rafmacalaba/data-use-mentions-extended huggingface dataset:
data and methods, Section 3 gives the results and Section 4 concludes.

# **2.  Data and Methods**

# **2.1 Data Sets**

# **Data set selection**

This study uses general household surveys and censuses rather than data sets that focus on disability, i.e., disability surveys. This stems from the objective to exploit data sets that are collected on a regular basis and may be potentially used to document and track human development for persons with disabilities. Of course, complementary work is needed to use disability-focused data sets, such as the Model Disability Survey developed by the World Bank and the World Health Organization (Cieza et al 2018).

We first examined nationally representative general household surveys and population censuses conducted in 136 LMICs (as defined by the World Bank) from 2009 to 2018 (Mitra et al 2021). The questionnaires of general household surveys and censuses were retrieved from online survey databases such as the International Household Survey Network (IHSN) Catalog, or the websites of individual National Statistical Offices. Questions were considered to be similar enough for inclusion in the study if they covered the four domains of functional difficulties recommended by the United Nations (2017, p. 207) for censuses. When functional difficulty questions followed a screener question like “Do you have a disability?” (e.g. Albania LSMS), we did not analyze the data as such screeners lead to under self-reporting.

From the data sets reviewed, this paper presents results for 21 LMICs with data sets using the Washington Group Short Set or similar questions where we could access general surveys or census data. We use 24 data sets as follows: eleven censuses (Dominican Republic, Indonesia, Kiribati, Mexico, Panama, Philippines, Rwanda, South Africa, Tanzania, Vanuatu, Vietnam), five Living Standards Measurement Studies (LSMS) (Ethiopia, Malawi, Nigeria, Tanzania, Uganda), four Household Income and Expenditure Surveys (HIES) (Bangladesh, Liberia
Based on the finding by de Groot et al. (2004) that institutional quality is important in explaining bilateral trade flows, the Corruption Perception Index is added to the model to represent institutional quality for both the exporting and the importing country (see table 4, specification 15).<sup>11</sup> These variables have the expected positive coefficients and are statistically significant.

Moreover, as de Groot e (2004) and Anderson and Marcouiller (2002) also find, when institutional quality is included, the coefficients of the GDP per capita variables become insignificant or have the wrong sign. This result could be explained by multicollinearity among the institutional quality variables and GDP per capita. Thus GDP per capita is omitted from further analysis. Specification 16 in table 4 contains the results: omitting GDP per capita reduces the adjusted _R_ -squared value only slightly, while it improves the coefficients of many of the statistically significant variables.

> 11 The analysis uses the Corruption Perception Index for 2004, from Transparency International (the index has a scale of 0–10, with 10 being least corrupt). Alternative variables to represent institutional quality were also tried, such as rule of law, regulatory quality, and control of corruption (all from the World Bank’s Governance Research Indicator Country Snapshot) and contract enforcement and registering property indices (from the World Bank’s Doing Business database). Because these alternative variables all turned out to be strongly correlated with the Corruption Perception Index, that index is the only one retained in the model.

17
_ESTIMATING FOOD PRICE INFLATION_ _19_ events have been more significant than others. For example, in Afghanistan, the average ratio between price increase and price uncertainty in local currency as measured over all time steps equals 0 _._ 66. At this value, inflation is positive but price uncertainty outweighs price increase and so there is no strong incentive to hold on to food to hedge against currency depreciation risks. This is opposed to Sudan, South Sudan and the Syrian Arab Republic, where the increase in food prices has not only been extremely high, but has also outweighed food price uncertainty by roughly a factor 2. Alternatively, food prices in Afghanistan rose by 14.09% annualized in 2020, which seems low when compared to some other high inflation events, but highly significant when compared to the relatively low price volatility over that same time period (inflation was 2.67 times volatility).

Inflation-targeting countries also aim for prices to be stable on low time frames. Tables A6 and A7 condense the high frequency results by presenting annualized figures. An FCS-wide average is included at the bottom. In total, price estimates for 1223 markets and 43 foods underly these figures. For all _year × countries_ combinations for which estimates are produced (361), only 48 annualized inflation rate estimates fall within the 0-2% targeting range, while 51 estimates cross a critical inflation threshold of 50%. The FCS-wide results highlight the elevated inflation levels during the period associated with the World Food Price Crisis of 2007-08 and the aftermath of the pandemic. The annualized monthly price change in 2008 peaks at geometric average of 19 _._ 52% across the full set of countries, while remaining mostly within a modest several percentage point range over the 2009 to 2018 period. The estimated average inflation rate then spikes again at 27 _._ 46% in the year of the pandemic, while the preliminary 2021 inflation estimate of 22 _._ 65% remains well above levels of the World Food Price Crisis of 2007-08. The results in table
|2011 – Census (village<br>level)–matched|0.49||
|---|---|---|
|2016-17 – Survey (full<br>sample)-matched|0.404<br>0.459|.055|
|Percentage difference bet<br>non-participant|ween participant and|13.6%|

