rafmacalaba/data-use-mentions-extended huggingface dataset:
did not include yet the potential congestion e¤ects of immigration on the labor market of receiving countries, nor did we examine the short-run e¤ects accounting for sluggish adjustment of physical capital to immigrants. This section deals with those issues and extends our robustness analysis.

## 5.1 Accounting for undocumented immigrants

By focusing on census data, our database fails to record undocumented immigrants in most countries, for whom systematic statistics by education level and country of birth are not available. An exception is the United States where demographic evidence indicates that most residents, regardless of legal status, are identi...ed in the census. However, other host countries provide no accurate data about the size and educational status of undocumented migrants. In their recent report, Kovacheva and Vogel (2009) estimate the size of irregular migrant populations in the European Union and in selected member states. Annex 1 of their paper provides lower and upper bounds for the proportion of undocumented in the immigrant population in 2002.<sup>13</sup> Data are available the for the 10 European countries in our sample - Belgium (11-18%), France (9-15%), Germany (14-20%), Greece (42-63%), Italy (53-75%), Netherlands (11-26%), Portugal (18-89%), Spain (8-29%), Sweden (1.7-2.5%), and the U.K. (11-21%).

Figure 6 gives the e¤ect of immigration on average wages when illegal immigration is taken into account where the ’Lower Bound’and ’Upper Bound’scenarios use the proportions listed above. Although there may be some instances of highly skilled undocumented migrants (such as those overstaying their H-1B visas in the United States), the majority of undocumented migrants are believed to be low-skilled. Hence, we make the two extreme assumptions that

> 13See http://irregular-migration.hwwi.net/Stock_estimates.6170.0.html.

16
and 38 .62; iii) s kewness = 0.25 and 0.25; iv) standard deviation = 0.61 and<br>2.45.<br>96% confidence interval 80% confidence interval<br>Gini index proxied(ROC economies) Gini index proxied(NROC economies)<br>(weighted by population) (weighted by population)<br><!-- End of picture text --> 96% confidence interval 40% confidence interval Poverty headcount of 3 percent 80% confidence interval Median Year = 2030 _Source:_ Penn World Table 9.0, World Development Indicators, World Bank PovcalNet, United Nations, and the author’s estimates.

Page **33** of **45**
electricity consumption but only a weak relationship between electricity consumption and GDP. For example, using a panel of Middle Eastern countries, Narayan and Smyth (2009) show that a one-percent increase in GDP leads to a 0.95 percent increase in electricity consumption, but a one percent increase in electricity consumption raises GDP by only 0.04 percent. In addition to the co-integration methodology dominating the literature, Inuwa et al. (2019) estimate the GDP-electricity consumption elasticity for the Economic Community of West African States with the System GMM estimator and produce a coefficient of 0.02.

The contribution of this paper to the literature is threefold. First, the paper uses a larger sample of countries than other studies that focus on regional data, and the additional information improves the estimators. Also, it provides elasticities for three sectors of agriculture, manufacturing, and services, which is important given the substantial variation in electricity intensity across sectors and its importance for sectoral growth. For example, electricity constraints might be less important for agriculture than manufacturing that relies primarily on energy-intensive technologies in production. In addition, sectoral elasticities help account for variations in the sectoral composition of GDP across countries and support better understanding of the implications of electricity constraints on GDP growth in various settings. Finally, the paper uses the panel GMM estimation approaches to address endogeneity and reverse causality bias in OLS estimates.

