and on-budget health aid are most likely correlated, _β_<sup>�</sup> _ON_ is biased unless the marginal effect of off-budget health aid on health spending is zero. The sign of the bias is ambiguous because it depends on the partial correlation between on- and off-budget health aid, which could be positive or negative.

This section has clarified the criticism of McGillivray and Morrissey (2000, p. 422), who claim that because a large portion of the aid that is reported by donors is not reflected in the public sector accounts of recipients, such aid measures “. . . are inappropriate for analyzing fungibility”. In addition, this section has shown that the use of recipient-reported aid data is also problematic unless separate data exist that can measure off-budget aid such that equation (1) can be estimated rather than equation (4). Off-budget aid is likely to be sizable in many countries and to vary both between and within countries. Thus, the effects of
|(0.124)|(0.127)|(0.151)|(0.151)|
|Observations|31,768|31,768|153,155|153,155|

_Source:_ Calculations based on the Cambodian Socio-Economic Surveys 1996, 1999, 2004, 2007, 2008, 2009, 2010, and 2011, and the Sri Lankan 1992–2002, 2008, 2011, and 2012 Labor Force Surveys. _Note:_ Standard errors are in parentheses. Standard errors are clustered at the industry level. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two. and three asterisks respectively.

The grand mean effects of the industries are calculated; post-MFA is a dummy equal to 1 for years 2005 and later; additional controls include age squared, industry, and occupation dummies. Occupation dummies are according to 1-digit ISCO-08; the omitted category is managers. Industry dummies include 15 categories: Agriculture and Forestry; Mining; Food, Beverage, and Tobacco; Textile and Apparel; Wood; Other Manufacturing; Utilities; Construction; Sales; Transport; Financial, Insurance, and Real Estate; Public Administration; Social Services; and Other Services. Log wages are measured as logarithm of real monthly individual labor income in 2005 prices. The omitted time category in cols (1) and (3) is 2004 and earlier; the omitted time category in cols (2) and (4) is 1998 and earlier.
**Figure 4: Poverty projections Official poverty line**

<!-- Start of picture text --> COVID-<br>19<br><!-- End of picture text --> <!-- Start of picture text --> 25.3 25.3<br>24.9<br>23.7<br>21.9 22.0 21.6<br>24.1<br>21.9 22.0<br>18.4<br>20.5 20.6<br>18.7<br>FLOODS +<br>16.7<br>COVID- POST-PANDEMIC RECORD PROJECTED<br>19 RECOVERY INFLATION RECOVERY<br><!-- End of picture text --> <!-- Start of picture text --> Nowcasting<br><!-- End of picture text --> <!-- Start of picture text --> Forecasting<br><!-- End of picture text --> Source: Authors’ calculation from HIES (2018/19) data.

Note: Projections use preferred specifications following equation (1), including information from real growth from eleven economic sectors to model labor income. _Projections using Headline CPI_ (solid line) use headline CPI for the whole series. _Projections using HH inflation_ (dashed line) are based on household-specific inflation, as described in the methodological section, until FY23. However, due to the lack of disaggregated inflation projections post-FY23, later poverty projections are deflated using expected headline inflation.

The household-level poverty rate from FY23 to FY25 is forecasted using headline CPI instead of COICOP-disaggregated inflation. These indicate the possibility of an economic recovery in later years, which is projected to bring poverty back to its initial level, marking a period of five years without additional poverty gains.

Figure 4 also shows that using an aggregate measure of inflation (headline CPI) underestimates the
2021). Similarly, women in Japan faced lower demand due to their high participation in fixed-term jobs and concentration in the service sector (Kikuchi et al., 2021). In the United States, the situation was similar, with a more significant contraction in participation and employment levels for married women with children, partly due to restrictions on childcare services (Albanesi & Kim, 2021). The impact of children on labor participation and employment was also found to be significant by other authors in the United States, particularly for women with children between 0-6 years and 6-12 years, in low-income jobs, and among Latinas and Blacks (Lim et al., 2021; Pitts, 2021; Fairlie et al., 2021). Comparable effects on participation were also seen in the United Kingdom<sup>2</sup> (Cattan et al., 2020), Australia (Churchill, 2021), and Canada (Quian & Fuller, 2021).

Extensive evidence also exists for developing countries. Using High-Frequency Phone Surveys (HFPS) conducted in 13 Latin American and Caribbean (LAC) countries, Cucagna & Romero (2021) analyzed the gendered impact at the onset of the COVID-19 crisis. They found that female workers were 44% more likely than male workers to lose their jobs, with a persistence in the job loss gap as the crisis continued. Furthermore, they found that 56% of job losses were explained by highly female-intensive sectors such as trade, personal services, education, and hospitality, and

> 2 However, Witteveen et al. (2020) find that in the same country, men suffered more layoffs during the pandemic’s early part than women.
Policy Research Working Paper 9163

# **Abstract**

This study uses loan-level data on syndicated lending to a large sample of developing countries between 1993 and 2017 to estimate the mobilization effects of multilateral development banks (MDBs), that is, their ability to crowd-in capital from private creditors. Controlling for a large set of fixed effects, the paper shows evidence of positive and significant mobilization effects of multilateral lending on the size of bank inflows. The number of lenders and the average maturity of syndicated loans also increase.

These effects are present not only on impact but last for up to three years and are not offset by a decline in bond financing. There is no evidence of anticipation effects, and the results are robust to numerous tests controlling for the role of confounding factors and unobserved heterogeneity. Finally, the results are economically sizable, indicating that MDBs can mobilize about seven dollars in bank credit over a three-year period for each dollar invested.

