rafmacalaba/data-use-mentions-extended huggingface dataset:
in 1990-94 to 31.4 percent of GDP in 2010-14) while intra-regional trade remained almost unchanged at about 30% of GDP over the past two decades.

# **3 Trade and Growth: A Detailed Econometric Analysis.**

This section estimates the long-term growth effects of the different dimensions of international trade integration. The regression analysis is conducted on (unbalanced) panel data of non-overlapping 5- year period averages for 174 countries (of which 45 countries are located in Sub-Saharan Africa) from 1970 to 2014. This section not only evaluates the impact of international trade integration on economic growth, but it also examines the effects of international trade on the sources of growth; namely, growth of capital per worker and total factor productivity (TFP) growth.

## **_3.1 International trade integration and economic growth_**

**Table 2** reports the GMM-IV system estimation of growth per worker on the different dimensions of trade integration; namely, trade volume, export product concentration, export market concentration, and the dependence on natural resource exports. Column [1] reports the effects of trade, and market and product concentration while controlling for the initial level of GDP per worker. This specification will be referred to as the baseline specification for the remainder of this section. Column [2] adds the ratio of natural resources exports to GDP to the baseline specification. Column [3] adds growth determinants like education (as measured by the secondary enrollment rate) and institutional quality (proxied by the ICRG Political Risk Index). Finally, column [4] adds financial development (credit to the private sector as % of GDP), government consumption (as % of GDP) and growth volatility to the specification in column [3]. The initial level of real output per worker, expressed in logs and included in all the specifications estimated in Table 2, has a negative and significant coefficient. This is evidence of conditional convergence in labor productivity.

Trade openness, as measured by real exports
obtain the age-specific labor participation rates by household.

National average school participation rates by age are calculated from official enrollment and drop-out rates for primary school, middle school, high school and university or college. Then we estimate the age-specific school participation rates of each household by solving a quadratic program, which minimizes the difference between the school participation rates of each household and the national average school participation rates, subject to the constraints implied by the base year skill composition of each household’s labor endowment.

The productivity increments associated with educational attainment enhancement are derived from the study by Shi, Sicular and Zhao (2002). These authors estimate wage (or shadow wage) equations for agricultural and rural, non-farm workers. These equations include educational attainment as an explanatory variable. Their estimates suggest that additional education has the greatest impact on rural non-farm wages, with one additional year of schooling boosting hourly wages (and, we presume, productivity) by 15%. Additional schooling also has an impact on agricultural productivity, with an additional year of schooling boosting total factor productivity on the farm by 2%. When adjusted for the share of labor in agricultural output, this is equivalent to a 2.5% increase in labor productivity. Specification of the values of the off-farm labor supply elasticity draws on the econometric work of Sicular and Zhao (2004) and Zhang, Huang and Rozelle (2002). Sicular and Zhao report results from a household labor supply model estimated using labor survey data from the 1997 CHNS data set for nine central provinces. From their labor supply equations for self-employed agricultural labor and self-employed nonagricultural labor, it is possible to calculate elasticities of labor transfer from farm to non-farm 12
simulate the future spatial distribution of urban land. The final projection outputs, which have a spatial resolution of 1 km, are presented at 10-year intervals from 2020 to 2100, with 2015 being the reference year for baseline urban extent.

To overcome the uncertainty associated with climate change and illustrate the range of possible outcomes, Chen et al. (2020) adopt the SSP framework in producing the urban expansion projections. The five SSP scenarios, which are central to the Intergovernmental Panel on Climate Change (IPCC) reports, describe socioeconomic changes that affect demographic, economic, and climate change dynamics. The SSPs are part of a new scenario framework established by the climate change research community to facilitate integrated analysis of future climate impacts, vulnerabilities, adaptation, and mitigation (Riahi et al. 2017). The three main sets of scenario drivers are (1) population and education; (2) urbanization, defined as the urban share of the population (Jiang and O’Neill 2017); and (3) GDP per capita and interpersonal income inequality.<sup>5</sup> The scenarios are each associated with certain levels of assumed mitigation and adaptation challenges: the sustainability scenario (SSP1), for example, is marked by low mitigation and adaptation challenges, while the regional rivalry scenario (SSP3) depicts a future with high challenges to mitigation and adaptation. It is critical to note that the SSP scenarios underpinning Chen et al.’s (2020) urban expansion projections frame our results on potential impact on crop and livestock production.

