rafmacalaba/data-use-mentions-extended huggingface dataset:
capital flows. To make these relationships more precise and to study the separate effects of some of these factors we provide some regression results in this section.

The main aim of the regressions is to try to explain the magnitude of the various types of capital flows for individual countries. Common with the existing literature (e.g., Calvo, Leiderman and Reinhart, 1993, Cuhan, Claessens, and Mamingi, 1998; Hernandez and Rudolph, 1995, and Taylor and Sarnio, 1997; Montiel and Reinhart, 1997, provides a review of this literature), we distinguish two groups of explanatory vaiiables: international factors ("pushfactors"); and domestic factors ("pull factors). Push-factors are thought of conditions in global capital markets that influence the supply of capital and aie outside the control of a particular recipient country. Pull-factors are thought of as country-specific factors and conditions influencing the interest of foreign capital of investing in that particular country. Some of these factors are under the control of the country; some are initial conditions; and others are outcomes, which are in part influenced by capital flows themselves.

For the group of pull-factors, we use the US-dollar, 6-month LIBOR interest rate, and the economic growth in OECD-countries. We expect that declines in wol1d interest rates will have a positive effect on capital flows to CEE/FSU as that will make the rate of return on investing in these countries higher relative to other alternatives. The effect of an increase in OECD growth rate is less obvious. On one hand, it will likely be associated with a rise in the rate of return on investment in OECD-countries, thus reducing the attractiveness of investing in transition economies. On the other hand, higher growth may raise the supply of savings in OECDcountries, thus stimulating capital flows.

The group of pull-factors is split into policy factors, i.e., "reform efforts," initial conditions, and "outcomes." Obviously, it is difficult to quantify the degree of policy reform a country has
- 10-

efficiency gains resulting from financial innovation and improvement in corporate governance associated with contractual savings development are likely to foster growth. In addition, while maximizing the risk adjusted rate of returns on managed funds, contractual savings institutions diversify their portfolio internationally, thus improving the allocation of capital on a global level, which, in turn, may favor growth in developing economies. Finally, faster growth, inducing a higher savings rate, would further promote growth in a virtuous cycle.<sup>20</sup>

# **_The international evidence_**

We now turn to study the behavior of contractual savings and non-life insurance institutions' portfolios in some OECD and developing countries. Figure 2 shows the importance of contractual savings around the world in terms of financial assets over GDP in 1996. In countries with long time reformed pension systems and important national provident funds, contractual savings have accumulated large amounts of financial assets (some of them higher than 100 percent of GDP). In countries that have not yet or have recently reforrned their pension system, contractual savings are less developed. In addition, in countries where enterprises carry important book reserves on their balance sheets against liabilities resulting from mandated or promised benefits to workers, have not developed their contractual savings sector (e.g., Germany, Italy, Austria, Korea) as countries of comparable level of economic development have.

**Figure 2: Contractual savings financial assets (GDP %,1996)**

<!-- Start of picture text --> 160% - E'"--- XL- -<br>140%-3<br>I IOM i i=<br><!-- End of picture text -->

**Source:** 1998 OECD Institutional Investors Statistical Yearbook and WB institutional investors database.

Other institutional investors, like non-life insurance companies, mobilize a considerably smaller volume of savings. Figure 3 shows that even in countries with 20 For discussions on the impact of capital market
rule of law and<br>accountability (23%)|
|**Timbuktu**|Greater responsibility in managing<br>ourownaffairs (32%)|Greater representation of the northern<br>population in national institutions (32%)|
|**Urban**|Greater responsibility in managing<br>ourownaffairs (33%)|Greater representation of the northern<br>population in national institutions (26%)|

> 25 We are only considering Songhai and Tamashek who represent 47% and 37% respectively of the survey population.