The estimation results indicate that electricity consumption has a positive and significant impact on the value added in all three sectors when using the sample of all countries, including high-income and nonhigh-income groups. However, the coefficients become insignificant for agriculture and services once high-income countries are excluded from the sample. These changes in estimation results reflect substantial differences in production technologies in agriculture and services between high- and non-highincome groups. Agriculture in developing countries primarily includes subsistence farmers who use manual labor to perform tasks, while agriculture relies on electricity-intensive technologies in advanced economies. At the same time, sectors with low-skill labor and manual tasks, such as trade, dominate services in developing countries
generally not, especially<br>recall surveys with aggregated<br>item list)|
|Direct<br>measurement|(+)|(+)|(−)<br>(computed as value divided by<br>quantity)|
|Collection<br>process|(−)<br>(especially with nonresident<br>enumerators)|(−)<br>(nonresident enumerators)|(+)<br>(based on actual transactions)|

_Source:_ Gaddis (2016, 10) Although not many Sub-Saharan African countries have conducted market price surveys (see the list of countries in Table A1 in Appendix A), those price data are potentially a good source for spatial price adjustment. The market price surveys are often conducted in a sample of communities or market centers close to the enumeration areas (EAs) in parallel with official nationally representative budget surveys. Typically, only one price is recorded for each item in each community. A major disadvantage of market price surveys is the lack of detailed quality information. Without detailed information about item descriptions, it is not possible to compare prices of the same products across regions. Gibson, Le, and Kim (2017) explain that Vietnam’s price survey was carefully designed to record detailed information for each product.

As stated earlier, CPI price data are another potential source for spatial price adjustment. Unlike market price surveys, CPI price data exist in almost all countries (Berry et al. 2019). Since the CPI is an indicator of inflation, it does not indicate spatial differentials in costs of living.<sup>10</sup> However, it is possible to calculate the spatial price index by using the CPI price data (not CPI values per se). The problem is that many countries collect price information only in urban areas (see Table A2 in Appendix A). Nevertheless, if the geographic coverage of the data is adequate, using the same price data for
actual tasks women perform, another one emerges: The interviews with rural women reveal some implicit definitions of work which affect how they conceptualize the tasks they themselves perform either on an occasional or on a regular basis. Altogether, the study finds three characteristics that - according to women - need to be in place to allow for certain tasks to be considered an economic activity, including ‘employment’ and ‘self-employment’. Conversely, if not in place, certain tasks disqualify from being identified as economic activities. First, these activities need to be carried out outside the home; second, they require an exchange of money for either goods produced, or services provided; and third, there needs to be a sufficient time commitment for certain tasks to qualify.

While few men and women in these communities seem to have access to regular employment for pay, it is important to note that they still respond differently to the household survey questions. Overall, there is a lack of paid jobs in the communities, and few men and women are involved in activities that secure a regular income. Men in the communities spend their time in precarious activities, either as day laborers or in small-scale agricultural production **.** Activities reported by men in the communities are mainly in agriculture, with about half of men respondents reporting that agricultural production is for selfconsumption, and half of respondents reporting that it is to sell in the market. Some men who sell agricultural production to the market complain of exploitative middle-men (coyotes). In the Southern Zones (Las Crucitas) where agricultural production is more challenging, men report working as day laborers in local estates (fincas). But this work is seasonal, exposing them to financial difficulties in seasons when crops are not being sold. Many men also report being landless, and those that can afford it rent plots of land. Importantly though, while some of those characteristics (lack of regularity, lack of direct income) affect women’s interpretation of ‘work’, this does not
# **<u>References</u>**

Adams, Richard H., and John Page. 2005. “Do International Migration and Remittances Reduce Poverty in Developing Countries?” _World Development_ , 33 (10): 1645–69.

Adams-Prassel, Abi, Teodora. Boneva, Marta Golin and Christopher Rauh. 2020a. “Inequality in the Impact of the Coronavirus Shock: Evidence from Real Time Surveys.” _Journal of Public Economics_ , 189: 104245.

———. 2020b. “Work Tasks That Can Be Done from Home: Evidence of the Variation Within and Across Occupations and Industries.” _Cambridge Working Papers in Economics_ 2040, Faculty of Economics, University of Cambridge.

Ahonen, Emily Q., and Fernando G. Benavides. 2006. “Risk of Fatal and Non-fatal Occupational Injury in Foreign Workers in Spain.” _Journal of Epidemiology and Community Health,_ 60: 424426.

