rafmacalaba/datause-encoder-data huggingface dataset:
<u>https://siteresources.worldbank.org/INTLSMS/Resources/3358986-1233781970982/58009881282216357396/7337519-1388758418241/GHS_Panel_Survey_Report_Wave_2.pdf.</u> NBS. 2016. _LSMS-Integrated Surveys on Agriculture: General Household Survey, Panel 2015/2016._ Abuja: NBS and Federal Ministry of Agriculture and Rural Development; Washington, DC: World Bank. https://microdata.worldbank.org/index.php/catalog/2734/related-materials.

NBS and World Bank. 2016. _Basic Information Document: Nigeria General Household Survey, Panel 2015/16_ . https://microdata.worldbank.org/index.php/catalog/2734/related-materials.

Nwankpa, M. 2014. “The Politics of Amnesty in Nigeria: A Comparative Analysis of the Boko Haram and Niger Delta Insurgencies.” _Journal of Terrorism Research_ 5 (1). <u>http://doi.org/10.15664/jtr.830.</u> OCHA (United Nations Office for the Coordination of Humanitarian Affairs). 2017. _Nigeria: 2018 Humanitarian Needs Overview_ . New York: OCHA.

<u>https://www.humanitarianresponse.info/sites/www.humanitarianresponse.info/files/documents/f iles/13022018_ocha_humanitarian_needs_overview.pdf.</u> Raleigh, C., A. Linke, H. Hegre, and J. Karlsen. 2010. “Introducing ACLED: An Armed Conflict Location and Event Dataset Special Data Feature.” _Journal of Peace Research_ 47 (5): 651–60. <u>https://doi.org/10.1177/0022343310378914.</u> Rockmore, M. 2017. “The Cost of Fear: The Welfare Effect of the Risk of Violence in Northern

- Uganda.” _World Bank Economic Review_ 31 (3): 650–69. https://doi.org/10.1093/wber/lhw025.

- Serneels, P., and M. Verpoorten. 2013. “The Impact of Armed Conflict on Economic Performance: Evidence from Rwanda.” _Journal of Conflict Resolution_ 59 (4): 555–92. <u>https://doi.org/10.1177/0022002713515409.</u>

Shemyakina, O. 2011. “The Effect of Armed Conflict on Accumulation of Schooling: Results from Tajikistan.” _Journal of Development Economics_ 95 (2): 186–200. <u>https://doi.org/10.1016/j.jdeveco.2010.05.002.</u> Singhal, S. 2018. “Early Life Shocks and Mental Health: The Long-Term Effect of War in Vietnam.” _Journal of Development Economics._ <u>https://doi.org/10.1016/j.jdeveco.2018.06.002.</u> Swee, E.L. 2015. “On War Intensity and Schooling Attainment: The Case of Bosnia and Herzegovina.” _European Journal of Political Economy_ 40 (A): 158–72. <u>https://doi.org/10.1016/j.ejpoleco.2015.08.001.</u> Verwimp, P. 2012. “Undernutrition, Subsequent Risk of Mortality and Civil War in Burundi.”

- _Economics and Human Biology_ 10 (3): 221–231. https://doi.org/10.1016/j.ehb.2011.09.007.

- Verwimp, P., P. Justino, and T. Brück. 2018. “The Microeconomics of Violent Conflict.” _Journal of Development Economics_ . <u>https://doi.org/10.1016/j.jdeveco.2018.10.005.</u>

- Weissman, M.M., D. Sholomskas, M. Pottenger, B.A. Prusoff, and B.Z. Locke. 1977. “Assessing Depressive Symptoms in Five Psychiatric Populations: A Validation Study.” _American Journal of Epidemiology_ 106 (3): 203–14. https://doi.org/10.1093/oxfordjournals.aje.a112455.