16
civil service bureaucracies di↵er greatly in whether and how they collect data on their performance. Unlike data related to the macroeconomy, households, firms, schools or labor markets, central statistical agencies are typically not involved in measuring government e↵ectiveness, and few international standards exist to aid cross country comparisons. To therefore quantify the delivery of public sector projects in our context, we exploit the fact that each Ghanaian civil service organization is required by OHCS to provide quarterly and annual progress reports. Organizations di↵er in their reporting formats and coverage, and some either did not produced reports for this time period or produced them in a format that was infeasible to code. We are thus able to use the progress reports of 30 Ministries and Departments (and our civil servant survey covers 2247 bureaucrats in these 30 organizations). Figure A1 provides a snapshot of a typical progress report and indicates the information coded from it, and the Data Appendix discusses the coding

> 4In Ghana, grades of technical and administrative bureaucrats are officially referred to as ‘senior’ officers while grades covering cleaners, drivers etc. are referred to as ‘junior’ officers, regardless of their tenure or seniority. While we restrict our sample to ‘senior’ officers in the formal terminology, throughout we use the terms senior and non-senior in their more colloquial sense to refer to hierarchical relationships within the professional grades.

8
**Figure 3. Firm ownership Figure 4. Firm size Figure 5. Distribution of firm age (number of employees)**

_Source_ : Authors’ calculation. _Source_ : Authors’ calculation. _Source_ : Authors’ calculation. _Note_ : Business’s age is truncated at 35.

By industry, information and communication technology (ICT) is the largest industry in the online job portal in terms of the number of employers and job postings (Figure 6). ICT constitutes 23% of the employers, and each employer has posted four jobs on average. The second largest industry is administrative, social, and personal services (15% and 4 jobs), followed by professional, scientific, and technical services (14% and 3 jobs), manufacturing (12% and 3 jobs), and wholesale, retail, hotel, and restaurant (11% and 2 jobs).

**Figure 6. Distribution of firms and the number of job postings per firm over industries**

_Source_ : Authors’ calculation using the data from Rozee.pk.

In sum, the comparison of the characteristics of Rozee.pk data and the labor market data clearly shows that Rozee.pk data represent a high-pay labor market domain where better qualified jobseekers and more productive employers participate. The labor force participants in Rozee.pk are younger and more educated and earn more than the average labor force in Pakistan. Females 7
# **4.2 Nigeria’s Reforms since 2003**

Nigeria consistently improved its ratings for the economic management and structural policies clusters between 2003 and 2009. Nigeria’s debt policy rating rose by 2 points from 2003 to 2009. Similarly, its trade policy rating rose by 1.5 points as well. The average rating for its economic management cluster went up by more than a point, while the average rating for structural policies rose by a point as well. The social inclusion and public governance clusters, however, have not seen any significant ratings changes.

Nigeria came up with the NEEDS (the National Economic Empowerment and Development Strategy) in 2004. This included reforms such as passage of a fiscal responsibility bill. Also introduced was the idea of a reference oil price, which was to guide fiscal decisions. The IMF saw NEEDS as a major first step towards a credible agenda of growth and poverty reduction. In the 2005 report, the IMF and IDA staff saw implantation of the strategy in 2004-05 as "impressive". In the 2007 progress report, the IMF staff state that macroeconomic policies improved, while there was still worry regarding fiscal expansion and quality of budget spending.

On other fronts, the NEEDS strategy included reforms on financial sector, transport system, accountability and transparency. The passing of the bill on Commitment to Extractive Industries Transparency Initiative, and the start of civil society involvement through the "Publish what you Pay" initiative meant that oil revenue data would be collected and published. Further promises on health, education and poverty eradication were made as well. That said however, the CPIA ratings show that WB analysts did not believe significant progress in these sectors were made, unlike in economic management and structural policies cluster.

The financial sector reforms in 2004 also forced consolidation in the banking sector by raising the minimum capital requirements by a ten-fold. This led to significant banking mergers and consolidation. The government also replaced a pay-as-you-go system with a fully funded pension system. After these reforms, domestic
**Figure 2.5: Rural Hourly Wage Distribution by** **<u>Sector of Activity (2002 Pesos), Mexico, 2003</u>**

