rafmacalaba/data-use-mentions-extended huggingface dataset:
Table 4 provides a summary of the funding needs under these assumptions by regions and lending categories. Sub-Saharan Africa and IDA countries explain the vast majority of the total estimated cost, 76 percent and 82 percent, respectively, which supports the World Bank’s commitment to supporting data production in the poorest countries. The total cost of technical assistance provision in all countries in support of a total of 390 surveys across the 15-year period of interest will be approximately **USD 253 million** , assuming a uniform technical assistance provision cost of USD 540,000 per survey that grows at the same rate as the survey implementation cost. **<mark>The total survey implementation and technical assistance costs amount to USD 945 million,</mark>** <mark>of which survey operation costs explain 73 percent.</mark> A main challenge of this estimation exercise is that for most of the 78 countries, actual unit cost estimates are not available and costs need to be imputed from our cost database of 18 countries. There are at least three approaches that could be used for the imputation of the unit costs. For our baseline estimates of USD 690 million for the cost of survey operations, we assumed that the unit cost of a country is the same as the regional average unit cost. A clear concern here is that East Asia and Pacific and Europe and Central Asia each have one actual unit cost estimate. Average unit costs can be calculated by country income classification as well. Finally, the average of all 18 countries could be chosen as the unit cost for all of the 78 countries.

Table 5 includes total survey implementation and technical assistance cost estimates based on 1) whether the region-specific, the income class-specific, and the global average unit cost is assigned to each of the 78 countries of interest; and 2) different assumptions on sample size (5,000; 7,000; 9,000; 11,000) and annual growth rate of unit costs (0 percent; 2
# Introduction

Internet surveys generally cannot match the coverage and quality of their more traditional in-person counterparts, but they remain important tools for rapid, remote, and low-cost data collection. This is particularly useful to track changes in response to a shock, as during the COVID-19 pandemic, or to provide an additional way to reach people when security or other circumstances preclude a face-to-face strategy, as in some fragile and violence-affected contexts. The self-administered online format may also encourage more honest responses to sensitive questions than are usually expected of an interviewer-administered survey. One case in which these attributes may be particularly valuable is in tracking exposure to violence and the various demographic, socio-economic, and mental health variables with which that exposure may be associated.

The West Bank and Gaza Poverty and Equity team had a unique opportunity to compare a Facebook survey with an in-person survey on these topics in 2022. Following the spike in violence in the West Bank and the attacks on Gaza in mid-2021, the World Bank co-led the production of a Rapid Damage Needs Assessment (RDNA). During this process it became clear that there was a large gap in the understanding of the mental health impacts on a population that is subject to continuous exposure to violence and conflict. To fill this gap, the West Bank and Gaza Poverty team together with local and international partners<sup>3</sup> designed and implemented the first nationally representative survey of mental health outcomes among Palestinians. The survey was conducted between March and May 2022. At the same time, the World Bank team ran a web-based survey on Facebook which matched, to the extent possible, the same questions contained in the in-person survey questionnaire. This Facebook survey built on the experience and expertise of the MENA Data Lab, which supported 15 online surveys advertised through Facebook in 2022/2023.

The main report on the in-person survey<sup>4</sup> outlines the relevant context of the West Bank and Gaza as well as theoretical frameworks linking mental health outcomes to socio-economic outcomes, experience of adversity
Table 1: Unionization shares by industry

||1996|2011|
|---|---|---|
|Mining and quarrying|0.71|0.75|
|Agriculture, hunting, forestry and fish|0.07|0.06|
|Manufacturing|0.43|0.36|
|Electricity, gas and water supply|0.44|0.47|
|Construction|0.20|0.11|
|Wholesale and retail trade|0.25|0.18|
|Transport, storage and communication|0.46|0.29|
|Financial intermediation, insurance, etc.|0.23|0.22|
|Community, social and personal services|0.41|0.57|
|Private households|0.12|0.00|

Notes: Share of unionized workers by industry. Own calculations based on 1996 October Household Survey and 2011-q1 Labor Force Survey.
by differences in some of the non-monetary variables used in the consumption model (reported in Table 2), which are likely driven by the two surveys having different data collection schedules. Yet, we argue that the difference is small enough and, in a context of limited data availability, this result lays reasonable pre-conditions to impute back into previous GHS rounds.

> 19 The imputation in the 2018/19 GHS should be considered as a check to the validity of the imputations in previous rounds. The official poverty estimate for 2018/19 should be calculated using NLSS data.

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nations accounting for at least 90 percent of world IT trade. These zero-tariff bindings were on an MFN basis and thus available to exports from any other WTO member country.

