rafmacalaba/data-use-mentions-extended huggingface dataset:
# _4.2. Food Security Measures_

We use the following common household level food security indicators, which capture broadly dietary diversity and consumption behaviors considered to be symptomatic of food insecurity.

- The Household Food Consumption Expenditures

Food consumption expenditures (FExp) represents a direct indicator of economic access to food, as it captures the value of food consumed by the household over a given time period. Our survey instrument included a detailed food consumption module which asked each individual _i_ in the treatment and control, from a list of food items _f ∈{_ 1,...,F _}_ , the quantities consumed by _qif_ by their household and the total value of the consumption _vif_ , over a 7-day recall period. The total value of food consumed by the household _i_ (FExp _i_ ) is calculated as:

- The Household Dietary Diversity Score (HDDS)

The HDD Score (HDDS) is also an indicator that captures (economic) access to food.<sup>8</sup> As a simple count of the number of different food groups consumed by a household during a defined period, it is meant

> 8The HDD Score is a modified version of the U.S. Household Food Security Survey Module (HFSSM), adapted for use in a developing country context.

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# **2 Model setup**

The framework is a small open New Keynesian model adapted from Obstfeld and Rogoff (2000) and Christiano et al. (2005), in which we include three production sectors: non-traded goods, traded goods, and a natural resource. The model includes the standard friction of investment adjustment costs, which is standard in DSGE literature. We also include the friction _`a la Calvo_ in the prices and wages setting as in Christiano et al. (2005) and Kopoin et al. (2013). The model is calibrated from the Uganda data and includes various exogenous shocks as well as various fiscal policy regimes.

## **2.1 Households**

The economy is composed of a continuum of infinitely-lived households of mass 1. Households obtain utility from consumption _ct_ , which is produced by domestic firms, and receive disutility from labor supply _lt_ . Accordingly, the preferences of the representative household are given by the following lifetime utility function, which is separable with respect to consumption and hours worked.

where _β_ denotes the household’s discount factor and _ψ_ is the inverse of Frisch elasticity of labor supply, whereas _γ ∈_ (0 _,_ 1) is the parameter that controls the extent of habit. Finally, **E** 0 denotes the conditional expectation operator evaluated at time 0.

Households are assumed to be able to borrow or lend freely in national financial markets by buying or issuing risk-free bonds denominated in units of consumptions goods, and those in the non-tradable goods sector set nominal wage using Calvo’s partial indexation mechanism. Finally, the representative household maximizes the aforementioned utility function subject to budget constraint in units of domestic composite consumption: 6
demand since firms become able to produce more output with the same labor, and there may be reductions in the sector’s employment in that region<sup>3</sup> . Furthermore, if in some sectors technological growth is labor biased, productivity growth may not translate into employment growth.

Ideally economists would like to have a direct measure for regional productivity growth. One approach is to estimate a regional production function and interpreting the residual as total productivity

> 1 Combes (2000) finds that sector concentration fosters employment growth only in two manufacturing sectors: Glass and Office machinery and computers.

> 2 Glaeser et al. (1992) and Henderson et al. (1995) both use data for the US and reach different conclusions. However, they analyze different time periods and the samples are constructed in different ways. In Glaeser et al. (1992) the sample includes only the six largest sectors for each city and, therefore, all sectors included have an initially high degree of concentration. Furthermore, non-manufacturing sectors such as wholesale and retail trade are heavily represented, while in Henderson et al. (1995), these are not included.

> 3 I thank Antonio Ciccone for pointing this out.

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reaction time and financial sector policy making activity, respectively. An in-depth analysis of temporary policy measures (for example, the effectiveness and potential unintended consequences of policy interventions) is left for future research.

# _1.1 Overview of policy response surveillance initiatives_

Various surveillance efforts exist which track the policy responses to COVID-19. Some take stock of a broad range of interventions, such as fiscal, monetary and prudential policies (e.g. IMF Policy Tracker, COVID-19 Financial Response Tracker (CFRT) by the Yale Program on Financial Stability, IDB Summary of Policy Measures, and OECD Policy Tracker); others focus exclusively on prudential regulatory measures (e.g. Institute of International Finance’s Regulatory Measures) or containment and economic support measures (e.g. Oxford COVID-19 Government Response Tracker (OxCGRT)).

Compared to these projects, our database offers three distinctive features. First, it tracks the financial sector policy response with a focus on emerging markets and developing economies (EMDEs),<sup>2</sup> although it also covers several advanced economies. Second, the database adopts a tiered classification system which provides a typology of policy measures to facilitate analysis. Third, each measure is dated and accompanied by the relevant authority which implemented the measure. Where available, a link to the primary source is also provided as well as a termination or extension date, if applicable. Taken together, these features enhance the ability of both policy makers and researchers to comprehensively analyze and benchmark policy responses and their impacts at different stages of the pandemic.

