rafmacalaba/datause-encoder-data huggingface dataset:
We infer oblast demand (and supply) of services, assuming that intermediate and final demand for services share a common intensity of demand in all oblasts as in the national model. For example, if telecommunications costs are x percent of the costs of nonferrous metals production in the national model, we assume that telecommunications costs are x percent of nonferrous metals costs in each of the oblasts. Demand for telecommunications from non-ferrous metals will differ across oblasts, however, since the share of total output attributable to non-ferrous metals differs across regions.

We have total external exports and imports by oblast, as well as the commodity structure of external exports and imports by oblast for the year 2001 (Regions of Russia 2001, <mark>tables 23.1 and 23.2</mark> ). We also have unpublished data supplied to us by Rosstat on inter-oblast exports and imports by sector. That is, for each of over 250 key commodities, we have an 88 by 88 matrix of bilateral trade flows among the oblasts.

Supply and demand balance by oblast and by commodity requires adjustment of trade intensities. These adjustments assure that oblast exports and imports in aggregate are consistent with national import and export values, we have to adjust the oblast import and export intensities. We did this using a methodology that minimized the sum of squares of the difference between the original data on exports and imports and the adjusted exports and imports data, subject to the constraints of supply-demand balance and consistency with the national model data. Since we had greater confidence in the validity of the oblast output data than the inter-oblast trade flow matrix, in this optimization process, we fixed the output levels of the oblasts at the levels we had calculated above. We do not need to make any other adjustments, as the production technologies are assumed consistent across the regions.

Since in every step of the process, we calculated oblast shares of the national input-output table, the process yields a set of 88 regional input-output tables which portray a regional disaggregation of the national input-output table. That is, summing over all the 88 input-output tables will yield the national input-output table, including wholesale and retail distribution margins, investment demand and government expenditure. Crucially, we may aggregate the 88 oblasts into a set of non-overlapping subsets and any such aggregation will yield a set of input-output tables that is fully consistent with the national input-output table. In particular, our seven region model is consistent with the national input-output table.

# **FDI Shares**<sup>**22**</sup>

We first employed the NOBUS survey to obtain the shares of workers working in multinationals service sectors in each business service sector in each oblast. We used this as a proxy for the share of output in each service sector in each oblast. We also obtained information from (1) our estimates from

> 22 We explain the methodology further in appendix A of Rutherford and Tarr (2006a).

17
# **F. BIBLIOGRAPHY**

- Andres, Luis, J. L. Guasch, S. Lopez Azumendi. 2006. “Assessing the Governance of Electricity Regulatory Agencies in Latin America and the Caribbean: A Benchmarking Analysis”. Policy Research Working Paper 4380. The World Bank. Washington DC.

- Baietti, Aldo, W. Kingdom, and M. van Ginneken. 2006. “Characteristics of well-performing public water utilities”. Water Supply and Sanitation Working Notes. The World Bank, Washington, DC.

- Department for International Development (DFID). 2005. “Review of China State-Owned Enterprise restructuring and Enterprise Development (SOERED) Project”. London.

- Erakovic, Ljiljana, and M. Wilson. 2005. “Conditions of Radical Transformation in State-Owned Enterprises”. British Journal of Management. London.

- Gomez Ibañez, Jose A. 2007. “Alternatives to Infrastructure Privatization Revisited: Public Enterprise Reform from the 1960s to the 1980s”. Policy Research Working Paper 4391. The World Bank.

- Government of Australia. 2005a. “Australian Government Presentation to The Policy Dialogue on Governance in China”. Beijing.

- __________1999b. “Principles and Better Practices of Corporate Governance in Commonwealth Authorities and Companies”. Sydney.

- Government of New Zealand, State Services Commission. 2007. “New Zealand’s State Sector Reform: A decade of Change”. From www.ssc.govt.nz

- Irwin, Timothy and Yamamoto, Chiaki. 2004. _“Some Options for Improving the Governance of StateOwned Electricity Utilities”._ Energy and Mining Sector Board Discussion Paper. Paper No. 11.

