rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Data Appendix**

## **Variable definitions**

**_Employment_** is the total sum of all unique IRP5/IT3(a) forms with positive and non-missing employment income per unique tax reference number. Temporary workers are weighted by the duration of their contract (see Pieterse et al., 2018).

**_Age_** is determined in reference to the birth year. Only when birth year is missing, age is considered to be entry (age 1) when firm is first observed in the data.<sup>40</sup> .

**_Unit of observation_** is the firm. In the SARS-NT Panel, a firm is defined as a CIT-registered entity that has completed an IT14 and/or ITR14 form (Pieterse et al., 2018). In other words, each unique CIT reference number is associated with a firm.

**_Firm size_** is determined as the number of workers employed by a firm. The employment variable is used to construct the categories of firm’s size. A firm-level labor indicator for the SARS-NT Panel is derived from the associated IRP5 forms (i.e. IRP5 and IT3a forms) submitted by PAYE-registered firms (Pieterse, 2018).

|**Firm size c**|**ategories**||**Age cate**|**gories**||
|---|---|---|---|---|---|
|1-19|1-9|1-9|1-5|1|1-5|
|20-99|10-19|10-19|6-9|2-5|6-9|
|100+|20-99|20-49|10+|6-9|10-19|
||100+|50-249||10+|20-29|
|||250-499|||30+|
|||500+||||

**_Employment Growth_** is calculated following Davis and Haltiwanger (1992,
215)|(0.145)|
|**N**|806|1503|1832|1832|4141|
|**Pseudo R-squared**|0.120|0.070|0.085|0.085|0.075|

Source: Author’s calculations based on ELMPS 1998-2018 Notes: *p<0.05; **p<0.01; ***p<0.001.

# **4.5.2. Entry of firms**

We turn now to entry of firms. We consider new firms to be those that started between waves. Figure 17 shows the percentage of firms that are new by subsequent wave formality. Informal firms are more likely to be new firms. In 2018, 73% of all informal firms were new compared to 55% for formal firms. Figure 18 shows the percentage of firms that are formal by whether they are new. The figure confirms that already established firms are more likely to be formal compared to new ones. Formality has fallen over time across both established and new firms. In 2018, 44% of established firms were formal compared to 26% of new firms. There is suggestive evidence that firms may start as informal (and later formalize). Taken with the higher rates of exit for informal firms, it may also indicate that this sector is more dynamic, has greater churn, or is a fallback form of employment (necessity entrepreneurship).

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# **6 Conclusion**

This paper examines the economic impacts of a large-scale marine pollution disaster on the employment and earnings of a local fishery community. The _Formosa_ incident, in which toxic wastewater was discharged into the ocean and damaged an entire ecosystem in the central – coast of Vietnam in 2016, presented a special case study for how the affected communities – saltwater fishers coped with the negative shock. We combine a novel satellite data capturing night-time light detected from fishing boats with the fishery earnings and labor-market information provided by the labor force surveys. We show that the disaster reduced incomes by as much as 46 percent for the immediate period after the _Formosa_ breakout. We further provide evidence indicating potential coping mechanisms. Upstream fishers who live closer to safe fishing grounds were likely to travel there and continue fishing, as shown by the intensified fishing activities in those regions after the incident took place. In contrast, downstream fishers, who live far away from safe waters, experienced more dramatic impact on average earnings. In terms of the labor-market responses, we find that these individuals substitute work hours in fishery with working secondary, non-fishery jobs. Both coping mechanisms are shown to help mitigate the income losses, even though far from entirely. We also find that the income damage to fishery diminished over time, even though it remained sizable and statistically significant at the end of 2016. Finally, we discover a positive spillover income effect on the freshwater fishery industry, which produces seafood’s substitute products.

Examining the impact of _Formosa_ on the affected population, and how these victims cope with the extreme shock, is relevant to the design of different assistance policies. We show that, even on average, the impact of _Formosa_ was not uniformly distributed among the victims. It is also evident that _Formosa_ did not just affect saltwater fishery in the four provinces located within the contaminated zone, but also the nearby regions and other industries as well. These findings should be factored into any top-down incentives to compensate and subsidize individuals and households.

#
possible that countries may have sought to also recruit older people, with basic general education qualifications, who were in other professions, and who were local. The certification result is more consistent with the common narrative, although the country estimates suggest that it is only in Mozambique where the effect is substantively large (albeit statistically insignificant).

# 4.4 Classroom observation and teacher absence

The last set of results comes from the analysis of the classroom observation and teacher absence data.<sup>21</sup> There is no consistent evidence that teachers hired after FPE do any worse on any of these

> 20 A bandwidth of 7 was chosen to report here for parsimony and because it is a relative midpoint. Results for bandwidths of 5 and 10 years are reported in Appendix Table 6.