31
# **2.3 Summary Statistics**

Table 1 shows summary statistics of takeover events for our sample of matched target firms. In our sample, 30.8% of events are horizontal takeovers defined as takeovers between firms in the same 3-digit industry. We characterize 8.1% of our events as a vertical takeover: a takeover between a service-industry firm and a manufacturing firm. This classification is not exhaustive and over half of the events in our sample meet neither takeover definition. Incorporating firm location, 67% of takeovers take place between firms in the same province and 20.3% take place between firms that are both in the same 3-digit industry and the same province. Further, 11.3% of events are in tradeable and 40.4% of events are in non-tradeable industries.<sup>7</sup> To assess how takeovers affect labor market concentration, we create a projected takeover-induced labor market concentration change measure using historical worker flows (see Arnold, 2021).<sup>8</sup> We find that only two of our takeovers have a non-negligible impact on labor market concentration.

Table 1: Takeover Characteristics

|Event Type|Share (%)|
|---|---|
|Horizontal (Same 3 digit industry)|30.8|
|Same province|67.0|
|Same province and 3-digit industry|20.3|
|Same province and 2-digit industry|23.8|
|Vertical|8.1|
|Between tradeable frms|11.3|
|Between non-tradeable frms|40.4|
|High Labor Market HHI Change|0.0|
|Number of Takeovers|1,013|

Note: This table shows the characteristics of the takeovers in our sample. All industry definitions rely on Standard Industry Classifications. A horizontal takeover is defined as a takeover between firms in the same 3-digit industry. A vertical takeover is defined as a takeover between a service-industry firm and a manufacturing firm. There are 12 provinces in the Netherlands. High HHI changes in the labor market correspond to takeovers that generate a change in HHI greater than or equal to the change of labor market concentration of 0.21 (see Arnold, 2021).

A few important conclusions can be drawn from Table 1. First, takeovers in this period are

> 7Tradability of a given product is identified using the industry code of the firm. A tradeable product is one that is sufficiently portable to be sold long distances or across borders. For example, massages are non-tradeable while computers are tradeable. We use the definitions put forward by OECD (2018).

> 8We follow an analogous procedure to Arnold (2021) to create a measure of expected labor market concentration for a given takeover. The primary difference in our approach is the difference in geography used. While Arnold (2021) uses United States commuting zones, we use labor market regions. The size of this geographic definition is between municipalities and provinces and is analogous to the commuting zone definition constructed by Statistics Netherlands. There were 36 different labor market regions in the Netherlands during our time of study.

7
Pakistan (Pakistan’s Central Bank, SBP hereafter)—The Export Finance Scheme (EFS) and the Long-Term Finance Facility for Plant & Machinery (LTFF)—that provide loans to exporters at concessionary interest rates to finance short-term working capital and longterm investment in machinery and equipment, respectively. These schemes are large and well established in Pakistan. EFS, which started to operate in 1973, provided loans worth US$3.8 billion per annum between 2015 and 2017, or 17.4 percent of Pakistan’s total exports. While LTFF is more recent and smaller in size than EFS, its loans outstanding are equivalent to 1.3 percent of the country’s exports over the same period.

To carry out our empirical analysis we combine customs transaction data that contain the universe of international trade transactions for firms in Pakistan with data from SBP that tell us which firms used EFS and LTFF and the value of the loans they requested from commercial banks in each fiscal year over the period 2015-2017. Our data shows that the firms benefiting from these loans are substantially larger than the average exporter and are concentrated in the clothing, textiles and apparel sectors.

In order to address the non-random selection of exporters into the export finance support programs, we use a ‘doubly robust’ matching estimator combined with covariate adjustment developed by Wooldridge (2007), following the policy evaluation literature. Doing so allows us to construct an appropriate comparison group to estimate what would have been the export performance of firms that made use of EFS and LTFF had they not participated in these schemes. Our estimates of the probability that firms participate in each scheme confirm that size (in terms of export sales) is a very strong predictor of participation, as well as being an importer and exporting a large share of products for which the schemes are available.<sup>1</sup> Crucially, for the purposes of identification, even after controlling for the aforementioned observable characteristics determining firms’ participation in the schemes, there remains substantial unexplained variation in firms’ treatment status. This ensures that we can find

> 1EFS is available to firms in all sectors with the exception of products covered in a negative list while LTFF is available to firms exporting in 20 sectors. Details of the requirements and restrictions imposed on firms to participate in each scheme are summarized in Section 2.