- Jones, Leroy P. 1991. “Performance Evaluation for Public Enterprises,” World Bank Discussion Paper n. 122, World Bank.

- Legreid, Per, P. G. Roness, and K. Rubecksen. 2006. “Performance Management in Practice: The Norwegian Way”. Oxford.

- Mesnard, Mathilde. 2000. “Institutional complementary and corporate governance: a reassessment of the Russian transition failure”. Paper for the International Society for New Institutional Economics’ Annual Conference 2000. Tuebingen. Germany.

- Nellis, John. 1988. “Contract Plans and Public Enterprise Performance”. Policy, Planning, and Research. Working Papers. Public Sector Management and Private Sector Development. The World Bank.

- Organization for Economic Cooperation and Development (OECD)a. (2004) “OECD Principles of Corporate Governance”. Paris.

_______ 2005b. “Corporate Governance of State-Owned Enterprises: a survey of OECD countries”. Paris.

________1999c. “Greek Public Enterprises: Challenges for Reform”. Paris.

________2005d. “OECD Guidelines on Corporate Governance of State-owned Enterprises”. Paris.

Shirley, Mary M. and Lixin Colin Xu. 1997a. “Information, Incentives and Commitment: An Empirical Analysis of Contracts between Government and State Enterprises”, Policy Research Working Paper n. 1769, World Bank.

23
induced vulnerability, which encompasses chronically poor households, and second, risk-induced vulnerability, which covers households that are not now poor, but that face a substantial risk of falling into poverty. The approach is widely favored among practitioners because it facilitates the generation of vulnerability estimates in the absence of panel data. Notable instances of such an application are described by Atamanov, Mukiza, and Ssennono (2022), Rude and Robayo-Abril (2023), and Skoufias, Vinha, and Beyene (2024).

While the Günther and Harttgen (2009) method offers the advantage of requiring only a single crosssectional household survey, the vulnerability estimates are representative at the same geographical level as the household survey. The method thus does not provide vulnerability rates at the spatial resolution necessary to implement the effective, highly disaggregated targeting sought for social programs. The proposed approach overcomes this limitation by recovering poverty rates and the shares of households at risk of becoming poor from welfare indicators imputed from census data. This information is then used to create a highly spatially disaggregated vulnerability map. As in Günther and Harttgen (2009), a household is considered vulnerable if it exhibits a probability of falling into poverty of more than 50 percent over the next two years. Methodological details are provided in the data and methods section.

# The concept of vulnerability

According to Dercon (2005, 2010), vulnerability is often defined as “the risk of households falling into or remaining in poverty because of either idiosyncratic hazards (due to characteristics of the individual household) or covariate aggregate hazards (external to the household).” This definition highlights several characteristics of vulnerability. First, both poor and nonpoor households can be vulnerable. Second, risk is a critical component of vulnerability. Third, risk is related to household- and community-specific characteristics.

Transforming this general definition into measurable concepts requires a precise understanding and the modeling of the income-generating function of households, particularly the expected mean and variance of income and consumption under different realizations of the potential shocks. Overall, there are three common empirical approaches to the measurement of vulnerability. These are known as the vulnerability to expected poverty, the vulnerability to expected utility, and the uninsured exposure to risk. Pritchett, Suryahadi, and Sumarto (2000) were among the first to introduce the concept of vulnerability to expected poverty, which defines vulnerability as the probability that a household will fall below a specific threshold, 5
The time period is 1950-2000. The data on Ginis come from WIDER and from world income distribution (WYD) project which collected detailed household survey data for more than one hundred countries covering the period from the mid-1980’s to the late 1990’s.<sup>18</sup> We have a total of 1067 Gini observations: 882 come from WIDER, the rest from WYD project.<sup>19</sup> The data on GDP per capita come mostly from the World Bank SIMA database. However, they are complemented by other sources, most notably from countries’ own statistical yearbooks and this particularly so for the countries that were not independent during the entire 1950-2000 period.<sup>20</sup> In the simplest formulation **1** (table 4, column 1), we use only one political variable: the three-year average lagged _Democracy_ from PolityIV database (version June 2000). The _Democracy_ variable is defined as “general openness of political institutions” and it ranges from 0 to 10 (higher values denote greater democracy). For ETHNO, we use an index of ethnolinguistic fragmentation.<sup>21</sup> The major interest of regressions like (1) lies in isolating the Africa-specific effect, that is testing whether African data behave differently than those from the rest of the world rather than in estimating the exact parameters of (1). We do this first by the inclusion of regional dummies.<sup>22</sup> The role of

