rafmacalaba/datause-encoder-data huggingface dataset:
> <sup>16</sup>

In examining the robustness of the results below, a number of other potential political characteristics of countries are also examined, including political and electoral institutions, ethnic heterogeneity, and voter information. Table 1 presents a summary of all of the variables used here. Note that each continuous episode of competitive elections constitutes an observation. All variables are therefore averaged over the duration of the episode.

**Table 1:  Summary statistics**

|**Variable**|**N**|**Mean**|**median**|**std dev.**|
|---|---|---|---|---|
|Rule of law|101|6.187|6.167|2.48|
|Share of government-owned newspapers/<br>market share of top five newspapers|75|.16|0|.32|
|Corruption in government|100|5.89|5|2.25|
|Bureaucratic quality|101|5.816|5.39|2.61|
|Gross secondary school enrollment|121|62.885|65.32|31.87|
|Public investment/GDP|86|0.039|0.032|0.027|
|Gov’t. wages/GDP|91|0.061|0.055|.035|
|Average continuous years of competitive electi|ons 137|13.35|5.5|17.65|
|Ethnolinguistic polarization|128|0.561|0.038|0.72|
|Newpaper circulation per 1000 inhabitants|118|119.66|71.99|131.2|
|Majoritarian (1) or non-majoritarian|137|0.328|0|0.47|
|Presidential (2), Semi-presidential (1),<br>or Parliamentary (0)|137|1.096|1.692308|0.95|
|Percent population young|133|0.336|0.353|0.1|
|Total population (10 millions)|135|33600000|8330978|104000000|
|Percent population rural|134|0.487|0.49275|0.22|
|Land (millions km<sup>2</sup>)|133|825394|143000|2190413|
|Total government expenditures/GDP|91|0.286|0.28|0.11|
|Primary school enrollment|123|99.47|100.7|17.59|
|Total education expenditures/GDP|115|0.034|0.032|0.02|

N.B. Observations are episodes of continuous competitive elections. Variables are the sum of the
Policy ReseaRch WoRking PaPeR 4574

# **Abstract**

This paper addresses labor markets in Haiti, including farm and nonfarm employment and income generation. The analyses are based on the first Living Conditions Survey of 7,186 households covering the whole country and representative at the regional level. The findings suggest that four key determinants of employment and productivity in nonfarm activities are education, gender, location, and migration status. This is emphasized when nonfarm activities are divided into low-return and highreturn activities. The wage and producer income analyses reveal that education is key to earning higher wages and incomes. Moreover, producer incomes increase with farm size, land title, and access to tools, electricity, roads, irrigation, and other farm inputs.

This paper—a product of the Sustainable Development Division, Social Development—is part of a larger effort in the department to reduce poverty and increase social inclusion. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dverner@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
Policy Research Working Paper 5926

# **Abstract**

This paper empirically investigates whether households affected by income shocks cope by reducing human capital investments. The analysis uses Crisis Response Surveys conducted in Armenia, Bulgaria, Montenegro, Romania, and Turkey during 2009 and 2010. A propensity score matching technique is adopted to compare health and education investment decisions among households that were affected by income shocks to the matched comparison group. The authors find that households affected by income shocks reduced some human capital investments. Interestingly, households in these five countries were more likely to adopt healthrelated coping strategies as opposed to education-related coping strategies. The results from Armenia, Bulgaria, Montenegro, and Turkey show that households affected by income shocks reduced their visits to doctors and reduced their spending on medicine and medical care significantly more than the matched comparison group. Households affected by income shocks reduced their education investments, but did not adopt harmful education-related coping strategies, such as withdrawing children from schools or moving children from costly private to cheaper public schools. These findings reveal that long-term and possibly intergenerational household welfare could be affected by short-run income shocks and hence underscore the need for governments to employ mitigation measures.

This paper is a product of the Human Development Economics Unit, Europe and Central Asia Region. 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://econ.worldbank.org. The authors may be contacted at bdasgupta@worldbank.org and majwad@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
from these studies and uses a larger sample of countries, our results on the effects of a persistent inflation shock are broadly in line with theirs.