> 21 Results for bandwidths of 5 and 10 years are reported in Appendix Table 7.

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complements: households with more irrigated plots, making more use of modern agricultural inputs, and with off-farm enterprises are less likely to send migrants due to dry spells. This is in line with hypothesis H3 about substitutability between _in situ_ adaptation and migration as alternative risk mitigation strategies for farming households.

In contrast, the role of consumption is not as strong as the one played by previous variables. The relationship with treatment effects distribution is almost flat, with a pertinent exception: households with the lowest absolute level of consumption per capita are more likely to send climate migrants (this effect is close to 10%). Remember that, compared to the asset index, the consumption data capture short- and medium-term deprivation, whereas the asset index is a better indicator of long-term wealth (Aiken et al., 2023). While one might be tempted to interpret this particular result as a hint that the poorest households, unable to smooth consumption, tend to engage in risk-coping migration, this is not the case: upon further investigation, we found that these households simply experienced substantially milder weather conditions compared to the rest of households. The GS-SPEI value for this lowest consumption decile is 0.11, against a sample mean of 0.23 (cf. Table A1). Therefore, this positive effect should be interpreted as the casual result of minor shock exposure rather than as a signal of an underlying heterogeneity related to the conditioning variable. Concerning human capital, we do not observe relevant heterogeneity with respect to the share of members with higher education. Finally, the last panel of Figure 3 shows variation depending on the intensity of the treatment, the reversed GS-SPEI. As this is a continuous treatment, it is interesting to inspect _ex post_ the distribution of treatment effects with respect to the treatment 'dose', i.e., the intensity and sign of the experienced weather conditions. The relationship appears strongly nonlinear, and drier conditions (upper deciles of the reversed GS-SPEI) are associated with larger negative effects.

Now note
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data and Investment Climate Survey for Mongolia (PICS) from 2004. Data for 2002 and 2003 was collected but we focus on the 2003 data because data for several variables of interest are not available for 2002. The survey covers Mongolian registered firms with at least 3 employees from manufacturing, construction, service, and tourism sectors.<sup>22</sup> The coverage rates of the number of firms in the four sectors are 81%, 70%, 56% and 53% respectively. The PICS survey data is matched at the firm-level with the second source of data, the firms’ tax reports submitted to the Mongolia tax office.

As a first shot at measuring the extent of underreporting by survey firms, we report the firms’ responses to the question “what % of total sales the _typical establishment_ in your area of activity reports for tax purposes” across city industry and firm size (Table 1). Firms report that the typical firms on average underreport 37.7% of their sales. Although underreporting in the city of Erdenet is significantly higher at 10% compared to Darkan and Hovd, the differences among the other 3 cities and across industry and firm size are not

**TABLE 1**

**Mean % of underreporting in sales by the direct approach**

||Variable|Obs.|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|---|
|**_City_**|_Ulaanbaatar_|179|37.7|29.2|0|95|
||_Darkhan_|44|34.6|26.6|0|85|
||_Erdenet_|46|43.5<sup>*</sup>|26.0|0|97|
||_Hovd_|28|33.7|
>situation is greatly affected by the Civil War that has engulfed that country for the past two decades. Data must be<br>handled with great care. Data on Wages and salaries as percentage of GDP are taken from IMF Background paper<br>No. SM/951258 of October 4, 1995 and relate to 1994. GDP, Average Government wages and Average Public<br>wages to per capita GDP are based on calculations emanating from this data. Non-agricultural employment data<br>are taken from International Labor Office's Yearbook of Labor Statistics 1995 and are for 1992. They reflect data<br>gathered through Establishment surveys, that is, data on the number of workers on establishment payrolls. This in<br>turn may result in an underestimation of employment.|
|---|---|
|Botswana|Botswana Central Government employment and local Government employment are taken from IMF Report No.<br>SM/94/278 of November 15, 1994 and relate to 1993. Education data is taken from UNESCO Statistical Yearbook.<br>1995 and relates to 1992. It includes 10409 primary education teachers and 4467 secondary level teachers.<br>Military employment data includes 7,000 personnel in the army and 500 in the air force. Wages and Salaries of<br>Consolidated Central Government relate to 1993. All other data are obtained through calculations emanating from<br>this data. Data on wages in manufacturing are taken from the ILO's Yearbook of Labor Statistics 1995 and refer to<br>1994.|
|Burkina Fas|o<br>Unemployment data are taken from the ILO's Yearbook of Labor Statistics 1995 and refer to 1994 and source IlIl,<br>Code 2:_employment_office_statistics_and include persons in employment who are seeking a change of job or extra<br>work and are therefore also registered at employment offices. This method of data gathering may result in an<br>underestimation<br>of the
We employ six monthly macroeconomic and financial variables in the SVAR model, ordered as follows: logs of seasonally adjusted industrial production ( _Y_ , 'output' hereafter), logs of seasonally adjusted consumer price index ( _P_ , ‘price’), domestic policy interest rates ( _MP_ ), overnight interest rates or 3-month or one-year government bond yields ( _Int_ ), logs of the nominal exchange rate relative to the US dollar ( _FX_ ), and the equity price index ( _S_ ).<sup>9</sup> We also include four exogenous variables to account for latent factors that can simultaneously affect endogenous variables: the international commodity price index ( _Cmdt_ ), U.S. federal funds rates ( _FFR_ ), the CBOE volatility index ( _VIX_ ), and a deterministic trend component ( _Trend_ ).<sup>10</sup> These data are sourced from Haver Analytics, national central banks, or Federal Reserve Economic Data (FRED).