Table 4 reports average tariff and non-tariff trade costs across SIC4 industries for five-year interval from 1992 to 2002. European tariff rates decline across a broad range of industries over time in the Electronic sector. Indeed, over the entire period, tariffs were halved for approximately 40 percent of industries. The rate of tariff declines, however, varies substantially across industries. According to the directives laid down by the Lugano and the Dresden agreements, European product standards have progressively been harmonized to international norms. The decline in the non-harmonized share of standards also differs across industries. The highest reduction is among industries producing household appliances, including cooking equipment, refrigerators, laundry equipment, and vacuum cleaners.

In addition to being a good match to the theory, the trade costs constructed here have several advantages. First, they are derived directly from a database used by firms to document the regulation requirements to export to the European Union. Second, they vary across industries and time. Even with these advantages, some caveats should be noted. First, the EUESDB does not

> 17Blind (2010) proposes a partial concordance from ICS codes to SITC codes. However, this bridge is too aggregate at the ICS level so individual SITC codes are mapped to a large number of ICS codes.

> 18PERINORM is a bibliographic database maintained by the British, French and German standard-setting bodies. It is designated to facilitate industry access to product standards and technical regulations.

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We use data from Morningstar Direct on the asset level portfolios of mutual funds to understand the different types of investors holding emerging market</mark>

> 14 In the Appendix, we provide additional results using jointly high-yield developed market firm bonds and emerging market firm bonds.

> 15 SDC Platinum contains a category that classifies the type of bond issued, which sometimes conflicts with our classification using the nationality of the issuer. If this category indicates that an emerging market firm issues the bond, we classify it as such regardless of the nationality of the firm provided by SDC. This affects only 300 observations (1.5% of our sample).

15
for two neighboring economies of relaxing transport and trade barriers, based on policies currently being considered. The analysis focuses on the aggregate impact of increasing market access and the extent to which it affects not only border regions but locations far away from the countries’ borders.

The model developed allows for multiple regions (districts and states) that trade differentiated products while facing bilateral trade costs. As in Allen and Arkolakis (2014), the trade costs are proportional to the least-cost route connecting any pair of locations, given the countries’ road networks. Workers who consume traded and nontraded goods are imperfectly mobile across regions. Along the lines of Krugman (1991), each region features free entry of firms, each with increasing returns, and thus positive agglomeration externalities.

The quantitative exercises are based on data on economic activity (employment and wages) across districts in Bangladesh and states in India, along with GIS data on the road network of each country. The GIS data allow changes in trade costs to be estimated more precisely than they could be using standard distance-based measures, given the heterogeneous conditions of roads in these countries. Using these data, we recover trade flows across locations and calibrate the rest of the parameters to different moments. We use standard values from the literature for the elasticities in the model.

By reducing transport and trade costs, the MVA would reduce the prices of final goods and intermediate inputs. These reductions would lead to increases in national real income of up to 7.6 percent in India and 16.6 percent in Bangladesh. Increasing the competitiveness of some states and districts more than others would increase economic activity in some areas, where firms would have to pay higher wages to attract enough workers to take advantage of the improved competitiveness and access to markets. The relocation of economic activity would lead to reductions in spatial wage inequality in India and a marginal increase in Bangladesh.

This paper is related to several strands of research. It contributes to the international trade literature on economic geography, including the work of
simulation results to the case of a differing pace of structural transformation across regions. Section VI explores the change in the distribution of regional intersectoral productivity gaps, in conjunction with recent patterns of structural transformation in Ghana. Section VII concludes.

# **II. Background**

# A. _Regional incomes_

Regional GDP estimates are unavailable for Ghana. Consequently, to obtain a picture of trends in income across regions, we use trends in average consumption (per adult equivalent) as a proxy. Figure 1 plots average consumption for nine of the country’s 10 regions benchmarked to Greater Accra (the richest region) in 2005/06 and in 2016/17, based on data from Ghana Living Standard Survey (GLSS) rounds. The GLSS are multi-topic, national household sample surveys administered by the Ghana Statistical Service (GSS) with data representative at the region level.<sup>3</sup> The regions are organized from the poorest (Upper West) to the second richest (Ashanti).

Regions have generally observed a growing gap in average consumption relative to Greater Accra. In addition, this growing gap is generally larger (in percent terms) for poorer regions than for richer regions. The pattern in trends suggests that poorer regions are falling behind Greater Accra at a faster rate than richer regions.

# B. _National and regional sectoral employment shares_

Figure 2a plots trends in employment shares by sector (agriculture, industry, and services) at the national level between 1960 and 2018 (almost a 60-year period). Ghana did not exhibit any strong signs of structural transformation during the subperiod from 1960 to 2000 (during the first 40 years of the full period); sectoral employment shares showed some fluctuation during this subperiod but no sustained upward or downward trends. Across this subperiod, agriculture accounted for, on average, about 55 percent of overall employment, followed by services (about 30 percent) and industry (about 15 percent).