# **2.2 Crop and livestock data**

The data on crop production are drawn from MapSPAM (Spatial Production Allocation Model) (IFPRI 2019). MapSPAM provides global gridded data on the production of 42 crops, which are aggregated into eight groups in our analysis: cereals, fruits, oils, pulses, roots, sugar, vegetables, and non-food crops (see Table 1). We focus our analysis on food crops.<sup>6
10 pocket spending. In other cases, the expenditure questions are in the health section. In some cases, some items are asked about in one section, e.g. the expenditure section, and other items are asked about in the other, e.g. the health section. We have used as much information as possible, and where the recall period is less than 12 months, we have annualized by multiplying the amount recorded by a timeneutral annualization factor (i.e. 12 in the case of a one-month recall, by two in the case of a six-month recall, and so on). Some surveys asked about all items of health expenditures in both the expenditure and health section. In these cases, we used whichever section had the longer recall period – usually the expenditure section – since we suspect that short recalls combined with a time-neutral annualization factor leads, on balance, to an overestimate of annual health expenditures.

We mostly relate out-of-pocket health expenditures either to total household consumption (in low- and middle-income countries) or to income (in high-income countries). Increasingly, surveys try to collect data on both, but it is still the case that income is typically not available for low- and middle-income countries. Where we can, we show how some indicators change as we move from consumption to income.

In the catastrophic expenditure analysis, we use thresholds of 10% and 25%. In the impoverishment analysis, we use the $1.90-a-day, $3.20-a-day and $5.50-a-day international poverty lines (IPLs) (Jolliffe and Prydz 2016; Ferreira and Sanchez 2017), as well as a $10.00-a-day line which may be better thought of as a global lowincome line rather than a poverty line per se (Kochhar 2015). In converting our health expenditure data to international dollars, and in deriving the IPLs, we convert values in different years to 2011 values using local CPIs and LCUs to international dollars using 2011 PPPs.

We checked the numbers underlying
31.7 percent).<sup>54</sup> The Roma population is significantly overrepresented among the multidimensionally poor since 25.9 percent of the AROPE individuals are Roma –whereas only 3.3 percent of the non-AROPE are Roma. A closer examination of the different components of the material and social deprivation dimension<sup>55</sup> indicates that between 65 and 75 percent of the Roma community cannot afford annual holidays, or unexpected expenses paid with own resources, or replacement of worn-out furniture, or replacement of worn-out clothes or cannot afford having two pairs of properly fitting shoes. These are the most prevalent categories of material and social deprivations among the Roma but also among the overall Bulgarian population – but at the national level, the prevalence is lower (between 34 and 41 percent). In turn, the items relative to internet access, arrears in housing costs and social inclusion are among the least prevalent items of deprivations, both among the Roma and the national population.

# **Further analysis reveals a greater intensity of deprivations experienced by the Roma**

**community.** When looking at the degree of overlapping deprivations, 3.8 percent of the Roma population experience both low work intensity and material and social deprivation, whereas only 1.0 percent of Bulgarians fall into this category (Table 10). 4.4 percent of the Roma population experience both low work intensity and are at risk of poverty, compared to 1.3

> 53 The Roma population interviewed in the survey represents 10.4% of the overall population. This figure from the EU-SILC is in line with the estimate of the Roma population in Bulgaria (10.2%) obtained using the data from the Council of Europe (2012).

> 54 Own estimates based on 2021 Bulgaria SILC. National estimates are also consistent with Eurostat and those published by the Bulgarian National Statistics Institute, and Roma estimates are also consistent with those published by the Bulgarian National Statistics Institute.