<!-- Start of picture text --> 0 30 60 90 120<br>Hourly Wage<br>Agriculture Industry<br>Services<br>Source: ENE 2003, 2nd quarter.<br>.04<br>.03<br>.02<br>Density<br>.01<br>0<br><!-- End of picture text --> Off-farm labor income is the most important income source for the rural population in Mexico. The share of off-farm labor income has increased its share of total income from 49 percent in 1992 to 66 percent in 2002 (see Figure 2.6 and Table 2.5). The high-productivity nonfarm income share increased nearly five-fold, while the low productivity income share fell during 1992-2002. In 1992, none of the seven regions had high productivity rural nonfarm income shares accounting for more than 7.5 percent of total income. Over the past decade these shares reached 51, 22, and 21 percent in the Pacific, Capital, and Gulf regions, respectively. In the Center and South region, the highproductivity rural nonfarm income share is still low, reaching only 5.6 and 9.9 percent respectively in 2002.

The farm-income share of total rural income fell dramatically during 1992-2002 according to the 1992-2002 ENIGH surveys (Table 2.4 and Figure 2.6). The share of agricultural income in total rural income fell from 50.8 percent in 1992 to 23.8 percent in 2002. The regions that experienced the largest reduction in the agricultural income share were the North, Capital, and Pacific regions with close to a 70 percent reduction in the agricultural income share. The South and Center regions experienced the lowest reduction, namely 38 and 30 percent, respectively. The South region still
**|-0.025|-0.031**|
|Mother's exposure to conict|(0.027)|(0.015)|(0.020)|(0.014)|
|Observations|2,165|2,163|2,169|2,164|
|Adjusted R-squared|0.131|0.114|0.132|0.110|
|Mother and other controls|Yes|Yes|Yes|Yes|
|Children contrls|Yes|Yes|Yes|Yes|
|Village xed eects|Yes|Yes|Yes|Yes|
|Number of clusters|373|373|373|373|
|Control mean (Mother's age 16 to 21)|-1.836|-0.580|-1.472|-0.423|

Note: Sample is children aged 0 to 59 months at the time of the survey. *** (**) (*) indicates signicance at the 1% (5%) (10%) level. Treatment on the treated estimates are reported based on a dierence-in-dierence specication. Standard errors are clustered at village level. Mother's age in the table refers to mother's age at the start of the war. Comparison cohort is children born to mothers whose age was 16 to 21 at the start of the war. Children born to mother's cohort 22 to 29 is a second comparison group that serves as a placebo test. Mother's controls are mother's years of birth xed eects, mother's month of birth xed eects, region specic trends. Household controls are indicator for high caste, female headed households, and whether residing in a rural area. Child controls are indicator if child is a girl, a twin, birth order xed eect, and xed eects for
there have been real success stories, a strong connection between taxation, responsiveness, accountability and state building is far from guaranteed.<sup>3</sup> Questions remain about whether current reform efforts have focused sufficiently on fostering broader development gains.<sup>4</sup> The limited focus on building fiscal contracts is evident in scant fiscal redistribution in many lowincome countries and correspondingly limited trust in tax systems. OECD data point toward far lower levels of fiscal redistribution among new members from the Global South.<sup>5</sup> Looking more deeply, while simulation estimates are inevitably imperfect, the Commitment to Equity Institute has sought to estimate total fiscal redistribution through taxes and direct transfers at the country level. The pattern is striking: while middle-income countries sometimes achieve significant reductions in extreme poverty, this is not the case for lower-income countries, where direct fiscal redistribution is limited by heavy reliance on indirect taxes and weak transfer systems (Figure 2). While the impact of taxes on poverty reduction likely looks more encouraging when the in-kind value of all public services funded by government revenues is considered, there remains ample evidence that service delivery targeting the poor is frequently ineffective.<sup>6</sup> Unsurprisingly, evidence of inequitable tax burdens and poor translation of revenue into services is reflected in limited trust in tax systems in much of the low-income world (Figure 3).

**Figure 2: Estimated Reductions in Extreme Poverty from Taxes and Transfers**

<!-- Start of picture text --> 20%<br>0%<br>-20%<br>-40%<br>-60%<br>-80%<br>-100%<br><!-- End of picture text --> _Notes_ : The poverty line is defined as $1.9 PPP/day. Positive values imply an _increase_ in extreme poverty after taxes and transfers.

