rafmacalaba/datause-encoder-data huggingface dataset:
|Size|Appe<br> and Covera<br>|ndix Table 1<br>ge of Diferent<br>|Samples<br>|||
|---|---|---|---|---|---|
||(1)<br>Number of<br>Firms|(2)<br>Share of<br>Total Formal<br>Firms<br>(%)|(3)<br>Share of<br>Employment<br>(%)|(4)<br>Share of<br>Credit Stock<br>(%)|(5)<br>Share of<br>Value Added<br>(%)|
|_Panel A: Universe of Firms_||||||
|Formal Firms|602,882|100|100|100|100|
|Active Firms|449,632|75|92|82|100|
|Credit Program Eligible Firms|434,411|||||
|Credit Program Users|102,648|||||
|_Panel B: Firms with Observables fo_|_r Firm-Level E_|_stimations_||||
|Default Model|96,411|16|61|51|67|
|Selection and Leverage Model|119,153|20|50|44|74|
|Firms With Previous Loans|63,867|||||
|Firms Without Previous Loans|55,286|||||
|Credit Program Eligible Firms|114,606|19|35|21|19|
|Firms With Previous Loans|59,541|||||
|Firms Without Previous Loans|55,065|||||
|Credit Program Users|40,901|7|14|9|7|
|Firms With Previous Loans|30,937|||||
|Firms Without Previous Loans|9,964|||||

This table reports summary statistics of the different samples used in this paper, i.e., formal firms sample, active firms sample, default model sample, and selection and leverage model sample. The sample of active firms corresponds to the set of firms with positive sales during 2019. The default model sample corresponds to the set of firms used to estimate the default model. The selection and leverage model sample corresponds to the set of firms used in the selection and default analysis in this paper. Columns 1 to 5 show, for each sample, respectively, the number of firms with data, the share of firms and aggregate employment they represent in the economy, the share of aggregate bank credit stock they capture, and the share of aggregate value added they generate. Employment and value added are calculated by aggregating data from tax records of all firms in Chile. Firms are classified across size categories based on their annual sales, according to the criteria defined by the tax authority. Firms with previous loans are those that either have bank credit outstanding in December 2019 or receive a bank loan over the period 2012–2019. On the other hand, firms without previous loans are those with no credit records in the banking system throughout the same period.

19
# **B.2 Categories of Places of Purchase**

Our core sample of 31 surveys has by construction a place of purchase for each household purchase. Evidently, the names of places of purchases (PoP) available to respondents differ across countries. However, the places of purchases can be classified into broad categories which are approximately equivalent across countries. We detail below the taxonomy used in this paper, which separates the consumption of goods into five broad categories of places of purchase, and services into four broad categories.

# _•_ **Goods**

(1) Non-market consumption (e.g. Self-production) (2) Market consumption, no store front (e.g. markets, street stalls) (3) Market consumption, corner and convenience shops (4) Market consumption, specialized shops (e.g. brand stores, bakeries)

- (5) Market consumption, large stores (e.g. supermarkets, malls)

# _•_ **Services**

(6) Services provided by institutions (e.g. bank, hospital, university) (7) Service provided by individuals (e.g. maid services, gardening) (8) Entertainment (e.g. restaurants, hotels) (9) Informal Entertainment (e.g. food truck)

# _•_ **Unspecified**

(99) N.A/other (e.g. other, not applicable, unspecified) The majority of countries have places of purchase for each of the five good categories. In some countries one of these categories is missing, all such cases are reported in the last column of table B3. Four countries in particular do not distinguish between specialized stores (category 4) and corner/convenience stores (category 3). For these countries we imputed at the decile level the relative shares of categories 3 and 4, based on countries with income up to 50% smaller or larger.

For services, it is more frequent that some of categories are missing. In particular some countries do not have a detailed list of institutions as potential places of purchases for services. These are typically also countries in which the share of expenditures with 77
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<br>P<br><!-- End of picture text --> _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 Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table 3:** Endowment, household and institutional characteristics of IDPs.

