rafmacalaba/data-use-mentions-extended huggingface dataset:
may be that the development profession does not know enough about how to assist a developing country with a fragile (sometimes failing and post-conflict) state transform itself into a developmental state. History may not be useful for this purpose. Besley (2012) argues for instance that successful state formation in Western Europe and China is not replicable as these states are specific products of history: thousands of years of effective centralized empire management in the case of China, and avoiding the destructive 22 In ongoing follow-up work, we test our hypotheses with available quantitative information. Since project outcomes are significantly correlated with measures of project size, extent of project supervision, evaluation lags and early-warning indicators flagging problematic projects during implementation (as shown convincingly by authors such as Denizer Kaufman and Kraay 2012), we use a large database on projects and project ratings from 1977 to 2012, to analyze econometrically economic policy projects (6% of total projects); public sector and governance projects (also 6%) and technical assistance projects. If governance-focused projects matter, this would be seen in an interaction term between other project types and governance projects on the outcome of these other projects, i.e., the value of governance projects is to enhance the outcome of the rest of the project portfolio. This interaction could be the larger, the greater the quality of the governance project itself. In other words, governance projects allow improvements in the portfolio of other projects: less emphasis on social assistance and more emphasis on income generation, and governance projects should increase the capacity to absorb more foreign aid (if we take into account the dollar value of other projects and their interaction with governance projects in terms of outcomes).

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u> ~~appendix~~ presents the summary statistics and source of each indicator. While value added and trade data are used directly in the analysis, labor indicators are used to calculate industry-specific factor shares. Following Grossman and Krueger (1992) we assume that each industry’s output is produced according to a constant returns to scale CobbDouglas technology using three main inputs: labor, human, and physical capital. For each industry, labor share is calculated as a product of average unskilled wages and total number of workers divided by industry output. Human capital share is calculated as the total wage bill divided by output minus the labor share. Finally, physical capital share is calculated as the residual.

While we acknowledge potential limitations of the pollution data and the analytical framework used, we believe that these exercises are in line with the literature and can help to provide insights for the ongoing policy debate.

# **V. Results**

This section presents and discusses the results from the two quantitative exercises. In each case we start by discussing the benchmark case (no technique effect) and move on to the alternative scenario. We present both individual-country results and the aggregate analysis for the region.

# _Decomposition Exercise_

Figure 1 compares the emissions from clean and dirty industries in the period before and after DRCAFTA assuming no technique effect. All countries experience a significant increase in pollution

> 10 Most papers in the literature estimate a combination of scale and technique effects.

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br>1993 1996 1999 2000 2003 2006 2007 1994 1997 1999 2002 2004 2007<br>1996 1997 1998 1999 2000 2007 2008 1994 1996 1999 2001 2004 2006<br>1994 1995 1997 1998 2001 2002 2004 2005 2008 1993 1995 1996 1998 2000 2001 2003 2005 2006 2008<br>1993 1994 1995 2001 2002 2003 2004 2005 2006 1993 1995 1997 1998 2000 2002 2003 2005 2007 2008<br><!-- End of picture text --> Source: Authors’ calculations based on World Bank and IMF data (2011). Notes: Unweighted averages. The data is not presented if there is at least one missing country observation within a group.

The resumption of positive growth rates by the late 1990s was associated once again with a significant deterioration of current account positions and unprecedented inflows of private foreign savings. But was this the result of a substitution between national and foreign savings or the consequence of the existence of high investment opportunities? The evidence seems to suggest that in emerging Europe this second phase of increased dependence on foreign savings was not, for the most part, a case of savings substitution (Figure 2). On the contrary, it appears to have been targeted at exploiting attractive investment opportunities in a rapidly improving business environment. Indeed, investment in many cases adopted an upward trend and domestic savings either increased or remained stable. More precisely, classifying emerging Europe into the three political groups related to the European Union (EU10, EU candidate, and EU neighborhood countries) reveals quite a unique international financial integration experience. The EU10 and the EU candidates, on average, were by the late 1990s importing capital on a larger scale (Figure 2, second and third chart) compared to the varying reliance on external finance in the case of the EU neighborhood (Figure 2, fourth chart). Yet, the process does appear to have reached its limit in many countries during 2007-08. Indeed, it is then that investment appears to have adopted a 5
Public parcel-level data on agricultural land sale and rental transactions that are regularly published as part of Ukraine’s land market monitoring system on the website of State Geocadaster (SGC) form the basis of our analysis. These data, which include the date of the transaction, the parcel’s cadastral number, price, the term for leases, and firm IDs for contracting parties that are legal entities,<sup>7</sup> have been used by others (Ibatullin _et al._ 2024; Kvartiuk and Martyn 2022).<sup>8</sup> We construct prices per hectare and convert to US$ using the monthly exchange rate in place at the time of the transaction.<sup>9</sup> Parcels’ cadastral number allows overlaying parcel polygons with publicly available crop maps to obtain the share of any given parcel under different types of land use. Crop maps also allow identification of land that is cultivated but not mapped in the registry, our indicator of informality. We further use parcel centroids to compute distances from every transacted parcel to key infrastructure objects such as roads, ports railways, and settlements that are likely to affect land prices and which we sourced from open street map. Finally, to measure soil quality, SoilGrids data (Poggio _et al._ 2021) is used to obtain pH, soil density, and nitrogen as well as organic matter in the 0-60 cm horizon for each parcel’s centroid as well.<sup>10</sup> Beyond these indicators that are routinely used in hedonic analysis, we define fields as contiguous units that are cultivated with the same crop and bounded by terrain features such as roads, rivers, wind breaks, stretches of forests or settlements that are difficult to modify in the short term. This definition, which is comparable to what is used in EU member states for agriculture sector monitoring, allows fields to be identified using machine learning algorithms trained on EU data using medium resolution satellite imagery, in our case stacks
- (6) Are specific forms of restrictiveness more relevant than others for manufacturing firms’ performance? To answer this question we replace the aggregate ‘rest’ indicator with restriction-specific indicators in equation (2).

