rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 11061

# **Abstract**

Despite the large fiscal footprint of corporate tax incentives, limited causal evidence exists on their impact on economic outcomes. This paper helps fill this gap by exploiting the phasing out of a large income tax exemption scheme for export- oriented firms in Tunisia. Using data on the universe of registered Tunisian firms, the analysis shows that the reform caused a decline in the entry of new firms in the sector previously benefiting from the incentives. However, the reduced entry did not translate into any effects on employment, revenue, or the wage bill, as the reform did not impact the activities of incumbent firms, which account for the bulk of economic activity in Tunisia. The findings are robust to addressing various threats to the empirical identification, and they confirm emerging evidence casting doubt on the importance of tax incentives to determine investments relative to other factors in an economy.

This paper is a product of the Economic Policy Global Department—Trade. 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 mcali@worldbank.org; giorgio.presidente@unibocconi.it; and tscot@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<
Figure 1: Percentage of People Living on Less Than US$1.25, US$2.50, and US$4 per Day, Late 2000s Weighted Average for Bolivia, Brazil, Ecuador, Guatemala, Mexico, and Peru <!-- Start of picture text --> 50<br>43<br>40<br>30<br>24<br>21<br>20<br>9 9<br>10<br>3<br>0<br><$1.25 <$2.50 <$4.00 <$1.25 <$2.50 <$4.00<br>Indigenous Nonindigenous<br>Percent<br><!-- End of picture text --> _Source_ : SEDLAC data (CEDLAC and the World Bank) in World Bank 2015a.

Even in the more developed economies of Europe, minorities often are among the most disadvantaged. The European Union (EU) Special Eurobarometer 437—a public opinion survey conducted regularly on behalf of the European Commission, which looks into attitudes and perceptions of Europeans toward discrimination based on different grounds—reports that “discrimination on the grounds of ethnic origin continues to be regarded as the most widespread form of discrimination in the EU (64 percent), and it is more likely to be seen as widespread than was the case in 2012 (+8 percentage points). More than half of respondents in the EU say that discrimination is widespread in their country on the basis of sexual orientation (58 percent; +12) and gender identity (56 percent; +11), both showing substantial increases from the 2012 survey” (European Commission 2015). As in Africa, Asia, and Latin America, discrimination in Europe seems to be associated with poverty and economic marginalization. In a recent study on poverty and unemployment rates among the Roma population in 11 EU member states, the European Union Agency for Fundamental Rights concluded that
go beyond the associated drop in household income, there may be increased insecurity about the future, and so on. The subjective impact of the crisis may therefore depend on the coping strategies adopted, the intrinsic value of employment, and the extent to which households value leisure, uncertainty or different types of expenditures. Finally, there could be systematic reporting biases if different groups have

> 3 Due to low response rates, actual or imputed rents are not included. Previous analysis showed that the consumption aggregates constructed from LiTS I compare well with conventional measures based on national accounts and household surveys (see Zaidi et al, 2009).

> 4 The greater impact of the crisis on the poor as well as the correlation between job loss and low consumption could be due to greater vulnerability of initially poor households to job loss, or to lower consumption caused by the job loss experienced by the household. The data do not enable a comparison between the two hypotheses as only current consumption is measured in LiTS II.

> 5 Because the LiTS data measure crisis impact at the household level, it is not possible to analyze differences at individual level. The lower likelihood that female-headed households were affected is consistent with the crisis impact on sectors that employed higher proportions of males (for example, construction).

9
Clearly, this type of feed-back effect would muddle the relationships; to avoid this problem, years during which the crisis is unfolding are typically excluded from the sample.

Another key element of our study was the construction of the banking crisis dummy variable. Beginning from a sample of all the countries in the world, economies in transition were excluded based on the view that the problems in these countries were of a special nature. The following step was to identify all episodes of banking sector distress, drawing from the surveys of Caprio and Klingebiel (1996) and Lindgren _et al._ (1996) and from other case studies. To distinguish between fragility in general and crises in particular, and between localised crises and systemic crises, we established – somewhat arbitrarily – that for an episode of distress to be classified as a full-fledged crisis in our panel, at least one of the following four conditions had to hold: the ratio of non-performing assets to total assets in the banking system exceeded 10 per cent; the cost of the rescue operation was at least 2 per cent of GDP; banking sector problems had led to a large scale nationalisation of banks; extensive bank runs took place or emergency measures such as deposit freezes, prolonged bank holidays, or generalised deposit guarantees were enacted by the Government in response to the crisis.

