rafmacalaba/data-use-mentions-extended huggingface dataset:
in each country, we can readily construct the ...rst two terms in equation (57), the present value of expected TFP growth, and the change in expectations of that quantity. The third term, which depends on the change in the capital stock can also be constructed using data from EU-KLEMS. We assume that the composite discount rate, �; is common across countries and we set it equal to 0:95:<sup>18</sup> For the expenditure and distributional shares, we use their country speci...c averages over the sample period.

The results are in Table 2. We see that assumptions about ...scal policy a¤ect the results in signi...cant ways. We ...rst illustrate our methods by discussing the results for the US, which are given in the last row. We then broaden our discussion to draw more general lessons from the full set of countries. In the ...rst column of Table 2, we construct the output data and the capital and labor shares under the assumption that government expenditure is wasteful and taxes are lump-sum. In this column, "utility-relevant output" comprises just consumption and investment, aggregated using weights that sum to less than one.<sup>19</sup> In this case, the average annual growth rate of welfare in the US is equivalent to a permanent annual increase in consumption of about 2.5 percent. Recall from Section 4.4 that this result applies whether we think of the US as an open or a closed economy. The same is true for all the other results in Table 2–all apply to open as well as closed economies. Now we study the case of optimal government spending, still under the assumption that taxes are not distortionary. Thus, at the margin the consumer is indi¤erent between an additional unit of private consumption and an additional unit of government expenditures. In this case, output consists of consumption,
adults make greater use of formal financial services.

Novel cross-country data on self-reported reasons for not having a formal account make it possible to identify barriers to financial inclusion. Moreover, the ability to disaggregate data by individual characteristics allows researchers and policy makers to identify population groups that are excluded from the formal financial system and better understand what characteristics are associated with certain financial behaviors.

As the first public database of indicators that consistently measure people’s use of financial products across economies and over time, the Global Findex database fills a big gap in the financial inclusion data landscape. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world save, borrow, make payments, and manage risk. The main indicators on the use of formal accounts and formal credit will be collected yearly, and the full set of indicators every three years.

**1**

MEASURING FINANCIAL INCLUSION
.18)|(0.32)|
|Panel A of Table 8: Capital market openness and develo|pment|||||
|De-jure fnancial openness index<br>-0.18|0.00|0.03|0.06|0.13|-0.17|
|(0.57)|(0.03)|(0.03)|(0.07)|(0.14)|(0.51)|
|Private sector credit (% of GDP)<br>-0.60|-0.00|-0.03|-0.13**|-0.23|-0.35|
|(0.78)|(0.04)|(0.05)|(0.06)|(0.18)|(1.53)|
|Panel A of Table 10: Changes in macroeconomic variabl|es before|frst cash f|ow|||
|Real GDP<br>0.110|0.000|0.013***|0.020**|0.019|-0.004|
|(0.147)|(0.004)|(0.005)|(0.010)|(0.015)|(0.058)|
|Local currency GDP defator<br>-0.047|0.000|-0.000|-0.005|-0.009|-0.021***|
|(0.032)|(0.001)|(0.002)|(0.004)|(0.006)|(0.006)|
|Local currency units per U.S. dollar<br>0.058|0.000|0.001|0.005|0
# _3.4. Harmonizing the approach across Countries_

In order to better assess the synthetic panel technique as it is applied simultaneously in the three different countries and settings we apply a number of data harmonizing protocols. First, to avoid potential bias from using panel data to conduct the tests, we follow Lanjouw et al. (2011) in by splitting every panel dataset in every country into two randomly drawn sub-samples and then treating one sub-sample from each round as two repeated cross-sectional surveys. We then use these two repeated cross-sections to estimate mobility by applying the method described in section 2. These results are then compared with the true panel consisting of the other sub-sample.

Second, we apply the same specifications across countries (to the extent possible based on data availability). Specification 1 is the most restrictive in that it only uses variables that best adhere to the time invariance assumptions: household head age, age squared, gender, years of education, and ethnicity. Specification 2 adds geographical controls and regional fixed effects while the specification 3 adds more flexible interaction terms between the first two specifications. Finally, specification 4 adds retrospective asset ownership information (Peru only).

Third, for each country we predict household income using time invariant characteristics from round 1 and the returns to those characteristics from round 0. This yields a synthetic panel that uses the actual welfare measure from round 1 and the simulated one from round 0. We repeat the procedure in the reverse order as an additional validation exercise.

