rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9960

# **Abstract**

This paper investigates the labor market outcomes for refugee and urban national communities in Kenya during the COVID-19 pandemic, using five waves of a novel high-frequency phone survey collected between May 2020 and June 2021. Even after conditioning on age, gender, educational attainment, and area of living, only 32 percent of refugees were employed in February 2020 compared with 63 percent of nationals. With the onset of the pandemic in March 2020, the share of employed for both refugees and nationals fell by around 36 percent, such that in May-June 2020, only 21 percent of refugees were still employed compared with 40 percent of nationals. Using a panel setup with wave and location fixed effects, the analysis finds that the recovery in the share of employed, hours worked, and household incomes was slower and often stagnant for refugees compared with the recovery of nationals. These differences cannot be explained by demographic factors, living in an urban or camp environment, having been employed previously, or sectoral choice, suggesting that a third, unobservable “refugee factor” inhibits refugees’ recovery after a major shock and aggravates preexisting vulnerabilities.

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 upape@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
1984 and 1985; it dropped to 7% in 1986, then increased to 15% in 1987, and dropped again to 10% and 9% in 1988 and 1989.

# **III Hypotheses about the Determinants of Decentralization**

Using a principal-agent (PA) approach, this section discusses what determines the way the government and SOEs decide internal and managerial incentives, financing arrangements, and control rights over production decisions. Consider this PA relationship: the government as the principal is risk-neutral, and the manager of a SOE as the agent is risk averse. For some reforms, I shall also consider a PA relationship between the manager as the principal and his employees as his agents. The government is postulated to have three goals: increase revenue (or profitability), retain control of SOEs which represent high control amenity -- that is, utility from directly controlling the firms - - to the government, and reduce the inequality of income across firms. Managers and employees have an informational advantage over the government that allows them to earn a rent, and that advantage, as a result, causes sub-optimal effort and investment levels. To reduce the informational asymmetry, the government can design managerial and employee incentives, control rights and performance contracts to align the objectives of **13** the SOEs with those of the government. The manager, of course, may opt for the more centralized status quo if the firm is better off than under the considered reform.

# _Incentives, Control and Risks_

In designing incentives the government would like to differentiate between firms in terms of the risks they face, and strike a balance between incentives and insurance.14 In

> **13** See Laffont and Tirole (1993), chapter 1, _A Theory of Incentives in Procurement and Regulation,_ the MIT Press: Cambridge, Massachusetts.

**14For** surveys on principal-agent models, see David Sappington, "Incentives in Principal-Agent Relationships," _Journal of Economic Perspectives, 5(2),_ 1991,
>8 503,352.80 664,992.10 1.32<br>9 663,107.70 952,224.20 1.44<br>10 1,441,124.00 2,661,722.00 1.85<br>Source: World Bank Group and staff estimates based on the 2017 ENPH<br>Note: The deciles were built using market income + per-capita pensions<br>Finally, Figure 1 presents the results of progressivity in relative (Panel A) and in absolute (Panel B) terms applying<br>the rescaling and without doing so. On the one hand, the empirical evidence shows that the results for the case<br>of Colombia do not seem to make much sense when performing this adjustment, the system goes from being<br>regressive to progressive, on the other hand, as the implementation of said rescaling is still under discussion, the<br>team decided not to include this adjustment in this version of the CEQ.<br>FIGURE 1: PROGRESSIVITY IN ABSOLUTE AND RELATIVE TERMS WITH AND WITHOUT RESCALING<br>Panel A: Progressivity in Relative Terms Panel B: Progressivity in Absolute Terms<br>60%<br>15%<br>50%<br>10% 40%<br>30%<br>5% 20%<br>10%<br>0% 0%<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>W/o rescaling With rescaling W/o rescaling With rescaling<br>Source: World Bank Group staff estimates based on the 2017 ENPH<br>Note: The deciles were built using per-capita market income + pensions<br>*The results presented in this paper are due to the short-term distributional effects of taxes and spending in Colombia.<br>Along these lines, for example, in the case of
policies in the area of cross-border supply of auditing and distribution services (commercial presence requirements), legal services being closed to foreigners, and labor market tests being applied for mode 4.