> 55
# **Table A14. Associations between SCP Quality Determinants and Labor Market Outcomes** **_Using Individual- and Program-Level Data for Ecuador_**

||**Ever**<br>**employed the**<br>**year after**<br>**graduation**|**Percent of time**<br>**employed after**<br>**graduation**|**Average**<br>**monthly**<br>**wage (log)**|
|---|---|---|---|
||<br>(1)|(2)|(3)|
|_Infrastructure_||||
|Number of workshops or labs available for practice||2.645***<br>(0.445)||
|_Costs_||||
|Government scholarships are used by some students||7.623*<br>(4.241)||
|_Training and curriculum_||||
|Analyze student performance to solve problems more than once||-8.775**||
|<br>per year||(3.818)||
||0.137***|7.221*|0.650**|
|Collect student satisfaction data more than once per year|<br>(0.0446)|<br>(3.678)|<br>(0.277)|
|Government standards are very important to update program|0.0546<br>|21.14***<br>|0.611**<br>|
||(0.0402)|(3.658)|(0.246)|
|Enrollment trends are very important to update program||-5.478<br
6 between net farm income or agricultural GDP and farm household welfare is fading, even in some low-income countries (Davis, Winters and Carletto 2009). Frequently, many of the poor, including the rural poor, are net-buyers of staple foods, causing them to be adversely affected—at least in the short run—by increases in prices of staple foods.

Second, the compilation of national household surveys that are comparable for crosscountry analysis has progressed rapidly such that there are now recent surveys for more than 100 countries available at the World Bank. That dataset (www.worldbank.org/prospects/gidd) has already begun to be used in conjunction with the World Bank’s LINKAGE model of the global economy to assess global income distribution issues (e.g., Bussolo, De Hoyos and Medvedev 2008).

Third, the World Bank has recently compiled a very comprehensive new global database that updates and expands substantially our understanding of the distortions to agricultural incentives in developing countries in particular.<sup>3</sup> Those estimates have since been expressed so as to make them usable in national and global economy-wide models (Valenzuela and Anderson 2008). They differ from the usual ones employed by trade modelers of developing country policies in that they are based on direct domestic-to-border price comparisons rather than (as with the GTAP dataset, see Narayanan and Walmsley 2008) on applied rates of import tariffs and other key border measures.

A first attempt to exploit those new methodologies and databases has recently been undertaken to assess the relative impacts on national, regional and global poverty and inequality of agricultural and non-agricultural trade policies at home and abroad. This paper summarizes and draws lessons from the papers that have emerged from that research project. At the outset it should be made clear that agricultural and trade policies are far from the first-best policy instruments for achieving national poverty or income distribution

> 3 That distortions database is documented fully in Anderson and Valenzuela (2008) and is based on the methodology summarized in Anderson et al
- Cull, Robert, and Lixin Colin Xu, 2003. “Who Gets Credit? The Behavior of Bureaucrats and State Banks in Allocating Credit to Chinese State-owned Enterprises,” _Journal of Development Economics_ 71(2): 533-559.

- Cull, Robert, and Lixin Colin Xu. 2005. “Institutions, Ownership, and Finance: The Determinants of Investment among Chinese Firms,” _Journal of Financial Economics_ 77, 117-146.

- Cull, Robert, Lixin Colin Xu, Tian Zhu.  2009. "Formal Finance and Trade Credit During China's Transition," _Journal of Financial Intermediation_ 18(2):173-192.

- Dethier, Jean-Jacques, Maximilian Hirn, and Stephane Straub. 2011. “Explaining Enterprise Performance in Developing Countries with Business Climate Survey Data.” _World Bank Research Observer_ 26, 258-309.

- Dollar, David, and Shang-Jin Wei. 2007. “Das (Wasted) Capital: Firm Ownership and Investment Efficiency in China.” IMF Working Paper 07/09. Washington, DC.

- Du, J., S. Girma. 2010. “Red Capitalists: Political Connections and Firm Performance in China.” _Kyklos_ 63(4), 530-545.

- Faccio, Mara. 2006. “Politically connected firms”, American Economic Review 96(1), 369-386.

- Faccio,  Mara, John J. McConnell, and Ronald W. Masulis, 2006. “Political connections and corporate bailouts”, Journal of Finance, vol. 61(6), 2597-2635.

- Faccio, Mara, and David C. Parsley. 2009. “Sudden deaths: Taking stock of geographic ties”, Journal of Financial and Quantitative Analysis 33(3): 683-718.

- Faccio, Mara. 2010. “Differences between politically connected and non-connected firms: A cross country analysis”, Financial Management, vol. 39(3), 905-927.