Although we cannot conclusively say much about Labor Force Participation Rates (LFPR) as we do not have data on the part-time and full-time nature of this work, ongoing evaluations may be able to collect and report on these data. Overall, we conclude that, in terms of labor outcomes, the program is achieving or is on track to achieve its objective of improving livelihoods by increasing employment opportunities for women in rural areas with more women now being engaged in self-employment (farm and non-farm) in the short-run.<sup>19</sup>

# **4.1.2 Borrowing, savings and assets**

As noted earlier the theory of change for the intervention is that women can gain agency and economic empowerment through formation of social networks and greater access to low-cost credit, combined with skills development (Sanyal, Rao, and Majumdar 2015). The primary funding mechanism for NRLM is micro-loans through community grants and the SHG-Bank Linkage program. The program also has a major focus on promoting savings using the group membership as a commitment device. Therefore, we investigate outcomes related to the extent and nature of borrowings, savings and various asset classes.

We start by looking at overall current savings and the amount saved in the previous 12 months.<sup>20</sup> We find that overall savings have gone up almost 18.6 percent and most of this increase is due to households shifting their savings to formal sources (45.3 percent higher savings in formal sources). But overall, there is only a 5.2 percent increase in share of formal savings to informal savings.
# **4. Gender Inequality and Development Today**

With a few exceptions, most of the cross-country literature reviewed in section 2 uses data on gender inequality that date back at least 10 years. This lack of recent data has occurred because the topic has been at the heart of the policy debate for decades, so academic researchers have been accumulating evidence since the late 1980s. Another contributing factor is that cross-country growth regressions analyze economic growth over long periods of time, typically since the 1960s or 1970s, and gender inequality must be measured at the beginning of the period to study its role as a determinant of economic growth.

The 2012 World Development Report presents a detailed account of the state of gender inequality today. Strikingly, health and education gaps have substantially narrowed and, in most cases, have closed altogether. However, these improvements have not been accompanied by economic and political parity or equality. This finding suggests that different policies are needed to promote economic and political parity indicative of the process of development, at least as it has been observed in today’s richest nations, because previous gender-equality policies have not automatically produced this parity.<sup>5</sup> In this section, we provide evidence on the state of gender-education gaps that have been the focus of most of the macroeconomics literature. The main purpose of this section is to illustrate the sensitivity of cross-country estimates to the choice of theory-related specification and of the sample. Readers interested in exploring gaps in other dimensions are referred to the World Bank’s 2012 World Development Report.

Figures 1 and 2 review two measures of the education gap: the female/male literacy ratio and the female/male secondary-education attainment for people over the age of 25. Both measures indicate that gender inequality in education had almost disappeared as of 2010. The median female-male literacy ratio was 0.99; over half of the sample countries had achieved gender parity, and only a handful had ratios below 0.80. The median of the female-male secondary-education attainment
# | Poticy ReseARCH WORKING PaPeR 2283

## Summary findings

This paper introduces a large new cross-country database on political institutions: the Database on Political Institutions (DPI).

Beck, Clarke, Groff, Keefer, and Walsh summarize key variables (many of them new), compare this data set with others, and explore the range of issues for which the data should prove invaluable.

Among the novel variables they introduce:

* Several measures of tenure, stability, and checks and balances.

* Identification of parties with the government coalition or the opposition.

* Fragmentation of opposition and government parties in legislatures.

The authors illustrate the application of DPI variables to several problems in political economy.

Stepan and Skach, for example, find that democracy is more likely to survive under parliamentary governments than presidential systems. But this result is not robust to the use of different variables from the DPI, which raises puzzles for future research.

Similarly, Roubini and Sachs find that divided governments in the OECD run higher budget deficits after fiscal shocks. Replication of their work using DPI indicators of divided government indicates otherwise, again suggesting issues for future research.

Among questions in political science and economics that this database may illuminate: the determinants of democratic consolidation, the political conditions for economic reform, the political and institutional roots of corruption, and the elements of appropriate and institutionally sensitive design of economic policy.