||Mean|S.D.|Min.|Max.|N|
|---|---|---|---|---|---|
|**Endowment level**||||||
|Ability to read|0.59|0.49|0|1|11854|
|Ability to write|0.58|0.49|0|1|11854|
|Education (years)|5.54|4.55|0|29|9699|
|Chronic disease or disability|0.08|0.27|0|1|11854|
|**Household level**||||||
|Household size|7.11|2.53|1|15|11854|
|Household head is female|0.45|0.50|0|1|11854|
|More females than males in the household|0.42|0.49|0|1|11854|
|Boys in the household (age: 0-14)|1.60|1.36|0|6|11854|
|Girls in the household (age: 0-14)|1.61|1.41|0|7|11854|
|More girls than boys in the household (age: 0-14)|0.49|0.50|0|1|11854|
|Men in the household (age > 14)|1.97|1.34|0|8|11854|
|Women in the household (age > 14)|1.93|1.18|0|8|11854|
|More women than men in the household (age > 14)|0.28|0.45|0|1|11854|
|**Institutional level**||||||
|Easy access to labor market|0.26|0.44|0|1|11854|
|Easy access to good markets and trade|0.33|0.47|0|1|11854|
|Easy access to health services|0.53|0.50|0|1|11854|
|Easy access to help with crimes and disputes|0.91|0.29|0|1|11854|
|Easy access to identification documents|0.63|0.48|0|1|11854|
|Local market nearby (<30mins)|0.66|0.47|0|1|11854|
|Health care nearby (<30mins)|0.70|0.46|0|1|11854|
|Access to land|0.31|0.46|0|1|11842|
|Reporting crimes and disputes|0.23|0.42|0|1|11854|
|Has form of legal identification|0.90|0.30|0|1|11843|
|Feels safe at night|0.58|0.49|0|1|1982|
|Feels safe during the day|0.93|0.25|0|1|1982|

22
Policy Research Working Paper 8342

# **Abstract**

Centralized targeting registries are increasingly used to allocate social assistance benefits in developing countries. There are two key design issues that matter for targeting accuracy: (i) which households to survey for inclusion in the registry and (ii) how to rank surveyed households. The authors attempt to identify their relative importance by evaluating Indonesia’s Unified Database for Social Protection Programs (UDB), among the largest targeting registries in the world, used to provide social assistance to over 25 million households. Linking administrative data with an independent household survey, they find that the UDB system is more progressive than previous, program-specific targeting approaches. However, simulating an alternative targeting system based on enumerating all households, they find a one-third reduction in undercoverage of the poor compared to focusing on households registered in the UDB. Overall, there are large gains in targeting performance from improving the initial registration stage relative to the ranking stage.

This paper is a product of the Strategy and Operations Team, Development Economics Vice Presidency. 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://econ.worldbank.org. The authors may be contacted at sbazzi@bu.edu.

_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 Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
that occur within different distance radii of the DHS household-cluster centroid. We count all events that occur within different distance belts—for example, 0–50 km or 51– 100 km. We take a threshold of 50 km to define distant events, but we conduct sensitivity analyses increasing and decreasing this threshold. We also build a continuous indicator that counts all events that occur beyond 50 km from the household cluster and weighs these events using the inverse of the distance. For example, all attacks within a radius of 51 km of the household cluster are assigned a weight of 1, and attacks farther away receive weights smaller than 1. We also consider the number of deaths as an alternative measure of violence.

We also use information on attacks conducted by the Fulani. Between 2008 and 2018, the ACLED data register 1,038 violent events in which at least one of the agents involved was Fulani. About 70 percent of these events are classified as violence against civilians and 25 percent as battles, while the remainder are explosions or remote violence. These events led to 7,236 registered fatalities. Most of the violent events are concentrated in the north central region. About 25 percent of the events occurred in Benue State, followed by 22 percent in Plateau State, and 9 percent in Taraba State (these statistics are not displayed in any table).

_Displacement data._ We use data on the number of displaced people from the IOM’s DTM project, particularly the site- and location-assessment databases. We use these data to calculate the number of IDPs who live near the household cluster. To do so, we count the number of IDPs residing within a 10 km radius of the DHS household cluster. In addition, we build binary indicators at the LGA level indicating the presence of IDPs.

Since July 2014, the IOM has been collecting displacement data from key informants (such as village chiefs and religious leaders) at IDP-hosting locations. These data include the count of IDP households, the estimated number of IDP people, and key information on the host location such as the presence, type, and conditions of essential infrastructure such as health facilities, water sources, and hand-washing stations. The data collection is conducted quarterly in all locations known to host IDPs in the six northeastern states in Nigeria. According to conversations and email exchanges with IOM experts, the focus 16
scale, larger banks might enjoy substantial cost savings and pass these lower costs to borrowers so we would expect a negative sign.