Table 1 shows a version of the regressions in our 1998 paper, in which the sample has been extended through 2002 and to include more countries. The number of crises episodes in 4 the baseline specification has risen from 31 to 77, a sizable improvement (table 2). The findings are by and large consistent with those of the earlier paper, indicating that the relationships are fairly robust.

Low GDP growth, high real interest rates, and high inflation are significantly correlated with the occurrence of a banking crisis. Thus, crises tend to manifest themselves during periods of weak economic growth and loss of monetary control. Exposure to real interest rate risk is also a source of banking fragility. This is consistent
al. (2023); Deshpande (2022)).

We study whether exposure to the COVID-19 pandemic influenced the labor market aspirations and expectations of young women in India, and specifically in the north Indian state of Haryana. The gender gaps in youth labor force participation and unemployment in Haryana are even larger than those at the national level. According to the 2022-23 Periodic Labor Force Survey, the labor force participation and unemployment rates for young women in Haryana are 12.5 percent and 21 percent, respectively, whereas the corresponding numbers for young men are 57.7 percent and 16.8 percent. We focus on aspirations for financial remuneration, which is one of the most important dimensions of what individuals value in a job.<sup>2</sup> We also examine whether the pandemic’s impact on willingness to migrate is an underlying channel for our main results. This line of inquiry is driven by the significant detrimental impact of the pandemic on rural-to-urban migration in India (Arora et al., 2023).

> 1Source: Periodic Labor Force Survey of India, July 2022-June 2023.

> 2In our sample, 93 percent of the respondents consider salary to be a very important aspect of a job.

1
it is clear that the magnitudes are quite small. There are several reasons that help explain this fact.

As discussed in Section 1, the reforms in cotton markets have been relatively successful but have not been smooth. Initially, the public monopoly was transformed into a private monopoly. Entry was useful in early stages, but the failure of the outgrowing scheme limited the expansion of the sector. In fact, the outgrower scheme was still failing in 1998, when the household survey data was collected. This means that the evolution in income shares that we can capture with the available data does not reveal the whole benefits on the reforms.

Using additional farm data, Brambilla and Porto (2005) report that the cotton reforms had two distinctive effects on cotton farming. First, there is a decline of the land area devoted to cotton in 1998-1999 (when the outgrowing scheme was failing) followed by a significant increase in area planted in 2000-2001, when the outgrower scheme was perfected with the entrance of Dunavant. Second, the authors find that farm productivity in cotton showed a similar pattern, declining in 1998-1999 and increasing in 2000-2001. In additional, their findings indicate that income shares increased, on average, by roughly 10-20 percent. These additional factors could help increase the average gains from a 12 percent price increase to roughly 2 percent of household income, on average. Although these figures are higher, the effects seem still fairly low. The study by Brambilla and Porto (2005) reveals another reason why the increase in cotton prices may not have large impacts. Productivity of smallholder cotton production has been traditionally very low in Zambia. Although there is some evidence that the reforms have caused productivity to increase in recent years, there is still room for improvements in this area.

One additional reason for the small impacts is that cotton activities are not really widespread in Zambia. More importantly, we have only considered a first order approximation to 15
iron supplement, intake of anti-malarial drugs, tetanus vaccination) and mother‘s educational level are measured for the mother.<sup>9</sup> Whether the child was born in a health facility is measured at individual level. Because vaccination records are not available for deceased children, immunization is measured as the fraction of children with all vaccines at the

> 7 All differences described in this paragraph are statistically significant at the 5% level.

> 8 Reported neonatal mortality rates are also higher for births to women with tertiary education, although because there are few such births, the confidence interval is large and this difference is only significant at the 10 percent level.

> 9 The DHS data on antenatal care was collected for the last birth only. For a child who is not the last birth, its value was approximated by its last born sibling. In the final sample (4668 observations), there are 1046 children (22.4%) who are not the lastborns.
However one wants to approach the issue, the raw material will be the change in real income and expenditure of different income deciles, and for this one must consult the evidence of household surveys.

For a wide cross-country analysis of movements in poverty and inequality around the time of financial crises, the Povcal database is an inevitable choice. It contains such data for 95 countries and between one and 14 different years (an average of 4-5 observations for each country). Matching this with the Caprio-Klingebiel banking crisis data set giving details on 180 crises in 149 countries (including advanced economies) offers the prospect of assessing correlations in a useful way (Caprio et al. 2005).

# _Timing_

Yet we have to be careful. Timing of the crises is one of the most uncertain aspects of the crisis database, yet issues of timing are crucial to our analysis of the effects of crisis on poverty and inequality, if we are to capture what (as argued above) are potentially contrasting short-term and long-term effects.