This paper — a product of Regulation and Competition Policy, Development Research Group — is part of a larger effort in the group to understand the institutional bases of poverty alleviation and economic reform. The study was funded by the Bank’s Research Support Budget under the research project “Database on Institutions for Government Decisionmaking”TM (RPO 682-79). Copies of this paper are available free from the World Bank, 1818 H Street, NW, Washington, DC 20433. Please contact Paulina Sintim-Aboagye, room MC3-422, telephone 202-473-8526, fax 202-522-1155, email address psintimaboagye@worldbank.org. Policy Research Working Papers are also posted on the Web at
4 costs of inputs, loss of competitiveness and, for the first time, hard budget constraints .] After some adjustment in production levels and reallocation of resources, various relative shocks ensued. Changes in relative costs and the collapse of domestic demand led to additional compression in productive activity, thus creating fiscal pressure. The ensuing high inflation proved destabilizing to the macroeconomic situation (see Table 2). The degree of labor shedding over this tumultuous period, however, was more moderate. The decline in output considerably exceeded the decline in employment, signifying continued labor hoarding and soft budget constraints initially, with reliance on attrition, extended unpaid vacation, early retirement, and exit from the labor force. The relatively moderate rates of unemployment can be explained by: (i) the discrepancy between the level of official, registered unemployed, and the level of _de facto_ unemployed; (ii) exit from the labor force; and (iii) measurement problems associated with informal sector activity. Labor market surveys in Russia, for example, yield significant discrepancies in the magnitude of unemployment relative to the official numbers (Commander and Yemtsov, 1995).

7. The success of the new UI programs has not been uniform across the CEE countries. Since initially implemented, several countries have adjusted the benefit parameters in the direction of tighter eligibility requirements, shorter durations or lower replacement rates, because of spiraling costs, which led to a squeeze on financing for active labor market policies. Counterexamples are provided by Poland and Romania, which experienced particularly high rates of unemployment and declines in living standards early in the transition period. Poland extended its duration maximum to 18 months in regions deemed to be in particular crisis, and Romania added a "social benefit" provision at a very low level of support (40 percent of the minimum wage, means-tested only), to last 18 months in addition to the regular benefit duration of up to 9 months, bringing the total duration to greater than two years. Despite the intention to increase protection of vulnerable populations
of picture text --> _Source_ : Author‘s calculations based on data from CASEN, MxFLS and ENAHO panel databases.

These figures result from the complete specifications of the models shown in annex (table A4); however, they are robust to different specifications (see table 4). As the middle class, ideally, should consist of those households facing a very low risk of falling into poverty over time, we define the income associated to that probability as the lower threshold that depicts the lower bound of the middle class.<sup>7</sup> While there is a strong association between income and vulnerability, it remains nevertheless difficult to anchor a threshold solely to vulnerability, since the curves do not suggest structural behavioral changes. In the case of the upper threshold we define it as $50 in PPP terms, being it an income amount that lies in the upper tail of the income distribution of all three countries.

> 6 Estimated as the average of the independent variables for an estimated probability range between 0.09 and 0.11. Tables A1-A3 in the annex show the coefficients and standard errors from equations (1) and (2) for Chile, Mexico, and Peru, respectively, while Figure A1 shows the correlation between the estimated probabilities from equation (1) and the fitted values from equation (2).

> 7 In order to not classify as middle class a number of lower class households if any of these incomes is used, we establish for strict comparability purposes a more demanding criterion of $10 a day. Of course, nothing comes without a trade-off. The use of the proposed threshold of $10 implies, conversely, that a number of middle class households in each country will be considered as lower class. For instance, if middle class in Mexico includes those households with per capita income at or above $9.7 a day, hence a 1.3 percent of these households will be lower class under the criterion of $10 (in
(pre-primary) and primary schooling but insignificant for students in lower and upper secondary schools.

Estimates using the time spent by each municipality in different color categories provide qualitatively similar results compared to findings using values of the warning index: an increase in dropout, reduction in promotion, and an increase in student switching from private to public schools when comparing municipalities in orange and red with those in green and yellow. We cannot reject that effects for municipalities that spent more time in the orange and red categories are statistically identical, suggesting no distinction between the highest warning levels.

Our results depend on the assumption that outcome trends in municipalities with greater and lower warning indices were parallel before the pandemic. While we cannot test this directly because the warning index did not exist before the pandemic, we carry out a placebo experiment to verify our parallel trends assumption. First, we drop observations from 2021. Then, we assign warning index values from 2020 to 2019 and indices from 2021 to 2020, as if the pandemic had begun one year earlier. We estimate differential effects of the warning index by year (for 2019 and 2020). The parallel trends assumption holds for dropout but is satisfied in part for other outcomes. However, pre-existing differences in trends are in the opposite direction to our effect estimates and economically small in magnitude, which lends some support that our main results are adequately approximating the differential effects of exposure to COVID-19 on educational outcomes.