# **5. Results**

## **5.1 Reform and Perception-Based Performance**

The firm-level data available from the World Bank Enterprise survey suggest that in the Indonesian service sector, foreign firms are larger than domestic firms. While 23 percent of foreign firms in the service sector have more than 100 employees, only 1 percent of domestic firms do. In addition, foreign firms tend to use better technologies and more sophisticated processes, as suggested by the fact that they are four times as likely to have international certifications than domestic firms within the same size class.

The performance of service sectors, as measured by the perceptions of their users, is negatively related to how restrictive they are toward FDI. We found that a one percent improvement in service sector performance as perceived by manufacturers was associated with a one percent reduction in the degree of restrictiveness toward FDI in those service sectors, as measured by the OECD restrictiveness index (see Figure 10, for visual inspection of the evolution of the two indicators).

Formally, we regress the index of restrictiveness on the perception based performance index, using a fixed effect model, which allows us to control for time-invariant sector specific factors that affect the perceptions of services performance, The point estimate for the elasticity of performance with respect to restrictiveness is -0.9, and significant at 1 percent. We cannot reject the hypothesis of the effect being different from -1.

The experience in freight and logistics services in Indonesia provides a concrete example of the link between policy restrictiveness and performance. If services related to freight and logistics services, for example, remain sheltered from foreign investment and competition, costs may remain high and service quality poor. This undermines the capacity of the private sector to benefit from business opportunities involving long-distance shipping logistics costs in Indonesia are
, Italy and England together form our group of “high performing countries” (HPC).

The specific target population of the PIRLS study was defined as the upper of the two grades with the highest share of nine-year-olds of a country at the time of testing. This led to a student population which was in the fourth grade in the countries considered in this paper, with the exception of England, where it was fifth grade. The mean age of the students in the

> 5 For detailed information on the study and the database, cf. Mullis et al. (2003), Martin et al. (2003), Gonzalez and Kennedy (2003), Campbell et al. (2001) and the PIRLS homepage at http://timss.bc.edu/pirls2001.html.

> 6 The United States could not be used for the analysis because two crucial student background variables, the number of books at home and parental education, were not administered in the US. This would result in a lack of comparability to the other countries, as well as to considerable bias in the estimation of the remaining student background and school effects in the US. Instead of the US, England is included in the analysis as a representative of the Anglo-American country group.

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in Ghana to 6,778 in Tanzania; a total of more than 145,000 adult children (ages between 20-69) are represented in the study. The data in all the countries are organized into five-year birth cohorts based on the children’s years of birth. In table 1, we also present the minimum sample size in each country, which corresponds to the smallest sample size of five-year birth cohort in each country.

In all the countries, years of schooling is coded as the number of years associated with the highest grade completed, and repeated grades are not counted. Parental educational attainment is the average of the years of schooling of the mothers and fathers. All the surveys contain information on the educational attainment of parents in two separate variables that differentiate the education of parents who are coresiding or not residing in the household. We used the personal identification numbers of fathers and mothers to create a pair of parents and children if the parents and children are still living together in a household. Using this information to create pairs of parents and children imposes a co-residence condition that reduces the sample size significantly. In addition to this information, all the surveys have another question that collects information on retrospective parental educational attainment among household members who are not co-residing in the household regardless of whether the parent is alive.

