rafmacalaba/data-use-mentions-extended huggingface dataset:
11 low- and middle-income African countries between the years 2010 and 2015 and Egypt in 2015: only South Africa and Namibia avoid egregious fiscal impoverishment among the populations at large. In Egypt, meanwhile, fiscal impoverishment is essentially zero.<sup>48</sup> Egypt’s fiscal profile—nontargeted expenditures that create cash or near-cash benefits that are larger (in absolute magnitude) for all individuals than the direct and indirect taxes collected from the same individuals—is rare. In other country and fiscal contexts, such a mismatch would be fiscally unsustainable. In Egypt (in 2015), however, the revenue shortfall was approximately 10 percent of GDP. The funding gap in FY15, as well in other years has been typically funded through government bonds.

> 46 See Enami, Lustig, and Tafqiri (2017) for the Iranian example of the same poverty‐ and inequality‐reduction impacts.

> 47 The measure of fiscal impoverishment (FI) was first proposed by Higgins and Lustig (2016). The FI headcount measures the proportion (out of the entire population) of individuals who were prefiscal poor and became poorer or prefiscal non‐poor and become poor as a result of taxes and transfers. The FI consumable income poor headcount measures the proportion (out of the entire post‐fiscal poor population of individuals who were prefiscal poor and became poorer or prefiscal non‐poor and become poor as a result of taxes and transfers).

> 48Fiscal impoverishment–headcount ratio and Fiscal Impoverishment–consumable income poor headcount ratio are both zero

> (0.0 percent) in Egypt using the 1.90 US$ per day (2011 PPP) international poverty line or Egypt’s national poverty line; and are 0.0 and 2.0 percent (respectively) in Egypt using the $PPP 3.20 per day (2011) international poverty line.

30
this two-visit setup, the recall length is shorter, especially for planting-related activities. Including this data set thus allows us to assess if recall effects vanish with somewhat shorter recall periods.

All three surveys are part of the Living Standards Measurement Study – Integrated Survey on Agriculture (LSMS-ISA) and, as such, the data sets contain an integrated household and agricultural component. The household survey component collects detailed socioeconomic information, including household-level data on consumption, income, assets and housing, and individual-level data on demographics, education, and health. The agricultural component collects detailed information, among other items, on agricultural inputs used and outputs produced, as well as output disposition, at the plot-level. The integrated survey further allows breaking down which household members are involved in agricultural production, who owns the means of production, and who is responsible for managing the household farm’s plots of land.

As ancillary data to validate our findings on farmer-reported crop production, we use the second round of the Methodological Experiment on Measuring Maize Productivity, Soil Fertility and Variety (MAPS) survey. The MAPS data, which Gourlay et al. (2017) discuss in detail, were collected in two rounds in 2015 and 2016 in Uganda, containing 900 maize-growing households in round 1 and 489 in round 2. The MAPS survey employed objective and subjective survey methods to collect information on maize production (crop cutting), area, soil fertility, and maize variety identification.

# Outcomes of interest

The analysis focuses on some of the agricultural outcomes of major policy interest, namely land input, crop production, labor and input use. The main dependent variable is the approximate length of the recall period between the interview and the activity relating to the outcome of interest (Table 1).

Land input reporting is assessed in two ways. First, as the total number of agricultural plots (or parcels) cultivated by the household. This variable is simply a count of the households’ agricultural plots (and parcels) which respondents list at the beginning of the
Soares, Liliana Sousa, Barbara Stallings, Miguel Sz ́ekely, Daniel Valderrama, Carlos V ́egh, Hernando Zuleta, and participants at seminars and conferences for helpful debates, suggestions, and comments. Daniel M. Angel, Elizabeth Jaramillo, Alejandra Mart ́ınez, Sebastian Melo, Juan Pablo Uribe, and Lucila Venturi provided outstanding research assistance. Finally, the authors would like to thank the National Institute of Statistics of Ecuador (INEC) for access to the Ecuadorian social security records (IESS), and the Ministry of Employment of Brazil for providing access to the Rela ̧c ̃ao Anual de Informa ̧c ̃oes Sociais (RAIS) data. The authors are particularly grateful to Roberto Castillo, Rosangela Farias, Natalia Garz ́on, Karla Marra, Jociany Monteiro, and Diana Zambonino for warmly hosting us and for their unconditional help with these administrative data. A supplementary online appendix for this article can be found at the _World Bank Economic Review_ website. The views expressed in this paper are those of the authors only and not those of the World Bank or the Inter-American Development Bank.
effects, and fiscal shock identification strategies (see Section 2 for discussion). Empirical work examining public investment multipliers in EMDEs is especially scarce. The main challenge in performing such research also rests in the difficulty of identifying changes in public spending that are uncorrelated with contemporaneous macroeconomic shocks, and that can thus be deemed as exogenous.