We also include some measures for the diversity of banks’ activities by including the _interest income share_ , defined as total interest income over total operating income and _intermediation_ , defined as total loans over total liabilities. According to Laeven and Levine (2005), a specialized loan-making bank will have a larger interest income share, while a specialized investment bank is expected to have a larger share of other operating income. If the Nigerian lending market is subject to more competitive pressure, specialized loan-making banks are better able to absorb this competition so we anticipate a negative relationship between the interest income share and the spreads. Also, banks that are more involved in intermediation of loans should be better prepared for competition and charge lower spreads so we again expect a negative association between intermediation and the various spread measures.

For measures of banking concentration, we include the _Herfindahl_ index in the deposit and lending market segment.<sup>9</sup> Figure 3 gives a graphical overview of the development of bank concentration in Nigeria. In comparison to many African countries, bank concentration has been very low in Nigeria.<sup>10</sup> Only with the announcement of the new capital requirements in 2004 and the ensuing merger and acquisition activities have we seen increased Herfindahl indices in both the deposit and lending market.

Finally, we account for potential effects of macroeconomic developments by including variables such as annualized quarterly _inflation_ , the _real Treasury bill rate_ , the quarterly _industrial production growth_ rate as well as the _exchange rate depreciation_ (proxied by its quarterly growth rate).<sup>11</sup> In the past, Nigeria has experienced a very volatile macroeconomy and changes in the industrial production index as a proxy for GDP growth attempt to capture any business cycle effects that might have an effect on bank spreads. Similarly, inflation can affect spreads if monetary shocks are not passed

> 9 The Herfindahl is the sum of squared market shares in the deposit or the loan market segment, scaled by 10,000. It ranges from 0 to 1. We scale the Herfindahl in order to make it more comparable with the scale of the dependent variables.

> 10 According to Cihak and Podpiera (2005) and for illustration, the Herfindahl indices in loans were 0.105 for Kenya, 0.112 for Tanzania and 0.160 for Uganda in June 2002. After the privatization of the government-owned bank UCB, Uganda’s Herfindahl index for deposits has increased to over 0.200 (Beck and Hesse, 2006).

> 11 The macroeconomic data is from the IMF International Financial Statistics Database.

12
Figure 3: Density of total test score for teacher training only intervention <!-- Start of picture text --> Panel A:- Baseline Survey Panel B:- Follow-up Survey<br>0 10 20 30 40 50 0 20 40 60<br>Total test score Total test score<br>Treatment Treatment<br>Control Control<br>.05 .04<br>.04<br>.03<br>.03<br>.02<br>Density Density<br>.02<br>.01<br>.01<br>0 0<br><!-- End of picture text --> Note: Total test score is the sum of math, reading, writing, listening and Peabody test scores.

Books only: Providing books had a strong impact on test scores. Books alone greatly increased test scores more than teacher extra training, but the books intervention still had a much weaker impact than training and books provided as a package. It improved scores in many more subject tests. For instance, it increased the total score by 20.6 percent of a standard deviation (Table 5). The density of the total test score for the treatment and control group of students exhibits a mildly stronger shift in mean score among the treated groups of students (Figure 4). The intervention improved the scores in two of the ve tests. It increased scores in reading and math tests by 22.2 and 25 percent of standard deviation, respectively. These improvements in test scores due to book provision are lower than the impacts under the joint provision of training and books.<sup>13</sup> The ndings that books improve test scores in some subjects, even when provided alone, is in line with a general narrative provided in the systemic review by Glewwe et al. (2013): when considering all the evidences holistically, textbooks and workbooks improve weakly learning outcomes. In addition, we nd that the return from the provision of books increases when it is jointly provided with teacher training. The latter result, along with the fact that training also works better when provided along with books, is evidence of the complementarity of education inputs.

> 13For group of students in books-only intervention, the standard deviations of test scores in Peabody, math, listening, reading, writing and total score are 2.39, 2.36, 1.80, 3.93, 2.02 and 8.13, respectively.

21
households in wealth category 3 with a post-secondary-educated head; and Middle, for all other households.

Table 1a displays mean scores for the 10 environment-related threats to livelihood across the three localities and three SES groups. The intergroup correlation coefficients in Table 1b reveal substantial variation, both within and across localities, that is summarized in Table 1c. The distribution is roughly symmetric, with one negative correlation (-0.20), first, second and third quartile points at 0.37, 0.51 and 0.64, respectively, and a maximum at 0.97.