2
exogenous source of size-variation under the assumption that the size of US firms is orthogonal to tax enforcement. Arguably, for large firms the IRS bases its decision directly on third-party reports and risk scores from economic activity and not indirectly based on size. In countries with high fiscal capacity, it is well-documented that non-compliance is concentrated among the self-employed and family firms with few employees (Blumenthal et al. 2001, Kleven et al. 2011). To alleviate concerns, we remove all firms with fewer than 5 employees in the US Census, which also matches the WBES sampling design. In Section 4.4, we show that the results hold when constructing the exogenous size-ranking with British and German firms in the Amadeus dataset, which are subject to stringent information reporting requirements.<sup>18</sup> Finally, in section 4.3 we show that for the richest countries in the WBES (countries with per capita GDP above $21,000)<sup>19</sup> the estimated coefficients of size on tax inspection is zero. This evidence supports the first stage identifying assumption that in high fiscal capacity countries, firm size is not driven by size-based inspection.

The IV strategy provides a causal estimate of the size-gradient under the first stage validity, discussed above, and the exclusion restriction. The exclusion restriction assumes that the US size-ranking of WBES industries only impacts tax inspection in the WBES country through the WBES size-ranking. If tax inspection depends on employee-size for the exclusion restriction holds as as only indirectly (e.g. by proxying capital) long technological demand for labor does not impact capital other than through labor input. However, this assumption fails if the first-stage coefficient does not capture technological differences in employee-size, but is instead an imperfect proxy for capital input demand. To try to address this issue we construct two measures of demand for capital input in the US industry distribution: the demand for external reliance (Rajan and Zingales 1998) and

> 18Amadeus data are collected from annual financial statements filed with the business registry and audited annual reports presented to shareholders. These firms are subject to a broader and deeper set of reporting requirements and hence in this sample tax inspection is unlikely to be driven by crude size-proxies.

> 19The $21,000 cutoff was chosen to correspond to the 90th percentile of income in the WBES such that these countries are comparable to the US in terms of fiscal capacity.

13
# **Table S25. Strategies to cope with income lost due to pandemic across gender of head of household**

||Relied on<br>Saving|Sale of<br>Assets|Reduced<br>Food<br>Consumption|Reduced<br>Non-Food<br>Consumption|Received<br>Assistance<br>from Friends<br>and Family|Received<br>Any<br>Assistance|
|---|---|---|---|---|---|---|
|Female Head|-0.105***|-0.021**|0.037|-0.068***|0.020|0.085***|
||(0.022)|(0.009)|(0.028)|(0.021)|(0.024)|(0.018)|
|Round 2|-0.029|-0.004|0.090***|-0.003|0.000|0.014|
||(0.024)|(0.010)|(0.022)|(0.019)|(0.016)|(0.010)|
|Round 3|-0.004|0.044***|0.146***|-0.040**|-0.009|-0.031***|
||(0.021)|(0.011)|(0.020)|(0.017)|(0.018)|(0.010)|
|Female × Round 2|0.096**|0.013|-0.033|0.083**|0.079**|-0.057**|
||(0.038)|(0.011)|(0.039)|(0.033)|(0.038)|(0.024)|
|Female × Round 3|0.027|0.007|-0.038|0.039|-0.008|-0.069***|
||(0.038)|(0.022)|(0.043)|(0.030)|(0.038)|(0.024)|
|Ethiopia|0.014|-0.036***|0.093***|0.134***|0.004|-0.007|
||(0.024)|(0.010)|(0.021)|(0.016)|(0.018)|(0.010)|
|Nigeria|0.088***|0.010|0.532***|0.191***|0.178***|0.058***|
||(0.030)|(0.013)|(0.026)|(0.020)|(0.022)|(0.011)|
|Uganda|0.225***|-0.017|0.265***|0.201***|0.165***|0.061***|
||(0.034)|(0.014)|(0.030)|(0.025)|(0.027)|(0.011)|
|Observations|10,618|10,618|10,618|10,618|10,618|22,664|
|R-Squared<br>_Note_: Each column rep|0.021<br>orts results from|0.022<br>a single regres|0.204<br>sion of a binary va|0.014<br>riable equal to 1|0.042<br>if the respondent|0.022<br>stated that the|