> 18 More on this in Milanovic (2002).

19 The data are of all kinds: recipients are either households or individuals, welfare aggregate is income or expenditures, the data are gross of taxes or net. As has become standard, we adjust for these differences in definitions by using dummies. It is a necessary adjustment, but not the one that can be enthusiastically endorsed. As Ivaschenko (2002) rightly observes, in many instances in transition countries, the bias of one definition over another is neither constant, nor is even the direction of the bias always the same.

20 For more information on GDP per capita series see Milanovic (2003).

> 21 The index measures the probability of two random individuals from a given country belonging to the

2 _ni_ same ethnic group. The index obviously ranges from 0 to 1. Its formula is 1-∑ ( ) where ni=size of _i_ -th _N_ group, N=total population. We use here the index calculated by Annett (2000) from the _World Christian Encyclopedia_ (Barrett, 1982). The advantage of this index compared to the more commonly used index derived from the Soviet sources (see Mauro, 1995; Easterly and Levine, 1997) is a much more detailed ethnic grouping, more recent data, and broader country coverage (150 countries). The data were kindly supplied by Anthony Annett.

22 For the reasons just explained, we keep regional dummies in all the regressions. Annett (2001, p. 17) gives an additional reason. When in his regression with government consumption as the dependent 16
# **1. Introduction**

Identifying population groups in need of financial and social assistance is a priority for policy makers in rich and poor countries alike. As living standards rise, attention tends to shift away from an exclusive focus on the poorest population groups to encompass also “vulnerable” groups that are perhaps better off than the former, but that still face a comparatively high risk of falling back into poverty. Welfare policy for these two groups can be quite different: longer-term and structural intervention such as educational investment is necessary to catapult the first group out of deep and possibly chronic poverty, while social protection programs may be more suitable for the second group. To identify these groups, two separate income thresholds can thus be defined: one is the poverty line and the other a higher-income line, hereafter referred to as the vulnerability line, below which non-poor households can be regarded as facing a heightened risk of falling back into poverty.

Poverty lines are well researched (see, e.g., Ravallion, 2012 for a review) and are periodically updated in most developing countries. Vulnerability lines are less commonly considered and where they are, they often employ varying concepts of vulnerability. For example, World Bank (1995) provides an assessment of poverty in Ecuador during the 1990s where multiple poverty lines are constructed and the highest of these lines is simply designated a “vulnerability line”. This designation does not regard households who fall below this line as facing some heightened risk of falling into poverty. Pritchett et al. (2000) address this concern by defining a vulnerability line as the level of income below which a household experiences a greater than even chance of experiencing an episode of poverty in the near future, but considers as “vulnerable” even those households that are currently poor. This perspective differs from our 2
# Table III: Informative Declarations to Tax Authorities

|(1)<br>Form|(2)<br>Purpose|(3)<br>Record Type|(4)<br>Coverage of<br>Corporations|(5)<br>Coverage of<br>Self-Employed|(6)<br>% Matched With<br>Income Tax Records|
|---|---|---|---|---|---|
|D151|Reporting of rm-rm transactions|Sales|46.5|38.9|37.0|
||N=17,125,017 (54.3% sales)|Purchases|49.3|54.1|65.5|
|D158|Reporting of transactions at auctions|Sales|0.5|1.7|16.4|
||N=369,002 (71.0% sales)|Purchases|0.3|0.9|23.5|
|D150|Withholding on purchases|State purchases|8.4|5.0|78.7|
||N=759,391 (98.5% state purchase)|Private purchases|0.22|0.08|39.9|
|D153|Withholding on card transactions for GST<br>N=4,198,384|Sales|11.5 [26.7]|5.8 [18.4]|67.8 [48.7]|

