rafmacalaba/data-use-mentions-extended huggingface dataset:
question necessarily precedes and motivates further work on measuring the welfare impacts of the policy (which we outline in the conclusion). In addressing this question, we account for two features of the firm location problem that have the potential to bias estimates if ignored. First, we non-parametrically account for regional attributes such as amenities, infrastructure, local public goods, and natural endowments. Since regional incentives are allocated to compensate for inter-regional differentials in local characteristics, it is imperative to adequately account for these factors in examining the contribution of these programs. Otherwise, there is a tendency to systematically understate their value. Second, our empirical analysis makes use of a linked headquarter-branch plant panel data set, which allows us to consider the role of intra-firm considerations in the location decisions of vertically integrated firms. If firms tend to locate branch plants in close proximity to their headquarters, and if those headquarters are disproportionately located in unsubsidized areas, this could be mis-interpreted as a failure of the fiscal incentive. While the empirical application focuses on Brazil, the estimation strategy outlined in the paper has broader applicability and can be used to examine similar issues across countries.

Our main findings are that subsidized credit offered to firms via the CF’s has worked in terms of industrialization in the Northeast. At the same time, we find that pull of the headquarters has a much bigger effect on entry than the CF’s for the minority of vertically integrated firms (i.e., with headquarters). This implies that, for vertically integrated industries, firm structure needs to play a part in the CF – i.e., reform to promote entry by headquarter firms into lagging regions. In more footloose sectors, such reforms would not be necessary.

6
Evidence for other middle and low-income developing countries is scarce, especially for the latter. For low-income developing countries, Girma and Kedir (2005) present evidence for Ethiopia. After controlling for endogeneity using parents’ education, they find that education is more beneficial to the less able. In particular, returns in the lowest (10<sup>th</sup> ) quantile of the earnings distribution (at about 20 percent) are twice that in the highest (90<sup>th</sup> ) quantile.

For South Africa, an upper middle income economy, Mwabu and Schultz (1996) derive quantilereturns estimates for white and non-whites in 1993. They find that, among Africans, returns do not increase by their decile in the distribution of residuals, while among whites, returns to higher education increase significantly, from 9 to 18 percent. This is interpreted as evidence that ability and higher education are complements for whites (one-third of whom obtained this form of education) and substitutes for African males, at least at the primary level. The returns to schooling in South Africa are high for an upper middle income country. In 2003, male returns were on average 16.4 percent and for females 18.7 percent (see Figure 7).

In Ghana, a low income country in Africa, the returns across the distribution are very flat – and low overall with an average return of only 8.2 percent – ranging from 7.3 to 8.7 percent for males. For Ghanaian females, however, schooling is much more equalizing, with returns of 14 percent at the bottom of the wage distribution and only 8 percent at the top; therefore highly equalizing.

For Kenya and Tanzania, our evidence is from employer surveys; therefore, the results may not be representative of the entire labor market. In both Tanzania and Kenya, education is highly disequalizing.

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where a government might be a real or perceived contributor to displacement or is harboring an overt or covert policy to reduce the number of refugees in the country.

With limited results in including FDPs in national sampling frames, alternative approaches remain necessary. The UNHCR, the World Bank and numerous scholars and practitioners worldwide are experimenting with satellite images and phone surveys to try detecting refugee and IDP populations that may escape the UNHCR and national registers with some initial encouraging results. In Lebanon, Jordan and the Kurdistan region of Iraq, Aguilera et al. (2020) designed sampling strategies for Syrian refugees with known ex-ante selection probabilities. They used a variety of data sources, including data collected by humanitarian agencies, and also employed geospatial segmenting to create enumeration areas where they did not exist. Systematic field experiments are also underway to test different sampling approaches for IDPs living in camps. For example, Himelein, Pape and Wild (forthcoming) compare the performance of five alternative sampling approaches (satellite mapping, segmentation, grid squares, “Qibla method,” and random walk). Different indicators are assessed including household size, consumption, poverty and ownership of assets. Using empirical evidence from a field experiment in an IDP camp in South Sudan, the total survey error of each sampling approach is compared to a census, allowing for the disaggregation of the total error into sampling and non-sampling components. One of the main findings is that the implementation of all approaches suffers from over-estimation of household sizes caused by a systematic tendency of enumerators to select larger households because they are more likely to find an adult respondent. Such studies with a focus on ground truthing remain important to validate sampling approaches for displaced communities.

