rafmacalaba/data-use-mentions-extended huggingface dataset:
a necessary condition to attain macroeconomic adjustment. Although often complicated by labor market regulations, the labor allocation between tradable and non-tradable industries is the fundamental ingredient to achieve structural adjustment. The presence of formal mechanisms of wage indexation has been usually mentioned as a deterrent of the wage flexibility needed for structural adjustment. In particular, the dynamic role of unions and minimum wages in pushing the entire wage structure ( and inflation) has also been pointed out as a crucial mechanism for creating wage rigidity in many countries. It is in this context that we examine whether the ERP was helped or hindered by the labor market and whether the reliance on market forces to reallocate and absorb labor was indeed justifiable.

This labor market study uses the Ghana Living Standards Survey (GLSS) data which provided sufficient information to monitor the trend in employment patterns, unemployment, migration, earnings, and the demand for labor. The study uses all three rounds of the GLSS data sets. The available data relate to the periods from September 1987-August 1988 (GLSS 1), October 1988-September 1989 (GLSS 2), and September 1991-September 1992 (GLSS 3)3.

This paper provides an overview of characteristics of labor markets in Ghana. Section 1 deals with the Supply of labor, labor force participation and its determinants. Section 2 is concerned with the absorption of labor- the sectoral pattern of employment, characteristics of the wage and non-wage workers, their sources of income- wages and earnings, characteristics of child labor, turnover rates and trends in earnings and employment. Section 3 deals with unemployment rates and its distribution by region. Section 4 deals with migration and the characteristics of migrant workers, while Section 5 deals with literacy rates, drop outs, characteristics of university graduates, returns to schooling and private and social returns to education. Section 6 concludes with a summary of major findings and implications.

> 3 One of the main issue with these three rounds of survey is the that of comparability, since the format of the questionnaire has changed slightly in GLSS 3
institutions that determine the provision of local public services (Acemoglu and Dell, 2010),<sup>7,8</sup> and migration frictions in the form of high trade costs (Acemoglue and Dell, 2010) and migration barriers (Skoufias and Lopez-Acevedo, 2009), Using household surveys from the early 2000s and Oaxaca’s (1973) decomposition, Skoufias and Lopez-Acevedo (2009) contribute a comprehensive study of spatial differences in welfare and their determinants within and across geographic regions above the first administrative level in 8 LAC countries.<sup>9</sup> Within any given region, they find that rural areas are poorer than urban areas primarily because of differences in education levels, and not differences in returns to education. Hence, they conclude that migration within regions has equalized these returns. By contrast, across geographic regions, differences in returns explain a larger share of the welfare gap, signaling that mobility barriers tend to grow with distance. Skoufias and Lopez-Acevedo (2009) also characterize the profile of domestic migrants, their incentives to migrate, and the characteristics of sending and receiving regions. Migrants are typically well-educated, young, and relatively well-off, come from regions with relatively low stock of skills, and therefore tend to drain the lagging regions from skilled workers, potentially exacerbating territorial inequality. Migrant remittances may have an offsetting effect (Acosta, Fajnzylber and Lopez, 2008), but research suggests that this effect is less likely to occur if most migrants are skilled workers. The latter tend to migrate on a permanent basis and are more likely to be affluent (Skoufias and Lopze-Acevedo, 2009). Kanbur and Rapoport (2005) also question the quantitative importance of remittances for reducing territorial welfare differences.

In sum, the existing literature relies on disparate data sources – either national accounts or household surveys – and looks at issues of territorial income differences and dynamics separately. Therefore, it provides an incomplete or inconsistent regional picture on the sources and evolution of territorial income differences in Latin America. By
shorter work history (if any) by age 22-24. At the same time, more experienced workers entering the labor market at younger ages (age 18-19) seem to be affected by the tax cut just like older workers: employment increases only at low-TFP firms (Table D2).