A few recent empirical studies on the effects of inflation shocks on public debt are summarized in Table 1(b). As far as we know, Afonso and Jalles (2019) is the only one that uses the local projection method as we do. While they use historical data before World War I and focus on a deflationary dummy, their results from the local projection are broadly in line with ours. Quantitatively, however, their results and End et al.’s (2015) results from panel regression, however, are smaller than ours. Cherif and Hasanov (2018) estimate a VAR model with the debt dynamics equation using the US data, and their estimated impact of an inflation shock on the debtto-GDP ratio is comparable to ours.

Besides the above-cited recent studies using simulation or estimation approach comparable to ours, there is a long strand of literature on the link between inflation and public debt. Using detailed historical data of public debt, Hall and Sargent (2011) and Abbas et al. (2011) provide an accounting decomposition of the evolution of public debt and calculate the contribution of inflation and find that inflation was not a consistent source of public debt reduction, but played an important role in certain periods, such as post-war United States until the 1960s. Giannitsarou and Scott (2008) explore the implications of rising public debt for inflation based on an accounting exercise of public debt. The literature on the fiscal theory of price level, including Cochrane (2011) and Davig et al. (2011), explores the theoretical possibility that a high level of public debt may lead to higher inflation. Doepke and Schneider (2006) study the impact of an inflation shock on wealth distribution across private holders of public debt.

# **III.   SIMULATION APPROACH**

# **A.   Methodology**

# **Debt dynamics equation**

Our simulation approach is based on the following framework that extends the standard debt dynamics equation by making explicit the exogenous inflation shock and making a distinction between short and long-term debt.

Abstracting from the stock-flow adjustment, debt dynamics can be expressed as: Next, make a distinction between short-term debt (b�� ) and long-term debt (b��). Furthermore, distinguish between the long-term debt issued in year t (b��, ��� ) and the long-term debt that was issued prior to year t (b��, ��� ). With these breakdowns, the debt dynamics equation is 4
Policy Research Working Paper 6098

# **Abstract**

This paper investigates the determinants of price differences and market integration among Indonesian provinces, using data from retail cooking oil, rice and sugar markets during the period 1993–2007, and from wholesale maize and soybean markets during the period 1992–2006. The authors measure the degree of integration using co-integration techniques, and calculate average price differences. They use regression analysis to understand the drivers of price differences and market integration. For rice and sugar, they find wide market integration and low price differences, in the range of 5–12 percent. For maize, soybeans and cooking oil, they find less integration and higher price differences (16–22 percent). Integration across provinces is explained by the remoteness and quality of transport infrastructure of a province. Price differences across provinces respond to differences in provincial characteristics such as remoteness, transport infrastructure, output of the commodity, land productivity and income per capita.

This paper is a product of the Poverty Reduction and Economic Management Unit, East Asia and Pacific Region. 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://econ.worldbank.org. The author may be contacted at gvarela@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
strong interest in Eskom’s performance related to multiple aspects, including the provision of electricity to support economic activity, the achievement of social and development objectives such as the provision of electricity to poor households, dividends, environmental hazards, and contingent liabilities.

It is important to differentiate and properly assess and manage the risks the government is exposed to from Eskom’s performance. If Eskom was unable to meet its obligations to service guaranteed debt or honor payments for electricity to IPPs, the government is obliged to step in and make a payment to creditors or IPPs.

Given the complex relationship between the guarantor (the government), and Eskom, the clear definition of a credit event is essential. Eskom has not defaulted in the past for any of the guaranteed debt or amounts owed to the IPPs and it may be unlikely to do so in the future. The government has a strong incentive to avoid outright default which may lead to an acceleration of loans and bonds by creditors, a disruption in the electricity supply, contagion to other SOCs, and broader negative macroeconomic repercussions. Hence, the government is likely to step in before an outright default occurs, as long as the government’s own fiscal strength enables it to do so. A narrow definition of a credit event would not capture these events as an actual default would not occur vis‐à‐vis creditors and the guarantee agreement would not be formally triggered.

Hence, the Credit Risk Directorate at NTSA has defined a credit event more broadly as any type of unplanned government support aimed at avoiding default.<sup>9</sup> These include:

- an unplanned capital injection, beyond any planned injection to support Eskom’s capital expenditure program;

- the temporary (or permanent) servicing of debt payments to the creditor (e.g. an annual principal and interest installment); and

- an immediate payment of all guaranteed debt outstanding.