<mark>Albanesi, Stefania, and Jiyeon Kim. 2021. “The gendered impact of the COVID-19 recession on the US labor market.” No. w28505. National Bureau of Economic Research.</mark> Aleksynska, Mariya and Ahmed Tritah. 2013. “Occupation–education mismatch of immigrant workers in Europe: Context and policies,” _Economics of Education Review_ , 36: 229-244.

Alon, Titan, Matthias Doepke, Jane Olmstead-Rumsey and Michele Tertilt. 2020. “The impact of covid-19 on gender equality,” _National Bureau of Economic Research_ , Working Paper 26947 doi:10.3386/w26947.

Amuedo-Dorantes, Catalina, and Susan Pozo. 2010. “Accounting for Remittance and Migration Effects on Children’s Schooling.” _World Development_ , 38 (12): 1747–59.

Autor, David H., and Michael J. Handel. 2013. “Putting Tasks to the Test: Human Capital, Job Tasks, and Wages,” _Journal of Labor Economics_ , 31 (S1): 59-96.

Autor, David H., Frank Levy and Richard J
trust funds to complement their bilateral programming when their aid agencies do not have sufficient presence or expertise in countries to implement programs effectively (OECD 2010: 40, 2011: 29). Donor officials state that the proliferation of trust fund aid is associated with a need for a new type of aid that is complementary to the existing multilateral and bilateral aid. They claim that multilateral core contributions to the World Bank and other MDBs cannot achieve some aims because contributions cannot be earmarked (IEG 2011a). The evaluation by the World Bank’s International Evaluation Group (IEG) (2011a: 6) thus concludes: “trust funds are a way to circumvent the allocation system of the MDBs’ [multilateral development banks] country‐based business model.”<sup>7</sup> Eligibility for IDA funds is based on being under a per capita Gross National Income (GNI) threshold and on a lack of access to non‐concessionary lending. Replenishments of IDA, and IDA policies regarding allocations and other issues, are negotiated every third year by donor countries. Throughout our sample period, IDA resources were allocated according to an explicit rule taking into account recipient need (as measured by low per capita income) and the quality of economic policies and governance, where the latter is assumed to increase aid effectiveness (e.g., Burnside and Dollar 2000, 2004). The World Bank’s “Country Policy and Institutional Assessments” (CPIA) measure the quality of policies and institutions, and are the most important element in the formula

> 7 The IEG (2011a) also found that six out of eight donors direct aid resources through trust funds “to issues or countries of national policy or public interest” and that five out of eight donors use trust funds to influence the World Bank. A UK official stated that his government supports large global funds in climate change, health and education because of “impatience with the existing multilateral system” (IEG 2011a: 6).

4
# **Export Diversification from an Activity Perspective: An Exploration Using Occupation Data**

Hagen Kruse<sup>a</sup> , Marcel Timmer<sup>a</sup> , Gaaitzen de Vries<sup>a</sup> , Xianjia Ye<sup>a</sup> a Groningen Growth and Development Centre, Faculty of Economics and Business, University of Groningen, the Netherlands _Keywords_ : Export diversification; International trade; Structural change; Occupations; _JEL codes_ : F19, O14, O33

## Acknowledgments

This paper is commissioned as part of the project ‘Leveraging trade for more and better job opportunities in developing countries’ by Maryla Maliszewska and Deborah Winkler, a product of the EFI-MTI-TIC-Trade and RI (ETIRI). The project 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 g.j.de.vries@rug.nl.
<!-- Start of picture text --> Ven. registered in RAMV<br>} [_Jo-10<br>[11 - 50<br>51 - 1,000<br>O.% MM 1,001 - 10,000<br>BSN MM 10,001 - 49,591<br>eat Ven. surveyed<br>2 catathe” ig i e 01-5<br>a agOe ce @ 6-100<br>NeOPOUt oaa) > @ 101-500<br>GS hn<br><!-- End of picture text --> <!-- Start of picture text --> .<br>“a venous Census 2018<br>Eoin -<br>ae — ee<br>wn . 2 An a HH 1001“Ty- 10,000<br>Wee eas tae! PAGESge WEE 10,001 -- 166,566<br>a<br>on ve he i LEY a<br>ye fans<br><!-- End of picture text -->
0313)|(0.0302)|
|Policy credibility|0.348***|0.346***|
||(0.0584)|(0.0585)|
|Positive perceptions||0.0338|
|of individual situation||(0.0290)|
|Region|X|X|
|Urban/rural|X|X|
|Gender|X|X|
|Income|X|X|
|Marital status|X|X|
|No. of HH members|X|X|
|Income|X|X|
|Education|X|X|
|Employment status|X|X|
|Observations|5,916|5,916|

