rafmacalaba/data-use-mentions-extended huggingface dataset:
details on road conditions (a panel of GIS data and maps for 1996, 2006, and 2016 for Ethiopia and 2003, 2009, and 2018 for Kenya that rely at least partially on actual road surveys).<sup>3</sup> Panels of roads from the same source are rare. Figures A.2 and A.3 show the extensions of the paved road network for Kenya and Ethiopia. Related papers studying the expansion of the Ethiopian road network are Adamopoulos (2019), Gebresilasse (2019) and Kebede (2019), all of which focus on the feeder roads from the 2016 Ethiopian Roads Authority survey.<sup>4</sup> .

We used two methods to map access to the electricity network: nighttime lights and maps of power transmission grids. Nighttime light data are available for most years and locations but convey imperfect information on electrification. Historical maps of electricity grids are more

> 3Data for Ethiopia are used in Croke and Duhaut (2020)

> 4The study by Moneke (2020), which focuses on all-weather (gravel, asphalt, or bitumen surface) roads, is closer to this study

6
<u>Annex1</u> Page 2 Once the 2011 census data are available, in principle it would be possible to apply the poverty mapping methodology for refining the expenditure distribution.

For examining housing affordability, most rules of thumb are based on income, not expenditure. However, estimates of income distribution by detailed income classes are not prepared in India. To convert expenditure to income, we assumed that the savings rate across income classes was 65 percent which is close to average propensity to consume out of household disposable income in the national income accounts in recent years. Lower income groups, which are of most interest to us, may save at lower rates, so to the extent that this is true, these estimates of income may be on the high side. We also corrected for the well documented difference between household consumption as estimated in the national income accounts by increasing consumption expenditure by 30%. For those who feel that this adjustment is not warranted, then our estimates of income are biased upward by the same factor for the two periods under study. As such, estimates of affordability of residential real estate based on these income distribution figures may be somewhat biased upward by our choice of adjustments. However, this may compensate to some extent for the perceived bias of NSS data toward lower income groups. Our results are striking in that—even with such generous estimates of income growth –a large share of the population, 50 percent, would have difficulty affording a new well located formal home. If one were to adjust income estimates downward, this implies an even larger share of the population would find new formal housing unaffordable.
not fi nd any correlation between teacher shocks and movements. Of course, it could still be that the correlation is in unobservables that do not co-vary positively with these observed characteristics; verifying (or not) the importance of selective matching versus precautionary investment remains an important agenda for our future research.

# 8 Conclusion and Caveats

This paper uses a household optimization framework to identify the impact of teacher-level shocks on students’ learning gains. Our data from Zambia show that shocks to teacher inputs have a substantial effect on student learning. In our preferred speci fi cation, shocks associated with a 5 percent increase in the teacher’s absence rate resulted in a decline in learning of 3.75 percent (English) and 4 percent (Mathematics) of the average gains across the two years. The identi fi cation strategy is based on a restriction of the sample, whereby we focus only on those students who remained with the same teacher in the two concurrent years that they were tested. This allows us to rule out changes in unobserved teacher characteristics as a confounding factor in the estimation procedure. We argue that the estimates thus arrived at are robust to omitted variable and misspeci fi cation bias.

What are the implications of this fi nding? To improve educational outcomes, governments should concentrate on providing resources at the school-level that cannot easily be substituted for by households. In a previous paper (Das and others 2004), we documented that educational funding is an important determinant of learning achievement. However, because schools spend the money received from the government on resources such as textbooks, there is a high degree of substitutability between public and private funding. Increases in the former crowd-out the latter. Contrariwise, this paper argues that households are unable to insure themselves against teacher-level shocks. Moreover, the estimated impact of teacher-level and student-level shocks are of roughly the same magnitude. This con fi rms fi ndings from other studies that teachers matter and further, raises the possibility that insurance at the school-level may be more bene fi cial than at the
area that maps the relationship between charge and the number of people who will take up the service at that particular charge. Such an estimated relationship can depict the ‘uptake rate’ for the service improvements. This interpretation of WTP survey data is common in the health economics and finance literature (see Bala et al., 1999 for an early exposition).

Second, we often collect information on various characteristics of the households and the communities they live in – income, assets, education, attitudes, location, and amenities – that is typically under‐utilized in interpreting and using WTP data. The heterogeneity that exists in the population may not be fully captured in the implementation of the randomized split‐sample 4
Policy Research Working Paper 9255

# **Abstract**

Although household well-being is anchored in long-term average rates of consumption, welfare comparisons typically rely on shorter-duration survey measurements. This paper develops a new strategy to identify the distribution of these long-term rates by leveraging a large-scale randomization that elicited repeated short-duration measurements from diaries and recall questions. Identification stems from diary-recall differences in reports from the same household, does not require these reports to be error-free, and hinges on a research design with broad replicability. This strategy delivers cost-effective suggestions for designing survey modules to yield the most accurate measurements of consumption well-being, and offers new insights for interpreting and reconciling diary-recall differences in household expenditure surveys.