# _1.2 Related literature_

This paper contributes to a small, but burgeoning literature that explores the impact of COVID-19 on the financial sector, analyzes drivers of the policy response and evaluates policy effectiveness.

**Impact of COVID-19 on the financial sector.** The financial sector has been put under strain by the COVID-19 crisis. Core markets became dislocated, EMDEs experienced massive capital outflows, and some borrowers faced liquidity and repayment challenges (e.g
# **VIII. References**

## **Adoho, Franck, Shubha Chakravarty, Dala T. Korkoyah Jr., Mattias Lundberg, and Afia**

**Tasneem.** 2014. "The Impact of an Adolescent Girls Program: The EPAG Project in Liberia" World Bank Policy Research Working Paper 6832.

**Aedo, Cristián and Sergio Nuñez.** 2004. "The Impact of Training Policies in Latin America and the Caribbean: The Case of Programa Joven" , Inter-American Development Bank Working Paper #R-483.

**Alzúa, María L., Guillermo Cruces, and Carolina Lopez Erazo** . 2013. "Youth training programs beyond employment. Evidence from a randomized controlled trial." Working Paper.

**Anderson, M. L.** 2008. "Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects" _Journal of the American Statistical Association_ , 103(484).

**Angelucci, Manuela and Orazio P. Attanasio.** 2006. " _Estimating ATT Effects with Non-Experimental Data and Low Compliance_ ." , No. 2368. IZA Discussion Papers.

**Attanasio, Orasio, Adriana Kugler, and Costas Meghir.** 2008. "Training Disadvantaged Youth in Latin America: Evidence from a Randomized Trial". NBER Working Paper 13931.

**Bandiera, Oriana, Niklas Buehren, Robin Burgess, Markus Goldstein, Selim Gulesci, Imran Rasul, and Munshi Sulaiman** . 2012. “Empowering Adolescent Girls: Evidence from a Randomized Control Trial in Uganda”. World Bank Working Paper No. 75566.

**Betcherman, Gordon, Martin Godfrey, Olga Susana Puerto, Friederike Rother, and Antoneta Stavreska.** 2007. "Global Inventory of Interventions to Support Young Workers: Synthesis Report" _World Bank_ , Global Inventory of Interventions to Support Young Workers: Synthesis Report.

**Bryson, A., R. Dorsett, and S
results to explore the spatial implications for countries’ 30x30 commitments.

Rather than presenting a detailed description of our methodology at the outset, we use our country cases to introduce its features sequentially. We begin with an econometric analysis that identifies the appropriate scale for envisioned protected areas in each country.

# **2.  Country Size and Protected Area Scale**

In each country, our sequential algorithm begins by overlaying the occurrence maps of all unprotected species on a spatial grid. It counts resident species for each grid cell and identifies the highest-count cell (P1) as the priority 1 candidate for protection coverage. In step 2, it sets aside the species resident in P1 and repeats the grid cell count for the remaining species. It identifies the highest-count cell as P2. The algorithm can repeat this exercise until all species have coverage in at least one grid cell.<sup>2</sup> A critical feature of our approach is the selection of grid scale for each country. We use a spatial grid whose cell area is the same as the average protected-area size, controlling for country size. Figure 1 displays the relationship (in logarithms) between country area and mean protected area (PA) size in sq km for 212 countries and political units.<sup>3</sup> The regression results in Table 1 confirm the strong visual relationship: Mean PA size increases by 0.614% with each 1% increase in country area, with very high significance (regression t-statistic of 15.47<sup>4</sup> ). Using this result, we can predict the mean PA size for any area and translate it to an appropriate grid cell resolution.<sup>5</sup>

> 2 When maximum resident populations are the same in more than one cell, the algorithm makes a random selection.

> 3 We compute average protected area size from WDPA (2024) and country area from the World Bank’s country shapefile (World Bank, 2024).
28 United Nations (1993). World Population Prospects : The 1992 Revision. New York.

United Nations (1995). World Population Prospects : The 1994 Revision. New York.