In addition, we apply household weights to address survey design and explore the use of alternative weighting schemes (no weights or individual weights in the robustness tests). For the upper bound mobility calculation, we apply 50 replications to estimate consumption in period 0 (and we also explore a higher number of replications as another check). Finally, we restrict our 10
We focus on four key outcomes of the simulation model to contextualize learning loss impacts: (i) Learning Poverty, (ii) Learning Adjusted Years of Schooling, (iii) Percent below minimum proficiency in the Programme for International Student Assessment (PISA), and (iv) Lifetime earnings.

Since the publication of the learning loss simulation results in June 2020 in <u>Azevedo, Hasan et al. (2021), the</u> paper has been downloaded more than 17,000<sup>3</sup> times and cited more than 550 times, emphasizing the interest and need to understand learning losses as the pandemic unfolds.<sup>4</sup> To date, the simulation results have helped ministries of education, key national stakeholders, and development partners advocate for and plan evidencebased recovery strategies for mitigating potential learning losses arising from COVID-19 school closures, as well as for tackling the chronic Learning Poverty problem that existed before the crisis.

Given the substantial learning data gaps<sup>5</sup> that accompany the long-lasting learning crisis, these simulation results are often the only source of evidence that can guide policymakers, educators, and researchers towards addressing and analyzing potential learning loss impacts on a global scale. Although they are well aligned with evidence on observed learning losses that has begun to come out, it is important to keep in mind that these results are simulations. They do not replace the value of actual data on learning losses or mitigation effectiveness, which is limited at present, particularly for low-income countries.

> 3 <u>https://openknowledge.worldbank.org/handle/10986/33945</u> 4 Based on Google Scholar statistics here: <u>https://scholar.google.com/citations?view_op=view_citation&hl=en&user=lTKXA78AAAAJ&citation_for_view=lTKXA78AAAA J:8p8iYwVyaVcC</u>

> 5 In July 2021, UNESCO, UNICEF, and the World Bank have joined forces
, harvesting and weighing, and (ii) comprehensive quality control measures applied to the incoming data throughout the fieldwork, there is reason to believe that the EACI crop cutting operations were not implemented as successfully. Figure 6 shows a scatterplot of SR and CC yields in the EACI data set and there is evidence of rounding errors in CC yields. Assuming that these errors are not correlated with the measures of inputs,<sup>18</sup> this would mostly create an attenuation bias in the coefficients for the respective inputs in the regressions of CC yields and could, therefore, explain in part the zero coefficients in Table 12. This finding underscores the need of quality (but still scalable) digital measurement devices when objective measurement is the goal.<sup>19</sup>

> 18We created a variable indicating whether an observation was rounded and it appears not to be associated with the value of the key inputs we study here. A close look at this issue is left to future work.

> 19In fact, discussion with CPS/SDR confirmed that during this round, the survey enumerators relied on scales which lacked accuracy and were scheduled to be replaced.

29
growth, including microdata from PNAD – National Household Survey. The results suggest that per capita incomes of the Brazilian states have already converged to their steady-state level and that they will remain unaltered due to huge educational and geographic differences between the states.

What accounts for these disparities? The North and Northeast of the country lag far behind other regions in quantity and quality of education, and these differentials are strongly correlated with differences in labor income, as human capital theory would suggest (Fiess and Werner 2004). However, education and other observable individual characteristics do not fully explain interregional wage and income differentials. Azzoni e Santos (2002) compared the differences in salary in the 10 largest Brazilian metropolitan areas in 1992, 1995 and 1997. Those salary differences remained significant even after controlling for cost of living measures, the traits of the workers (education, age, sex, race and family position) and the traits of their jobs (occupational position, sector and experience). Fiess and Werner (2004) estimated mover/stayer models of wage and migration. They found that low-education Northeasterners could boost their wages by 80% through migration; the differential declined with higher levels of education. Evidently, judging from the evidence cited above, the substantial degree of interregional migration is not sufficient to induce convergence between the regions.

Brazilian policy has been concerned with reducing interregional inequalities. Best known, perhaps, are the national programs of tax and fiscal incentives, including the Constitutional Funds, subsidized loans from development banks including BNDES and BNB, the Zona Franca of Manaus, and FINOR. Some programs were explicitly targeted on poor regions. For instance, Projeto Alvorada sought to provide education and other services to states (and later microregions) with below-median HDI. Individual states use fiscal incentives to influence intrastate location of industrial firms, but the scope and impact of these programs is not comprehensively known. Other programs 2 The others were in the North Region.
Egleston, B. L., S. M. Miller and N. J. Meropol (2011). "The impact of misclassification due to survey response fatigue on estimation and identifiability of treatment effects." Statistics in medicine **30** (30): 3560-3572.

Epley, N. and T. Gilovich (2001). "Putting adjustment back in the anchoring and adjustment heuristic: Differential processing of self-generated and experimenter-provided anchors." Psychological science **12** (5): 391-396.