_Note_ : Each column reports results from a single regression of a binary variable equal to 1 if the respondent stated that the household relied on that strategy to cope with the loss of income and 0 if they did not. The binary variable is regressed on indicators for each country, with Malawi as the base case, and round, along with an indicator equal to 1 if the household head is female, as opposed to male. Data come from all rounds of phone survey data in which FIES questions exist. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

52
- 33 -

urban polarization in developed countries (See Table 1.2). Finally, LAC countries are also fairly industrialized: the contribution of their industrial sector to GNP formation exceeds the average 36 percent value recorded for the developed countries in Argentina, Chile, Peru and Venezuela and is already close to this value also in Brazil, Mexico and Uruguay (see Table 3.7). Thus, conventional indicators of development such as industrialization and urbanization do not by themselves explain the wide discrepancy of urban service levels between LAC countries and the developed market economies.

3.09 Public Sector Dominance? The importance of the public sector, both in terms of revenues and expenditures, is no greater in LAC countries than it is in the so called market economies, which are in fact "mixed" economies. In the average, the expenditures of a Latin American government account for 31 percent of GDP, against 41 percent in the more developed countries (see Table 3.9a). In this respects, one could argue that the role of government is less significant in LAC countries than it is in the industrial market economies. But, if one were to measure fiscal effort as the proportion to which GDP is taxed by the general government, LAC countries' proportion falls below 25 percent

||Table|3.9a:<br>|GENERALG|OVERNMEN|T EXPEND|ITURES A|S PERCEN|TAGE OF|GDP a/|||
|---|---|---|---|---|---|---|---|---|---|---|---|
||1975|1976|1977|1978|1979|1980|1981|1982|1983|1984|Average|
|LAC SAMPLE_bI_|26.2|25.5|26.3|27.3|25.9|27.1|30.0|33.7|32.4|31.0|<br>28.5|
|MARKET ECONOMIESb/|39.2|38.7|38.3|38.9|39.1|40.9|41.3|42.8|42.8|41.0|<br>40.3|

_I/_ Total expenditures include expenditures and lending minus repayment. General Government comprises central, regional and local governments. This is different from 'Total Public Sector' which includes decentralize public enterprises. h/ Weighted averages. Comparability over time affected by avaLlabllity of data.

Table 3.9b: GENERAL GOVERNMENT REVENUES AS PERCENTAGE OF GDP a/

||1975|1976|1977|1978|1979|1980|1981|1982|1983|1984|Average<br>=|
|---|---|---|---|---|---|---|---|---|---|---|---|
|LAC SAMPLE_k/_||||||||||||
||22.3|21.9|24.1|24.7|24.4|24.4|24.5|26.3|25.8|24.6|<br>24.3|
|MARKET ECONOMIES_kb_||||||||||||
||36.3|36.3|36.8|36.5|36.7|37.9|38.7|39.4|38.9|39.0|<br>37.7|

a/ Total Revenues lnclude revenues and grants. _k/_ WeLihted averages. Comparability over time affected by the availability of data.

Source: IMF, Government Finance Statistics, various years.
# **4. Crisis impacts on social mobility, life satisfaction and perceptions of government performance**

# **4.1. Social mobility**

Respondents in LiTS II were asked to “imagine a 10-step ladder where on the bottom, the first step, stand the poorest 10 percent of people in [their] country and on the highest step, the 10th, stand the richest 10 percent of people in [the] country”. They were then asked to state on which step of the 10 their household rests today, where it stood four years before and where it is likely to be in four years’ time. The stated position<sup>11</sup> can be interpreted as a subjective ranking of a household's social standing: it is (weakly) correlated to the measured consumption aggregate but, similar to class, it is better predicted by education and asset ownership.<sup>12</sup> Such subjective views of relative social standing are important because relative incomes are known to play a larger role in satisfaction with life or happiness than absolute incomes.<sup>13</sup> We find that respondents who place themselves at a higher point on the ladder have significantly greater life satisfaction than those who report lower positions. And social mobility matters for life satisfaction: those who think that they have slipped down the ladder over the previous few years are significantly less satisfied with life.

