rafmacalaba/data-use-mentions-extended huggingface dataset:
linkages were also high with manufactured exports of China and those of the United States at 34% and 25% respectively that year. At 8%, the region’s linkages with India’s manufactured exports were smaller but still substantial. By contrast, West European manufactured exports have had relatively few linkages with Africa and the four external comparators.

Western Europe’s linkages to Germany’s manufactured exports were predominantly downstream but included a large upstream component. The region's linkages to manufactured exports of China, the United States, and India were almost evenly divided between upstream and downstream components with upstream linkages being slightly higher. Upstream and downstream linkages of Western Europe’s to US manufactured exports and the upstream linkages to Germany’s manufactured exports have declined significantly in the past 20 years, during which the downstream linkage to German exports has grown by an even larger amount, and the region’s linkages with manufactured exports of China and India developing over the period.

# **6. Establishment-Level Analysis of GVC Participation**

Global or otherwise, a value chain defines a series of transactions taking place at the level of the firm or business establishment. Typically, we consider a business establishment to be part of a global value chain if it is exporting downstream goods that it has produced using imported inputs. To identify instances of participation in GVCs from cases of non-participation in the production activities of the population of establishments in an economy, we analyze the factors that affect participation at the firm level. For this purpose, we use data from the World Bank Enterprise Surveys and estimate a linear probability model of firms’ GVC participation: (3) PP(yy= 1 | XX) = X where equals 1 if a firm operates along a GVC and 0 otherwise. We adopt two definitions of GVC participation. The first one is a standard definition in which a given firm is considered active yy in GVC if it is exporting and importing. Alternatively, we follow a less exclusive definition where a manufacturer is
percent in the general Bulgarian population. Remarkably, 23.7 percent of the Roma population are simultaneously at risk of poverty and materially and socially deprived, while this figure is much lower at 7.5 percent among Bulgarians. The most severe situation is observed among individuals simultaneously deprived in all three dimensions, with 17.9 percent of the Roma population experiencing poverty, low work intensity, and material and social deprivation, compared to 2.9 percent in the general Bulgarian population. Overall, these findings underscore the disproportionate levels of multidimensional poverty and social exclusion faced by the Roma population compared to the general population of Bulgaria as well as the intersecting challenges.

**Table 10.** Percentage of individuals deprived in each AROPE dimension, National average and Roma population (2021)

|**AROPE**|**Bulgaria**|**Roma**|
|---|---|---|
|**Deprived in only 1 dimension**|||
|low work intensity|0.9|0.3|
|severe material and social deprivation|7.7|15.8|
|at risk ofpoverty|10.4|12.9|
|**Deprived in only 2 dimensions**|||
|several material and social deprivation + low work intensity|1.0|3.8|
|at risk of poverty + low work intensity|1.3|4.4|
|at risk ofpoverty+ several material and social deprivation|7.5|23.7|
|**Deprived in 3 dimensions**|||
|at risk of poverty + low work intensity + several material and social<br>deprivation|2.9|17.9|
|national AROPE rate|31.7|78.7|

Note: Percentage of individuals (using survey weights). See Table 1 and Table 2 for a detailed description of each AROPE dimension.

Source:
observations that lie at the 95<sup>th</sup> percentile of the labor productivity distribution. Therefore, the higher the value of the labor productivity efficiency, the closer the country’s labor productivity to the global labor productivity frontier implying that the country’s labor productivity is globally competitive. Finally, data on trade are sourced from the CEPII-BACI dataset. The dataset contains bilateral import and export values and quantities across many countries at the 6-digit harmonized system classification (HS). Using appropriate concordance table, we map the trade data into Broad Economic Classification (BEC) Revision 4 to identify imported goods that are classified as intermediate and capital goods. For each African country, we then aggregate the imported good type (i.e., capital and intermediate good) across the partner countries into two categories based on their origin. Consistent with our research objective, we consider two origins: the Global North and the Global South. Our definitions of the Global South and Global North strictly follow Demir and Razmi (2022). Table A1 reports the correlation matrix among the variables of interest in our analysis.