This paper is a product of the Poverty and Equity 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 nkrishnan@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
**The pass-through of international commodity price shocks to producers’ welfare: Evidence from Ethiopian coffee farmers**<sup>∗</sup>

Hundanol A. Kebede<sup>†</sup>

# **1 Introduction**

What is the effect of frequent booms and busts in international commodity prices on smallholder farmers in developing countries who rely on production of these commodities for their livelihood? This is particularly relevant question because conventional consumption smoothing options such as bank loans or personal savings are likely to be meagre in these parts of the world. A number of studies show that informal risk-sharing arrangements among members of relatives or communities insure (perhaps imperfectly) household consumption against income shocks (see, for instance, Townsend, 1995; Jacoby and Skoufias, 1997; Fafchamps and Lund, 2003; and Gertler and Gruber, 2002), so that temporary income shocks may not necessarily have severe consequences on household consumption. However, some of the shocks that are discussed in these studies are idiosyncratic income shocks, such health or unemployment shock to household head. The effectiveness of informal risk-sharing against commodity price shocks that are correlated across households in a village is not clear, and is an empirical question.

In this paper, I use a unique household level panel data to estimate the pass-through of international coffee price shocks on household consumption, labor supply, and welfare in Ethiopia. A number of studies have looked at the effect of commodity price shocks on different outcomes such as child labor and schooling (Kruger, 2007; Cogneau and Jedwab, 2012; Carrillo, 2020); child mortality (Miller and Urdinola, 2010; Haaker, 2018); and long-term adult mental health ∗I would like to thank the editor and three anonymous referees for their helpful comments. I would also like to thank Kerem Cosar, James Harrigan, John McLaren, Caglar Ozden, Sheetal Sekhri, and Sandip Sukhtankar for their comments and encouragements. This paper has also benefited f rom comments received from participants in UVA’s Development workshops
importance, albeit difficult circumstances, of monitoring the welfare impacts of the COVID-19 pandemic on households, the World Bank administered High Frequency Phone Surveys (HFPS) in more than 100 countries, including Indonesia (for more details: <u>https://www.worldbank.org/en/country/indonesia/brief/indonesia-covid-19observatory). The surveys are conducted regularly and are aimed to gather information on key household socio-</u> economic indicators, including employment, as well as access to public services and safety nets.

5
Figure 4: Parallel trends assumption <!-- Start of picture text --> Panel A: Lights per capita growth Panel B: Population denisty<br>No mining Active mining No mining Active mining<br>.4<br>200<br>150<br>.2<br>100<br>0<br>50<br>-.2 0<br>1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>Year Year<br>Panel C: Spatial Gini Panel D: Spatial Sen index<br>No mining Active mining No mining Active mining<br>3.5<br>0<br>3<br>-.02<br>2.5<br>-.04<br>2<br>-.06<br>1.5<br>-.08<br>1<br>-.1<br>.5<br>-.12<br>1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 0<br>1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>Year<br>Source: Authors' construction using lights and population data. Year<br>Population density<br>changes relative to 1997 changes relative to 1997<br>Mean per capita lights growth<br>Sen Index<br>Spatial light Gini index<br>changes relative to 1997<br>changes relative to 1997<br><!-- End of picture text --> Panel B of Figure 4 shows the average annual changes in population density – our migration measure – relative to 1997. That panel shows that the annual population density changes before the accession were steady and trending in parallel between the treated and control districts. Significant changes in population densities are observed in the treated districts in the postaccession period. These upward trends suggest that in
_ANDR EE_<sup>_ ́_</sup> _SEPTEMBER 28, 2022_ _6_ tail risks up to 3 years ahead. The current paper essentially proposes a middle ground between the work of Wang et al. (2020, 2022) and Andr ́ee et al. (2020), combining the multi-year outlook properties provided by the first approach and the predictive optimization of the latter, while aiming for global coverage such as attempted by Martini et al. (2021); Foini et al. (2022).

The importance of maintaining current outlooks on global food security developments, along with mechanisms to update and revise projections systematically and timely, is likely to increase in coming years as many drivers of food insecurity are projected to worsen into the 21st century. Success in poverty reduction has largely coincided with substantial loss of pristine natural environments (Stern et al., 1996; Andr ́ee et al., 2019a). Degraded environments, more frequent climatic extremes, and a continuously growing population will continue to put pressure on future agricultural systems (Grainger, 1990; Ingram et al., 2010; Myers et al., 2017; Diogo et al., 2017). These developments put the rural poor that rely on local natural assets for income and food consumption particularly at risk (Duraiappah, 1998; Barrett and Bevis, 2015; Barbier and Hochard, 2018).

