rafmacalaba/data-use-mentions-extended huggingface dataset:
. Given the small number of observations, we aim for a parsimonious specification, including cropland per capita—as a proxy for demographic pressure exerted on land utilization—an institutional variable, and controls for the macro-economic and socio-economic contexts. Table 3 below shows the coefficient estimates, starting with cropland per capita in column (1) and subsequently adding the Quality of Land Administration rate in column (2) as well as two controls in column (3): GDP per capita and the Gini Index. In columns (4) and (5), we substitute the Quality of Land Administration with other institutional variables, namely our index of Rural land tenure insecurity and the index for Enabling the business of agriculture, respectively. We do not include the institutional variables simultaneously due to both our concern to keep a parsimonious specification and the high correlation between these institutional variables. In Table 2, a negative sign is associated with a lower fixed effect and thus with a slower pace of cropland loss in regression (1).

For each variable, the sign of coefficients and the order of magnitude remain the same across specifications. Our preferred specification is column (3), which has the greater number of observations, although columns (3)-(5) consistently point towards significant comparable effects for both cropland per capita and the institutional variable. Column (3) shows that in countries that have greater cropland per capita, cropland disappears at a slower pace: A one standard deviation decrease in cropland per capita leads to 5.8 percent increase in cropland losses over the study period. This can be due to increased 11
control for some omitted variables by including GDP/capita as our measure of level of

# development in all of the regressions.

Our approach in this paper is that high inventories are an optimal response to particular characteristics of a developing country. An alternative approach is that high inventories represent firm inefficiency, a result of poor management perhaps. We would not expect this type of inefficiency to be correlated with any of our variables once we control for level of development, and, for this reason, we do not address this type of explanation but rather focus on correlation with country characteristics.

# **3. Data Description**

It is difficult to obtain consistent time series data on inventory holdings for developing countries. The aggregate data reported in the national accounts is the change in inventories rather than the stock of inventories; often this data is based not on an inventory survey but on the difference between production and sales which leads to highly inaccurate data.1<sup>2</sup> Most national statistics agencies do have inventory stock data but they do not publish it. In order to report the size of the country's industrial production, the statistics agency typically carries out a firm survey or census, which asks about total inventory holdings at the beginning or end of the year. More detailed surveys break down inventories into three or more categories: raw materials inventory, goods-in-process inventory, and finished goods inventory. Many surveys also request data on raw materials consumed in production. The United Nations, in its World Programme of Industrial Statistics, surveyed the statistics departments of countries around the world, requesting

> 12 We note, however, that the initials results of our research using the aggregate inventory levels computed from the National Accounts data were not inconsistent with the stylized observation that developing countries hold more inventory than developed countries.

11
Policy Research Working Paper 8668

# **Abstract**

This paper estimates the effect of worldwide tax changes on output following the narrative approach developed for the United States by Romer and Romer (2010). The analysis uses a novel dataset on value-added taxes for 51 countries (21 industrial and 30 developing) for the period 1970–2014 to identify 96 tax changes. It then uses contemporaneous economic records to classify such changes as endogenous or exogenous to current (or prospective) economic conditions. In line with theoretical distortionary and disincentive-based arguments, and using only exogenous tax changes, the main finding is that the effect of tax changes on output is highly non-linear. The tax multiplier is essentially zero under relatively low/moderate initial tax rate levels and more negative as the initial tax rate and the size of the change in the tax rate increase. Based on a global sample, these novel non-linear findings suggest that the recent consensus pointing to large negative tax multipliers in industrial countries, particularly in Europe (e.g., Alesina, Favero, and Giavazzi, 2015), (i) is not a robust empirical regularity, and (ii) is mainly driven by high initial tax rates in these countries. The paper also shows that the bias introduced by misidentification of tax shocks critically depends on the procyclical or countercyclical nature of endogenous tax changes. The relevance of the arguments is evaluated both for the novel global sample and for Romer and Romer’s U.S. dataset.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region. 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/ research. The authors may be contacted at gvuletin@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange
Column (4) introduces the interaction terms between education and ownership allowing for an analysis of ownership-specific returns to education. The estimated impacts of additional schooling seem to be largest in the state sector and lowest in the informal wage sector. The positive slope of the interaction term between foreign ownership and education confirms that the impact of foreign ownership on wage rises with educational attainment. The implied wage premium relative to informal wage sector turns out to be largest for the foreign sector at around 58 percent<sup>35</sup> followed by state (52 percent) and private sectors (13 percent).