Since the early 2000s, the employment share in agriculture began to decline
foregone earnings could not be calculated because there are too few cases in the CILSS for which income from child labor is reported.

_Demand factors._ As a household survey, the CILSS does not furnish data on employment opportunities for children. Likewise, the data on wages for children cannot be used here because the number of cases of reported wages are too few to use a cluster- or region-specific averaging procedure (as we did for the cost of schooling) to produce a useable exogenous measure of wages. Hence, the model below does not include any direct demand variables. As a (weak) proxy, we have included in the model dummy variables for the region of residence of the household.

# A **Model for the Determinants of Child** Labor

Several formal models of the household economy that explicitly take into account the economic contributions of children have been discussed in the literature (Levy, 1985; Rivera-Batiz, 1985; Sharif, 1994). Much of this work is based on Rosenzweig and Evenson (1977). The setting is the standard constrained utility maximization model of the household. A consumption vector is maximized, subject to the resource endowment of the household and the market determined returns to these assets. A structural formulation of this assets deemed exogenous. The child labor equation can then include an instrumental variable for income, e.g. its value predicted by the first equation.

31
- (iv) The investment data only exist up to 1998 which is before the major recent downturn. An obvious issue is whether countries with better regulatory frameworks have had a smaller fall in private investment  following the post1999 downturns.

Pargal reports estimates from two fixed effect regressions to explain the determinants of private investment levels. The equations are pooled across all 5 sectors and all 9 countries in her sample. The first set of estimates covers all years and the relevant regulatory variable is a simple 0/1 dummy for each sector, country or year on whether or not a regulator existed (in legal terms).

The second set of estimates reports the effect of a variety of 5 major regulatory governance features:

- 1) Regulator inside or Outside the Ministry 2) Appointment of Regulatory Commissioners by Executive alone or with involvement of Legislature

- 3) Number of regulatory commissioners (Note not regulatory _staff_ numbers.) 4) Regulator funded by central government 5) Rate or return or price cap regulation.

The equations are estimated on the pooled sample but only for the years after the regulatory body for the country and sector was (legally) established.

Finally, regressions for the 9 countries with the single regulatory variable were estimated separately for private investment in each of the five sectors.

The main result is that the variable for legislation to open up the sector consistently dominated the regulatory variables. This is true both for the pooled regressions and the individual sector (including the electricity industry) regressions. Indeed, the simple regulatory variable never had a t-value greater than 1. The question is whether or not this is a robust finding.

We have pointed out above that the ascription of the relative effects of regulation and liberalisation is very difficult – particularly if they are both measured by legislative indicators and the change was in the same law. This is confirmed empirically. Pargal notes in a footnote that the coefficient on the regulatory variable had higher t-values when the liberalisation variable was omitted<sup>48</sup> . Unfortunately, we
- Clarke, G., C. Z. Qiang, L.C. Xu. 2015. “The Internet as a General-Purpose Technology.” _Economics Letters_ 135, 24-27.

- Clarke, G., Y. Li, and L.C. Xu. 2016. “Business Environment, Economic Agglomeration, and Job Creation around the World.” _Applied Economics_ , 48(33), 3088-3103.

- Claessens, S., E. Feijen, and L. Laeven, 2008. “Political Connections and Preferential Access to Finance: The Role of Campaign Contributions.” _Journal of Financial Economics_ 88(3): 554-580.

- Cull, R., L. Gan, N. Gao, L.C. Xu. 2019. “Dual credit markets and household access to finance: Evidence from a representative Chinese household survey.” _Oxford Bulletin of Economics and Finance_ 81(6), 1280-1317.

- Cull, R., W. Li, B. Sun, and L.C. Xu. 2015. “Government Connections and Financial Constraints: Evidence from a Large Representative Sample of Chinese Firms.” _Journal of Corporate Finance._ 32: 271-294.

- Cull, R., L.C. Xu, and T. Zhu. 2009. "Formal Finance and Trade Credit During China's Transition," _Journal of Financial Intermediation_ 18(2): 173-192.

- Croqvist, H., F. Yu. 2017. “Shaped by their daughters: Executives, female socialization, and corporate social responsibility.” _Journal of Financial Economics_ 126, 543-562.

- Croson, R., U. Gneezy. 2009. “Gender differences in preferences.” _Journal of Economic Literature_ 47(2), 448-474.

- Croson, R., and N. Buchan. 1999. “Gender and culture: International experiment evidence from trust games.” _American Economic Review_ 89(2), 386-91.

- Cull, R., and L.C
a shorter labor module for wage employees. If both household and labor force surveys are available, the labor force surveys provide better coverage of the four dimensions even if only for wage employees.