- Fan, Joseph P.H., T.J. Wong, Tianyu Zhang. 2007. “Politically connected CEOs,
# **5.2 Implications for policy makers**

The key implication for policy makers from this study is that there is a clear need to foster greater recovery of employment among formal firms in Zambia. Non-trivial numbers of workers who were previously employed by formal firms no longer had formal jobs even over a year after the pandemic began. This means that across the economy as a whole, it is likely that formal employment has not rebounded to anywhere near pre-pandemic levels. There may be a need for government support to expand opportunities for workers to join formal firms. A potentially promising sign that emerges from the econometric analysis is that firmspecific factors, more than structural factors, appear to be associated with whether firms experienced a decline or recovery in employment. This may imply that there is greater scope for specific actions to be taken by firms to increase their number of employees. If structural factors were more closely correlated with changes in employment (e.g. the sector that firms operate in), there would potentially be far less scope for policy makers to have an influence.

# **5.3 Contribution to knowledge**

We draw on two distinct but complementary data sources that illustrate this same pattern. To the best of our knowledge, this is the first attempt to use both survey and tax administrative data to track how firms have recovered from the pandemic. We have carefully examined the strengths and weaknesses of both data sets to try to ensure as much comparability as possible. We hope that this effort will be replicated and improved upon elsewhere as triangulating findings across different data sources can provide both researchers and policy makers far more confidence in the robustness of the results. Particularly, the use of tax administrative data as a low-cost way to monitor economic activity is rather novel, and this study shows how it can complement the largest existing effort to collect survey data from firms across countries, which is undertaken by the World Bank Enterprise Analysis Unit.

31
|Share in total expenditure<br>8.5<br>12.9|5.8|
|---|---|
|_Fuel plus transport_||
|Per capita expenditure||
|(YER/year)<br>15,086<br>48,790|8,040|
|Share in total expenditure<br>9.7<br>14.0|7.0|
|Source: Based on HBS (2005/2006) and SAM (2009).||

# **Options for Petroleum Subsidy Reform**

This paper analyzes two major scenarios: an accelerated reform path (Reform Option 1) where all subsidies are cut within one year, and a more gradual reform option (Reform Option 2) that phases out subsidies over a period of three years (Table 5). In the accelerated scenario, subsidies would be eliminated from an estimated 391 billion YER in 2009 to zero in 2011.<sup>9</sup> This would, _ceteris paribus_ , imply a reduction of the fiscal deficit by one-half, from 6.9 percent to 3.5 percent of GDP, with remaining surplus from reform of 215 billion YER. In the gradual reform scenario, subsidies are phased out by equal amounts (130 billion YER) from 2011 to 2013. _Ceteris paribus_ , the total savings from reform are smaller due to continued fiscal costs for subsidies in 2011 and 2012, thus the fiscal deficit is reduced more slowly.

Table 5. Fuel Subsidy Reform Options

||Re<br>|form 1, ac<br>|celerate<br>|d<br>|Refor<br>|m 2, gra<br>|dual<br>|
|---|---|---|---|---|---|---|---|
||2009|2011|2012|2013|
th........<br>o.....<br>o....<br>....<br>o.........|15|
|VI.|Mag|nitude of the Farm-Nonfarm<br>Linkages.nkae**eOg*Oe<br>s****|O*0<br>18|
||A.|<br>Capitalflo.s<br>....................<br>.<br>|18|
||B.|<br>Laborflos.|19|
||C.|<br>Backward ln<br>k<br>a<br>g<br>es........<br>...<br>0.........<br>......<br>. .....|<br>19|
||D.|<br>Forward li<br>n<br>k<br>a<br>g<br>es.....<br> <br>|20|
||E.<br>|<br>Consumption<br>lns21<br> <br>||
||_Fe_|Ca.t<br>ion|23|
|VII.|Pro<br>|bable Magnitude of the Agricultural Growth<br>||
||Mu|tipliers......<br>......<br>....<br>.........<br>. ....<br>. ..........
studies on developing economies broadly find that the net effect of the first wave of digitalization on jobs has been positive, albeit with differences according to type of employment. Hjort and Poulsen (2019), for example, conclude that the reinstatement effect outweighs the substitution effect of automation associated with faster internet access in SubSaharan African countries. Studies of the impacts of ICT adoption on jobs have also investigated location-based differences. In Tanzania and Nigeria, working-age individuals living in areas covered by mobile internet experienced an increase in wage employment (Bahia et al. 2020, 2021).