# _3.2 Model Specification_

To examine how structural transformation is affected by the patterns of trade in Africa, we estimate the following empirical model: Where, the subscripts , , and denote country, region, and year, respectively. The level of economic transformation i is independently captured by structural change ( i ) and ii rr tt productivity convergence ( Fi ), then i i , Fi } . is initial industrial structure, measured as SS ZZ a country’s value-added share in 2000. is the corresponding coefficient to be estimated. ii is SS ∈{ZZ ψψ an indicator measuring the pattern of trade, while is the corresponding coefficient. The trade ii,ii φφ TT patterns we are interested in are capital and intermediate goods African countries source from θθ either the global North or South. Because of the high correlation between the trade variables (see Table A2), in the estimation
(given by the observed infrastructure data), respectively. We can see also that the average income increases by 3.4% and 10.7%, while for median income, the gains are slightly smaller, 2.85% and 10.25%.

When the government can decide on both types of investments, the gains are substantially larger. This is shown in the first two columns of Table 7. When budgets are kept separated, the Efficiency + Coordination allocation generates median income, average income, and welfare gains of 14.88, 15.85, and 19.75 percent, respectively. If we also allow for a joint budget, the gains are even larger. The Fully Optimal solution produces welfare gains of around 22%, comparable in magnitude to the gains in average and median income. In the Fully Optimal counterfactual, it is important to note that, besides reassigning investments to different road segments, the government also increases the share of total expenditure on roads.

Table 7 provides clear evidence that the government benefits from the complementarities of investments in both electricity and road infrastructures. When comparing the Efficiency + Coordination scenario to the Efficiency-Roads and Efficiency-Electricity cases, we see an increase of 1.26 percentage points in the welfare gains due to a larger degree of efficiency in the allocation within each infrastructure budget. Moreover, comparing the Fully Optimal and 29
# **4 Data and descriptive statistics**

## **4.1 Data sources**

The empirical analysis in this paper combines and examines several sources of panel data for the period 1995-2015, which we can link through a consistent definition of industries. In this section, we provide a brief description of each data source, while giving further details in Appendix B.1.

The stock of industrial robots by industry, country and year comes from the International Federation of Robotics (IFR). These data are based on yearly surveys of robot suppliers, and cover about 90 percent of the industrial robots market. The IFR measures both deliveries and stocks of “multipurpose manipulating industrial robots” based on the definitions of the International Organization for Standardization.<sup>11</sup> The data are disaggregated roughly at the three-digit level for manufacturing and roughly at the two-digit level for non-manufacturing activities. The IFR data are regarded as the best source of information on industrial robots (Graetz and Michaels, 2018; Acemoglu and Restrepo, 2017b). Nonetheless, these data have some limitations. First, about 30 percent of industrial robots are not classified into any industry. Following Acemoglu and Restrepo (2017b), we allocate these unclassified robots to industries in the same proportion as observed in the classified data. Second, the IFR data do not cover “dedicated industrial robots”, which are defined as automatically controlled machines for only one industrial application. Third, the IFR data have incomplete coverage for a number of countries; as a result our panel is unbalanced. We construct the robot stock for each country-sector-year using the perpetual inventory method assuming a depreciation rate of 10%. We prefer this measure over the measure of the robots stock reported by the IFR because that measure assumes that robots do not depreciate for a period of 12 years, yet lose all their economic value after exactly 12 years. We believe that gradual depreciation is a more plausible description of reality, but use the IFR measure of the robots stock
TABLE 4: CORRELATION BETWEEN PSYCHOLOGICAL CONSTRUCTS AND ECONOMIC SUCCESS