But how does this relative openness compare with goods trade policy (as measured by their tariffs)? Most high-income economies present relatively liberal policies for both manufactured goods and services (Figure 4). Some are much more liberal for goods than they are for services (e.g. Indonesia and Oman) and _vice-versa_ (the Republic of Korea, Chile and South Africa). Upper middle-income economies tend to be more protective for goods trade, whereas lower-middle income economies tend to have restrictive policies for trade in both goods and services. Also, economies in East and South Asia are appreciably more restrictive in services than in manufactured goods trade. Latin American economies are surprisingly open in services, while most of them retain substantial goods trade barriers (bottom left quadrant).

**Figure 4:  Services trade and goods trade policies, 2016**

<!-- Start of picture text --> Goods vs Services Trade Barriers<br>IDN IND<br>CHN MMR<br>PAN<br>PHL MYS<br>OMN THA EGY<br>TUN<br>VNM<br>ISL TUR ISR KAZ LKA ARG<br>MEX<br>SGPCHENOR CAN AUS NZL CRI EU23 UKRRUS KOR COL URY BRA PAKKEN NGA BGD<br>HKG USA PER DOMZAF CHL<br>JPN<br>ECU<br>0 5 10 15<br>Median<br>Applied Tariff Rates Manufactures (trade weighted) 2016<br>Color key: blue = OECD; navy = ECA; orange = EAP; dark orange = SAS;<br> green = LAC; black = SSA or MENA.<br>For Myanmar, Sri Lanka, Panama, Peru and Thailand 2015 tariff data are used.<br>Average STRI 2016 used for 23 EU member states.<br
shaking) at a given frequency or 'return period' based on a probabilistic analysis (more details are presented in Appendix A). Although, the potential damage captured through the TH indexes is not a projection, it allows us to observe which types of natural events could affect more in the areas where households reside. But it is worth noting that TH scores do not represent hazard risks per se, they are only physical hazards from probabilistic models, and their use for risk-related assessment is discouraged. ThinkHazard! methodology provides a general view of the hazards for a given location to promote disaster and climate resilience. It is a simple flagging system highlighting the hazards in an area. Moreover, the scores emphasize the likelihood of different natural hazards affecting areas. Thus, the data can show some uncertainties in specific areas that fail to cover the full range of hazard categories and intensities (see more detail in ThinkHazard! Platform, 2022).

The TH indexes are available from level 2 of administrative units (ADM 2) of FAO's classification. In the case of Brazil, the index is used at level 1 because the geographic variable available in the Household Survey is only at the state level. To capture the differences among administrative units of level 2, we use the average of the indexes of level 2 (see Appendix A). So, this country's index cannot be compared with the other countries' TH indexes. We use data from local areas to control for exposure variables ( ). By definition, exposure is the presence of people, livelihoods, species or ecosystems, environmental functions, services, resources, infrastructure, or economic, social, or cultural assets in places and settings that EE could be adversely affected by events (IPCC, 2014). Following this definition, we include population density (people per sq. km of the district or state area), percentage of cropland, and urbanization of the district or state area. The data source of type of land is from the MODIS
estimates with a degree of caution, this is suggestive evidence that African countries may have been more successful in reducing poverty than currently thought.

The paper is organized as follows. Section II explains why CPIs in Sub-Saharan Africa may be biased and outlines the Engel curve method. Section III describes our empirical methodology, while section IV describes the data sources. Section V shows estimates of the CPI bias and conducts several robustness checks. Section VI assesses the implications of CPI bias for measured poverty trends. Section VII concludes.