Based on the sample of 40 developing countries used in the analysis, the two most common dimensions covered in the data are income and working conditions, followed by benefits and job security. Little additional information is available to capture separate potential fifth and sixth dimensions on safety and training, respectively. This analysis considers not only the availability of survey questions, but also the share of valid responses to survey questions, the implicit assumptions built into the questionnaire, such as whether universal health care should be provided, as well as how much additional information is gained by including an indicator. For instance, self-reported indicators that are found to be positive for virtually all respondents, such as subjective skill level, do not add much information because most respondents would not report that they are unqualified for their current job.

Labor force and household surveys classify employment types differently. For example, labor force surveys distinguish among several types of workers and often have country-specific employment categories. For the countries in which the analysis relies on household surveys, only general employment status is available. As a result, this paper chooses four broad employment categories as dictated by the survey with the fewest number of categories: wage employees (formal or informal), self-employed workers, unpaid workers, and other workers.<sup>19</sup> However, the quality of the data across employment types

> 19 Other workers include employers and individuals with job descriptions that do not align with the other three categories, such as apprentices. The survey with the fewest number of categories is the 2017 Labor Force Survey in Türkiye.

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Methodology. Our method consists in collecting Census or register data from a large set of receiving countries, with the highest level of detail on birth countries and (at least) three levels of educational attainment: s = h for high skilled, s = m for medium skilled, s = l for low skilled and s = u for unknown. Low skilled workers are those with primary education (or with 0 to 8 years of schooling in countries where the highest diploma is not provided); medium skilled workers are those with secondary education (9 to 12 years of schooling); high skilled workers are those with tertiary education (13 years and above). The unknowns are either due to the fact that some immigrants did not declare their educational attainment or to the absence of data in some receiving countries. Educational categories are built on the basis of country speci...c information and are compatible with Barro and Lee data on education in the sending countries. A mapping between the country educational classi...cation is sometimes required to harmonize the data<sup>6</sup> . Let Mt;s<sup>i;jdenotethestockofadult</sup> individuals born in j, of skill s, living in country i at time t.

Building an aggregate measure of emigration per educational attainment requires a rule for sharing the unknown values. Two resonable rules could be considered: either unknown values can be distributed in the same way as the known values or they can be assimilated to unskilled. We combine both rules depending on the information available in the receiving country. For receiving countries where information about immigrants’ education is available, we assimilate unknowns to unskilled workers. For example, Australian immigrants who did not mention their educational attainment are considered as unskilled. In receiving countries where no information about skill is available, we transpose the skill distribution observed in the rest of the OECD area. Note that the latter skill distribution is speci...c to each country of birth. For example
’ _International Country Risk Guide_ and the measures of checks and balances and competitive elections from the _Database on Political Institutions_ .  These are discussed below.

> 2 Based on World Development Indicators PPP adjusted income per capita, and using the Database on Political Institutions variables _Executive Index of Electoral Competition_ ( _EIEC_ ) and _Legislative Index of Electoral Competition_ ( _LIEC_ ), and _Tenure of System_ ( _tensys_ ) which are explained in more detail below.
with a reduced supply of bank credit, suggesting that a bank lending channel was operative in Uganda, although it was weaker than in advanced economies.

# 2.5. Shortcomings in the analytical capacity of central banks

Because monetary policy affects the economy with lags, an important component of inflation targeting—or, for that matter, any other activist monetary policy regime—is the ability of the central bank to forecast with a modicum of accuracy its target variables on the assumption of unchanged policies as well as to assess the effects on those variables of potential changes in the settings of its instruments. In many advanced economies and non-LIC EMDEs, these tasks are performed using structural macroeconomic models of the economies in question. However, few LIC central banks have such models with proven track records (IMF 2015). Although work on such models is underway at many LIC central banks, the task is formidable, not least because of the lack of relevant historical data, insufficient knowledge about the macroeconomic structure of the economies concerned, rapid structural change in the economy, and shortages of research expertise. The analytical capacity of LIC central banks—even their ability to monitor and assess recent and current economic developments—is generally hampered by serious data deficiencies (Gemayel, Jahan, and Peter 2011; IMF 2015). Thus, data on economic developments in informal sectors, which are often large, are typically absent or grossly inadequate. Official estimates of real gross domestic product (GDP) are typically available only with annual frequency and often with substantial lags.<sup>6</sup> Labor market data, including on wages and unemployment rates, are generally poor. The absence of a well-defined term structure of yields in financial markets makes it difficult to assess market expectations of future monetary policy actions. Finally, estimates of inflation expectations are generally unavailable because of the absence of survey evidence and market-based measures derived from differences between yields on comparable indexed and non-indexed securities.

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