||Personal<br>Initiative<br>(1-5)|Error<br>management<br>(1-5)|Self Efficacy<br>(1-5)|Entrepreneurial<br>locus of control<br>(1-5)|Entrepreneurial<br>Identity<br>(1-5)|Attitude to Risk<br>(1-8 where a<br>higher score is<br>less risk averse)|Entrepreneurial<br>Activity Planning<br>(1-3)|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Success: Business Profits above<br>|0.0993***|0.137***|0.202***|0.303***|0.155***|0.0496|0.135***|
|median at baseline (Yes=1; No=0)|(0.0263)|(0.0267)|(0.0278)|(0.0354)|(0.0386)|(0.103)|(0.0198)|
|Mean for low profit businesses at|4.342|4.210|4.049|3.556|4.236|4.196|2.076|
|baseline PI/BSED study|(0.0708)|(0.0719)|(0.0750)|(0.0954)|(0.104)|(0.278)|(0.0535)|
|Observations|2001|2001|2001|2001|2001|2001|2001
Figure 1: Household business outcomes: monthly averages <!-- Start of picture text --> (a) Monthly Suspension Filings (HBTC) (b) Monthly Closure Filings (HBTC)<br>Average Commune Monthly Suspension Filings Average Commune Monthly Closure Filings<br>Apr<br>May<br>Aug<br>Aug<br>2016 2017 2018 2019 2020 2016 2017 2018 2019 2020<br>Month Month<br>Average Suspension Filings per Commune Average Closure Filings per Commune<br>Pre-Covid Mean Pre-Covid Mean<br>(c) Weekly Work Hours (LFS) (d) Monthly Income (LFS)<br>Average Weekly Working Hours Average Monthly Income (Inflation Adjusted)<br>Sept<br>Sept<br>Apr<br>May<br>2018 2019 2020 2018 2019 2020<br>Month Month<br>Average Weekly Working Hours Average Monthly Income (Individual)<br>Pre-Covid Mean Pre-Covid Linear Trend<br>40<br>30<br>2<br>20 1.5<br>1<br>10<br>Number of Closure Filings .5<br>Number of Suspension Filings<br>0 0<br>50<br>6500<br>45 6000<br>40 5500<br>5000<br>35<br>Average Weekly Working Hours<br>Average Monthly Income (thousands) 4500<br><!-- End of picture text --> Note: This figure shows monthly-average plots of four commune-level household business outcomes: monthly suspension filings (panel A), monthly closure filings (panel B), weekly work hours (Panel C), and individual monthly income (Panel D). Blue/solid lines indicate monthly average values. Red/dashed lines represent pre-COVID means (2018-19). Data on household business suspension and closure filings are obtained from the Household Business Tax Census. Data on individual work hours
the final income aggregate is net of costs, as opposed to gross (which could overestimate the income a household has at its disposal). Taxes are the only cost that has been subtracted from gross income earned to create net income earned (Quiñones et al. 2009).

Before undertaking the empirical analysis, both the income and consumption variables of each of the countries included in our sample were converted into constant 2011 international dollars—in purchasing power parity (PPP)—and expressed per capita per day. Furthermore, observations with negative and zero incomes were excluded from the analysis, because some indices of inequality are defined only for positive values. Accordingly, the sampling weights of households—used in all calculations—have been recalibrated in such a way that estimates from the samples after deletion of nonpositive records are forced to fit the initial population-level information on the households’ geographical location and area of residence (rural versus urban).<sup>8</sup> Table 1 presents distributional summary statistics for the survey-based consumption and income variables used in this study. Compared to income, consumption expenditure typically produces lower estimates of inequality, independently of the measure that one considers—the Gini coefficient, the mean log deviation (MLD), or the Theil index. As mentioned in the previous section, this is to be expected and can be explained by a declining marginal propensity to consume and by the fact that consumption surveys tend to understate the spending at the top. Instead, an argument for using consumption rather than income is that data on the former are often of a higher quality in developing and emerging economies and are less vulnerable to idiosyncratic shocks, as households tend to smooth their consumption over time. Because estimates of inequality will be biased if computed using any single one of these variables, what is needed to obtain consistent estimates of inequality, we argue, is a combination of the information coming from the consumption and income data. Section 4 appeals to multiple-imputation methods in order to achieve this.