World Bank (1993). World Tables of Economic and Social Indicators, 1950-1992 (ICPSR file 6159). Washington, DC: World Bank, International Economics Department (producer), 1993. Ann Arbor, MI : Interuniversity Consortium for Political and Social Research (distributor), 1997.
effects. All regressions include 133 randomization PRA controls and control for data collection rounds in which outcomes were collected. SEs are clustered at the household level. Outcomes are listed on the left and primary outcomes are described in detail in Online Appendix Text 1. (***) (**) (*) denotes significance at (1%) (5%) (10%) level. Column (1) reports the mean and the standard deviation (in parentheses) of each outcome for the primary woman (respectively, column (4) for the primary man) of the control group. Column (2) reports the treatment estimates in levels with robust standard errors (in parentheses) for the primary woman (respectively, column (5) for the primary man). Column (3) reports the naive p-values [in brackets] for the primary woman (respectively, column (6) for the primary man). Primary woman (respectively, primary man) sample size for each treatment estimate is reported at the bottom of the panels: it lists the total number of observations across all data collection rounds ( _n*t_ , where _n_ is the individual HH) and the number of unique HHs ( _N_ , in parentheses). All market work participation estimates are whether the HH member engaged in an activity over the past month. Labor force participation is defined by the International Labour Organization as the working-age population that engages actively in the labour market, either by working or looking for work. HH = household; PRA = participatory rural appraisal; SE = standard error; SD = standard deviation.

# **5.5 Household Income and Revenues**

Five years after the asset transfer, treatment households report higher and more diversified sources of income and revenues compared to their counterparts in the control group: our proxy for monthly household income and revenues is 32% (p-value = 0.001) larger than the control group (Table 6).<sup>26</sup
transfer rates were nearly identical in program and comparison schools (not shown).

In November 2000, cohort 1 students took end-of-year Grade 5 exams, and these are used as baseline test scores in the evaluation.<sup>6</sup> In March 2001, the NGO held meetings with the headmasters of schools chosen for the program to inform them of program rules and to give each school community the choice to participate. Headmasters were asked to relay information about the program via a school

> 5 Although mandatory school fees were abolished in 2003, as described above, the NGO continued to pay grant money directly to schools with scholarship winners in 2003.

> 6 A detailed project timeline is presented in Appendix Table A.  Unfortunately, there is incomplete 2000 baseline exam data for cohort 2 (when they were in grade 4), and thus baseline comparisons focus on cohort 1.

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This is because a larger difference between the income and hours worked elasticities and larger income effects both imply that high income individuals have higher preferences for consumption, so that redistributing away from them is less desirable than in the Mirrleesian benchmark. The takeaway is that labor supply elasticities not only impact the efficiency costs of taxation, but also encode important information about the equity benefits of taxation through the information they contain about the drivers of income inequality.

The rest of the paper proceeds as follows: Section 2 discusses related literature, Section 3 illustrates how labor supply elasticities can be used to recover preferences in the context of a labor supply model in which productivity is equivalent to the hourly wage and individuals only choose hours worked, Section 4 extends our analysis when productivity is not equal to the hourly wage (i.e., individuals choose effort per hour as well as hours worked), Section 5 discusses our empirical implementation, Section 6 discusses how our findings impact optimal taxation, and Section 7 concludes.

# **2 Related Literature**

This paper is related to four different strands of literature: (1) decomposing income inequality empirically into wages and hours, (2) survey evidence on the determinants of income inequality, (3) the relationship between income inequality determinants and redistribution, and (4) the invertibility of economic systems.

First, this paper is related to an empirical literature focused on statistically decomposing income inequality into wage heterogeneity vs. hours worked heterogeneity. Haider (2001), for example, decomposes the variance of income into wage and hours worked, finding that most of income variance is due to wage variance, although a non-negligible amount of income variance is due to hours variance (and the covariance between hours and wages). Doiron and Barrett (1996) also find that income variance is driven more by wage heterogeneity for males; however, they find that the opposite is true for women. Gottschalk and Danziger (2005) and Blundell et al. (2018) have similar findings. Our paper
Policy Research Working Paper 9808

# **Abstract**

Carbon pricing is increasingly used by governments to reduce emissions. The effect of carbon pricing on economic outcomes as well as mitigating factors has been studied extensively since the early 1990s. One mitigating factor that has received less attention is education quality. If technological change that reduces the reliance of production on emissions is skill-biased, then carbon pricing may increase the skill premium of earnings and subsequent wage inequality; however, a more elastic skill supply through better education quality may mitigate adverse economic outcomes, including wage inequality, and enhance the effect of carbon pricing on technological change and subsequently emissions. A general equilibrium, overlapping-generations model is proposed, with endogenous skill investment in which the average skill level of the workforce can affect the need for emissions in an aggregate production function. This study uses data on industrial emissions linked to the Organisation for Economic Co-operation and Development’s Programme for International Assessment of Adult Competencies dataset for European Union countries. The findings show that, within countries, cognitive skills are positively associated with employment in industries that rely less on emissions for production and in industries that, over time, have been able to reduce their reliance on emissions for production. In the estimated general equilibrium model, higher cognitive skills reduce an economy’s reliance on emissions for production. Having higher quality education—defined as the level of cognitive skills attained by workers per unit of cost—increases the elasticity of skill supply and, as a result, mitigates a carbon tax’s economic costs including output loss and wage inequity, and enhances its effect on emissions reduction. The implication is that investments in education quality are needed for better enabling green technological innovation and adaptation and reducing inequality that results from carbon pricing.