Standard errors clustered by region in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Data source: World Bank survey on trust (2021) 20
from this model shows that in an economy in which consumers have homogeneous skills (productivity) and there are different degrees of firm ownership, the negative price effect of market concentration is greater than the positive profit effect of firm ownership so that greater market concentration increases the poverty index.

The second set of results shows how in an economy wherein consumers have heterogeneous skills, there is a range of market concentrations for which the positive profit effect offsets the negative price effect as long as the degree of firm ownership among low-income consumers is sufficiently large and there is a significant productivity gap between less and more highly skilled workers. This second set of results suggests that declines in poverty in this context are only possible in the case wherein the poor have access to a share of oligopolistic rents. Although this scenario seems highly unlikely, this result has important implications for public policy for economies with less-than-perfect markets and where the social welfare function is to eradicate poverty. Following the fundamentals of the second welfare theorem, this result suggests the possibility for governments of taxing extranormal rents extracted by firms with market power and redistributing them through targeted _lump-sum_ social transfers, thereby contributing to poverty reduction by offsetting the welfare losses deriving from the price effect. The policy implications of these results follow Auriol and Warlters (2004), who find that raising the barriers to entry can be consistent with a deliberate government policy aimed at boosting tax revenue. Market entry fees, the authors argue, encourage the emergence of large taxpaying firms that collect rents and are easy to tax at low administrative cost through entry fees and profit taxes.

3
# FIGURE 4 Exchange rate responses to domestic shocks

# A. Monetary policy shocks

<!-- Start of picture text --> Percentage points<br>3.5 Interquartile range Median<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>All Advanced EMDEs<br>economies<br><!-- End of picture text -->

# C. Domestic demand shocks

<!-- Start of picture text --> Percentage points<br>1.2 Interquartile range Median<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>All Advanced EMDEs<br>economies<br><!-- End of picture text -->

# E. Domestic supply shocks

<!-- Start of picture text --> Percentage points<br>1.0<br>Interquartile range Median<br>0.5<br>0.0<br>-0.5<br>All Advanced EMDEs<br>economies<br><!-- End of picture text -->

# B. EMDEs:  Monetary policy shocks

<!-- Start of picture text --> Percentage points<br>4 Interquartile range Median<br>3<br>2<br>1<br>0<br>Trade Exch. Inflation Energy Current<br>openness regime target account<br>D. EMDEs: Domestic demand shocks<br>Percentage points<br>2 Interquartile range Median<br>1<br>0<br>Trade Exch. Inflation Energy Current<br>openness regime target account<br>F. EMDEs: Domestic supply shocks<br>
empirical work, H, is essentially equated with the quantity of schooling. Given that cross country data on school attainment is readily available, there is a large empirical literature on explaining earnings differentials and returns to schooling. The Mincer earnings function takes the general form: where E is earnings typically measured in logarithms, S is school attainment, Z is labor market experience and W is a vector of other factors affecting earnings. Barro and Lee (1993, 2001) developed internationally comparable data on years of schooling using a combination of census data, literacy data and enrollment data. Using Barro-Lee data, the estimated return to schooling in (2) is measured by β1 . However, this estimate is biased since school attainment, S, is correlated with factors like ability and family inputs. In fact, test scores have been added to the Mincer model above to control for such ability differences. If the definition of human capital is expanded to include non-cognitive skills (interpersonal skills, communication ability, ability to work with teams, etc.) estimating E becomes even more complicated.<sup>8</sup> Over the last 30 years, a substantial amount of attention has focused on estimating the economic returns to schooling worldwide as depicted in (2). A fundamental econometric issue in many of these studies relates to causal effects; if people with more ability also tend to obtain more schooling, separating returns to schooling as distinct from returns to ability is not

> 8 See Lemieux (2006) for a retrospective article on the performance of the Mincer equation 30 years after its publication. Lemieux concludes that the Mincer equation has held up remarkably well in estimating wage determination provided some adjustments are made such as including a quadratic term to account for the convex relationship between schooling and earnings.