A novel aspect of this paper is the use of survey-based expectations for future inflation, output growth, FX rates, and interest rates. Consumer and business expectations are sourced from monthly surveys conducted by national central banks or statistical offices, while professional forecasters’ expectations come from Consensus Economics surveys.<sup>11</sup> The availability of expectations data varies across EMEs. As a baseline, we prioritize consumer or business expectations, which tend to show greater variability, and supplement with professional forecasters’ surveys when national consumer and business surveys are unavailable or too short for some EMEs. In the SVAR model, we incorporate one-year-ahead expectations.<sup>12</sup>

# **III.2 Identification and estimation**

The baseline identification follows the standard recursive scheme using the Cholesky decomposition of the variance-covariance matrix, as in Christiano et al. (1999) and many others. The monetary policy instrument (i.e., policy interest rates) is placed after the output and price variables, in line with the Taylor-rule
Figure 2: Concentration Curves of Selected Education and Health Services <!-- Start of picture text --> Current primary school attendance Contraception<br>(for primary school age population) (married women, modern method)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>cumulative proportion: children 7-14 years cumulative proportion: married women<br>2000 2005 2000 2005<br>2011 2014 2011 2014<br>Antenatal Care Skilled Birth Attendant<br>(received 4 or more ANC visits from skilled health personnel) (skilled health personnel)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>cumulative proportion: last birth in previous 5 years cumulative proportion: births in last 5 years<br>2000 2005 2000 2005<br>2011 2014 2011 2014<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>cumulative proportion: chidren attending schools cumulative proportion: using modern contraception<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 cumulative proportion: attended births 0<br>cumulative proportion: received ANC<br><!-- End of picture text --> Note: The diagonal (45 degree line ) is the line of equality. The values in the horizontal axis are ranked by wealth. Source: Authors' computation from the DHS data.The values are for all regions except Addis Ababa.

39
differences are not statistically different and hence we cannot draw conclusions with the existing evidence.

Lastly, in an attempt to better understand the relationship between public markets and buyout investments, we estimate models where our measure of buyout investments is adjusted by the total market capitalization of public firms instead of including public market activity as a control variable. If institutional reforms make being public more costly for firms and hence lead to a substitution of private financing for public financing, we would expect to find a positive coefficient on our reform variables. That is indeed what we find: buyout activity relative to public market activity increases following investor protection reforms. This suggests that one reason buyout activity increases following investor protection reforms might be that firms substitute private equity financing for public financing which increases the demand for private capital and leads to more buyout investments.

# **6 Conclusion**

Despite the tremendous increase in global buyout investments over the last two decades, there is a significant lack of systematic studies exploring the country-level drivers of buyout investments. Our study aims to fill that gap by using comprehensive data on buyout investments across 61 countries over 1990–2017. We find evidence that macroeconomic conditions such as unemployment as well as financial development and regulatory environment all are important determinants of international buyout investments. Our findings suggest that countries with lower unemployment and higher stock and credit market activity receive more buyout investments. We also explore regulatory reforms regarding investor protection and contract enforcement and find that countries receive more buyout capital following the implementation of these types of reforms.

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rural divide: municipality population data that are used to stratify the sample, and interview-coded data on area size category. We classify urban/rural based on the former, but use interview-coded data when this is not available. The correlation between the population-based and interviewer-coded urban/rural categorizations is very strong: in Sub-Saharan Africa, 94 percent of respondents in cities with populations of 500,000 or more are classified as urban and 95 percent of respondents in towns and villages under 10,000 are classified as rural.

> 12 Local examples were provided, such as cooperatives in Latin America.

> 13 The excluded category includes “in the home” (because of the sensitivity of asking this question in face-to-face interviews in the home) and other assets such as gold and livestock, as well as other formal markets, such as equity purchases.

> 14 In addition to having an account, formal saving is also conditional on an individual’s ability and willingness to save. This may be associated with cyclical macroeconomic conditions, idiosyncratic shocks (such as illness or unemployment), as well as cultural attitudes toward saving. An important caveat is that the data were collected in 2011, following the global financial crisis, which might have affected individuals’ ability to save.