Notes: This table provides information about the nature and coverage of third-party informative declarations used by the tax authorities in Costa Rica, for 2006 to 2014. Columns 1-3 provide the form number, its purpose, the number of observations and the record type. Columns 4 and 5 display the share of income tax lers covered by the dierent third-party declarations, distinguishing corporations and the self-employed. Column 6 displays the share of informative declarations which are matched with an income tax declaration. In the last row, the shares in brackets refer to the match rate with the monthly sales tax declarations for 2008-2014. The shares are calculated on the pooled data for all years/months. All declarations identify the reporter and taxpayer by their administration-internal anonymous tax ID, and provide information on the transaction amount, and (where applicable) the amount of the tax withheld. Amounts are accrued. Since January 2012, all declarations must be prepared using the DECLAR@7 software. Sanctions for noncompliance with the obligation to submit informative declarations are specied in the Codigo de Normas y Procedimientos Tributarios. All declarations are annual, except D153, which is monthly. D151 requires reporting of transactions >2.5 mio  annually with a transaction partner, and transactions of >50,000  annually for rent, commissions, professional services or interests. For D150, the withholding rate is 2% and 3% respectively for state and private purchases. For D153, the withholding rate is rm specic, following the schedule in Table II. For more information on the ling of informative declarations, see http://www.hacienda.go.cr/contenido/12997-declaraciones-informativas.
With respect to risk coping measures, we use data on household consumption, loans and remittances, and adult labor supply.

**Consumption.** The GLSS reports households’ frequent consumption (excluding medical expenditures) during the six days before the interview. Panel B of Table 1 shows its sample average. Households observed before the implementation of the NHIS reported an average weekly consumption of 104,420 GHS ($11). Households observed after the NHIS reported an average of 88,530 GHS ($9). As with reported medical expenditures, reported consumption is highly skewed. We, therefore, use a logarithmic transformation of consumption in our analysis. Panel B in Figure 3 shows the respective RD graph. While there is no change at the discontinuity, we observe a significant drop in the average household consumption two months after NHIS implementation, which will not be picked up in the regression discontinuity analysis. Overall, the average consumption was higher during the six months after NHIS implementation than in the six months before, as seen by the fitted lines.

**Loans and Remittances.** Households were also asked about formal loan take up, the quantity borrowed, and remittances received by the household that were used for health purposes. The variables are reported over the year before the interview. Panel C of Table 1 displays that about 5% of households in our sample observed after the introduction of the NHIS borrowed a loan, compared to 7% among households observed before the NHIS. The latter had a higher average borrowing than the former. This difference is not statistically significant.

Remittances received in households observed before the implementation of the NHIS amounted to 96,650 GHS ($10), compared to 77,170 GHS ($8) among households observed after. This is a sizeable difference. The low value of remittances is because only about 2 percent of households reported receiving any remittances. To deal with the large number of zeros in the loans and remittances amounts, we use a logarithmic transformation of the quantity that households borrowed and received through remittances in our analysis. Panel C in Figure 3 shows the RD graphs for the amount borrowed through formal loans (in logs) and remittances received (also in logs). In line with the sample averages, we do not find any discontinuity in the amount borrowed around the date the NHIS was introduced. We no longer observe any differences in remittances when examining the data around the discontinuity.