_Source_ : Jellema, Lustig, and Papelbon (
.lb)<br>I (QB * PB)<br>y y<br><!-- End of picture text --> Table 4.1 shows total gross value of output for these 17 industries as derived from the industrial censuses, expressed in both national currencies with conversion at the average official exchange rate of 1.5375 pesos to the cruzeiro in 1975. In the last column of the table, Mexico/Brazil gross output ratios are presented. In the case of Brazil, our sample covered 42 per cent of gross value of output, in Mexico 38 per cent.
default, moral hazard on the part of borrowers can increase ex ante demand for credit and ex post default. However, anticipating more default motivates creditors to restrict lending ex ante, thus limiting access to credit. Our review of the literature explores to what extent the predictions of our simple model match the empirical evidence on debt relief, borrower expectations, and moral hazard.

The remainder of the paper proceeds as follows. Section 2 lays out a stylized model of debt relief and presents comparative statics describing the effects of debt forbearance and debt restructuring under alternative model assumptions. In section 3, we discuss the various types of debt relief programs implemented in practice and review the existing evidence on the effect of these programs. Section 4 presents evidence on the effects of debt relief on borrower expectations, using survey data from a population of microfinance borrowers with recent exposure to debt relief. Section 5 discusses promising directions for future research and concludes.

# **2 The Costs and Benefits of Debt Relief: Conceptual Framework**

This section presents a simple model of consumer borrowing, default, and debt relief policies that builds on Indarte (2022, 2023). We use the model to highlight differences in the costs and benefits of different debt relief policies.

> 3 See also Badarinza et. al. “How has the pandemic affected household finances in developing economies?” Economics Observatory Blog, June 2021.

4
samples for which different types of predictors are selected when implementing LASSO to select variables for the EBP model. Predictors are grouped into eight categories: Weather and climate variables, Vegetation (specifically the Normalized Difference Vegetation Index), Land Cover Classification (LC), modeled population estimates, pollution indicators, distance to different locations, and latitude and longitude (GPS). The results are shown in Table 10.

When predicting assets, Pollution, Distance, and Land Cover are selected most frequently, more than 96 percent of the time. Weather and vegetation are also quite common, at 90 and 88 percent, respectively. GPS and population are less common, as they are only selected 58 and 52 percent of the time, respectively. Patterns are fairly similar for poverty prediction. Vegetation, Land cover, and distance to major cities are also frequently selected. Weather-related variables, however, are only selected in 65 percent of the samples and the GPS indicators (latitude and longitude) are almost never selected.

Selection by LASSO is a coarse measure of the importance of predictor variables. We therefore perform Shapley decompositions of the predictors in one sample per country and indicator using the XGBoost results, following Lundberg et al. (2019). For each sub-area, this procedure calculates the average of the absolute value of changes in the prediction of assets or poverty due to each predictor variable, taken over all possible orderings of the predictors in the tree. These average Shapley values are then averaged across all sub-areas in the sample. The variables with the largest Shapley values for each country and indicator are listed in Table 11 .

In nearly all cases, the landcover shares (coverfraction) stand out as key variables in the XGBoost models. These include the fraction of the sub-areas classified as urban, shrub, grass, bare, trees, crops, and water. On average these variables and their higher-level aggregates account for almost half of the top 10 variables for asset prediction and 60 percent of the top 10 variables for poverty prediction. Pollution variables
the population.<sup>4</sup> For costs of education and taxes, we rely on administrative data. More detailed information can be found in the Annex 2.

The rate of return is calculated for adults at different ages of re-entering the education system starting with adults at the age of 20 ending at the age of 55 with five-year intervals.