Sampling FDPs outside camps faces a different challenge, especially when a list-based approach is not possible. FDPs can be spread in low density across a larger population requiring an area-based adaptive sampling approach. The approach continues to list additional enumeration areas systematically selected, e.g., by selecting neighboring enumeration areas, until a sufficient number of FDPs are
two days, then total exports in 2011 would have been 16.4 percent lower than they actually were. Using transaction-level data from El Salvador, Carballo, Graziano, Schaur & Martincus (2022) show that upgrading transit systems to streamline border processing in developing countries can be a high-return investment (with a back-of-the-envelope estimated return of US$3 to 1).

> 36Link to PIDA Priority Action Plan

> 37Considering that the general equilibrium analysis is done in comparative statics we do not apply a discount factor to compute the return.

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Table 7: Domestic Implicit Subsidy Provided by Gazprom

||Via arxears|Via barter|Total as % of GDP|
|---|---|---|---|
||($ billion)|($ bilon)||
|1993|1.4|1.4|1.4|
|1994|1.6|1.7|1.3|
|1995|3.4|3.7|1.8|
|1996|3.6|3.7|1.8|
|1997|3.3|3.5|1.6|
|Total|**12.7**|13.5||

Source: staff calculations

# _Power Utilities - RAO UES (UES)_

Like Gazprom, the electricity sector is heavily involved in the nonpayments web, and serves as a major channel for large implicit subsidies to domestic industries. Table 8 shows that the electricity generation sector is also plagued by low collection rates and by low cash collections, i.e., by both arrears and NCS. These phenomena are unique to FSU countries, as a detailed multi-country World Bank study of the energy sector shows.<sup>25</sup> The problem is virtually absent in the transition countries of central Europe and has been largely solved in the Baltic Republics.

Table 8: Composition of Power Utilities Sales by Means of Payment*

||199.|1997|
|---|---|---|
|Sales|100|100|
|Cash & liquid equivalent|20|20|
|Bank bills|11|6|
|Offsets and barter|49|62|
|Unpaid arrears|20|12|

* _Including intra -industry transactions_

Source: UES, MFK Renaissance, Brunswick Warburg Table 9 presents our estimate of the implicit subsidy flow from the electricity generation sector. According to it, the average annual net subsidy
then decreased from According to Rahman and Haque (1988), poverty decreased from the mid 1970's to the mid 1980's. For the later period, estimates based on group data for the Household Expenditure Surveys published by the Bangladesh Bureau of Statistics indicate that in both rural and urban areas, poverty increased in the late 1980's and early 1990's (Khundker, Mahmud, Sen, and Ahmed, 1994; Hossain and Sen, 1992). This was confirmed using the unit level data of the surveys up to 1991-92 (Wodon, 1995, 1997). Additional work based on a smaller survey conducted by the Bangladesh Institute of Development Studies within rural areas also show an increase in poverty in the late 1 980s, followed by a decrease between 1990 and 1994 (Rahman and Hossain, 1995; Rahman, Hossein, and Sen, 1996).
elasticity was –0.345. Additionally, recent decreases in tobacco consumption have occurred in lockstep with increased prices of cigarettes from the pass-through of excise and ad valorum taxes.<sup>7</sup>

## **3. Model**

In this paper, the impact of raising tobacco taxes in Russia is estimated using an extended cost-benefit analysis as in other studies (Pichón-Riviere et al. 2014; Verguet et al. 2015). The paper analyzes three factors to estimate how tobacco taxes affect household incomes. First, assuming tobacco consumption does not change, tobacco taxes directly affect household income because the share of household budgets allocated to tobacco purchases increases as the tax increases. Second, household medical expenses decrease as a result of a reduction or widespread cessation in tobacco consumption. Third, households experience a positive income change because of additional years of labor recovered through the extension of life expectancy. The aggregate effect of a tax policy is estimated as follows: Income effect = change in tobacco expenditure (A) + lower medical expenses (B) + rise in income (C) (1) For further details on the methodology used, see annex A; refer also to Fuchs and Meneses (2017a).