**Adult Labor Supply.** To analyze intra-household labor supply, we use the employment participation rate and hours worked over the week before the interview. Panel D of Table

11
take the form of monthly payments for not engaging in coca cultivation and/or assistance in the development of new (legal) agricultural activities. Although these programs have been relatively successful at the local level, their dependence on continuing national and international funding undermines their potential success in the long run. Governments in the three producer countries often rely on funding from developed countries to finance alternative livelihood programs and have to decide which regions to allocate the scarce funding, at the expense of experiencing increases in coca cultivation in those regions that cannot be targeted with these programs (UNODC, 2005a). For instance, after the implementation of alternative development programs in Aguatya and Bajo Huallaga (the two regions in Peru with the largest proportion of coca cultivation during the 1990s), coca cultivation had almost disappeared by 2004. Nevertheless, the high prices of coca leaf induced coca growers in other regions without any form of government attention (in the form of alternative programs, health and education services, etc.) to increase coca cultivation. As a result, coca cultivation, if anything, has remained relatively stable in Peru during the last six years.

UNODC together with government agencies in producer countries collect statistics on the number of eradicated hectares of coca crops. As explained before, in Peru and Bolivia eradication is done manually, whereas in Colombia it is mostly done by aerial spraying of herbicides in the coca fields.<sup>33</sup> This accounts for the difference between the average number of eradicated hectares per year in Bolivia (about 10,100 has per year between 1999 and 2005) and Peru (about 9,800 has per year), compared to Colombia (close to 110,000 has per year). Eradication in the three countries is undertaken by governmental entities (DIRECO in Bolivia, DIRAN—Antinarcotics Police—in Colombia, and CORAH and DEVIDA for

> 33 See González (2006) for a thorough description of aerial eradication programs in Colombia.

29
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4 natural resources correlates with the construction of tall buildings whose economic rationale is questionable, for example very tall buildings that are more expensive to construct than other types of buildings. Since resource-rich countries do not have a larger construction sector, as measured by cement use or construction GDP, but use more cement in their tall construction sector than industrialized economies, the expansion of the sector might have occurred at the expense of non-tall construction.<sup>4</sup> Fourth, an important theme in the macro-urban literature is the fact that urban-biased policies cause urban primacy (Ades and Glaeser, 1995). In particular, governments use resource rents to implement policies that disproportionately favor the largest city (Bates, 1981; World Bank, 2020). However, using both country- and city-level data, we do not find that resource-rich countries have relatively higher urban primacy rates. Indeed, resourcerichness appears to have the same consequences for _all_ population size categories of cities.

we various that that more Finally, perform analyses suggest having consumption cities might have economic implications for a country. Using evidence on the number of years of schooling, education quality, and the returns to education in urban areas globally, we actually do _not_ find that consumption cities have significantly less human capital than production cities. Yet, for a given level of human capital, consumption cities have more informality and higher employment in urban non-tradables than production cities. This implies that the human capital in consumption cities is employed in less productive sectors and jobs, at least in the short to medium run. Indeed, controlling for selection and sorting, we find large wage gaps between urban tradables and urban nontradables. In addition, we find that returns to experience in urban areas are overall lower in countries with more consumption cities. Finally, we discuss various studies showing that agglomeration economies are possibly weaker in urban non-tradables.

In addition to the structural change literature, we contribute to the macroliterature on the determinants and characteristics of urbanization across development countries (Gollin, Lagakos and Waugh, 2014; Gollin, Jedwab and Vollrath, 2016; Lagakos, 2016; Jedwab and Vollrath, 2019; Lagakos, Marshall, Mobarak, Vernot and Waugh, 2020; Lagakos, 2020; Porzio, Rossi and Santangelo, 2020; Gollin, Kirchberger and Lagakos, 2021;

> 4These results on possibly “inefficient” construction echo the work of Collier, Kirchberger and S ̈oderbom (2016) for roads and link this work to recent studies on the global role of market constraints (Kirchberger and Beirne, 2021) and geographical constraints (Jedwab et al., 2020b; Ahlfeldt and Jedwab, 2022) in construction. See Kirchberger (2020) for a survey of the literature on construction in developing countries.