# **8 Conclusion**

This paper studies the implications of payroll tax cuts in a setup where firms play an active role in wage determination. A key prediction of our framework is that the tax policy can have heterogeneous impacts across firm types. High-productivity firms offering good jobs respond to the tax cut by raising wages, while low-productivity firms will respond mainly on the employment margin. As a result, the tax policy will change the composition of jobs in the economy.

We exploit the introduction of age-dependent payroll tax reductions in Hungary and use rich administrative data to provide empirical support for these predictions. In response to a large tax cut, we estimate that both employment and wages increased among treated 36
ENIGH 2006 from 20,900 households and 83,600 individuals; and the HBS 2006 from 8,600 households and 34,900 individuals. We restrict the samples to children aged 15, for which we have 7,626 observations in the PNAD 2006; 22,600 in the SUSENAS 2005; 1,921 in the ENIGH 2006; and 683 in the HBS 2006. Although some children in boarding schools and other institutions are likely to be out of the sample frame, those samples should otherwise be representative for the total population of 15 year-olds.

In these four countries, these are the staple surveys for assessing the distribution of household income and, in some cases, consumption expenditures. But they also collect information on other topics, including labor supply, education and migration. We use information on parents' characteristics for estimating the total population of 15 year-olds in groups defined by similar gender, mother's education and father's occupation. The classification of the family background variable can be made comparable with the ones in the PISA by appropriate aggregation of coding categories. Parental characteristics are missing for orphans, children who do not live with their parents, or whose parents did not report their education. For instance, the information on mother's education is missing for about 15.0% of 15 year-olds in the PNAD 2006, 8.7% in the SUSENAS 2005, 11.9% in the ENIGH 2006, and 3.8% in the HBS 2006. When comparing the two surveyed populations, children with missing parental background information in the household surveys are not dropped, but associated with those with the same information missing in the PISA survey.

# **3. Measuring Inequality in Educational Achievement**

Measures of inequality in educational achievement are based on distributions of standardized test scores ( _yij_ ), constructed from the IRT-adjusted scores ( _xij_ ) by means of a transformation such as equation (2). In the case of PISA, the transformation is given by (2) exactly
# **1 Introduction**

There is a growing body of literature on the impacts of labor-replacing technologies. Most of this literature is focused on developed economies, since they are at the frontier of adoption of new technologies, and concerns are rising about the negative impacts on workers. The findings of this empirical literature show that while these technologies can bring about significant efficiency gains (Graetz & Michaels 2017), they can also have negative impacts on employment outcomes (Acemoglu & Restrepo 2017).

However, evidence for developing countries is scarce. This is driven not only by data constraints, but also because poorer countries in general have not been as successful as their richer counterparts at adopting new technologies at such a large scale. This article argues that developing countries can still experience some disruptive effects by being exposed to automation in developed economies. For example, automation in the US may reduce the demand for Mexican goods if it allows to reduce marginal costs and prices of US products. If Mexico’s exports to the US decline as a result, this could in turn displace Mexican workers. In other words, automation in the US can substitute away workers in Mexico by disrupting trade.

Concerns about the destruction of jobs in developing countries that were originally off– – shored from high-income economies a process known as reshoring are rising. Several media outlets have reported that firms in the United States and Europe have started to repatriate some of their production processes.<sup>1</sup> However, empirical evidence about the existence and magnitude of this phenomenon is scarce. De Backer et al. (2016) report that even though company surveys and anecdotal evidence suggest that the reshoring movement is gaining steam, empirical evidence does not support such claims. In contrast, Allen (2017) argues that economic history suggests that some of the concerns about reshoring are warranted. While the first industrial revolution brought progress to Western economies, many

> 1See, for instance, Coming home (The Economist, January 19th 2013) and Reshoring
worker in the region as a whole (0.3 percent per year). We argue that the contribution of natural capital is important in countries of the region that are abundant in energy commodities (for instance, Chad, Republic of Congo, Gabon, and Nigeria) and metals and mineral ores (say, Botswana, Democratic Republic of Congo, and Zambia).