# **Benefits from additional education**

Additional income as a consequence of improved formal education depends primarily on value of prior professional experience, unemployment, gender, and marginal tax-rate: _(i) Value of prior professional experience_ <u>. The value of experience and education is inter-</u> dependent because the embedded level of knowledge accumulated through experience in a job-position is closely tied to the employee’s level of education. While a part of job experience, such as integration into a large company, is generic across education levels, other kinds of job-experiences are job-specific, and therefore not valuable in other jobpositions. The calculations assume that prior knowledge have some value, namely that an adult completing a degree will have a similar wage growth and similar nominal wage premium as a young graduate. Since the wage premium is added to the wage level the student had before reassuming studies, prior knowledge is given some value. This assumption follows Blondahl _et al_ (2002).<sup>5</sup> _(ii)_ _<u>Unemployment</u>_ <u>.</u> The risk of unemployment varies with age and education. Annex table 3 details the unemployment rate by education level. We also propose an alternative scenario that disregards the difference in unemployment across education levels. This alternative scenario shed light on the impact of unemployment on returns.

> 4 They are Barranquilla, Bogotá, Bucaramanga, Cali, Medellin, and Pasto.

> 5 Because of lack of longitudinal data, little is known about how labor markets respond to timing of education. Less ability to learn, less
on the conditions associated with World Bank policy loans, the authors<br>find that borrower countries that vote with the United States at the United Nations are<br>required to enact fewer domestic policy reforms, and on fewer and softer issue areas<br>(prior action data set for DPLs).|
|Corral and McCarthy,<br>2020|Yes (Table<br>A1.2 and<br>Text)|Do quality-at-entry assessments enhance the delivery of<br>development projects at the IDB?|In the IDB, quality-at-entry project assessment - especially higher scores on project logic<br>and economic analysis - is correlated with implementation performance. However,<br>monitoring and impact assessment scores had limited impacts on performance.|
|Deininger et al., 1998|Yes (Table<br>A1.2 and<br>Text)|Does economic and sector work (ESW) improve the quality of<br>World Bank loans? How do managers decide on staff time<br>allocation trade-offs?|The article sets out an idealized model of decision-making in which a country manager<br>makes an allocation of resources between lending services and economic and sector<br>work. ESW has a significant positive impact on the quality of World Bank loans, but<br>country managers choose to allocate resources in favor of preparation at the expense of<br>ESW due to disbursement concerns.|
|Denizer et al., 2013|Yes (Table<br>A1.2 and<br>Text)|To investigate the relative importance of country-level<br>“macro” factors and project-level “micro” factors in driving<br>project-level outcomes.|Country level “macro” measures of the quality of policies and institutions are strongly<br>correlated with project outcome, however, the success of individual development<br>projects varies much more within countries than it does between (80-20). Project size,
_GLOBAL FOOD INSECURITY OUTLOOK_ _13_ observed intervals in the post 2019 data, something a simple cross-validation exercise using the 2015-2019 data would not reveal. The application will show that the synthetic data strategy increases prediction performance tremendously on holdout data selected around the edges of the sample space.

One important parameter in the synthetic data algorithm is the cutoff year that determines how many synthetic cases should be added, 1999 being all synthetic data and 2015 being no synthetic data. The synthetic cases are generated with a model that uses all 1999-2019 data, but the final forecast model may not necessarily benefit from training on the full synthetic history. The deeper history on the one hand allows the model to learn a richer representation of the data, but on the other hand the deeper history is more difficult to predict by the imputation model and so the quality of the synthetic cases may be lower. To find the right cut-off, holdout predictions of the final prediction model using all synthetic data cutoff years 1999 _,_ 2000 _, ...,_ 2015 have been validated. The optimal cutoff was selected as 2005, which thus covers synthetic cases leading up to and following the previous global food crisis around 2008.

# **III. Results**

# _A. Validation Results_

Three main models are estimated. _Linear_ uses a simple linear regression, trained only on observed data, thus without addition of any synthetic cases. _Cubist_ trains a Cubist regression on the same original data. _Cubistsynth_ estimates the Cubist regression with synthetic cases. Prediction performance is estimated using several setups. In all the experiments, the models are optimized for R<sup>2</sup> and the associated MAE and RMSE are given for completeness.<sup>16</sup> First a standard 10-fold cross-validation exercise was performed. This application estimates each model 10 times on a 90% sample so that predictions are validated 10 times on the 10% held out observations. The results show that the Cubist model beats the linear model by a large margin,