# **4. Data and descriptive statistics**

## **Data**

For the current analysis, we mainly used the data provided by the RLMS-HSE. This is a rich set of data on individuals and households and covering incomes, expenditures, and health status as well as attitudes toward smoking. The RLMS-HSE is an annual survey that allows researchers to construct panels to follow the same individuals over a few years at a time. The latest available wave of the survey when the analysis was conducted was the 2016 round.

The information on attitudes toward smoking is available on households and individuals. In the family questionnaire are questions about how much households spend on cigarettes and how many packs were purchased during a recent period. This allows the unit values (individual prices) of cigarettes
|0.072|-0.344|-0.014|
|- Preschool attendance|0.006||-0.241|-0.657|-0.004|
|- Primary school attendance|0.013||-0.230|-0.647|-0.009|
|- Secondary school attendance|0.015||-0.157|-0.573|-0.009|
|- Tertiary attendance|0.005||0.240|-0.177|-0.001|
|- Special education attendance|0.000||-0.788|-1.204|0.000|
|**All education attendance**|0.039||-0.147|-0.563|-0.023|
|**All in-kind transfers**|0.079||-0.039|-0.455|-0.039|

_Source_ : Own calculation based on SLCHBS 2018.

# **5.6. What Is the Marginal Contribution to the Redistributive Effect of Fiscal Interventions?**

Other than in the area of tertiary education, Grenada’s fiscal policies contribute to the reduction of inequality. In this part of the paper, we use the definitions of the marginal contribution to the redistributive effect of fiscal interventions, mainly those contributions with respect to the relative size of specific items as well as vertical equity, included in the concepts presented in the CEQ framework. Both help with showing which interventions (or groupings of them) are effective in reducing inequality through fiscal policy. This exercise shows that, with the exception of tertiary education, the rest contribute to the reduction of inequalities. When 17
1:** **<u>Correlation of selected rankings for bribe payments</u>**

|**_Transaction_**|Construction<br>permit|Operations<br>license|Government<br>Contract|
|---|---|---|---|
|Water<br>connection|0.65|0.65|0.41|
|Construction<br>permit||0.82|0.62|
|Operations<br>license|||0.68|

We then simply aggregated the eight indicators into a composite one using equal weights and compared it to the CPI. The correlation between the two indicators is about 0.7. Hence it seems that the CPI may capture important information about overall corruption, but possibly with a large error margin.

We then explored whether the differences between firm level responses and those of experts generating the CPI might have a systematic reason. We speculated that the experts who cannot be fully familiar with corruption in a country might draw conclusions about corruption by 4
allowing only differences in the stock of schooling and the returns to human capital. Then, we find that relative _pcGDP_ is 53 _._ 5 percent, accounting

> 48See Web Appx. Fig. 8(a) for the mortality distributions.

> 49See Web Appx. Fig. 8(b) for the distribution of schooling we use, based on the I2D2 data (1990-2016).

> 50We compare GDP per capita in levels. An alternative approach would be to compare GDP per capita in log levels. In that case, the gap is of 51%. In any case, the main point of our inquiry in this section is to compare the relative contribution of education and experience, so the absolute metric is not important.
possible drivers of the variation in _β_<sup>ˆ</sup> _i_<sup>_gdp_</sup> , _β_<sup>ˆ</sup> _i_<sup>_de f_</sup> and _α_ ˆ _i_ . We therefore also consider traditional scale variables, such as the level of economic development (i.e., GDP per capita) a measure of overall GDP and population to control for an economy’s size, and the degree of openness. We further include data on financial sector supervisory structures from Melecky and Podpiera (2012). These contain measures of the degree of integration in prudential supervision, the pursuit and integration of business conduct supervision, and central bank independence. The Kaufmann _et al._ (2010) governance indicators are also included.<sup>16</sup> This yields a total of 42 economic, financial and institutional development indicators.