Fernald, L. C., R. Hamad, D. Karlan, E. J. Ozer and J. Zinman (2008). "Small individual loans and mental health: a randomized controlled trial among South African adults." BMC public health **8** (1): 409. Haushofer, J. and J. Shapiro (2016). "The short-term impact of unconditional cash transfers to the poor: Experimental Evidence from Kenya." The Quarterly Journal of Economics **131** (4): 1973-2042. Hopkins, D. J. (2011). The Priming Power of Spanish: Testing the External Validity of a Survey Experiment, Citeseer.

Irwin, M., K. H. Artin and M. N. Oxman (1999). "Screening for depression in the older adult: criterion validity of the 10-item Center for Epidemiological Studies Depression Scale (CES-D)." Archives of internal <u>medicine</u> **159** (15): 1701-1704.

Kohout, F. J., L. F. Berkman, D. A. Evans and J. Cornoni-Huntley (1993). "Two shorter forms of the CES-D depression symptoms index." Journal of aging and health **5** (2): 179-193. Lerner, J. S. and D. Keltner (2001). "Fear, anger, and risk." Journal of personality and social psychology *
Finally, the bottom graph of Figure 1 shows the changes in domestic sourcing of MNEs. We first calculated the country average separately for each survey and then calculated the change in the average between the last two survey years for each country (standardized to represent the change over 5.8 years, the average time between two surveys). For almost two thirds of all countries, the intensity of domestic sourcing increased. The average increase of +3.5% across all countries tends to underscore the magnitude of the change. For a third of all countries (11 out of 34) the measure increased by more than 10%, while for only 2 countries it declined by more than 10%.

Given that there are on average 2.04 surveys per country, each country-year average is calculated using approximately 38 firm responses. It is no surprise then, that the standard deviation for the country-level changes is even higher than for the levels. Another reason for caution is that for some countries the annualized change is calculated for two years that are relatively close and then extrapolated for a 5.8 year period that is standardized over all countries. This is likely to raise the importance of measurement error.

The second GVC measure we have calculated is the fraction of inputs of domestic producers that is imported. As shown in the Appendix, the WBES in principle contains a question that allows one to distinguish between firms that sell finished or semi-finished goods mostly to final consumers and firms that sell intermediate inputs. Taglioni and Winkler (2014) propose to calculate this second GVC measure only for the subsample of producer firms, i.e. excluding suppliers. Unfortunately, in most surveys conducted in sub-Saharan countries this question was not posed to the firms. The harmonized dataset available on the WBES web site does not contain the responses to this question.

For comparability across countries and over time, we calculated the measure for all domestic firms, i.e. firms that report to be at least 90% private-owned by domestic owners,
(2009), Sahoo and Dash (2012), and Sahoo et al. (2012) find a positive correlation between infrastructure investment and economic growth in India, South Asia and China, respectively.

A few studies are carried out for South America <mark>(German-Soto</mark> & <mark>Bustillos</mark> 2014; Urrunaga and Aparicio 2012). Following Holtz-Eakin (1994), <mark>German-Soto</mark> and <mark>Bustillos</mark> (2014) estimated the production function using panel data with fixed effect and found positive links between infrastructure and growth. Urrunaga and Aparicio (2012) find that public infrastructure is important in explaining temporary differences in regional output.

# **_3.2 Economic Growth Effects of Sector-Specific Infrastructure_**

Instead of investigating the impact of aggregate public infrastructure investment, a few papers aimed at specific sectors, studying the economic growth effects of sector-specific infrastructure investment (Zou et al. 2008; Herrerias 2010; Czernich et al. 2011; Banerjee et al. 2012), mostly for the case of China. Specifically, Zou et al. (2008) use both panel and time-series data along with fixed effect and Granger causality test to examine the effect of transportation infrastructure on economic growth, suggesting that the greater growth level is, to some degree, attributed to the better transportation system. A meta-analysis conducted on the effect of transport infrastructure investment on economic growth (Melo et al. 2013) suggests that transport’s impacts are larger for the United State than Europe; the effects are also larger when using roads compared to other modes of transportation. It also shows that if the study is not controlling for unobserved heterogeneity and spurious correlations, then it will obtain higher estimates. Herrerias (2010) investigates whether an investment in equipment/machinery impacts the economic growth effect in China and finds that it does significantly. Banerjee, Duflo and Qian (2012) also study the effect of the transportation system on regional economic performance
Policy Research Working Paper 10066