# **II. Data and Methods**

# _A. Target Variable_

The analysis targets the prevalence (rate) of severe food insecurity. This metric is derived from the Food Insecurity Experience Scale (FIES) and is used to monitor progress toward the UN’s SDG of Zero Hunger by tracking food insecurity at three levels: food security, moderate food insecurity, and severe food insecurity.<sup>5</sup> This data is available for individual countries from the FAO (Food and Agriculture Organization) as 3-year centered moving averages. The FAO also publishes annual figures, but only at an aggregated level
these studies use data on developed countries only. This may be of little relevance for developing and transition economies which do not have a well-established regulatory framework, and highlyqualified public sector managers as those in the United Kingdom. Thus, the frequent rejection of the hypothesis that privatization leads to performance improvements (only 28 of the 87 studies report such findings) may not be surprising.

The more recent empirical literature on privatization focuses on developing countries (LaPorta and Lopez-de-Silanes, 1997) or on a cross-section of industrialized and semiindustrialized countries (Boubakri et al., 1997) of firms in (mostly) competitive sectors. These studies use larger datasets which allow for a direct test of the relative efficiency of state-owned versus private enterprises, but do not take into account the effect of different institutional environments. No study to-date has, for example, estimated the separate effects of privatization and stabilization (hardening of budget constraints). An exception is Picot and Kaulmann, 1992 which controls for country and sector effects in a cross-section of six developed countries. Nevertheless, privatization is found to have a positive effect on profitability, labor productivity, and new investment flows in all three studies.

Much of the empirical literature on the benefits of privatization preceded the theory of privatization and hence lacked a methodological framework to analyze its findings. It did, however, serve -- coupled with a healthy doze of faith -- as the basis for the large scale privatization programs in the transition economies. Now, equipped with both the SV model on the benefits of privatization and data from seven transition economies, it is an opportune time to test some of the hypotheses on the relationships between privatization and firm performance.

# **11. The new theory of politicians and firms**

With the collapse of central-planning in Central and Eastern Europe and the (then) Soviet Union interest in the theory of privatization emerged. The pathbreaking study in this literature (Boycko et al., 1993) was motivated by (and influenced) the Russian mass-privatization program. The model
## **Chart III.3 DUMPING INVESTIGATIONS BY SECTORS**

# **1990-2004**

|**Sectors**|**Number of I**|**nvestigations**|**Percentage **|**breakdown**|**Wo. Duties/ **|
|---|---|---|---|---|---|
||**Conducted**|**Wo. Duties**|**Conducted**|**Wo. Duties**|**Conducted**|
|Agricultural products|3|3|8.1|21.4|100.0|
|Chemicals|10|2|27.0|14.3|20.0|
|Petrochemicals|7|1|18.9|7.1|14.3|
|Tires|2|2|5.4|14.3|100.0|
|Textiles|3|3|8.1|21.4|100.0|
|Tableware and crockery or china|2|0|5.4|0.0|0.0|
|Iron & Steel Products|9|2|24.3|14.3|22.2|
|StationaryBatteries|1|1|2.7|7.1|100.0|
|**TOTAL**|**37 **|**14**|**100.0**|**100.0**|**37.8**|

Source: Calculations by the authors based on data from the Ministry of Trade.

As for the safeguards, the sectoral breakdown is relatively different according to the type of instrument (Chart III.4). Most of the investigations under the WTO safeguard were made for textiles and apparel, and home appliances.
- Decarolis, F., Fisman, R., Pinotti, P., and Vannutelli, S. (2020). Rules, discretion, and corruption in procurement: Evidence from italian government contracting. Technical report, National Bureau of Economic Research. 12

- Fazio, D. (2022). Rethinking Discretion in Public Procurement. 8, 12

- Fenizia, A. (2022). Managers and Productivity in the Public Sector. _Econometrica_ , 90(3):1063– 1084. 17

- Ferraz, C. and Finan, F. (2011). Electoral Accountability and Corruption: Evidence from the Audits of Local Governments. _American Economic Review_ , 101(4):1274–1311. 16

- Ferraz, C., Finan, F., and Szerman, D. (2015). Procuring Firm Growth: The Effects of Government Purchases on Firm Dynamics. 14

- Flynn, M. S. and Pessoa, M. (2014). _Prevention and Management of Government Arrears_ . International Monetary Fund. 2

- Foremny, D., Freier, R., Moessinger, M.-D., and Yeter, M. (2018). Overlapping political budget cycles. _Public Choice_ , 177(1):1–27. 16