In all transition subregions, households on the lower steps of the ladder report small improvements in their position (compared to four years before), whereas those in the middle and at the top of the ladder are more likely to report large falls. The higher a household considered itself to be four years before, the larger its perceived drop down the social ladder. This pattern may be explained by the fact that those at the top have more to lose, as those at the bottom cannot fall much in relative terms. However, it is also consistent with preliminary evidence based on household surveys (Sulla, 2011), which suggests that consumption during the crisis increased slightly in many transition countries for the poorest ten percent of households and fell most for the richer households (and that inequality fell slightly). In summary, although poor respondents

> 11 Most respondents believe that they are in the middle, regardless of their objective consumption level. The median position on the ladder is between step 4 (lower-consumption group) and step 5 (higher-consumption group).

> 12 See Bourdieu, 1979.

> 13 See Easterlin (1995). Kahneman et al (2006) argue that the correlation between satisfaction with life and income results from a "focusing illusion" because the life evaluation question draws “people's attention to their relative standing in the distribution of material well-being”.

17
**Table 17. Between-group differences in entry salary: 1980-86 and 1990-97 cohorts**

|||**1980-86 Cohor**|**t**||1990-97**Cohor**|**t**|
|---|---|---|---|---|---|---|
||Total<br>Difference|Characteristics|Structure|Total<br>Difference|Characteristics|Structure|
|MI-M2|9.54|-1.86|11.40|8.75|3.13|5.62|
|MI-Fl|23.82|15.70|8.12|10.29|6.27|4.02|
|Ml-F2|30.86|20.12|10.74|15.34|9.02|6.32|
|M2-FI|14.28|17.56|-3.28|1.54|3.14|-1.60|
|M2-F2|21.32|21.98|-0.66|6.59|5.89|0.70|
|FI-F2|7.04|4.42|2.62|_5.05_|2.75|2.29|

**Table 18. Between-group differences in entry grade: 1980-86 and 1990-97 cohorts**

|||1980-86**Cohort**|||**1990-97 Cohor**|**t**|
|---|---|---|---|---|---|---|
||Total<br>Difference|Characteristics|Structure|Total<br>Difference|Characteristics|Structure|
|MI-M2|0.56|-0.27|0.83|0.42|0.21|0.21|
|Mi-Fl|1.52|0.97|_0.55_|_0.55_|0.42|0.13|
|M1-F2|2.00|1.02|0.97|0.88|0.55|0.33|
|M2-FI|0.96|1.24|-0.28|0.13|0.22|-0.09|
|M2-F2|1.44|1.29|0.15|0.46|0.34|0.11|
|FI-F2|0.48|0.05|0.43|0.33|0.13-|0.20|

45
green trade can spur the demand for skilled and semi-skilled workers in new green emerging industries, it can also raise concerns about gender inequality (McFarland, 2013; Rocha and Piermartini, 2023) or skills intensity and income inequality (OCDE, 2014), even depending on the types of green goods industries or sectors.

This paper examines the green goods trade landscape in the Philippines to understand the current level, evolution over time, and predominant green industries sectors. We estimate the ad valorem equivalent (AVE) of non-tariff measures (NTMs) on green goods to assess the cost of regulations on green goods imports and identify the most burdensome NTMs and candidates for regulatory climate-friendly trade policy reform. Finally, since reforms of NTMs will increase imports and exports of green goods, we assess the relationship between the imports and exports of green goods in the labor markets. This is done using trade data and labor force survey data at the industry level for the Philippines.

The Philippines makes for a compelling case for this analysis. As this country is vulnerable to climate change impacts like extreme weather events, agricultural disruptions, or rising sea levels, bolding the green trade can support the country’s transition towards a more sustainable area, enhance resilience, and alleviate environmental risks. First, as the Philippines heavily relies on fossil fuel imports, investing in green energy such as hydropower, wind, or solar can reduce the dependence on fossil fuel imports. Second, regarding international commitments, since the Philippines is a member of several agreements in addressing climate change issues like the ASEAN agreement, participating in green trade can help this country align with international obligations and enhance its commitment to addressing climate change. Finally, and more importantly, promoting green goods imports and exports can drive job creation, increase wages for people, and support economic growth (World Bank Group, 2022).

The paper finds that the Philippines’ share of green goods trade is relatively low, partly due to the high incidence of NTMs on imports of green goods. The paper finds five measures out of about 90 as reform candidates, which are costly and have a higher incidence 2