Combining these two variables, we have been able to identify parental schooling for more than 95 percent of adult respondents in each country. Because of the lack of long panel or administrative data, most studies in the literature have used cross-sectional data and co-residence to identify child-parent pairs, mostly father-son pairs (see, for example, Emran and Shilpi, 2015; Hnatkovska et al., 2013; Jalan and Murgai, 2007). Using co-residence identification has three important implications for the analysis. First, because the distribution of education across both generations is different in the subsample of adults who live together with their parents and versus the total population, sample selection problems arise that bias the intergenerational elasticity downward. For instance,
This instrumented _θ_ elasticity is at the lower end of the estimates provided in the literature. Costinot et al. (2012) is the closest comparison given their use of industry-level regressions of productivity data and trade. Using a cross-sectional analysis of producer price data for OECD countries in 1997, they derive their preferred estimate of 6.5, which is substantially larger than this study’s estimate of about two. On the other hand, Costinot et al. (2012) derive a quite similar elasticity of 2.7 when they consider labor productivity metrics, the metric considered here. They too have IV estimates that are considerably larger than OLS estimates. More broadly, Eaton and Kortum (2002) provide larger initial estimates of the θ elasticity, with a preferred estimate in the range of eight. Simonovska and Waugh (2014a) revisit these results with a new estimator and come to a preferred estimate of about four. Overall, this study’s estimates are again lower than this connected work. The robustness checks described below find _θ_ estimates that continue in this ballpark, never exceeding four. Thus, this empirical approach consistently derives _θ_ estimates that are among the lowest in the literature, with some part of this difference due to methodology but an important part being substantive in interpretation.

It is important to identify the dual meaning of the higher IV results compared to OLS with respect to the _θ_ parameter. In the Ricardian model, a higher _θ_ parameter corresponds to a reduced scope for intraindustry trade due to comparative advantages across varieties. IV estimations thus suggest that OLS specifications overestimate the scope for intraindustry trade because they understate the link between country-industry productivity improvements and their associated export volumes. Both impetuses can be connected to Ricardian theories of comparative advantage for trade, but the role of the structural _θ_ parameter needs to be carefully delineated. This partitioning can also have important consequences for views of development and export success. Compared to OLS, the IV results shift more emphasis toward fundamental country-industry productivity improvements rather than intraindustry varieties; yet,
available for a large number of firms over a six year period. The use of such direct measure of innovation activities differentiates this study from most of the existing work with firm-level datasets.

There are several studies that analyze the effects of licensing reform in Indian economy. In one of these studies Aghion et al. (2008) analyze how the effects of this reform on firm revenues varies across states with differing restrictiveness of labor regulations. My analysis differs from their work in two main aspects. I use a firm level panel dataset whereas their data is at industry level. Hence the policy question I investigate is how the reform affects firm dynamics rather than industry dynamics. Their focus is on revenues whereas mine is on innovation and growth. In another study Chari (2010) focuses on the effects of the licensing reform on total factor productivity. Using the same dataset as Aghion et al. (2008) he focuses on the licensing reform in 1985. Lack of a panel dataset prevents to perform a firm-level productivity analysis using a production function estimation method. It also prevents to observe the heterogeneity in firms’ responses to the policy change. Using the same dataset as Aghion et al. (2008), Chamarbagwala and Sharma (2010) analyze the effect of licensing reform on skill upgrading where they measure skill upgrading as employment and wage bill shares of white-collar workers. None of these studies can analyze growth at firm level as the data they use is a repeated crosssection of firms.

Removal of the distortive regulations can have significant contribution to firm performance. The analysis of Hsieh and Klenow (2009) on within-industry dispersion in factor productivities in China and India show that the removal of plant-level distortions could result in as much as a 30% improvement in aggregate productivity. Chari (2010) shows that the licensing reform in India improved productivity by 22%. In this study, I show that the licensing reform increased innovation rate by five percentage points. The empirical analysis presented in the paper is supported by a dynamic model of innovation.
<!-- Start of picture text --> Ethiopia N.E Nigeria Somalia South Sudan Sudan<br>60 60 60 60 60<br>50 50 50 50 50<br>40 40 40 40 40<br>30 30 30 30 30<br>20 20 20 20 20<br>10 10 10 10 10<br>0<br>0 0 0 0<br>Male-headed HH Female-headed HH<br>Perecentage of Multidimensionally Poor Perecentage of Multidimensionally Poor Perecentage of Multidimensionally Poor Perecentage of Multidimensionally Poor Perecentage of Multidimensionally Poor<br>50-59% 60-69% 70-79% 80-89% 50-59% 60-69% 70-79% 80-89% 50-59% 60-69% 70-79% 80-89% 90-100% 50-59% 60-69% 70-79% 80-89% 50-59% 60-69% 70-79% 80-89%<br><!-- End of picture text --> Source: Authors’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017).