# **4. Panel Data Analyses**

# **4.1. Model Specification**

A series of cross-sectional regressions in Section 3 compared the wage levels of individuals working in different employer ownership categories, controlling for observable personal and other characteristics. One of the limitations of this approach is that the coefficient for the foreign dummy is biased if some unobservable characteristics (e.g., ability, willingness to work harder) are correlated with foreign ownership.

In order to address selectivity bias, several recent studies use matched employer-employee panel data and trace the change in wages of _identical_ individuals over time. Some studies compare the wage growth of workers whose employers experience foreign takeover with that of workers whose employers do not. For instance, Andrews _et al._ (2009) find that the takeover effect is small in Germany; and Martins (2004) and Heyman _et al._ (2007) show that the wage consequences of the acquisition of domestic firms may even be negative in Portugal and in Sweden respectively. Other studies focus on worker mobility between foreign and domestic firms to identify the wage effect. For instance, using matched employer-employee panel data for

> **35** The effect of foreign ownership in the log term is calculated at the sample mean of education level: 0.46 ≈ -0.029 + 10.4*0.047.

19
Policy Research Working Paper 10396

# **Abstract**

Work-related migration has many potential drivers. While current literature has outlined a theoretical framework of various “push-pull” factors affecting the likelihood of international migration, empirical papers are often constrained by the scarcity of detailed data on migration, especially in developing countries, and are forced to look at few of these factors in isolation. When detailed data is available, researchers may face arbitrary choices of which variables to include and how to sequence their inclusion. As male and female migrants tend to face occupational segregation, the determinants of migration likely differ by gender, which compounds these data challenges. To overcome these three issues, this paper uses a rich primary household survey among migrant communities in Indonesia and employs two supervised machine-learning methods to identify the top predictors of migration by gender: random forests and least absolute shrinkage and selection operator stability selection. The paper confirms some determinants established by earlier studies and reveals several additional ones, as well as identifies differences in predictors by gender.

This paper is a product of the Gender Global Theme and the Office of the Chief Economist, East Asia and the Pacific Region.. 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 dhalim@worldbank.org and sseetahul@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 Bank for Reconstruction and Development/World
selection methods except Bayesian Model Averaging (BMA). The square brackets reflect the probability of each variable’s model inclusion under BMA. The General model includes all control variables. BIC and AIC are the specifications (from all 17million combinations of controls) that minimize the Bayesian and Akaike Information Criteria respectively. ***, ** and * reflect significance at the 1, 5 and 10 percent levels respectively.

# 6. Estimating urban costs

Our meta-analysis in section 5 suggests that urban costs play a major role in explaining differences in agglomeration benefits across studies. Most studies measure “gross” agglomeration benefits of density rather than advantages of locating in crowded, congested and crime prone places that “net” out these benefits. In sum, the evidence base for urban costs is underdeveloped. Using novel data on crime, congestion and pollution from hundreds of cities around the world, including those in developing countries, we present estimates of density elasticity of urban costs in Table 7. The table makes the following points: First, urban costs are significantly higher in _levels_ in developing countries, and this is true across our measures of congestion and crime, and depending on the specification, pollution, suggested by the positive developing country dummy (and mirroring Figure 4). In levels, for the average city density in our data, pollution is on average 16-28 percent higher (although not significantly different from zero in column 1), 19-30 percent more hours are lost hours to congestion and homicides are 4 times as likely.

Second, although developing countries have higher urban cost levels, their cost _elasticities_ may not be different than that observed in developed countries. Relative to developed countries, the elasticity of 23
control for any unobservable time-invariant country characteristics. We also add time dummies to capture global macroeconomic variables, such as oil price, interest rates, etc. To reduce endogeneity concerns, we include one year lags of business environment variables. However, most of our results are not sensitive to using lags.<sup>15</sup> We estimate this model by country and year fixed effects regressions with errors clustered on the country level to capture any serial correlation of errors within country.

Results are presented in Table 4. We find that reforms to the business environment have a predicted significant impact on new business registration. Thus, improvements (i.e. reductions) in starting days, procedures or employment rigidity all result in increased business registrations.

With respect to governance indicators, we find that neither variable is significant in fixed effects model. Our explanation for this finding is that governance is a slow moving variable and improvements in governance can only be observed over longer time horizons. Our time frame is only six years, which might be insufficient to register any significant within country variation in governance indicators. This does not invalidate our earlier conclusion that governance has a significant positive long term impact on subsequent business registrations.

The corporate tax rate and financial development are also not significant in the fixed effects model, likely for the same reasons we discussed above for governance indicators.