The only systematic econometric cross-country study of which I am aware that attempts to detect the impact of banking crises on inequality is Lopez (2004).<sup>17</sup> The goal of his paper is to explore the impact of different policy approaches on inequality and growth in the short run and in the long run. He uses a somewhat larger database, that used by Dollar and Kraay (2002) (updated), and focuses on changes in inequality over five-year intervals. Lopez’s solution to the timing problem is to take non-overlapping five-year intervals and choose the latest available Gini coefficient in each interval. For banking crises (only one of his explanatory variables), he uses the number of years in the relevant five-year period for which a crisis is ongoing.<sup>18</sup> Lopez employs the Arellano-Bond GMM econometric methodology which involves double differencing of this data, and this arguably places a premium on data accuracy.<sup>19</sup> Interestingly, he
. _Disasters_ **34** , 552–570 (2010).

49. Gourlay, S., Kilic, T., Martuscelli, A., Wollburg, P. & Zezza, A. Viewpoint: High-frequency phone surveys on COVID-19: Good practices, open questions. _Food Policy_ **105** , 102153 (2021).

50. Carletto, G. & Banerjee, R. Strengthening Disaster Resilience: A Microdata Perspective. in _Capitalism, Global Change and Sustainable Development_ (ed. Paganetto, L.) 87–96 (Springer International Publishing, 2020). doi:10.1007/978-3-030-46143-0_6.

51. Carletto, G., Chen, H., Kılıç, T. & Perucci, F. Positioning household surveys for the next decade. _Stat._

- _J. IAOS_ **38** , 1–24 (2022).

20
3.4 Floor area adjusted population densities can sort between crowded and livable cities Population density is the most common measure of urban concentration. Most measures of population density take the form of people/urban built-up area or people/total urban area. However, population density is an imperfect measure of urban proximity as it does not account for the quality of built structures.

Figure **8** a shows a scatter plot of population density and city incomes. Population density is measured as people per built-up area of the city, for 400 cities worldwide. Two striking features emerge. First, population densities decline with incomes, as countries move across stages of economic development. Kinshasa, Mumbai and Karachi are four to five time more densely populated than Shanghai, Tokyo or London. Second, naïve measures of population density can conflate crowding with livable densities. Kinshasa, Mumbai and Hong Kong are three cities with similar population density but at very different levels of income.

While Hong Kong’s residents can live in tall buildings with adequate floor space, over 50 percent of Mumbai’s population lives in slums. Most of Kinshasa’s population lives in squalid conditions. In low- and lower-middle-income countries, population densities in the range of 50,000 people per km2 are reflections of squalor and crowding, not livable densities. Such crowding has been associated with high degree of contagion risk during the COVID-19 pandemic, where residents do not have physical space to be socially distant and need to access shared toilets and water kiosks in their neighborhood (Lall and Wahba 2020).

To untangle crowding from livable densities, we introduce a new measure called the Floor-area adjusted population density (FAPD), which we measure using the heights data available from DLRs WSF 3D database. We first compute total floor area using the heights data as follows: Where TFA is the total floor area. The average floor height is assumed to be 3 meters. This measure can be compared across pixels in a city, or aggregates compared across cities. Using
The questions on the UB scheme include the availability and maximum duration<sup>6</sup> of UB after one, five, and ten years of employment, the minimum contribution period to be eligible for UB, and eligibility requirements as stipulated in the labor laws of the country. The questions on ALMP include the availability of PES and the types of PES offered; the availability of a national skills/training fund, and government incentives for firms to provide training to their employees.

It is important to note that there is no notable change in UB and ALMP indicators from 2019 to 2020. This can be attributed to two factors. First, data is based on labor laws, which typically do not undergo frequent changes. Second, data collection cycle spanned from May of the preceding year to May of the subsequent year for both years. Consequently, the 2020 data does not adequately capture the majority of the UB and ALMP reforms related to the Covid-19 crisis, which commenced in March 2020. Moreover, the bulk of the UB and ALMP reforms implemented between March and May 2020 fall outside the scope of this dataset, including changes in UB amounts, wage subsidies, public works initiatives, and unemployment assistance, among others. The data collected in 2020 captured reforms in 12 economies between May 2, 2019 and May 1, 2020. Ten of these economies undertook reforms in response to the Covid-19 pandemic, but they did not lead to a significant change in the overall data.<sup>7</sup> Therefore, to examine the UB and ALMP reforms carried out during the Covid-19 pandemic, the paper utilizes the comprehensive Social Protection and Jobs Policy Inventory of the World Bank collected during March 2020-January 2022.