# **II. Literature review**

# _CPI bias in Sub-Saharan Africa_

The CPI measures the rate at which the prices of a specific basket of goods and services change from month to month. To compute the CPI, NSOs require price and quantity data for a variety of goods and services. Virtually all NSOs in Sub-Saharan Africa run regular monthly consumer price data collection programs, which form the basis for computing the CPI. In addition, estimating a weighted average of price changes relative to a base period requires data on consumed quantities as budget shares. These quantity data typically come from nationally representative household budget surveys (HBS), which most NSOs field in irregular intervals (see Beegle et al 2016).<sup>2</sup> Most NSOs use a fixed-base Laspeyres-type index and staged aggregation approach to compute the CPI (see United Nations 2009). In a first step, individual price quotations are combined to elementary aggregates that represent broad goods categories purchased by consumers in a specific locality and type of outlet. In the second step, elementary aggregates are combined to commoditygroup indexes (for example, food and beverages, apparel, transport) and ultimately the all-item CPI using weights (budget shares) estimated from the HBS for a base period.

It is well-known that Laspeyres indexes tend to overstate changes in the cost of living. This is because they hold quantities fixed in the base period and disregard consumer substitution behavior

> 2 The term HBS is used to denote national household surveys collecting consumption data,
30 impact of autonomization on the resource-intensity of care. Table 4 points to autonomization increasing the number of images and laboratory tests at least in provincial and district hospitals; Table 5 shows that for these lower-level hospitals, this is also true on a ―per case‖ basis. By contrast, in central hospitals autonomization apparently led to _fewer_ lab tests, as well as fewer surgeries. This might be because the fee differential between central and other hospitals is smaller than the cost differential, and autonomization stimulated managers in central hospitals to urge their staff to economize on lab tests and surgeries.

# **VII. CONCLUSIONS**

Our results suggest then that Vietnam‘s autonomization policy probably led to more hospital admissions and more outpatient department visits, though the effects are not large. The policy did not, however, apparently affect bed stocks or bed-occupancy rates. Nor does it seem to have increased hospital efficiency, but rather left the typical hospital‘s cost structure unaffected. Oddly, despite the volume effects and the unchanged cost structure, our results do not point to autonomization leading to higher total costs. By contrast, we _do_ find some evidence that autonomization probably led to higher out-of-pocket spending on hospital care, and higher spending per treatment episode. The estimated effects vary, however, in size depending on the data source and the level of the hospital. Some are quite large (around 20 percent and in some cases even larger), but the variation suggests that one should probably treat them with some caution. We find no evidence that autonomy affected in-hospital death rates or complication rates, but in lower-level hospitals it did apparently lead to more intensive style of care, with more lab tests and imaging per case.

While the results from the hospital and household data tell broadly similar stories as far as utilization and out-of-pocket spending impacts are concerned, there are differences in
sectors and aggregate productivity changes that are the result of labor re-allocation between sectors with different productivity levels and trends (McMillan et al., 2014; de Vries et al., 2015).<sup>6</sup> We therefore rely on data from the Groningen Growth and Development Centre (GGDC) 10 Sector database (Timmer et al., 2015), with additional information on data and methodology provided in appendix B.2.

The more disaggregated GGDC data reveal that much of the increase in the service employment share since 1990 can be attributed to expanding market services. The strongest employment increase, especially after 2000, took place in the sector comprising wholesale and retail trade, hotels, and restaurants (‘wrt’, see Figure 4 and appendix Table A.1). Not only does this sector have the lowest labor productivity among the 10 sectors we analyze, but also when comparing this sector across 29 developing countries, as done in McMillan et al. (2014: Table 2), Ghana is the country with the lowest labor productivity within this sector. In our view, this suggests that highproductivity service sectors are not able to absorb most of the labor supply that is especially freed up by productivity increases in agriculture. This may be due to supply constraints (Ghana only ranks 120<sup>th</sup> in the Doing Business report) and/or because the freed-up labor supply does not have the skills to enter high-productivity services (Ghana ranks bottom in PISA scores) and hence end up in the insulated low-productivity sectors such as retail trade.<sup>7</sup> However, also highly productive service sectors such as transport or business services expanded employment.