# **_4.3 The Impact of the Crisis on New Business Creation_**

In this section we formally investigate the relationship between crisis and new firm registration that we presented graphically earlier in the paper. These graphs show a decline in Entry Density that was visible in 2008 and 2009 for high income countries and in 2009 for

> 15 While endogeneity concerns are not completely eliminated with the inclusion of one year lags, using lagged regressors has an additional benefit that it allows us to include year 2009 in the regressions as the GDP data and governance indicators are not yet available for 2009 as of this draft.

17
# Figure VIII: Years of Schooling by Sector

<!-- Start of picture text --> 15<br>10<br>5<br>High-Skill Services<br>Low-Skill Services<br>Manufacturing<br>Agriculture<br>0<br>7 8 9 10 11<br>Log GDP p.w.<br>Average Years of Schooling<br><!-- End of picture text --> _Notes_ : The figure plots average years of schooling by sector for all country ˆ year pairs in IPUMS International, against GDP per worker from version 9.0 of the Penn World Tables (Feenstra et al., 2015).

cluded for the computation of agricultural wages. However, the estimated returns to schooling are not dramatically affected when computed using self-employed income as well (Herrendorf and Schoellman, 2017; Rossi, 2022).

**New cohorts are disproportionately more likely to work outside agriculture, contributing significantly to structural transformation out of agriculture** (Schoellman and Hobijn, 2017; Porzio et al., 2022). Porzio et al. (2022) show that a key reason for this is that more recent cohorts are typically more educated, which gives them a comparative advantage in non-agriculture. Human capital growth across cohorts is both a cause and a consequence of structural transformation; on one hand, the entry of more educated cohorts allows the nonagriculture sector to expand (Figure VII.a), while on the other the increase in the demand for agricultural labor driven by technological change and income effects incentivizes the accumulation of human capital (Figure VII.b). These conclusions are consistent with a broader literature arguing that the expansion of formal education is a key ingredient of the transition from stagnation to growth (Galor, 2005).

**Overall, these findings describe a complex relationship between human capital, structural transformation, and economic development** . While human capital growth is a potential tool to accelerate the economy wide transition out of
POLICY RESEARCH WORKING PAPER 1918

# **Summary findings**

Banking markets are becoming increasingly international through financial liberalization and general economic integration.

Using bank-level data for 80 countries for 1988-95, Claessens, Demirgii~-Kunt, and Huizinga examine the extent of foreign ownership in national banking markets. They compare net interest margins, overhead, taxes paid, . and profitability of foreign and domestic banks.

The comparative functions of foreign banks and domestic banks is very different in developing and industrial countries, possibly because of a different customer base, different bank procedures, and different regulatory and tax regimes: • In developing countries foreign banks tend to have greater profits, higher interest margins, and higher tax payments than do domestic banks.

• In industrial countries it is the domestic banks that have greater profits, higher interest margins, and higher tax payments.

It is common to read, in the literature on foreign banking; that the entry of foreign banks can make national banking markets more competitive, thereby forcing domestic banks to operate more efficiently. Claessens, Demirgii~-Kunt, and Huizinga show that increasing the foreign share of bank ownership does indeed reduce profitability and overhead expenses in domestically owned banks - so the: general effect of foreign bank entry may be positive.

Interestingly, the number of foreign entrants-matters more than their market share,'suggesting that they affect .. local bank competition more on en!ry rather tpan after gaining a substantial market share.

This paper - a joint product of the East Asia and Pacific Region and the Development Research Group- is part of a larger effort in the Bank to study the effects of increasing global integration of financial services. Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Rose Vo, room MC4-404, telephone · 202-473-3722, fax 202-522-2530, Internet address hvol@worldbank.org. The authors may be contacted at sclaessens@worldbank.org, ademirguckunt@worldbank.org, or hhuizinga@worldbank.
Also, one should select the values for the key normative parameter related to mortality. Our results reveal the importance of establishing what is the plausible range of values for the normative weight given to mortality. Again, preference elicitation methods should provide a sense of this range.<sup>22</sup> θθ

# **REFERENCES**

Alkire, Sabina, et al. _Multidimensional poverty measurement and analysis_ . Oxford University Press, USA, 2015.

Alkire, Sabina, and James Foster. "Counting and multidimensional poverty measurement." _Journal of public economics_ 95.7-8 (2011): 476-487.

Baland, Jean-Marie, Guilhem Cassan, and Benoit Decerf. "" Too Young to Die": Deprivation Measures Combining Poverty and Premature Mortality." _American Economic Journal: Applied Economics_ 13.4 (2021): 226-57.

Baland, Jean-Marie, Guilhem Cassan, and Benoit Decerf. "Poverty-Adjusted Life Expectancy." (2022).