This paper is a product of the Education Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at
# **2. The role of Special Economic Zones**

Accepting that agglomeration is both necessary and desired to achieve the benefits of regional scale, the question is how these processes can be best facilitated. The customs union and the wider processes of integration within the EAC should help overcome barriers to agglomeration caused by trade policies, border-related issues, investment, and the free movement of labor. But even assuming these processes are efficient, a number of barriers still remain. These include:

- Lack of reliable infrastructure, in particular transport, power, and telecommunications;

- Difficulty in accessing serviced industrial land and slow, cumbersome processes to secure it;

- Red tape and slow decision making relating to licenses, permits, and other aspects of starting and operating a business; and

- Information failures that prevent the private sector from coordinating investment activity.

One instrument used internationally to overcome such barriers to investment and agglomeration is special economic zones (SEZs). Special economic zones are spatially delimited areas within an economy that function with administrative, regulatory, and often fiscal regimes that are different (typically more liberal) from those of the domestic economy. Operating through a variety of different forms such as “export processing zones”, “economic processing zones”, “free zones”, and “foreign trade zones”, SEZs aim to overcome barriers that hinder investment in the wider economy, including restrictive policies, poor governance, inadequate infrastructure, and problematic access to land. SEZs have been an important policy instrument for many governments seeking to attract foreign investment, promote export-oriented growth, and generate employment. Their popularity as a policy tool has grown enormously in the past 20 years – in 1986, ILO’s database reported 176 zones in 47 countries. By 2006 it reported 3,500 zones in 130 countries (Boyenge, 2007).

While there is a long history of failure around the world in using instruments like SEZs to promote investment in remote regions, the evidence suggests they can be highly effective when targeting regions that
concentration on access to finance is also zero. However, in countries with high government ownership, the negative impact is significant.

The average government ownership in our sample is 0.25 with a standard deviation of 0.23. These numbers suggest that in a country with average government ownership the impact of Lerner is 0.79, while in a country that is one standard deviation above the average the impact is about twice as large, at 1.5. This translates into a decline in the probability of access to finance of about 10 percentage points as a result of a one standard deviation increase in the Lerner index in a country with high government bank ownership. The same impact is only 5 percent in a country with average government bank ownership and close to zero in a country without any government bank ownership.

To summarize, we find that the impact of competition and concentration depends on the environment in which banks operate. This may explain the contradictory results observed in the previous literature, as in some countries the negative impact of low levels of competition may be mitigated by some positive factors such as availability of credit information or the overall level of financial development, while in other countries the impact may be exacerbated by factors such as high government bank ownership.

# **6. Conclusions**

The theory on the impact of bank competition on access to finance offers conflicting predictions and the empirical literature provides mixed results and suffers from a number of limitations. Combining multi-year firm-level data on access to finance with panel country-level data on bank competition, this paper offers new evidence on the link between competition and access to finance. One advantage of our dataset is that it allows us to control for unobserved differences between countries, using country fixed effects. Thus, we are able to isolate within country variation in competition and access to finance. Also, contrary to other studies that equate concentration with competition, we conduct estimations using direct measures of banks’ pricing behavior.

Our results indicate that higher bank competition, as measured by
/sup> From the matrix, it could be argued that the difference in means between the two groups (High-Democracy & High Financial Openness group versus Low Democracy &-Low Financial Openness) was highly significant at 0.1% level.

Table 2 reports the statistical summary of the key variables classified by countries' degree of openness. Of interest here is the wide difference in countries' degree of redistribution as measured by the ratio of government social expenditure to GDP. Thus, countries classified as financially open spend on the average 22.3% of their GDP on social expenditures as compared to 6.7% among financially closed countries. Furthermore, the view that financial openness, democracy, and government social spending go hand in hand is confirmed by the econometric results shown in Figure 3. The cross-country data, with sample size ranging from 70 to 140, shows statistically significant results for all three relationships based on a z-test, with the highest correlation between political liberty and government social expenditures.

> 30 An.alternate measure of openness of the economic nature is the Fraser Intitute's Economic Freedom of the World rankings. These rankings are strongly correlated with the Financial Openness Index (0.70) and show a correlation of 0.49 with the Democracy Index Thethe 95% confidence interval forthe difference inmeans is 11.15 21.74

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