# **iii) Data availability**

NTSA uses a variety of information sources to inform risk analysis. Annual reports and financial statements (income statement, cash flow statement, and statement of financial position) are an essential resource. Statements are prepared according to International Financial Reporting Standards, audited, and are usually available within six months following the end of a fiscal year (ending March 31). In addition to backward‐looking financial statements, NTSA receives corporate plans. Corporate plans describe planned performance, and initiatives and capital expenditure programs for a five‐year period. Corporate plans also contain projected financial statements.

As no SOC benefitting from guarantees has defaulted in the past, no historic default data are available. However, the Credit Risk Directorate is in the process of compiling a database of credit events consistent

> 9 This is particularly relevant when estimating probabilities of distress and when defining rules for accessing future contingency funds for payouts related to guarantees.

10
# **Figure 3.2   Frequency Distribution of  Welfare change as a percentage of consumption**

<!-- Start of picture text --> Distribution of welfare results<br>4.5<br>4<br>3.5<br>3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>4 6 8 10 12<br>Welfare change: % of consumption<br>Percentage of solutions<br><!-- End of picture text -->

- More than 80% of the solutions in the interval 6%-8%

- 6.4% of the solutions below 6%; 13.0% of the solutions above 8%

- Minimum value: 4.5%; Maximum value: 11.4%

- Mean value: 7.1%; Median value: 7.1%.

Source: Jensen, Rutherford and Tarr (2007).

102
(3) and (4), except that the key parameters are now specific to each group e: In these equations, wn<sup>eisthewagefornativeworkers,φe</sup> n<sup>istheelasticityofnativelabor</sup> supply, and s<sup>e</sup> m<sup>istheimmigrantshareofthewagebillforgroupe.</sup>

# 3 Data

## 3.1 Data

The Labour Force Survey (LFS) of Malaysia provides annual data on various characteristics of the labor force and the structure of employment. The main survey is conducted by the Department of Statistics of Malaysia. Data are available for the years 1990 to 2010, with the exceptions of 1991 and 1994 when the survey was not conducted and 2008 where the survey weights were not available. Wage and income data were not collected until 2007 when an additional module was included in the survey. As a consequence, our main analysis is conducted with data from 2007 to 2010, with the earlier data used to help construct our instrument and conduct a number of robustness checks. The main survey samples, on average, 1 percent of the Malaysian population, while the supplement surveys around 0.5 percent of the working population.

Table 1 presents summary statistics for three years of the survey: 1990, 2007 (the first year of the supplement) and 2010. This is a period of rapid economic growth for 17
productivity, through which resource booms could negatively affect local income, especially in rural areas.

# **3.2 Empirical evidence**<sup>16</sup>

In this section, we discuss the empirical evidence on the local impact of resource abundance. We focus on quantitative academic studies.<sup>17</sup> In contrast to the country-level literature, the empirical literature exploiting within-country variation is more recent and less developed. There are, however, a growing number of studies already expanding this literature.

## **3.2.1 Impact on growth**

A first group of studies replicates the growth regressions used in the cross-country literature, but with data at the state, province, or county level (table AB.1). Similar to early work suggesting the presence of a resource curse, these papers also document a negative relation between measures of resource abundance and growth. For instance, using a cross-section of state-level data from the United States, Papyrakis and Gerlagh (2007) find a negative relation between income growth and the share of primary sectors in local GDP. James and Aadland (2011) and Douglas and Walker (2013) use more disaggregated data from U.S. counties and find similar results. Outside the United States, Zuo and Jack (2014) document a similar negative relation using data from Chinese provinces.

It is, however, difficult to interpret these results in a causal way. These studies share many of the limitations of the cross-country literature, such as omitted variable bias, reverse causality, and measurement error.

## **3.2.2 Impact on employment and local living standards**

> 16 See tables in Appendix B for a summary of the papers discussed in this section.

> 17 There are, however, several case studies examining the role of extractive industries, such as mining, on local communities. See, for instance, McMahon and Remy (2001) for a review of case studies in Canada, Latin America, and Spain. There is also a growing body of work on local value added in extractives supply chains, which is informative of the potential impact of mining on local economies. This includes industry-sponsored reports (International Council of Mining and Metals [ICMM]) and work funded by the World Bank (Burkina Faso, Central Asia, Ghana, Liberia, Tanzania, Zambia, Zimbabwe). The work funded by the Bank has not yet been assessed, but tentative conclusions are that in weak private sector environments, there tends to be a very low proportion of manufacturing value added in supplying mines and a somewhat higher service value added. This is after excluding supply of locally produced fuel, electricity, and cement.