> 8 Calibration
energy use in developing countries. Among these studies, Dai et al. (2012) use household expenditure data in China from 1985 to 2009 to investigate how household consumption expenditure change with rises in income and infer the impacts on carbon emissions. As income rises, the authors find that households spend more on transport and energy-related products, leading to a drastic increase in carbon emissions. To reduce carbon emissions, they suggest a shift of household expenditure from material and transport products to service-oriented goods. In the same vein, Wang et al. (2016) use the social accounting matrix of China to investigate how household expenditure affects carbon emissions through income distribution in rural and urban areas. They find that carbon emissions increase with income levels for urban and rural households. They report that rural households spend a large share of their income on carbon-intensive expenditures like transport. From another perspective, some studies focus on the relationship between vehicle ownership and greenhouse gas emissions. For instance, Vasic and Weilenmann (2006) propose a comparison between the emissions from motorcycles and cars. The authors mention that motorcycles emit less carbon dioxide than cars since they are lighter and less fuel-consuming. We 6
estimations in Columns 5-6 are built on the 1997 Census of Manufacturers. This dataset is a universal survey of U.S. manufacturing establishments conducted every five years. We estimate the local conditions by county and industry. We again normalize the local activity measures by the area of the county. We do not attempt to align U.S. industries with Indian industries, and we recognize that there are substantial differences in survey techniques and measurement quality between the two countries. Likewise, there are more advanced techniques for both production function estimation and urbanization/agglomeration premium estimation. But for our purposes, these two samples and transparent regressions provide an intuitive platform to observe differences between the two countries.

> 8  For example, Ciccone and Hall (1996), Duranton and Puga (2004), and Rosenthal and Strange (2004, 2011). Indian studies include Lall et al. (2004), Lall and Mengistae (2005), Deichmann et al. (2008), and Fernandes and Sharma (2011).

10
A key underlying requirement for successful fiscal decentralization is a uniform structure of subnational government accounts that are regularly and properly audited. There are problems with the availability of such a comprehensive data system in most developing countries. A census of government finances, reporting the actual financial outcomes for every subnational government, is essential information if the performance of the intergovernmental system is to be monitored. Yet, it is not common in developing countries to have an up-to-date information system that describes the finances of subnational governments in detail. Rarer yet is a fiscal analysis model that is used to track the performance of local government finances (Bahl, 1999b).

# **7. Breaches in Sequencing: Causes and Risks**

The primary question we raise in this paper is whether sequencing matters in developing and implementing a fiscal decentralization policy. Our answer is that it does, and that mistakes in sequencing can lead to significant resource costs and possibly to a loss in confidence in the decentralization initiative.

We also raise two secondary questions in this paper. First, we ask whether there is something akin to an optimal sequencing for decentralization policies and implementation. Our answer is that there is, and that following these sequencing rules might minimize the costs and the risks of implementing fiscal decentralization. Second, we ask about the extent to which countries follow these optimal sequencing rules. The answer would appear to be that in general, they do not.

What to make of this gap between theory and practice? One might consider a couple of conjectures. First, sequencing does not matter. Second, there is a lack of information or there are institutional and political constraints. In this section, we address in more depth these two possibilities.

# _7.1. Sequencing Does Not Matter?_

One might argue that the order of introducing decentralization policy does not matter, that sooner or later the structure of decentralization in a country will end up at the same place no matter how one goes about it. Look for example at the U.S.
**Table 1 Summarizing the municipality-level analysis**

||Effect on average|Effect on|Effect on|
|---|---|---|---|
||incomes (% change)|Poverty|Inequality|
|Bolivia|2.9|Decrease|Decrease|
|Brazil|-11.9|Increase|Increase|
|Chile|-6.7|Increase|Neutral|
|Mexico|0|Neutral|Neutral|
|Peru|-2.3|Increase|Neutral|

Source: Andersen and Verner (2010) Assunção and Chein Feres (2009) provide an estimate of the poverty impacts of climate change based on cross-sectional data at the municipality level in Brazil. They first estimate the impact of climate change on agricultural productivity (a proxy for the output-climate elasticity) measured as agricultural output per hectare in each municipality. Next, they use IPCC’s temperature and rainfall projections for 2030-2049 to build a different climate vector for each municipality, which is then used to obtain the percent change in agricultural productivity induced by climate change. Their estimates suggest that global warming is expected to decrease the agricultural output per hectare in Brazil by 18 percent, with the municipality-specific estimates ranging from -40 to +15 percent.