- Gadenne, L. (2017). Tax Me, but Spend Wisely? Sources of Public Finance and Government Accountability. _American Economic Journal: Applied Economics_ , 9(1):274–314. 10

- Gadenne, L. and Singhal, M. (2014). Decentralization in Developing Economies. _Annual Review of Economics_ , 6(1):581–604. _eprint: https://doi.org/10.1146/annurev-economics-080213040833. 2

- García-Santana, M. and Santamaría, M. (2023). Understanding Home Bias in Procurement. Publisher: World Bank, Washington, DC. 3, 11, 12

- Grossman, G. and Lewis, J. I. (
potential issues also need to be weighed against the alternative of not conducting interviews during the crisis.<sup>6</sup> For each of the four countries, Appendix Table A1 presents characteristics of the sample of households included in the last face-to-face survey and compares it to the subsample of households that were included in the first round of the HFPS. The samples are similar in terms of household and household head characteristics, though there are some differences in household head education and in asset ownership, especially in Ethiopia. Furthermore, as Kugler et al. (2021) describe, household heads and their spouses are overrepresented relative to other household members in the HFPS and as a consequence, employment rates in the sample are higher than in the full population when only one respondent is interviewed. Using different weighting strategies to correct for potential bias, Kugler et al. (2021) show that sampling bias is of similar magnitude across gender, education, and urban/rural groups, so that the HFPS can accurately capture inequalities in employment impacts between these groups, which is the focus of our analyses. Still, in some countries, given that most of the respondents are heads of household, the HFPS may significantly overestimate employment for young workers compared to adults, thereby underestimating the employment penalty faced by youth that are not head of households. This is important to keep in mind as it can affect our estimates of inequalities by age groups. In summary, our results are best seen as reflecting the situation of household heads, rather than the general working age population in these countries.

For the analysis in this paper, we use HFPS data collected between April 2020 and March 2021. Due to differences in the timing of the surveys, this period covers 10 rounds for Ethiopia and Nigeria, 8 rounds for Malawi, and 5 rounds for Uganda. The dates of data collection in each country, sample size and average stringency index for each round are presented in Appendix Table A2.

# **Variables and descriptive statistics**
# **Figure 9 Total fiscal risks from early termination of public-private partnership portfolios of South Asian countries, as percentage of GDP**

<!-- Start of picture text --> 0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>Afghanistan Bangladesh Bhutan India Nepal Pakistan Sri Lanka<br>Low Medium less low High less medium<br>Percent<br><!-- End of picture text -->

## _Source:_ Authors’ calculations.

_Note:_ Total fiscal risks are the maximum expected loss at the 99 percent confidence over the entire contract period and are expressed as percentage of GDP of a single year.

Pakistan faces the most significant fiscal challenge from early termination of PPPs in South Asia, largely because of its low revenue mobilization capacity. South Asian governments are quite different in terms of their revenue mobilization capacity, which helps determine their ability to absorb the fiscal costs from early termination of PPPs in infrastructure. The Government of Bangladesh’s revenues represent only 9.6 percent of GDP, the Government of Pakistan’s revenues represent 15.6 percent of GDP, while the Government of India’s revenues represent 20.5 percent of GDP.<sup>19</sup> Pakistan’s fiscal risks represent just below 3.3 percent of the government’s annual revenues. Even though Bangladesh’s fiscal risks are low, relative to the size of its economy, they are significant compared to the annual government revenues (figure 10), posing a significant fiscal challenge to the country, if only the revenues of a single year are available to absorb the costs of early termination of PPPs.

> 19 As of 2017, from World Revenue Longitudinal Data (WoRLD), available at http://data.imf.org/?sk=77413F1D- <u
Policy Research Working Paper 9897

# **Abstract**

This paper studies the effect of a change in the amount of cash assistance provided to Afghan refugees returning from Pakistan on household outcomes post-return. Using a regression discontinuity design, it measures the impact of a large exogenous change in cash assistance amounts on post-return outcomes in a quasi-experimental setting. Administrative data and post-return monitoring data suggest that more than 16 months after their return, returnees who received a larger cash allowance of $350 per returnee— equivalent to 2.5 times the average annual pre-return annual income—were better off than those who received a smaller cash allowance of $150. Recipients of the $350 cash assistance were more likely to invest in durable assets, such as a house (17 percentage point difference); recipients of the $150 cash allowance were more likely to use the assistance for immediate food consumption needs (40 percentage point difference). Households that received $350 per returnee were significantly more likely to have been issued legal documentation for their household members. In line with the literature on cash assistance, the change in cash assistance had no effect on post-return employment outcomes. The findings provide new evidence on the effects of unconditional cash transfers on refugee reintegration and show that larger cash transfer programs can have a large and long-term impact following refugees’ return.

This paper is a product of the Poverty and Equity 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 nkrishnan@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