**Figure 4 Source of growth in Sub-Saharan Africa, 1996-2014: The role of natural resource wealth**

_(Annual average percentage change, population-weighted)_ <!-- Start of picture text --> 4.0<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>Sub-Saharan Africa SSA Oil Rich Countries SSA Metal Rich SSA Non-Resource Rich<br>(SSA) Countries Countries<br>Physical Capital Natural Capital Human Capital TFP Output<br><!-- End of picture text --> _Source: The data has been collected from PWT 9.0 (Feenstra, Inklaar and Timmer 2015)._ Figure 4 plots the growth decomposition that includes the role of natural capital for different country groups in the region. The contribution of natural capital, as expected, is greater among resource rich countries than among non-resource rich countries. During the period 1996-2014, oil rich countries grew at an annual average rate of 3.9 percent and natural capital explains about 10 percent of the observed growth (contributing with 0.4 percent per year). The contribution of natural capital to output per worker growth is significantly larger among countries in the region that are abundant in minerals and metals (with a contribution of about 1.2 percent per year). However, the contribution of TFP growth for this group becomes detrimental, thus
following regression: where we interact each term in regression equation (11) with the tax cut rate, _TCRit−_ 1. To calculate _TCRit−_ 1, we need to rely on the previous year’s wage and so we can only estimate this regression for workers who worked in the previous year (incumbent workers).

31 We only see annual income for employment relationships spanning the entire year. This is a common feature of administrative social security data (see e.g. German IAB data).

27
# **E.22 Geo-coding Procedure**

The VHF data includes information on individuals’ residences, including their district, chiefdom, and village or parish. We use this information to place observations within sections. Our geo-location protocol involves several steps. First, a human coder inspected and cleaned all district and chiefdom names that did not exactly match the conventional spelling. Of 85,410 entries in the case data, we can code the chiefdom of residence for 97% of observations.

Second, we employ fuzzy string matching to match the available village or parish names to gazetteer files of placenames from Sierra Leone. Fortunately, in the chiefdoms that include our sample, only 14 confirmed, suspected, or probable Ebola cases do not include village or parish information.<sup>A10</sup> We employ the gazetteer file from Open Street Map ( `www.openstreetmap.org/` ), which includes 9,975 entries, ranging from hamlets to cities. We prefer this list to the 2004 census data from Sierra Leone, which only provides names for around 5,000 localities. Moreover, during the Ebola epidemic, Open Street Map mounted a humanitarian effort aimed at updating and verifying information on the locations of villages and roads in Sierra Leone.<sup>A11</sup> Ten sample entries from OSM gazetteer file:

|`osm_id`|`name`|`c`|`oordinates`|
|---|---|---|---|
|`1 27565056`|`Freetown`|`(-13.26802 `|`8.479002)`|
|`2 314001434 `|`Bo`|`(-11.73665 `|`7.962065)`|
|`3 314005602 `|`Kenema`|`(-11.18639 `|`7.885936)`|
|`4 314007819 `|`Koidu`|`(-10.97163 `|
model to account for the interactions among households in the local markets for land and capital, while paying due attention to nonseparability of household decisions flowing from the presence of significant transactions costs.

Taylor and Adelman (1996) pioneered the use of general equilibrium models at the village level. Their model closely follows the structure of macro level models, for example by modeling production at the sector level, which misses the impact of nonseparability of household decisions. We take a different approach to village modeling, placing differences in household response due to nonseparability at the center of the model. This household perspective on general equilibrium modeling results in a different model structure than used in macro level general equilibrium models and in existing village level models. We model production activities as being household-specific. This allows for idiosyncratic household responses consistent with nonseparability of production and consumption decisions. A second major difference is the nesting structure used for modeling production decisions. For each activity the production structure is calibrated based on the household survey data<sup>1</sup> . As a result we have household specific production functions, capturing differences in household access to inputs<sup>2</sup> .

By placing households at the center of the model, the village model used in this study is able to capture differences in production decisions reflecting differences in access to inputs, interactions between household production and consumption decisions, and interactions among different households within a village economy.