Our goal here is to relate the cross-country variation in _β_<sup>ˆ</sup> _i_<sup>_gdp_</sup> , _β_<sup>ˆ</sup> _i_<sup>_de f_</sup> and _α_ ˆ _i_ to the level of development of the economy of interest. Once the variation in these coefficients is linked to a set of relevant indicators, we will be able to determine equilibrium credit provision for a specific country based on its development stage, its financing needs, and the capacity of its financial sector to meet these needs. Our proposed framework will therefore enable macroprudential supervisors to gauge current credit provision in the economy against what is needed to maintain a financially stable economic growth path over the medium to long run.

The relationship between the coefficients and the considered development indicators 16A description of the explanatory variables that we use is provided in the Data Section.

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# **1 Introduction**

Firm takeovers where one firm absorbs another firm are common phenomena in markets worldwide. Such consolidations can not only impact product competition, prices, and consumer welfare but can also be consequential for workers (Autor, Katz, Patterson and Reenen, 2020; Azar, Marinescu and Steinbaum, 2022; Benmelech, Bergman and Kim, 2022; Rinz, 2022). Understanding the shortand long-term impacts of consolidations on worker outcomes is important for effective regulation of consolidations and the design of labor market, social insurance, and safety net policies.

This paper studies how takeovers affect the labor market outcomes of workers at consolidating firms by using administrative data on takeovers in the Netherlands from 2011 to 2015. Leveraging rich individual-level information on workers at firms involved in takeovers, we find evidence of heightened job loss post-takeover for workers at acquired firms, leading to long-term negative impacts on workers’ labor market outcomes, such as earnings and total income. These negative effects are pervasive across many types of takeovers. Characterizing the types of workers affected, we find that “over-placed” workers who have higher-than-expected pay prior to the takeover and “duplicative” workers with skills that are likely already present at the acquirer are harmed the most. These results are consistent with _labor restructuring_ , where consolidating firms restructure their workforce in order to decrease labor cost and thereby increase efficiency post-takeover.

We first document the short- and long-term impacts of takeovers on incumbent workers at the firms targeted by takeovers (“target firms”). Our analysis matches target firms to control firms that have not participated in any consolidation activity during our study period and then characterizes differential worker outcomes using an event study framework. We find that takeovers can have a large and long-lasting impact on workers at target firms. While workers who are present at treated and control target firms four years prior to the takeover have similar earnings and employment trajectories prior to the takeover, our event study estimates indicate that these workers fare quite differently post-takeover. There is substantial job loss among the
Figure 10).<sup>17</sup> Performance in the mostly high income Gulf states is noticeably stronger, but still well below their income group average. The standout performer is the UAE, ranked 24th in the world in the 2010 LPI, with a score comparable to that of Korea.

> 16 The cost measure does not include tariffs or trade taxes. Only official costs are recorded. Inland transport costs are based on distance to the shipping port. The methodology, surveys and data are available at <u>http://www.doingbusiness.org.</u>

> 17 The Logistics Performance Index (LPI) is based on a worldwide survey of operators on the ground (global freight forwarders and express carriers), providing feedback on the logistics “friendliness” of the countries in which they operate and those with which they trade. The methodology, surveys and data are available at <u>http://www.worldbank.org/lpi.</u>

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AL fell and REE increased over this period controling for demographic composition. The analysis also uncovers evidence of household scale economies in TEE. Households with more members, but similar demographic composition and per capita expenditures, have a lower AL that is only partly offset by higher REE. Although household scale economies contribute little to the main findings on population TEE, this supports the conjecture in (Deaton and Paxson 1998) that “caloric overheads” (fixed costs of common household activities in developing countries, such as travel for work, buying/selling goods, or collection of water or fuel) could explain the common empirical finding that larger households have lower per capita food expenditures holding constant demographic composition and per capita expenditures.