In light of these caveats, in this paper we aim to contribute to this literature in a number of ways. First, we use the data on public investment for a broad sample of 129 EMDEs over a long span of time (1980-2019). The large sample is helpful to better understand implications of public investment in EMDEs as a whole, as well as explore heterogeneity across country groups. Second, we bring together many of the methodological improvements in recent empirical research focusing on the role of initial conditions in determining the macroeconomic effects of public investment. These include: (i) prevailing business cycle conditions at the time of the shock; (ii) fiscal space; (iii) public investment efficiency; and (iv) the initial public capital stock level. While these themes have been touched upon in some earlier studies, no paper has yet brought them all together and

> 5 Public investment in the context of the paper refers to general government gross fixed capital formation.

2
WPS3843

# Regional Subsidies and Industrial Prospects of Lagging Regions

by Alexandre Carvalho DIRUR, Instituto de Pesquisa Econômica Aplicada (IPEA) <u>alexandre.ywata@ipea.gov.br</u> Somik V. Lall Development Research Group, The World Bank <u>slall1@worldbank.org</u> Christopher Timmins Department of Economics, Duke University <u>timmins@econ.duke.edu</u>

### **ABSTRACT**

Large and sustained differences in economic performance across regions of developing countries have long provided motivation for fiscal incentives designed to encourage firm entry in lagging areas. Empirical evidence in support of these policies, however, has been weak at best. This paper undertakes a direct evaluation of the most prominent fiscal incentive policy in Brazil, the Fundos Constitucionais de Financiamento (Constitutional Funds). In doing so, we exploit valuable features of the Brazilian Ministry of Labor's RAIS data set to address two important elements of firm location decisions that have the potential to bias an assessment of the Funds: (i) firm “family structure” (in particular, proximity to headquarters for vertically integrated firms), and (ii) unobserved spatial heterogeneity (with the potential to confound the effects of the Funds). We find that the pull of firm headquarters is very strong relative to the Constitutional Funds for vertically integrated firms, but that, with non-parametric controls for time invariant spatial heterogeneity, the Funds provide statistically and economically significant incentives for firms in many of the targeted industries.

JEL Classification: L22, R12, R58 World Bank Policy Research Working Paper 3843, February 2006 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely
is also true when it comes to the ability of fiscal systems to mitigate existent gaps across gender, racial and other groups in a country’s population. Therefore, updated fiscal analyses are fundamental to designing well-informed policy actions, as governments seek to be as effective and efficient as possible to protect the most vulnerable populations.

The results in this paper are consistent and complementary to a recent study on fiscal incidence analysis for the case of Brazil. In a recent study, Silveira and Palomo (2023) use the latest national household budget survey (Pesquisa de Orçamento Familiar, POF 2017-2018) and find that direct cash transfer and in-kind transfers are the main poverty and inequality reducing mechanisms of the Brazilian fiscal system. Using self-reported data in the survey, the authors document the regressive character of the public servants’ pensions system ( _Regime Próprio de Previdência Social_ , RPPS) and regressive impacts of indirect taxes. Differently from Silveira and Palomo (2023), our paper models and simulates direct taxes and contributions instead of using survey responses. By considering different elements like job status and family composition, our estimates aim to add more consistency between the reported income and the direct taxes assessment. We also model rural pensions as a separate cash transfer and contributory pensions are treated as deferred income, while their treatment of government transfers is estimated as an alternative scenario. An advantage of our study is its cross-country comparability to the large body of work based on a comparable methodology. However, we are not able to separate between the pensions system for private-sector workers, the _Regime Geral da Previdência Social_ (RGPS), and the pensions system for public servants, the RPPS (for details on the pension system, see Paiva (2016)).