24
# **Policy Research Working Paper Serles**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS1235 Eastern Europe's Experience with<br>Banking Reform: Is There a Role for<br>Banks in the Transition?|Alfredo Thorne|December 1993|N. Jose<br>33688|
|WPS1236 The Impact of Two-Tier Producer<br>and Consumer Food Pricinq in India|Maurice Schiff|December 1993|S. Fallon<br>38009|
|WPS1237 Bank Performance and the Impact<br>of Financial Restructuring in a<br>Macroeconomic Framework: A New<br>Application|Yavuz Boray<br>Hector Sierra|December 1993|C. Lim<br>30864|
|WPS1238 Kenya: Structural Adjustment in the<br>1980s|Gurushri Swamy|January 1994|V. Saldanha<br>35742|
|WPS1239 Principles of Regulatory Poiicy<br>Design|David E. M. Sappirgton|January 1994|WDR<br>31393|
|WPS1240 Financing the Storm: Macroeconomic<br>Crisis in Russia, 1992-93|William Easterly<br>Paulo Vieira da Cunha|January 1994|R. Martin<br>39026|
|WPS1241 Regulation, Institutions, and<br>Commitment in the British<br>Telecommunications Sector|Pablo T. Spiller<br>Ingo Vogelsang|January 1994|B. Moore<br>35261|
|WPS1242 Financial Policies in Socialist<br>Countries in Transition|Boris Pleskovic|January 1994|M. Jandu<br>33103|
|WPS1243 Are Institutional Investors an<br>Important Source of Portfolio Investm<br>in Emerging Markets?|Punam Chuhan<br>ent|January 1994|R. Vo<br>31047|
|WPS1 244 Difficulties of Transferring Risk-<br>Based Capital Requirements to<br>Developing Countries|Edward J. Kane|January 1994|P. Sintim-Abo<br>38526|
|WPS1245 The Adding-Up Problem: Strategies<br>for Primary Commodity Exports<br>in Sub-Saharan Africa|Takamasa Akiyama<br>Donald F. Larson|January 1994|A. Kim<br>33715|
|WPS1246 Determinant, ot Cross-Country<br>Income Irequality: An "Augmented"<br>Kuznets' Hypothesis|Branko Milanovic|January 1994|R. Martin<br>39065|
|WPS1 247 Complex Transactions Under<br>Uncertainty: Brazil's Machine Tool Ind|Andrew Stone<br>ustry|January 1994|P. Sintim-A!:<br>37644|
|WPS1248 Do Domestic Firms Benefit from<br>Foreign Direct Investment? Evidence<br>from Panel Data|Brian Aitken<br>Ann Harrison|February 1994|D. Ballantyne<br>37947|
individual characteristics of household members, more effective targeting strategies might become possible by relying on the imputed dataset.<sup>4</sup>

**We follow the approach developed by Ruben (1986) and identify matching variables, based on which we concatenate both datasets to ultimately employ multiple imputation methods** . There are many different approaches to statistical matching. Lewaa et al. (2021) provide an overview and divide the different methodologies into parametric, nonparametric, and mixed approaches. We follow early work by Ruben (1986) and first identify potential matching variables. These are variables that are part of both surveys and help to identify households that resemble each other. Nevertheless, not all overlapping variables should form part of the matching variables, but only those relevant to the target variable and similar in distributions across surveys (Serafino and Tonkin, 2017). We harmonize these variables with each other and apply a lasso regression to identify those variables that are most relevant to explain energy spending shares. Next, we concatenate both datasets based on these variables. While the household budget survey contains information on energy spending shares and energy poverty, these variables have missing values in the EU-SILC. After concatenating both datasets, we apply multiple imputation methods.

**To address one of the shortcomings in the literature raised by Lewaa et al. (2021), namely the lack of quality assessments, we explore several different imputation models.** We employ linear regression imputation models, predictive mean matching (PMM), and truncated regression imputation models. For each model, we explore three different imputation specifications by varying the inclusion of survey weights and the survey sampling design as additional matching variables. To evaluate the different models, we apply several validity tests. We compare the joint distribution of the imputed and observed energy spending share (overall and by subgroups), analyze if results differ across imputations, and compare the consistency of mean imputed energy expenditure by variables used in the statistical matching and those not used.

> 4 We address these type of research questions in Robayo-Abril and Rude (forthcoming).

5