To quantify the contribution of structural change to aggregate productivity growth, we use different methodologies to decompose the change in average labor productivity into different components, each with its own economic interpretation (McMillan et al. 2014; de Vries et al., 2015; McMillan et al. 2017; see appendix
|TableA9: Second-stage|— Impacts of hos|tingrefugees on househo|ld welfare|and poverty|
|---|---|---|---|---|
||Consumption|Consumption poverty|Wealth|Wealth poverty|
||FE-IV|FE-IV|FE-IV|FE-IV|
||(1)|(2)|(3)|(4)|
|Refugee Intensity|-0.447**|0.345**|0.122|0.093|
||(0.222)|(0.145)|(0.120)|(0.130)|
|Round FEs|YES|YES|YES|YES|
|Fixed Effects (FEs)|HH|HH|HH|HH|
|Controls|YES|YES|YES|YES|
|R-squared|0.064|0.036|0.187|0.114|
|# of EAs|433|433|433|433|
|N|12937|12937|13522|13522|

_Notes:_ This table provides 2SLS (household FE-IV) estimates of the impact of logarithm of refugee intensity on logarithm of household consumption expenditure per capita (column (1)), a binary indicator of household consumption poverty status (column (2)), “started log” of household wealth score (column (3)), and a binary indicator of household wealth poverty status (column (4)). Household sample weights from ESS2 applied. All regressions control for household FEs, round FEs, and 12 time-varying control variables. The coefficient estimates for the 12 control variables are not reported here, but can be available on request. Robust standard errors clustered at EA level are given in parenthesis. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

59
# **2.2. Methodology for GHG Emission Accounting**

Emissions from fossil fuel consumption are estimated by multiplying fuel consumption by their emission coefficients or carbon content. The emission coefficients are calibrated using fuel consumption and CO2 emissions data maintained by International Energy Agency (IEA, 2009). Fossil fuel related CO2 emissions reduction under a scenario is calculated by subtracting emissions under the scenario from that in the baseline.

GHG emissions from land-use change are calculated by multiplying the amount of forest land converted to crop land by the carbon stock change entailed in the conversion. We followed the IPCC Tier I approach, according to which, three types of carbon pools are accounted for: (i) change in carbon stock from aboveground biomass; (ii) change in carbon stock from below ground biomass and (iii) change in soil carbon stock. While calculating CO2 emissions from deforestation, we differentiated CO2 emissions per hectare of land across agroecological zones (AEZ). Emission stocks by type of AEZ are presented in the Appendix.

While estimating GHG emissions from aboveground and underground biomass, special care is taken to differentiate between the cumulative and annual emissions. This is an important issue as emission accounting for fossil fuel consumption and land-use change are different. For fossil fuel, the difference in emissions between the scenario and the baseline in a year is annual emissions, whereas the same in the case of land-use change is cumulative emissions. In other words, the change in land-use related GHG emissions between a biofuel scenario and baseline in a year cannot be directly added to the similar change in emissions from fossil fuel consumption as the former is a cumulative emission whereas the latter is an annual emission. Figure 2 helps illustrate the difference between the cumulative and annual emissions from land-use.

9
# Tables A.13. Explaining conservative social norms, by gender **Table 13.1. Explaining Social Norms – Females only sample**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||PNB Index -<br>HH Roles|PNB Index -<br>Public Space|SNE Index -<br>HH Roles|SNE Index -<br>Public Space|Sanctions<br>Index|
|Respondent’s age in years,<br>squared|0.00<br>(0.00)|-0.00<br>(0.00)|-0.00<br>(0.00)|-0.00*** (0.00)|-0.00<br>(0.00)|
|Years of education, derived from|-0.01**|-0.02***|-0.01|-0.02*** (0.01)|-0.11***|
|education level|(0.01)|(0.01)|(0.01)||(0.03)|
|Indicator for respondent having|-0.02|0.00|-0.05|-0.02|0.23|
|childyounger than 5years|(0.02)|(0.03)|(0.03)|(0.04)|(0.17)|
|Respondent lives in urban area|0.01<br>(0.05)|0.04<br>(0.15)|-0.07<br>(0.05)|-
# 1. INTRODUCTION