The remainder of this paper is organized as follows. Section 2 shows the details of the methodology and presents the data sources. Section 3 presents the main results, while Section 4 shows the fiscal incidence analysis focused on the demographic groups of interest. Section 5 discusses international comparisons. Section 6 concludes.

5
We derive industry productivity as where _TFPRs_ is the industry-weighted average of firms’ revenue productivity. Last, aggregate output can be expressed as a function of _Ks_ , _Ls_ , and _TFPs_ :

# **3.2 Measurement and Calibration**

We use the data described in Section 2.1 to calibrate the parameters of our model and measure firms’ marginal revenue products and revenue productivities. Industries in the model correspond to the four-digit industries within the manufacturing sector according to the ISIC Rev. 3 industry classification.<sup>8</sup> We measure firms’ value added, _PsiYsi_ , as the difference between gross revenue and intermediate inputs. We use four-digit industry deflators for gross revenue and intermediate inputs, provided by the data set, to deflate our estimate of firms’ value added. Industry value added, _PsYs_ , is measured as the sum of all firms’ value added within industry _s_ . The capital input, _Ksi_ , is measured as the book value of fixed assets, which we deflate using the gross revenue deflators. To control for differences in human capital, hour requirements, and rent sharing across plants, we follow Hsieh and Klenow (2009) and use the wage bill deflated by the intermediate input industry deflator as the measure for labor, _Lsi_ . In a robustness check, we also consider hours worked for our measure of labor.<sup>9</sup> As described above, we calculate effective tax rates as the ratio between a firm’s profit taxes and its gross accounting profits. We denote the measured firm-specific effective tax rate as _t_<sup>ˆ</sup> _si_ . Two things should be noted. First,

> 8. The number of four-digit industries ranges from 80 and 85 depending on the year analyzed.

> 9. Due to data availability, we only carry out this analysis for the period 2001-2007.

15
–S59.

- Ma, Xiao, Alejandro Nakab, and Daniela Vidart, “Human Capital Investment and Development: The Role of On-the-job Training,” Working papers 2021-10, University of Connecticut, Department of Economics May 2021.

- Matsuyama, Kiminori, “Agricultural productivity, comparative advantage, and economic growth,” _Journal of Economic Theory_ , 1992, _58_ (2), 317–334.

- Melitz, Marc J. and Stephen J. Redding, “Trade and Innovation,” NBER Working Papers 28945, National Bureau of Economic Research, Inc June 2021.

- Minnesota Population Center, “Integrated Public Use Microdata Series, International: Version 7.3 [dataset],” 2020.

- Porzio, Tommaso, Federico Rossi, and Gabriella Santangelo, “The Human Side of Structural Transformation,” Technical Report 2020.

- Restuccia, Diego, Dennis Tao Yang, and Xiaodong Zhu, “Agriculture and aggregate productivity: A quantitative cross-country analysis,” _Journal of Monetary Economics_ , 2008, _55_ (2), 234–250.

- Roback, Jennifer, “Wages, rents, and the quality of life,” _Journal of Political Economy_ , 1982, _90_ (6), 1257–1278.

- Rodrik, Dani, “Premature deindustrialization,” _Journal of Economic Growth_ , 2016, _21_ (1), 1–33.

- Rosen, Sherwin, “Wages-Based Indexes of Urban Quality of Life,” _Peter Miezkowski and Mahlon Straszheim, eds., Current Issues in Urban Economics, Johns Hopkins_ , 1979.

- Sachs, Jeffrey D and Andrew M Warner, “Natural resource abundance and economic growth,” Technical Report, National Bureau of Economic Research 1995.

- and , “Fundamental sources of long-run growth,” _The American economic review_ , 1997, _87_ (2),

- 184–188.