9
026|0.030|0.044|0.010|
|# of EAs|433|433|433|433|290|143|
|N|33691|33691|28806|28548|20907|7641|

_Notes:_ This table provides estimates of the impact of logarithm refugee intensity on whether an individual has had salaried employment. Individual sample weights from ESS2 applied. The first four columns report results for all individuals while columns (5) and (6) report results for rural and urban individuals, respectively. All columns control for round FEs. Further, column (1) controls for woreda FEs, column (2) controls for EA FEs, and all other columns control for individual (ID) FEs. Columns (4) to (6) additionally control for 12 time-varying characteristics. The coefficient estimates for these 12 control variables are not reported here, but can be available on request. Robust standard errors clustered at EA level are given in parenthesis. I report the overall and the within R-squared in the first two and other columns, respectively. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

46
1 input-output relations such as the number of workers per client or connection. The _frontiers_ recognise the much more complex nature of interactions between inputs and outputs. The _cost frontiers_ show the costs as a function of the level of output (or outputs) and the prices of inputs and is generally much more useful to regulators who are assessed to assess the wedge between tariff and minimum costs. The _production frontiers_ reveal technical relations between inputs and outputs of firms and provides a useful backup when cost frontiers are not easy to assess due to lack of data.' The inclusion of control variables in the specification of the functional relations estimated ensures that the various operators of a same activity are effectively comparable. Indeed, once the frontier has been estimated, the efficiency of a specific operator can be assessed in relation of the performance of the best operators in the industry when these are confronted with the same factors constraining the performance of the operator being assessed.<sup>2</sup> The productivity indicators although theoretically inferior to efficiency frontiers are quite commonly used by regulators to assess the performance of utilities. They are useful complements to efficiency frontiers, but seldom good substitutes. However, in most countries, data limitations makes them the only game in town so they tend to be used in conjunction with various types of quality indicators to obtain a multidimensional snapshot of a firm's performance. Moreover, the experience suggests that efficiency frontiers are not flawless. Even when the data controlled by the firm can be requested from the operators, it is not seldom that the data needed to identify the specific constraining characteristics of the activity analysed. This results in an impossibility to decompose the degree of efficiency that the firms can control from factors that influence costs but l In choosing between the estimation of a production or a cost function, the regulator needs to take into account the specificities of the sector he/she is working on. An important characteristic in regulated utilities is that in general, the firms are required to provide the service at a
***|0.000|420|3.8|0.5|0.8|0.156|274|
|Internal evaluations of staff|6.7|-4.0|8.5|0.265|418|5.9|3.6|1.3|0.350|262|
|Number of staff meetings in past<br>3 months|9.8|-1.6|4.2|0.518|420||||||
|Solicitation of patient opinion|0.660|-0.078|0.246|0.425|420|0.371|-0.143|0.355|0.385|278|
|Staff leaving the facility in past<br>12 months:|||||||||||
|Number left|0.6|0.1|-0.3|0.782|420|0.4|0.2|-0.1|0.605|278|
|Any left|0.302|-0.091|0.008|0.939|420|0.129|0.138|0.091|0.695|278|

Note: data from health facility assessments. All regressions are the difference-in-differences specification with facility fixed effects and controlling for exposure to the collaborative quality improvement and citizen scorecards interventions. Standard errors are clustered at the district and survey round level with wild bootstrapping. Level of statistical significance: *p<0.10; **p<0.05; ***p<0.01 25