The link between agricultural productivity and poverty is explored by means of a crosssectional regression of the poverty rate at the municipality level against the log of the agricultural output per hectare and the log of the total population in the municipality. Using instrumental variable methods to account for the correlation between agricultural output and the error term of the regression, they estimate that doubling agricultural productivity reduces poverty at the municipality-level by 12.8 percentage points. Based on this estimate they predict that climate change will increase the poverty rate in rural areas by 3.2 percentage points. Considering that the actual poverty rate is 40 percent, the authors claim that the number of poor in Brazilian rural areas will increase by 8 percent. The estimates also reveal interesting
in the Annex. The latest version of the REAL-IO software is available at http://www.real.illinois.edu/realio/realio1.0.msi The example data sources (e.g., OECD STAN Input-Output Database for 44 countries and Inter-country inter-industry) are also included.

# **6 Spatial Analysis of Regional Economies**

Regional economics offers a rich empirical setting for the application of geospatial methods, as our review of regional economic development research has highlighted a number of theoretical constructs that call out for empirical measurement. In this section, we outline a number of such methods, linking each to particular theoretical concepts and discuss their implementation and use in the analysis and monitoring of regional economic systems.

We begin with measures of inequality, polarization and concentration for a national system composed of interacting regional economies. Closely related to, but distinct from, polarization measures are measurements of spatial clustering, which include global measures of spatial autocorrelation. We provide an overview of so-called global measures of spatial autocorrelation as well as their combined use with measures of polarization. We then shift the spatial focus to discuss local measures of spatial association. These provide the ability to detect hot(cold)-spots of economic activity that might be responsible for the overall patterns detected by the global measures or spatial-outliers that depart from the global pattern.

The measures of polarization, clustering, and local spatial association are all focused on static patterns measured at one point in time. Recent work in spatial statistics and GIScience has focused on developing new methods that extend these to consider the dynamics of these patterns. Methods of exploratory space-time data analysis can be used to study questions of spatial income mobility that has to date been largely absent from the debates surrounding the regional equity-efficiency trade-off. We close this section with an overview of recent developments of machine learning in GIScience which may offer new approaches to the study of regional economies.

38
<mark>proprietorship firms vs. the rest, and another for partnerships or shareholding companies with nontraded shares vs. the rest. The data source is ES.</mark>

# 3.2.2 _Additional explanatory variables_

For robustness, we experimented with a few additional variables. These variables did not have any significant effects on the sales decline gap or on the results for the main explanatory variables listed above. The additional variables for the pre-pandemic period and taken from the ES include: a dummy variable equal to 1 if the firm has a woman owner and 0 otherwise; a dummy variable equal to 1 if the firm suffered losses due to crime during the year and 0 otherwise; and the growth rate of employment over the last three years. Additional variables for the period after the outbreak of the pandemic taken from COV-ES include the percentage of sales made online and the percentage of the workforce working remotely.

# **4. Empirical results**

# _4.1 Mean differences between SMEs and large firms_

Table 1 provides descriptive statistics and means tests of the main variables between SMEs and large firms. The decline in sales for SMEs is higher than for large firms by 12.2 percentage points (34.8 vs. 22.6 percentage points). The difference is significant at the 1 percent level. This is the (unconditional) total gap in sales decline at the mean between SMEs and large firms (henceforth “gap” or “total gap”). Figure 3 provides the kernel density plot of the sales decline for SMEs and large firms.

The differences in the level of the explanatory variables between SMEs and large firms help explain the “endowment” effects in the decomposition analysis. Unless stated otherwise, all 17