- Schiavina, M., A. Moreno-Monroy, L. Maffenini, and P. Veneri, “GHSL-OECD Functional Urban Areas 2019,” _EUR 30001 EN, Publications Office
0/1)|7.281|35.702<sup>_∗_</sup>|-32.669|13.936|-13.611|0.010|-0.003|-0.141<sup>_∗_</sup>|-0.177<sup>_∗∗∗_</sup>|-0.028|-0.046<sup>_∗∗∗_</sup>|
||(51.534)|(18.055)|(35.492)|(10.783)|(8.153)|(0.035)|(0.029)|(0.074)|(0.054)|(0.026)|(0.012)|
|County, Wave, Strata FE|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Observations<br>|30,550|30,545|24,354|30,739|30,788|30,884|30,896|30,892|30,900|30,897|30,901|
|_R_<sup>2</sup>|0.108|0.042|0.018|0.087|0.068|0.099|0.092|0.079|0.151|0.061|0.063|

This table shows OLS regression estimates using the first five waves from the Rapid Response Phone Survey data in Kenya. All regressions include the set of controls described in Table 1. Robust standard errors clustered within counties are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

When it comes to household actions specific
Figure B13: Heterogeneity in poverty gradient across countries, DOU <!-- Start of picture text --> MWI<br>NER<br>UGA<br>LSO<br>AGO<br>BFA<br>GNB<br>TCD<br>GHA<br>SEN<br>GIN<br>CIV<br>TZA<br>ETH<br>MRT Urban center<br>Urban cluster<br>GAB<br>−.6 −.5 −.4 −.3 −.2 −.1 0<br>Regression coefficients and confidence intervals<br><!-- End of picture text --> Source: International Urban Poverty Database. Each dot represents the regression coefficients _DOUj_ ( _i_ ) of regression 5 for each country _c_ , excluding control variables. Confidence intervals are represented with a line. The dependent variable _POVi_ is a dummy variable taking the value 1 if a household’s per capita expenditure expressed in PPP and spatially deflated falls below the poverty line. _DOUj_ ( _i_ ) is a vector of dummy variables: "Urban cluster" ("Urban area") takes the value 1 if household _i_ lives in an "Urban cluster" ("Urban area") as defined by the DOU method. The baseline category is "Rural". GHSPOP 1km is used.

54
# **5. Data and stylized facts on FDI in Africa**

# **5.1. Data**

The analysis is done on a sample of 16<sup>3</sup> African countries over the period 2001-2012.

Primary school enrollment<sup>4</sup> (defined as the total enrollment in primary education, regardless of age, expressed as a percentage of the population at the official primary education age), trade openness (exports plus imports as a share of GDP), fixed line telephone subscription (per 100 people), and labor productivity (output per employed individuals, generated based on 2005 constant prices) are accessed from World Bank’s World Development Indicators (2016).

Bilateral FDI stock of BRICS and G7 countries are from UNCTAD statistics (2016). BRICS’ and G7 countries’ direct investment stock to an African country _i_ at time _t,_ is calculated as the sum of FDI from individual BRICS and G7 countries at time _t_ . Since the UNCTAD data does not contain sector level bilateral FDI flows, data on Greenfield investments are used to examine the recent trends in sectoral investments. The Polity2 governance index, which is reported on a scale of -10 to +10, with -10 indicating strongly autocratic and +10 strongly democratic political systems, is obtained from the Polity IV Project (Marshall and Jaggers 2011). Tables A.1 and A.2 of the appendix contain respectively, the sample of countries used in this study and summary statistics for selected variables.

# **5.2. Stylized facts on FDI in Africa**

To get a sense of the sectoral level flows, we exploit data on Greenfield FDI in Africa, which has grown by almost five-fold since 2000. It increased from US$ 12.5 billion in 2000 to

> 3 The availability of data on BRICS FDI restricts us to 16 African countries.

> 4 Primary school gross enrollment ratio can exceed 100% due to the inclusion of
restrictions on wages, especially because we rely on the district ...xed e¤ects in the estimation.<sup>19</sup> Since data on malaria prevalence from pre-eradication era (before 1945) are not available at the DSD level, we devise an alternative way to exploit DSD-level variations in LDO restrictions due to di¤erences in historical malaria prevalence.