As the world’s poor in developing countries are disproportionately engaged in agriculture, increasing agricultural productivity and income is key to raising rural living standards <mark>(Castañeda et al. 2018; World Bank 2007)</mark> . Women comprise a large share of the agricultural labor force in developing countries – e.g., 49% in Africa, 37% in South Asia and 21% in Latin America (FAO 2011). Yet there is overwhelming evidence of a gap in agricultural productivity that is in favor of male farmers, which could lead to missed opportunities for improving the earnings of poor rural households.

A large body of earlier literature explored the sources of the gender productivity gap and found that women tend to have less access to productive resources such as land, labor, other inputs (e.g., fertilizer, improved seeds), credit, or extension services; experience lower quality inputs (e.g., inferior soil quality); <mark>have lower access to transport and markets; and face greater institutional constraints such as</mark> inadequate land rights, low tenure security <mark>, unequal gender norms and discrimination (e.g., Croppenstedt, Goldstein and Rosas 2013; FAO 2011; Quisumbing et al. 2014;</mark> and Udry 1996). Most studies found that the gender gap is reduced or even disappears, once controlling for input usage and other factors of production. It was subsequently argued that gender gaps could be overcome if female farmers had the same level of access to inputs and other agricultural resources as men. However, with a few exceptions, most of these earlier studies were limited in geographical scope.<sup>1</sup> A set of recent papers revisited the issue using nationally representative data (e.g., <mark>Aguilar et al.. 2015; Ali et al. 2016;</mark> Backiny-Yetna and McGee 2015; <mark>
(mazot), diesel oil (solar), LPG and gasoline – the cost shares of gross revenues that would H otherwise prevail without both protection and subsidies for an individual input, a gi , can again be written in terms of the currently observed cost share, a gi , and the ad valorem protection rates: And so the practical ERP formula above in Section 3.a. above for applied tariffs and the ad valorem tariff equivalents due to NTBs should be modified somewhat to account for

> 7 Lederman (2005) does not appear to apply the energy subsidies per unit of energy use from a particular source, SI, likely due to lack of appropriate detail in the input-cost-share data. As discussed further on, such source-specific subsidy data is now available from Abouleinein, , El-Laithy and Kheir-El-Din (2009).

7
Table A5: Estimation of propensity score for schools that joined Mais Educação in 2010, using pretreament data (2009)

||Panel A:|Fifth-grade schools|Panel B: N|inth-grade schools|
|---|---|---|---|---|
||Coefficient|_P_value|Coefficient|_P_value|
||(1)|(2)|(3)|(4)|
|∆ Portuguese grade|0.00|0.63|-0.01|0.00|
|∆ Mathematics grade|0.01|0.00|0.01|0.00|
|∆ Dropout rate|0.01|0.31|0.02|0.00|
|Portuguese grade|0.03|0.00|0.03|0.00|
|Mathematics grade|-0.05|0.00|-0.02|0.00|
|Dropout rate|0.04|0.00|-0.02|0.00|
|IDEB Index|-0.94|0.00|-1.18|0.00|
|∆ IDEB|0.56|0.00|0.80|0.00|
|Municipal population|0.00|0.00|0.00|0.00|
|Students' enrollments|0.00|0.00|0.00|0.00|
|IDHM|11.26|0.00|10.51|0.00|
|Urban|1.13|0.00|1.10|0.00|
|Basic sanitation|0.18|0.44|0.13|0.66|
|Library|0.15|0.00|0.01|0.81|
|Sports court|-0.08|