Considering the relative contribution of each indicator to multidimensional poverty between the two types of households, we see that being deprived in the indicators of bank account and school attendance in Ethiopia; bank account and years of schooling in Sudan; years of schooling and school attendance in Nigeria; and legal identification and school attendance in South Sudan are the main contributors of poverty to both types of households. In South Sudan, early marriage contributes more to poverty in female-headed households, while school attendance contributes more to poverty in male-headed households. In Nigeria, the relative contributions of early marriage and lack of legal identification are greater in female-headed households. The source of deprivation is slightly different for male- and female-headed households in Ethiopia, where lack of physical safety, early marriage, and lack of legal identification contribute more to female-headed households, while years of schooling and school attendance are much larger contributors to poverty in households headed by men.

Table 6 refines
in order to showcase the efficacy of our proposed measurement method. In the current global development context, both the United Nations through the Sustainable Development Goals<sup>13</sup> and the World Bank through their Report of the Commission on Global Poverty (World Bank, 2017) have acknowledged the need for assessing, monitoring, and alleviating poverty in multiple dimensions besides the monetary dimension. In practice, most non-income dimensions are assessed by ordinal variables. In this section, we show how our measurement tools may be applied to analyse inter-temporal sanitation deprivation in Bangladesh.

For our analysis, we use the nationally representative Demographic Health Survey (DHS)

> 13Available at https://sustainabledevelopment.un.org/sdgs.

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_013)|(0_._032)|(0_._010)|(0_._012)|(0_._005)|(0_._036)|
|Adjusted _R_<sup>2</sup>|0_._980|0_._915|0_._848|0_._979|0_._825|0_._978|
|Observations|9_,_280|5_,_271|43_,_541|9_,_999|36_,_541|3_,_406|
|Destination fxed efects|Yes|Yes|Yes|Yes|Yes|Yes|
|Product-time fxed efects|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes_ : This table presents the effects of distance on intra-national trade costs. All regressions use the adjusted price gap between trading pairs and include product-time and destination fixed effects ˆ multiplied by (1 _− ρod_<sup>_k_)</sup><sup>_/ρ_ˆ</sup><sup>_k_</sup> _od_<sup>.PanelAincludesallgoods,includingimportedgoods.PanelBconsiders</sup> locally produced goods only. Product-time clustered standard errors in parentheses.<sup>_∗∗∗_</sup> significant at 1%,<sup>_∗∗_</sup> at 5%, and<sup>_∗_</sup> at 10%. Source: Authors’ estimates based on national CPI data and distances calculated using Google.

# **5 Discussion of results and robustness checks**

The baseline estimation presented above uses geodesic distance as the main exogenous variable shifting trade costs. However, this measure may mask cross-country differences in the quantity and quality of road infrastructure. For example, if country A has more roads connecting two locations than country B, then the cost-advantage of having more roads would be reflected in a lower coefficient in country A. Figure 1 suggests road infrastructure certainly varies across the countries in our sample. In the same vein,
/sup> there are exemptions or reduced contribution rates for the poor or otherwise disadvantaged (in the case of Taiwan (China) the poorest 1% of households are fully exempt, and a further 4.5% have their contributions paid in full or subsidized). In Japan, contributions emerge as proportional despite the fact that different insurance plans (there are over 5000 plans under the UHI umbrella) have different contribution rates.<sup>16-18</sup> The progressiveness of private insurance—especially so in Taiwan (China)—reflects the fact that it is the better off who buy it. In both Hong Kong (China) and Taiwan (China), private insurance buys supplementary cover. Out-of-pocket payments emerge as proportional in Hong Kong (China) and Korea, mildly regressive in Taiwan (China), and regressive in Japan. The

> * A positive value of Kakwani’s12 index indicates a progressive payment structure, a zero value proportional payments, and a negative value a regressive structure. The data shown are from O’Donnell et al.<sup>4</sup>

> †  The Kakwani indices for total payments are a weighted average of the Kakwani indices for the individual payments sources where the weights are the revenues shares.
.18)||
|Distance-discounted domestic credit to private sector<br>(global)|||2.21***<br>(0.65)|
|Observations|2,511|2,511|2,521|
|R-squared|0.99|0.99|0.99|
|<br>Kleibergen-Paap rk LM statistic|11.90***|12.55***|11.47***|
|Cragg-Donald Wald F statistic|12.37|14.48|11.63|

Notes: Standard errors clustered at the country level in parentheses, *** p<0.01, ** p<0.05, * p<0.1. Here common controls and constant terms are omitted for brevity. All regressions include both country fixed effects and year fixed effects. The dependent variable at the second stage is DGE-based output informality (in percent of GDP). Domestic credit to private sector is in percent of GDP (taken from WDI). See sections 3 and 4 for detailed data descriptions and empirical methods. GDP per capita is in constant 2010 U.S. dollars. Data are between 2000 and 2018.

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