**Table 1a:  Livelihood-related change - Scores by locality and socioeconomic status Locality codes: 1 - Dwarir Jangle; 2 - Kumirmari; 3 - Kankandighi**

|Locality||1|||2|||3||
|---|---|---|---|---|---|---|---|---|---|
|SES|Low|Middle|High|Low|Middle|High|Low|Middle|High|
|Households|23|61|46|3|15|3|20|43|22|
|**Change **||||||||||
|Tree Loss|7.2|7.4|7.7|6.0|5.2|5.0|7.8|7.2|7.4|
|Livestock Loss|4.1|5.4|5.9|7.7|7.8|9.0|8.2|7.3|8.1|
|CropDamage|6.0|5.7|6.4|6.7|6.9|6.7|5.4|6.1|6.5|
|Fish Loss|2.1|5.2|6.9|5.3|5.3|7.3|3.8|5.2|4.5|
|Waterpollution|8.4|6.7|6.9|6.3|4.9|5.0|4.7|4.1|3.5|
|Soil FertilityLoss|5.0|3.5|2.5|4.0|4.3|3.3|6.2|4.6|5.5|
|Water Loss|3.1|2.8|2.2|6.3|5.0|4.3|6.1|5.6|5.8|
|Pests and Insects|4.7|2.9|1.9|3.7|5.6|4.7|5.6|4.7|4.2|
|**~~Table 1b: Corre~~**<br>Animal Diseases|**~~lation coef~~**<br>4.1|**~~ficients - L~~**<br>2.8|**~~ivelihoo~~**<br>2.4|**~~d-relate~~**<br>5.0|**~~d change s~~**<br>5.0|**~~cores~~**<br>4.0|4.0|4.8|4.7|
|<br>Out-Migration|<br>1.3|<br>2.1|<br>1.6|<br>4.0|<br>4.3|<br>5.7|3.3|5.2|4.7|

**Table 1b: Correlation coefficients - Livelihood-related change scores**

|Locality<br>||1<br>|||2<br>|||3<br>||
|---|---|---|---|---|---|---|---|---|---|
|SES|Low|Middle|High|Low|Middle|High|Low|Middle|High|
|Low|1.00|||||||||
|1<br>Middle|0.72|1.00||||||||
|High|0.54|0.97|1.00|||||||
|**Table 1c: Distrib**<br>Low|**ution of c**<br>0.33|**orrelation**<br>0.63|**coefficie**<br>0.66|**nts**<br>1.00||||||
|2<br>Middle|0.13|0.38|0.44|0.70|1.00|||||
|High|-0.20|0.38|0.53|~~7~~<br>0.60|0.79|1.00||||
|Low|0.41|0.44|0.32|<br>0.50|0.52|0.20|1.00|||
|3<br>Middle|0.04|0.45|0.46|0.61|0.64|0.58|0.72|1.00||
|High|0.09|0.35|0.33|0.60|0.64|0.45|0.82|0.94|1.00|
# **1. Introduction**

Are multinationals likely to reverse 30 years of globalization? Will global value chains that have benefited some developing countries become increasingly regional? How are businesses reacting to friend-shoring requests by policy makers? The reconfiguration of global value chains can have significant economic implications for the world. For instance, Cerdeiro et al. (2021) estimate that technological decoupling can lead to GDP losses of 5 percent for many countries. At the same time, Brenton et al. (2022) predict that reshoring production to high-income countries and China could drive 52 million people in developing countries into extreme poverty.

Rising geopolitical tensions between the US and China, the Russian Federation’s invasion of Ukraine, and the Covid-19-related supply chain disruptions have led some policy makers and businesses to revisit the benefits of globalization. These events have also highlighted the risks associated with overdependence on countries that are not considered strategic allies. For instance, US Secretary of the Treasury Janet Yellen called US firms to increase friend-shoring with the aim of having production spread across countries “ _[...]that have strong adherence to a set of norms and values about how to operate in the global economy and about how to run the global economic system_ ” to reduce trade partners’ geopolitical leverage from market positions in critical raw materials, technologies, or products that could disrupt the US economy.<sup>1</sup> Recent work shows that FDI is increasingly concentrated among countries with similar UN voting patterns (Aiyar et al., 2023; IMF, 2023).<sup>2</sup> In the specific case of the US, Alfaro and Chor (2023) document an increase in greenfield FDI

> 1 - <u>https://home.treasury.gov/news/press releases/jy0714#:~:text=We%20cannot%20allow%20countries%20to,work%20better%20for%20American %20workers.</u>

> 2 Aiyar et al. (2023) estimate a gravity model using the information on Ideal Point Distances (IPD) developed by Bailey et al. (2017) to show that “geopolitical distance” is an important determinant of FDI during the 2003-2021 period.

2