Table 1 provides the descriptive statistics of UB and ALMP indicators in 2019.<sup>8</sup> As seen from the table, 47.6 percent of 191 economies have UB schemes and 38.7 percent offer UB after one year of contribution. As expected, eligibility conditions
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population density could be correlated with the error term in the equation. To test this possibility, the spatial autoregressive instrumental variable (SPIV) model was performed. Taking advantage of lagged variables is one approach. The population density in 2007 is used as an instrument in this model.

35. As shown in **Table 5** , the results are broadly unchanged. The Hausman endogeneity test statistics indicate that the SPIV models are better. The test statistics are estimated at 8.68 and 16.51, which can reject the hypothesis of no autocorrelation. Based on the SPIV estimation, the land prices are found to decrease with the accessibility to jobs (JAI). The elasticity is 0.108. The other coefficients are broadly similar to the above results: The access to taxi-be is important. Road infrastructure and all-weather road connectivity are also found to be essential. While proximity to amenity zones is appreciated, the areas that are vulnerable to flood are not preferred.

**<u>Table 5. Spatial autoregressive IV estimation results</u>**

||<br>Coef.|<br>Std.Err.||<br>Coef.|Std.Err.||
|---|---|---|---|---|---|---|
|ln_MIN_|-0.131|(0.132)|||||
|ln_JAI_||||0.108|(0.065)|*|
|ln_POPDEN_07|0.063|(0.037)|*|0.049|(0.037)||
|ln_CSB_|-0.031|(0.029)||-0.030|(0.029)||
|_VUL_|-0.025|(0.008)|***|-0.024|(0.008)|
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incorporating future commodity prices either directly or indirectly into its planning. Even allocating money to basic agricultural research involves some judgment about future relative scarcities. The accumulating evidence suggests that this dilemma can be resolved if the government - simply does not try to outguess the market that is, anticipate the direction of future prices independently of current market prices. Whether these guesses are based on complicated econometric models of individual commodity systems or on attempts to discern underlying trends in historical price data, the record suggest they will be wrong in a very important way. Price forecasts based on either of these two techniques do not provide as good an estimate of future prices, even when the time horizon is as long as a decade, as the current Drice at the time the forecast price must be used in determining goveranent investment and program priorities.49 5.44 The implication for the role of government in price stabilization schemes is stark. Unless a strong case can be made for reasons of efficiency and income distribution to attempt short-run price stabilization, the government should probably find other instruments to reach its policy objectives. Stabilizing rice prices clearly makes sense for these reasons if the levels do not deviate too much from border prices for too long a time, where 'too much' and "too long' can be determined only relative to budgetary flexibility and the seriousness of any spillovers from rice price stabilization to other commodities or other countries.

5.45 The dilemma hero has been stressed before. Policy that stabilizes rice prices is important for political and economic reasons, but distortions caused by too much stabilization create rigidities in the whole agricultural economy. Long-run economic policy must promote efficient allocation of resources; otherwise there will be too little output to worry about distribution or food security. But granting this long-run goal, how much premium can a poor country pay in the short run to achieve food 49/ See Robert J. Schwartz, "Optimal Trends for Forecasting Prices: An Empirical Assessment of Three Grains," Ph.D. Dissertation,
**Figure 2: Output Impact in Different ‘Green’ Sectors: BBIN Scenario (% change)**

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# **7. Conclusions**

This is the first major attempt to develop a gender-differentiated data set for South Asian countries, within the widely used GTAP framework, coupled with the GTAP-POWER model that includes renewable sectors, to examine the nexus between trade, green economy, and gender. In order to generate gender differentiated employment and wages data in the current GTAP database, this study compiles gendered employment data and industry average wages across industries and occupations for each country and reconciles the data using the Yearbook of Labor Statistics from the International Labor Organization (ILO). The model and data set have been derived from a large number of survey data sets in South Asian countries. The data gaps for the select countries were filled using the data obtained from several published government sources. Female labor force participation and the gender wage gap were calculated using data disaggregated by economic activity according to the International Standard Industrial Classification of All Economic Activities (ISIC) at the 2-digit level of the classification and for the latest data available for that country. The study identifies the targeted value chains and economic activities, particularly among green trade sectors, that will benefit women in South Asia using the newly constructed model and database.

A few more innovations were introduced in the model; first, we introduce a multi-level nest in the labor demand, wherein perfect substitution happens between male and female composite labor while the different types of skilled labor substitute each other