> 1  Our estimation also rejected the commonly assumed separability of factors and intermediate inputs, and this assumption was therefore dropped in the village equilibrium model. 2  Detailed description of the village equilibrium model and calibration procedures can be obtained from the authors.

10
groups may not be connected, and thus do not benefit from any such subsidy. These households would therefore not be affected at all by an increase in user charges, except insofar as better cost recovery might permit utilities to expand services. As such, this analysis overstates the number of households that would actually be affected, and abstracts from the very real divide between low income households that benefit from connections and the subsidies that accompany those connections and those that do not. However, even correcting for this coverage gap still suggests that moving to full cost recovery could have widespread enough impacts that most policymakers would hesitate to move too far or too quickly in that direction.

> 11 Calculations are based on simulation of the cost of a minimum subsistence consumption bundle of both WSS and electricity services provided at the respective tariffs. Distribution of income data needed to estimate percentage of affected households was derived from household surveys.

17
Figure 5 reports the total number of restrictive measures registered between 2003 and 2022, where it is evident that there was a large increase starting in 2020. Figure 6 disentangles the measures into three types: export barriers, import barriers and industrial policies. Clearly, the latter ones are the predominant force leading the total number of measures reported in Figure 5.

The data suggest that the most prevalent restrictive measures take the form of subsidies, followed by trade related investment measures (see Figure 7a). In terms of liberalizing measures, the most frequent ones appear to be linked to the elimination of non-automatic licensing or quotas (see Figure 7b).

To capture the significance of the new trade restrictions and industrial policy measures, we also investigate the information at the most granular level offered by the Global Trade Alert database. We find that these measures include diverse, finely targeted, and technocratic forms of public support to production and exports, as well as financial guarantees. Unsurprisingly, the measures are mostly implemented by rich countries and some of the larger developing countries. For example, we see a significant increase in the use of export bans and subsidies in ICT and technology.

Table 1 reports two examples that allow understanding the virtues and potential limits of the GTA. Example #1 is an intervention by the US government to support General Motors and Chrysler through a capital injection. The intervention was announced on March 30, 2009, and is recorded as affecting only specific firms. The HS6 products affected by this interventions represent different types of cars (of different cylinder capacities), which, as is well known, are the main final products produced by General Motors and Chrysler. Example #2 reports instead the establishment of a China-backed state fund to promote the domestic integrated circuit industry. The fund was announced in 2014 for a duration of 10 years. The form of the intervention was state loans available to all firms. The HS6 products reported as potentially affected by this intervention include products at different stages of the semiconductor value chains, from
have analyzed the UNPS. Sheehan and Barrett (2017) provide a broad overview of input use in six Sub‐ Saharan African countries using the 2010 wave of the UNPS. Their work highlights the relatively low input use in Uganda compared to Ethiopia, Malawi, Niger, Nigeria, and Tanzania, and that the average amount of inorganic fertilizer applied per hectare of 0.7 kilograms for Uganda is substantially lower than the cross‐ country average of 56 kilograms per hectare (see Figure 2.3).

**Figure 2.3. Use of inorganic fertilizer and application rates, 2010**

<!-- Start of picture text --> 90<br>77.3<br>80<br>70 64.3<br>60 56.3 55.5 56.9<br>50<br>41.4<br>40 35.2<br>30 25.2<br>17 16.9<br>20<br>7.7<br>10 1.7 3.2 0.7<br>0<br>Malawi Ethiopia Nigeria Niger Tanzania Uganda Average<br>Fertilizer use (% of HH) Average nutrient application rate (kg/ha)<br><!-- End of picture text --> Source: Sheehan and Barrett (2014), using comparable LSMS‐ISA data.