Battiston, Diego, et al. "Income and beyond: Multidimensional poverty in six Latin American countries." _Social indicators research_ 112 (2013): 291-314.

Becker, Gary S., Tomas J. Philipson, and Rodrigo R. Soares. "The quantity and quality of life and the evolution of world inequality." _American economic review_ 95.1 (2005): 277-291.

- - Bolch, Kimberly, Luis F. Lopez Calva, and Eduardo Ortiz Juarez. "“When Life Gives You Lemons”: Using Cross - Sectional Surveys to Identify Chronic Poverty in the Absence of Panel Data." _Review of Income and Wealth_ (2022).

Chukwuonye, Innocent Ijezie, et al. "Prevalence of overweight and obesity in adult Nigerians–a systematic review." _Diabetes, metabolic syndrome and obesity: targets and therapy_ (2013): 43-47.

Decerf, Benoit. "Why not consider that being absolutely poor is worse than being only relatively poor?." _Journal of Public Economics_ 152 (2017): 79-92.

Decerf, Benoit. "
>0.00<br>0.00<br>0.00<br>0.00<br>0.00<br>es. Using the sample for the full sample of 678 farmers, except for 13 farmers<br> +_βFFlexiblei_ +_αc_+_εit_, where_i_corresponds to a farmer,_Y_ is the outcome of<br>we report the mean and the standard deviation of the outcome for the control<br>inal study design. We use data from the Commercialization survey conducted<br>commended dosages of Urea, DAP and KCl, respectively. In columns 4-6, we<br>p_<_0.1.|
|---|
|(1)<br>(2)<br>(3)<br>Absolute difference, ap<br>At sowing<br>Urea (kg/ha)<br>DAP (kg/ha)<br>KCl (kg/ha)<br>Extension (1=Yes)<br>-1.71<br>0.35<br>-1.43<br>(1.98)<br>(2.43)<br>(1.32)<br>Individual recommendation (1=Yes)<br>3.04<br>3.23<br>1.82**<br>(2.08)<br>(2.02)<br>(0.90)<br>Grant (1=Yes)<br>-30.34***<br>-16.26***<br>-13.41***<br>(2.00)<br>(2.25)<br>(0.85)<br>Flexible (1=Yes)<br>3.54*<br>2.37<br>
the period 2000-2016 was used based on the value added and employment data from the World Development Indicators and International Labor Organization. Within-sample as well as out-ofsample GDP-employment elasticity estimates were produced under each methodology and used to estimate employment growth based on observed GDP growth. The results of this analysis show that the fourth approach based on average ratio of annual percentage changes in employment and GDP exhibits

> 4 For example, following this approach (using macro data from Eurostat and the World Bank World Development Indicators), the observed 50 percent of GDP growth in European Union countries during 1995-2005 implies 50 percent growth in employment, in contrast with the 10 percent actual growth in employment observed over the same period.

> 5 Other works in the area include Kaldor (1966), Parikh (1978), Rowthorn (1975a,b), Saget (2000), Döpke (2001), Kapsos (2006), Pattanaik & Nayak (2014), or Olusoji (2016). 6 We thank Aart Kraay for his help on identifying this alternative estimation method. It is important to note that this approach is underlined by the assumption that the growth of employment and productivity are bivariate normal.

> 7 Okun’s law (Okun, 1962) offers an alternative way to compute employment elasticities based on the documented inverse relationship between the unemployment rate and GDP growth (see Ball et al., 2017 for a recent summary). While there have been sector-focused versions (Goto and Bürgi, 2020), one key challenge in applying this approach in the context of developing countries is that unemployment rates alone do not describe labor market supply-demand gaps, given sizable informal employment and inefficient labor use across different sectors. In this case, GDP growth leads to a combination of job creation and increased labor productivity. In developing countries significantly lagging behind the frontier economies, employment effects would be dampened.

3
)|0.0030***<br>(0.0003)|
|Observations|888,813|888,813|888,813|888,813|888,813|888,813|
|_R_<sup>2</sup>|0.6351|0.7739|0.3913|0.6313|0.7656|0.3890|
|Kleibergen-Paap rk Wald-F-stat||||115.912|115.912|115.912|
|Importer-exporter-year efects|Y|Y|Y|Y|Y|Y|
|Industry-year efects|Y|Y|Y|Y|Y|Y|

_Notes_ : Table reports OLS and IV results of equation (23) in text, using the baseline estimation sample and an alternative robotization measure (the observed stock of robots, ignoring depreciation). Columns (1)-(3) report the OLS results, while columns (4)-(6) report the IV and corresponding first stage estimates. Robust standard errors clustered by developed country are presented in parentheses. ***1% level, **5% level, *10% level.