# 4.2. Identifying Instruments for Land Restrictions: Interaction of District Level Malaria with DSD Level Geographic Characteristics

There were signi...cant variations in the historical malaria prevalence across di¤erent DSDs within the same district, and it is only natural that the district level average by itself would be of little help in understanding the variations across DSDs. For example, in Ja¤na district, Ja¤na city was almost malaria free while south Ja¤na su¤ered from severe malaria in early 1930s (Newman, 1965, p. 35). Our approach to constructing instruments that represent historical DSD level malaria incidence is to ...nd DSD characteristics that can essentially be used as “weights" to recover the variations in malaria prevalence across di¤erent DSDs within a district from the district average malaria data. For this purpose, we use proportion of land above 1000 feet elevation, and a measure of inland water in a DSD. The higher elevation of a DSD makes it less susceptible to malaria as it is di¢ cult for anopheles mosquitos, the carrier of malaria parasite, to survive and breed in high altitude. For example, in Badulla district of Sri Lanka, historically the mountainous region was e¤ectively malaria free, but the low lying area was infested with highly endemic malaria (Newman, 1965, P. 35). The spleen rates reported in Rustomjee (1944) show the e¤ect of altitude clearly; the average spleen rate for 1938-41 was 2.5 percent for areas above 3000 feet and the corresponding ...gure for the areas below 1500 feet was 43.
II, in part motivated by the proximity to the relatively high quality of services in the area that was upgraded. CONDER is working on trying to resettle these residents.

Urban upgrading of precarious settlements can also generate incentives to invasions indirectly, as there are often incentives for beneficiaries to sell their houses and invade elsewhere. A CONDER/AVSI survey indicates that a relatively low number of residents in a section of Ribeira Azul (17% of a 280 sample) sold their homes to move elsewhere. These data, however, refer to formal transactions only, and thus it is not known if and how many houses have been sold informally. In any case, according to impressionistic perceptions by field workers, the turn-over of residents has been quite low. In contrast, in other upgraded _favelas_ in Salvador the picture is strikingly different – for instance, in the _favela_ Silvio Leal (in the region of Pau da Lima), the turn-over of residents was about 75 percent. This appears to be closer to the norm in Salvador.

Beyond the approaches used in Ribeira Azul, a more systematic approach to slum prevention will be necessary for scaling up. One of the new policies being considered is the provision of serviced land for the poor. While the availability of appropriate public 19
for own consumption and the imputed value of owner-occupied dwellings and other income sources. None of these measures are readily available in the survey data. We now describe our approach to incorporate them in the analysis.

Value of own consumption was estimated in the following way. First, self-consumption was estimated from the POF survey using the ratio of households’ reported value (of withdrawals from business or own production) to disposable income. Next, the average of these ratios by state and percentile was estimated and then imputed to households in PNAD-C according to their state of residence and position in the income distribution. Each household’s monetary value of own consumption was obtained by multiplying the imputed ratio by disposable income.

18
firm-level data from India for the period 1990-2017. The study classifies the depth of GVC participation following Veugelers et al (2013) and Gereffi and Fernances-Stark (2016) and looks at different type of service inputs, i.e., the composition of services as well as the origin of services. Indian firms integrated in GVCs play an important role in India’s economy, contributing a disproportionate share of output and exports. Due to its rapid economic progress, its successful market liberalization and integration into the global economy and its recent reforms in the service sector, India offers an excellent case study to evaluate the relation between service inputs, GVC participation and firm productivity. The paper complements existing work on GVCs in India such as Goldar et. al (2018) and Banga (2016).

We estimate the productivity premium associated with varying depths of GVC integration and different intensities and types of services used as inputs. First, GVC integration is associated with higher productivity levels. Firms in GVCs have a productivity premium between 13 and 22 percent relative to domestic firms. The productivity premium increases with the depth of integration into GVCs - firms integrated at an intermediate and high level have a much higher productivity premium relative to firms participating in GVCs in a limited or low level. Second, there is variation across industries, with the productivity premium

> 1 Note: GVC participation is the ratio of a country’s GVC exports to total exports where GVC exports include the foreign valueadded content of exports and the domestic value-added content of exports that are re-exported by the importing country to a third country. Productivity here refers to labor productivity in PPP terms.

3