# **Abstract**

The spatial distribution of economic activity is known to depend on trade costs, both international and domestic. This paper examines the interplay between these external and internal trade costs using a model of trade and production that is tested with the organized manufacturing sector data for India from 1989 to 2009. The analysis establishes that the trade liberalization episode of the early 1990s helped spread manufacturing away from the primary region (districts closest to ports) to the secondary region between 1994 and 2000. Such dispersion of activity away from the primary to the secondary region was driven by high internal trade costs that insulated manufacturers from import competition. This trend reversed post-2000, a period of massive decline in internal trade costs, attributed to the Golden Quadrilateral highway upgrades. During this period, the districts along the highway network in the secondary region gained market access and manufacturing activity, while those off the network lost. Irrespective of the period, or the nature of trade costs, manufacturing activity in the interior region (districts farthest from ports) remained depressed, thereby emphasizing the importance of complementary conditions in driving territorial development.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at agrover1@worldbank.org.

_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 those of the authors. They do not necessarily represent the views of the International
ification, payment). These data have been extensively used and validated in empirical research using data on public finances in Brazil (Gadenne, 2017; Corbi et al., 2019; Shamsuddin et al., 2021). Moreover, the federal government performs several checks to guarantee an adequate level of quality. Our data is much more granular than SICONFI, measuring individual commitments, verification and payments, but once we aggregate at levels such as municipality-year we should expect to match totals from SICONFI. A natural validation of the quality of our dataset therefore is to compare our aggregates with those provided by SICONFI.<sup>20</sup> We perform the following exercises. First, we aggregate both amounts committed and paid at the municipality-year level in our new dataset and compare these values with information from SICONFI.<sup>21</sup> Formally, we compute Dmt = (Tmt<sup>BE−T SICONFI</sup> mt )/Tmt<sup>SICONFI</sup> , where Tmt<sup>BE</sup> represents total expenditures for municipality m and year t as calculated from our budget execution data and Tmt<sup>SICONFI</sup> represents total expenditures as calculated from SICONFI data.<sup>22</sup> In Figure 3, we present the histogram of the percentage deviation of _committed amounts_ from SICONFI, across states. Our key takeaway is that for five states (CE, MG, PB, PE and SP), our aggregates are almost identical to those from SICONFI - for each state, over 75% of deviations are below 1%, and often precisely zero. For PR and RS our deviations are centered around zero but with larger mass slightly above or slightly below - in both states three-quarters of deviations are in the range [-0.5%, 5%], but with more mass for larger absolute deviations in some municipality-years. We also present the same deviations but considering total committed amounts at the
Indonesian exporters.

While data on import tariffs are relatively more available, a key innovation of this paper is the extensive coverage of NTMs, which represent increasingly important but still under-researched trade policy instruments (Nicita, 2012). Specifically, we address the data limitation by assembling a novel hand-collected database on NTMs from different regulatory agencies in Indonesia for the period 2014-2018. We then link these NTMs (as well as tariffs) to firm-level customs data at the 10-digit HS level, the most detailed product classification 1
_proportionality assumption_ to approximate the main use of imports. That is, every domestic sector is assumed to import inputs in the same proportion as its economy-wide use of that input. For example, if an industry such as electronics relies on semiconductors and 10% of all semiconductors are imported, it is assumed that 10% of the semiconductors used by the electronics industry is imported. With this assumption, scholars have been able to link the flow of imported inputs to the flow of exported goods within the same global production network. Hummels, Ishii and Yi (2001) and Johnson and Noguera (2009) have used this approach to quantify the import content embodied in a country’s exports. Recent studies, then again, have questioned the accuracy of the proportionality assumption. Winkler and Milberg (2009) have shown that, in Germany, the cross-sectoral variation in the use of imported inputs differs significantly from the cross-sectoral variation in the use of domestic inputs. Koopman, Wang and Wei (2008) show that China’s policy preferences for processing exports has led to a significant difference in the intensity of imported inputs in the production for processing exports than in other productions (for domestic final sales and non-processing exports).

A third approach has been to use firm-level data on multinationals to measure the dispersion of GPNs. Collinson and Rugman (2008), Rugman, Li and Oh (2009) and Rugman and Oh (2009), for example, use data on the geographic distribution of assets for large multinational firms to measure the dispersion of GPNs. Hanson, Mataloni and Slaughter (2005) use BEA data on U.S. multinationals to estimate the drivers of trade in intermediate inputs for further processing between parent firms and their foreign affiliates. These studies, however, give an incomplete and potentially biased picture of the organization of GPNs since many multinationals outsource a large portion of their manufacturing activities to external firms (Gereffi, 1999; Gereffi, Humphrey and Sturgeon, 2005; Swenson, 2006; Bonham, Gangnes and Van