Evidence about the effects of robots on jobs in advanced economies has not coalesced around a definitive narrative. In an analysis considering both the displacement and productivity effects, Acemoglu and Restrepo (2020) show that the introduction of one industrial robot per 1,000 workers has reduced the employment-to-population ratio in the United States by 0.2 percentage point and wages by 0.4 percent. A similar study on EU countries using the same density of industrial robots estimates a reduction in the employment-to-population ratio of about 0.2 percent (Chiacchio, Petropoulos, and Pichler 2018). The adverse effect of robots on employment in the EU is echoed in the findings of Carbonero, Ernst, and Weber (2020). Yet a study of seven European countries using firm-level data concludes that industrial robots have no direct effect on employment (Jäger, Moll, and Lerch 2016). Antón et al. (2022) caution against drawing clear conclusions about how employment was impacted by robot adoption in Europe between 1995 and 2015, showing that the effect is sensitive to the time period, country sample, and model specification.

> 9 The “task-based” framework followed previous investigations of “skill-biased technological change.”

> 10 Acemoglu and Restrepo (2018) use data from Lin (2011) to show that about half of the employment growth over 1980-2015 took place in occupations in which job titles or tasks changed.

> 11 However, own-industry labor share losses are not outweighed by gains
# **Tables and Figures**

Table 1: Norm-related variables from DHS surveys by domain (top panel) and WVS attitudes (bottom panel)

|**Gender norm-influenced**<br>|**outcomes**<br>||
|---|---|---|
|**Do**|**main**|**Measure**|
|Economic<br>empowerment<br>Human Capital|Economic empowerment<br>Human Capital|Labor force participation<br>Years of education*|
|Marriage and children|Human capital|Age at marriage|
|||Age at first birth|
|Intrahousehold|Decision|Say in HH decisions – mobility,|
|bargaining and agency|making/autonomy|householdpurchase and health^|
||Intrahouseholdpower|Educationgapwith spouse|
|||Agegapwith spouse|
||Control over own|Marriage to first birth interval|
||body/aspirations re<br>children|Son preference (difference in ideal<br>number of boyandgirl children)|
|||Difference between desired fertility and<br>realized fertility **<br>IPV experience ^^|
|**Gender norm-influenced**<br>|**attitudes**<br>||
|**Do**|**main**|**Measure**|
|Human capital||University is more important for a boy<br>than agirl *|
|Voice and agency||Both the husband and wife should<br>contribute to household income ^|
|||Justification for IPV ^^|
|Fertility||A woman has to have children to be<br>fulfilled******|
|Economic empowerment||When jobs are scarce, men should have<br>more right to ajob than women**#**|

The symbols *, ^, **, ^
syste <mark>m dataset for</mark> the time period January 2008 to December 200 <mark>9.</mark><sup>84</sup> We follow Bessembinder et al. (2009) by eliminating cancelled, corrected, and commission trades from the data. We merge the remaining bond trades with the Fixed Income Security Database (FISD), which contains information regarding each bond’s rating, time to maturity, coupon payments, and other characteristics. Our sample includes all U.S. corporate bonds listed in <mark>FISD t</mark> hat satisfy a set of selection criteria commonly used in the corporate bond literature.<sup>85</sup> We remove all bonds with equity or derivative features (i.e., callable, puttable, and convertible bonds), bonds with warrants, bonds with floating interest rates, and bonds with any credit enhancements. We also eliminate all bonds that have less than one year to maturity or have been issued less than a year ago, and require all bonds to have an assigned rating and SIC code. As we are interested in industrial firms, we remove all financial firms from the sample (SIC codes 6000-6999). So as to not bias our analyses, we also remove from the sample Ford, General Motors and Chrysler. To be able compute daily bond returns, we also require bonds to have traded on two consecutive days over the sample period. Our final sample consists of 508 bonds from 269 firms. � Bond returns are calculated as: where _Pt_ is the clean price of the bond, _AIt_ is the accrued interest over one period, and _Ct_ is the coupon payment whenever it is paid (in which case _AIt_ = 0). To remove potential data entry errors, we follow Bessembinder et al. (2006) by requiring that the absolute value of the return be less than 30%. As there are multiple bonds for each firm, company-level returns are computed by