This paper is a product of the Knowledge and Strategy Team, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at broccolini.chiara@gmail.com, glotti@iadb.org, alessandrom@iadb.org, apresbitero@imf.org, and rstucchi@iadb.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its
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In all countries the formal systems have been relatively successful as well, and, as will be discussed below, became increasingly differentiated in function and governance from the nonformal system.

In the middle and later stages of systems development all three countries invested in larger numbers of institutions, more modes, and the development of policy and management capacity.

In sum, the record suggests that by beginning with relatively small investments in formal institutions, and gradually increasing the complexity of development in terms of the variety of modes. As policy, planning and management capacity developed, these countries were able to build incrementally from experience.

(d) Responsive Planning: In general, these systems were planned in response to -- not in anticipation of -- employment demand. In early stages, manpower data and manpower forecasting capacity were largely absent. Investments were justified on the basis of demonstrated industrial expansion. The development of strong institutional linkages between training and employment to facilitate incremental and localized planning and adjustment were, in retrospect, important alternatives to formalized manpower forecasting. In this context, the location of training institutions became an important variable. In Korea sites were chosen to be close to expanding enterprises; in Brazil, mobile training units were created to take training to the trainees.

(e) Early and Sustained Involvement of Enterprises: In all three countries sustained efforts were made to establish linkages between training and employment for all modes (in Brazil, these linkages had been part of the SENAI tradition since the 1940s). Enterprises played active roles in curriculum and enrollment decisions and in the design and provision of on-the-job training experience and apprenticeships. What may be most important is the consistency of commitment in training agencies to the importance of these linkages. Combined with expanding employment and increasingly effective enterprise management, this helped create a supportive "ethos" in which the needs of employers were taken seriously and reflected in training plans and curricula.

In later stages, the nonformal systems began to branch out to provide a range of training services to enterprises
Rwanda; Hardy and Kagy (2018), for Ghana; Islam et al. (2020) and Allison et al. (2021), for global analyses. Gaps in monthly profits, not all of them statistically significant, are summarized in Campos et al. (2019) for 14 SSA studies. Similar patterns are documented for high-income countries (e.g. Fairlie and Robb (2009); Rosa and Sylla (2016)).

These unconditional gaps typically narrow when controlling for sector and firm size (for example, Rijkers and Costa (2012); Hallward-Driemeier (2013); Gui-Diby et al. (2017); Chaudhuri et al. (2020)) because women business owners often operate in sectors where profit margins are comparatively low, and have fewer employees than their male counterparts (Amin (2011); Campos et al. (2018)). At the same time, several cross-country studies have documented that women-led businesses are more profitable and larger when they are in male-dominated sectors (Campos et al., 2019). However, firm characteristics do not explain the entirety of the gap in productivity, sales revenues, and profits. Islam et al. (2020) find that the labor productivity gap remains unchanged even with a wide range of controls. Similarly, in a study on the garment-making industry in Ghana, Hardy and Kagy (2018, 2020) find that the men-owned micro-enterprises generate significantly higher profits than women-owned micro-firms even after accounting for a wide range of firm, firm owner, and product characteristics.<sup>2</sup> Finally, Bruhn (2009), using data from Latin America and controlling for enterprise characteristics, finds gender differences in productivity for micro and small enterprises, but not for medium-sized and large firms.<sup>3</sup> Several explanations for these gaps in performance have been suggested, ranging from lower levels of business capital for women compared to male peers, less labor used by female-owned firms, and differences in the adoption
>(0.08)<br>-0.014<br>(0.21)<br>0.415<br>(0.26)<br>9.388_∗∗∗_<br>IV-1<br>3<br>37<br>nt 2005 int<br>anced. Dat<br>edasticity a<br>nstant term<br>tes signifca|
|egressions for fund<br>owth rates*|**AF2**<br>**AF3**<br>2.471<br>0.896<br>(1.13)_∗∗_<br>(0.27)_∗∗∗_<br>-0.450<br>-0.038<br>(0.18)_∗∗_<br>(0.09)<br>-0.158<br>-0.009<br>(0.11)<br>(0.03)<br>2.743_∗∗∗_<br>5.750_∗_<br>1.599<br>58.865<br>-1.311<br>IV-2<br>S-GMM<br>3<br>67<br>75<br>353<br>m GDP data in consta<br> size, and is hence unbal<br>re in log form. Heterosk<br> (GMM only) and a co<br> percent level, _∗∗_indica|
|hmark r<br> GDP gr|**AF1**<br>1.441<br>(0.56)_∗∗∗_<br>-0.354<br>(0.15)_∗∗_<br>-0.076<br>(0.06
# **Table 7: Regression coefficients of individual nutritional outcomes on the DHS household wealth index and household consumption per person**

||BMI|DHS<br>Height-for-<br>age|Weight-for-<br>height|BMI|LSMS<br>Height-for-<br>age|Weight-for-<br>height|
|---|---|---|---|---|---|---|
|Benin|0.194***|0.189***|0.080***||||
|Burkina Faso|0.279***|0.281***|0.097***|n/a|0.377***|-0.016|
|Burundi|0.242***|0.505***|0.115***||||
|Cameroon|0.285***|0.451***|0.257***||||
|Congo, Rep.|0.265***|0.292***|0.051**||||
|Côte d'Ivoire|0.203***|0.279***|0.043||||
|Congo, Dem. Rep.|0.276***|0.378***|0.073***||||
|Ethiopia|0.352***|0.374***|0.229***|n/a|0.037|0.025|
|Gabon|0.182***|0.397***|0.056**||||
|Gambia|0.208***|0.297***