The potential gains to input use appear quite high in Uganda given cultivatable land and output prices compared to other LSMS‐ISA countries. Binswanger and Savastano (2014) develop a measure of agro‐ ecological potential (AEP) using international crop prices, the share of land under each crop, and the potential of the crop in that area from the 2005/06 and 2009/10 LSMS‐ISA waves. The AEP is based on attainable crop yields across all agricultural zones using data from the International Institute for Systems Analysis and the Food and Agriculture Organization for medium input levels (Tóth
# **4. Data and Descriptive Statistics**

The first wave of the Somali High Frequency Survey (HFS)<sup>6</sup> was conducted in 2016. The second wave conducted in 2017 extended coverage to include nomads and households in insecure areas. The sample of IDPs and non-displaced Somalians is representative of the Somali population living in secure areas.<sup>7</sup> We use the second wave of the HFS which samples 4,780 households (26,317 individuals). IDPs account for 33 percent of the survey population, most of whom reside in IDP settlements in the South West and in urban Jubbaland.

**Table 1. Household and household head characteristics, by displacement status and gender**

||Settl|ement I|DP|Non-se|ttlemen|t IDP||Non-IDP|||Total||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Female|Male|All|Female|Male|All|Female|Male|All|Female|Male|All|
|_Female-headed (%)_|||55.4|||41.7|||51.0|||50.5|
|De jure|31.0||31.0|25.6||25.6|26.7||26.7|||27.0|
|De facto|68.9||68.9|74.4||74.4|73.2||73.2|||72.9|
|_Household characteristics_|||||||||||||
|Urban (%)||||38|32|34|79
study migration. Despite some recognized limitations, _multi-topic_ household surveys hold great potential when it comes to analytically addressing complex, multidimensional phenomena such as climate and migration, which require data on multiple livelihood dimensions and possibly over extended periods of time. When georeferencing is used in household surveys it allows for integration with other data sources, offering even greater potential to achieve higher levels of accuracy and granularity at lower cost as well as to increase the relevance of survey data production and their downstream reuse (e.g., for ground-truthing and calibration purposes).<sup>6</sup> In the last few years there has been a progressive push towards repositioning household surveys in the face of an evolving data environment and new demands for policy-relevant data in different fields (Carletto and Gourlay, 2019; Carletto, 2021; United Nations Statistical Commission, 2021). The COVID-19 pandemic accelerated this process and proved that household surveys are flexible enough instruments to respond effectively to modern data collection challenges. New modes of data collection as well as integration with alternative data sources have been tested and validated. Leveraging technological innovations, more can be done in the near future to improve survey methodologies and data collection methods (Zezza et al., 2022;

> 6 This, of course, must be done in compliance with adequate security standards to protect confidentiality (United Nations Statistical Commission, 2021).

4
components of<br>aggregate demand, 2014<br>Percent<br>40<br>30<br>20<br>10<br>0<br>Investment Exports Private Government<br>consumption expenditure<br><!-- End of picture text -->

- F. New trade measures

<!-- Start of picture text --> Number of new policy measures<br>Trade restrictions<br>2,000<br>Trade restrictions excl. U.S.<br>1,600 Trade liberalization<br>1,200<br>800<br>400<br>0<br>2009 2011 2013 2015 2017 2019 2021<br><!-- End of picture text --> Sources: Auboin and Borino (2018); Constantinescu, Mattoo, and Ruta (2020); CPB Netherlands Bureau for Economic Policy Analysis; Global Trade Alert Database; World Bank.

Note: EMDEs = emerging market and developing economies; GVC = global value chain.

A. World trade refers to average of imports and exports, indexed to 1970 = 100. The historical trend is computed over 1970-2008, smoothed using a Hodrick-Prescott filter.

B. Estimates from an error correction model estimated over the period 1970-2019. The model allows both the long-run elasticity of trade with respect to income (which captures trend, or structural, factors) and the short-run elasticity (which is relevant to short run or cyclical developments). For further details on the model specification, see Constantinescu, Mattoo, and Ruta (2020).

C. Trend levels in 2019 are obtained on the basis of the historical average trend growth computed over the period 1995-2008 and rebased to 100. Bars below 100 show deviations of actual 2019 levels from