We expect the results from this study to contribute additional evidence to the growing literature quantifying the short-term human capital costs from the COVID-19 pandemic in developing countries (Tadesse and Muluye, 2020; World Bank, 2021; <mark>Bundervoet</mark> _<mark>et al</mark>_ <mark>., 2022</mark> ). While previous studies have documented the generalized negative consequences of the crisis (Moscoviz and Evans, 2022), we argue that the pandemic did not affect all children equally. We 4
8</sup> This assumption implies that the imputed food share corresponds to the share of food of households with the same characteristics but whose total consumption corresponds to the food line. These households have a consumption strictly smaller than the poverty line, hence they typically spend a larger share on food, which yields a rather “low” poverty line.<sup>9</sup>

# **2) Details on the measure of household consumption**

# **Total expenditures versus welfare aggregates**

The welfare indicator used in Egypt’s official monetary poverty figures is annual household consumption. The consumption aggregate includes all spending on consumption of goods and services, including food and beverages; clothes and footwear; housing; housing services (water, electricity, gas, and fuel); furniture and household equipment and maintenance; health care; transportation; wired and wireless communication; culture and recreation; education; restaurants and hotels; and miscellaneous goods and services. The implicit value of housing is also added. Food subsidies, which lower the price of a few basic food items, are accounted in the welfare aggregates, while energy subsidies are not.

When performing this analysis, a series of obstacles prevented us from satisfactorily replicating the official poverty figures in 2015 and 2017/18. First, we only have access to half of the sample for HIECS 2015. Second, we do not have information on the exact food subsidy schemes used in 2015 and in 2017/18.

> 8 Under this assumption, the two coefficients β and γ “disappear” because ln(1)=0.

> 9 The alternative is to impute the share of food for households for which food consumption is equivalent to the food line. This alternative requires proceeding by iterations until one reaches a fixed point.

19
in 1993, because the availability of household surveys in Africa is more limited before this date.<sup>7</sup> Given the lags in data preparation, we were also unable to add another more recent benchmark year beyond 2008.<sup>8</sup> We have established two rules for allocating surveys to benchmark years (similar to Lakner and Milanovic 2015): (1) surveys need to be within two years of a benchmark year, and (2) surveys in consecutive benchmark years should be at least three and no more than seven years apart from each other. However, we had to make a few exceptions to ensure that the household surveys included in our database achieve a sufficient coverage of the African population.<sup>9</sup> PovcalNet provides the average incomes of the 10 decile groups within a country, but it does not provide more detailed information on the distribution in its main output.<sup>10</sup> We have constructed two versions of the database. First, we only use the raw data available in PovcalNet and construct a data set consisting of country decile groups. Second, we fit a parametric Lorenz curve to the decile groups and build a data set of percentile groups (for each country-year).<sup>11</sup> The second data set has the advantage of providing more detail on the country-level distributions, but this comes at the cost of requiring an additional parametric assumption. Our main set of results uses the data set of percentile groups but we present robustness checks using the more aggregated data. For the three surveys where we use survey data directly, we can of course construct percentile (or decile) groups directly. In the analysis, each quantile group is population-weighted, so every person is assigned the average per capita consumption expenditure of her quantile group in the country-year distribution.

Throughout, welfare is measured using per capita consumption expenditure, measured at the household level and expressed in 2011 PPP-adjusted USD.<
renegotiation.46 The country’s president also threatened to give Glencore’s monopoly trading rights to ExxonMobil. Glencore’s upstream interests may have also made the company more amenable to renegotiate.47 In 2018, as part of its IMF program, Chad completed its debt restructuring with Glencore, which lengthened the maturities on the debt, lowered interest rates and made the debt more counter-cyclical by including contingencies which adjust repayment depending on oil price.

The pandemic has accentuated the already sharp debt sustainability challenges in a number of developing countries. In response, the G-20 has agreed to an initiative which enables the temporary suspension of debt servicing (DSSI) for the world’s poorest countries. However, most RBLs were outside its scope, mostly because only official lenders participated and not commercial lenders or commodity traders.48 Further consideration should be given to how international financial institutions may be able to assist RBL borrowers to help restructure their debt effectively (in a way that also ensures inter-lender equity) in the eventuality of debt burdens becoming unsustainable.

# 6. Conclusions

In this paper we analyze the main features and the impact of resource-backed loans on borrowing countries. While our review was restricted to publicly disclosed information about borrowings by central governments or SOEs in Sub-Saharan Africa during the 2004-2018 period, our conclusions are relevant to developing countries in general and may also be applicable to other forms of collateralized borrowing.

We found that while RBLs have limited use in a handful of countries, they represent a sizable proportion of debt in many of them. The countries that rely most on this form of borrowing are generally less transparent in their debt reporting, especially with regards to SOE debt, and collateralization is not disclosed in public debt records. Within our analysis sample, we found wide variations in how favorable RBL borrowing rates were. While a few RBLs extended by bilateral lenders (Korea, China) have outstandingly low rates, most come with rates well above the median. This is markedly the case for the few RBLs extended by commercial commodity traders.