Second, the estimates imply that TEE fell by over 100 calories per day over this period for individuals aged 15 or older, which on its own would be enough to account for most of the decline in caloric intake in NSS data during this period. However, over the 1983-2012 period, India underwent substantial demographic change, with the share of the population aged 14 and under falling from 40% to 30%. Children have much (about 40%) lower TEE than adults, due to both lower REE (due to smaller size) and lower AL (particularly in rural areas where primary/middle-school attendance appears much less physically active than adult activities). Thus even though TEE is falling for both adults and (to a lesser extent) children throughout this period, demographic change at the population level is more than enough to offset this “within” effect, except after 2005 when activity levels fell particularly quickly.

The findings of flat population TEE and modest gains in nutritional status over time are discussed in light of the potential contribution of measurement error and changes in the disease environment to the (Deaton and Dr`eze 2009) puzzle of declining measured caloric intake . (Smith 2015) argues that the NSS estimates of caloric intake may be biased down, especially in recent years, due to rising consumption
For coastal flood hazards, we use a global coastal flood map with 3-arcsecond resolution, generated using the LISFLOOD-FP hydrological model<sup>43</sup> , with DEM MERIT as an input<sup>42</sup> . Coastal flood simulations are forced by extreme sea level scenarios derived from reanalyzing waves (using the <mark>WAVEWATCH-III mode</mark><sup>44</sup> <mark>)</mark> and storm surges (using the DFLOW-FM model<sup>31</sup> ), combined with tidal information<sup>45,46</sup> . As with fluvial and pluvial floods, we consider 100-year events.

Global flood maps do not incorporate the effects of artificial flood protection structures like dikes. This data limitation is pervasive in the literature, as there is no complete global inventory of flood defenses. Ongoing initiatives, such as the FLOPROS database, could eventually fulfill this need, but are still falling short of comprehensive coverage<sup>47</sup> . The use of undefended flood maps is likely to result in overestimating exposure in locations where flood protection systems defend against 100-year floods (or higher). Case studies and World Bank country risk assessments suggest that the vast majority of flood-exposed people in LICs and LMICs have no protection from a 100-year flood. Many LICs lack even basic drainage systems to manage light flooding. It is likely that only HICs and some UMICs offer such flood protection standards to a significant share of their populations; however, frequent flood disasters in these countries also demonstrate that coverage is far from complete.

This study considers a 1-in-100 year return flood intensity to reflect relatively rare and intense disasters. However, the concept of return periods is easily misunderstood and the probability underestimated. A 1- in-100 year flood has, on average, a 1 percent probability of occurrence in any given year, which translates to a 10 percent probability in a decade, or 50 percent in a lifetime (68 years).
# **Figure 24: Total Reserves and Net Trade**

## **Germany**

<!-- Start of picture text --> a) Levels b) As a Percentage of GDP<br>300 10%<br>250 8%<br>200<br>6%<br>150<br>4%<br>100<br>50 2%<br>0 0%<br>Total Reserves, nominal Total reserves to GDP<br>Net trade in goods, nominal Net trade to GDP<br>The Netherlands<br>c) Levels d) As a Percentage of GDP<br>80 15%<br>60<br>10%<br>40<br>5%<br>20<br>0 0%<br>Total Reserves, nominal Total reserves to GDP<br>Net trade in goods, nominal Net trade to GDP<br>China<br>e) Levels f) As a Percentage of GDP<br>2500 50%<br>2000 40%<br>1500 30%<br>1000 20%<br>500 10%<br>0 0%<br>-500 -10%<br>Total Reserves, nominal Total reserves to GDP<br>Net trade in goods, nominal Net trade to GDP<br>US$, billions Percentage<br>US$, billions Percentage<br>US$, billions Percentage<br><!-- End of picture text --> Source: World Bank, World Development Indicators.

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