9
(Chart 6). Compared to the euro area, where the financial sector assets account for some 570 percent of GDP, financial sector assets in the Czech Republic are still relatively low. The stock market is shallow in the Czech Republic, with capitalization of around 38 percent of GDP in 2010 (25 percent in 2003).

While the majority of loans to the private sector come from local banks, nonbank financial intermediaries (such as other companies engaged in lending or in leasing companies) also play some role (Chart 7). In 2004 their share in the loans to the nonfinancial private sector was almost 20 percent. However, increasing competition from banks and tax changes in past three years (which made leasing financing less favorable than earlier) have contributed to the decline of the relevance of these institutions. In 2010, their share in the loans to the nonfinancial private sector reached only 11 percent.

A remarkable increase in loans can be seen to the real estate industry, due mainly to the activities of real estate developers (Chart 8). Companies active in real estate markets now have an even higher share of loans than do manufacturing companies.

Direct cross-border credit to the private sector also plays a role in the Czech Republic, mainly in the case of nonfinancial corporations. Due to the large share of foreign ownership in the corporate sector resulting from past foreign direct investment, corporations often use intragroup financing directly from their parent companies abroad. An analysis of loans taken by nonfinancial corporations reveals that in 2010 slightly more than 50 percent of credit came from the domestic banks while the rest was from other nonbank financial intermediaries, from other (domestic) nonfinancial corporations, and from nonresidents, which include both parent nonfinancial companies abroad as well as direct cross-border loans from foreign banks (Chart 9).

5
conclude that trade costs, not migration frictions, represent a major constraint to the efficient distribution of economic activity and to high sustainable economic growth in Latin America.

# **6. Conclusions**

Transportation costs can hinder economic development with uneven effects across space within countries and regions. Barriers to the mobility of workers in the economy could have analogous results, by not allowing labor to allocate optimally across space. In this paper, we assess the magnitude of migration frictions in Latin America c. 2000 and quantify the effects of cuts in trade and migration frictions on spatial development in the region. We show that reductions in trade costs in an optimal way can be key for the long-term spatial economic development in the region and the potential to speed up its convergence to advanced countries’ standards of living.

Using the dynamic spatial general equilibrium model in Desmet et al. (2018) and fine-grained spatial data, we first calibrated migration entry costs using information on differences in initial utility across regions within the Latin American countries. We find that in most Latin American countries, migration entry costs calibrated based on spatially differential initial utility are on average substantially lower than those obtained assuming uniform within-country initial utility. Their within-country dispersion is also mostly lower. International comparisons suggest that the adjusted migration frictions are also smaller on average in Latin America than those in advanced economies, but trade frictions are considerably higher, even after adjusting for distance.

We then use two counterfactual simulations to assess the relative importance of the two types of mobility frictions. The first counterfactual explores the effects of reductions in transportation costs due to optimal expansion of the domestic road networks in most Latin American countries. We show that such a reduction increases the present discounted value of real per capita income in the region on average by 15.1% – a much larger magnitude than the one obtained with static quantitative trade models because of dynamics gains. The optimal improvements in connectivity also tend to stimulate the economic development of the relatively more populous and productive locations in the Latin American countries
mean flood intensity from the Global Flood Monitoring System (GFMS) data set (Merz et al., 2007), elevation, and distance to river. The flood shock measure takes a value of one where the flood index is more than 30 percent above the average flood index value. The drought measure is based on the difference between the National Oceanic and Atmospheric Administration (NOAA) African Rainfall Climatology version 2 (ARC2) flowering period rainfall in the current period versus the historical average. We use a dichotomous variable that takes a value of one when current period rainfall is at least 30 percent below the historical mean and is otherwise equal to zero.<sup>8</sup> Finally, we control for climate conditions by including historical flowering period rainfall mean and coefficient of variation, as well as standard production function variables. As shown in Online Appendix 1, the flood and drought shocks have significant negative impacts on plot-level maize yields.

# **3.3. Aid Distribution and Explanatory Factors**

For aid distribution, we use monthly data on the number of households who received food and cash aid in TA’s where aid was distributed.<sup>9</sup> The vast majority of aid was distributed through a consortium of international and local NGOs, coordinated by the Ministry of Disaster Management Affairs and the WFP (Babu et al., 2018). The monthly data are aggregated, starting with the month that distribution began following the end of the current period rainy season through to March of the subsequent rainy season. We subsequently divide the number of households to whom aid was distributed by the projected total number of households in the TA to get a measure of aid availability within the TA. To simplify terms, we hereafter use “aid distribution” to refer to this variable.

> 8 McCarthy et al. (2021) use a slightly different measure for drought. That measure is semi-continuous, taking the absolute value of the percent difference between current period rainfall and the historical