rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: DID Results - static

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Ln(N frms)|Ln(N entries)|Ln(N exits)|Ln(N employess)|Ln(Wagebill)|Ln(Revenue)|
|_Full Sample_|||||||
|Ofshore _×Post_|-0.041|-0.669***|0.053|-0.044|0.041|-0.096|
||(0.03)|(0.10)|(0.11)|(0.06)|(0.08)|(0.11)|
|Observations|1,094|1,094|1,094|1,076|1,076|1,094|
|N sectors|61|61|61|60|60|61|
|R-squared|0.91|0.93|0.90|0.76|0.74|0.85|
|_Manufacturing Sample_|||||||
|Ofshore _×Post_|-0.112**|-0.808***|-0.214|0.044|0.196*|0.147|
||(0.03)|(0.11)|(0.13)|(0.06)|(0.08)|(0.08)|
|Observations|504|504|504|486|486|504|
|N sectors|28|28|28|27|27|28|
|R-squared|0.83|0.86|0.82|0.79|0.73|0.75|

Note: this table illustrates the results of estimating equation (2) with OLS, for the full sample and manufacturing industries only. The dependent variables are the log of: number of firms, entries, exits, employees, wage bill, and revenue. The specification includes 2-digit industry-year fixed effects. Standard errors are clustered at the industry level.

# **5.1.3 Robustness results for industry-level effects**

We present a battery of tests based on alternative specifications of the industry-level DID. First, Figure A4 presents unweighted results, which appear consistent with the baseline analysis.

Second, we consider an alternative specification of our outcomes. In Figure A5, we show results on the cumulative effects of the reform using the same empirical specification as Garrett et al. (2020), where we define outcomes as relative changes with respect to 2013 (pre-reform) levels and weight observations by the 2013 participation of each sector in total aggregate outcomes. Since this specification shows cumulative, and not period-byperiod, differences between offshore and onshore, the small positive effects on the offshore sector are more pronounced, particularly for the manufacturing sector. They do reinforce the message, nonetheless, that we strongly reject a decrease in economic activity in the 24
<!-- Start of picture text --> Benchmark<br>south Upper tet . south Africa's<br>Affica Middle SBA ithe performance vs.<br>Income Benchmark<br>Deviation of +10% or more from Benclunark<br>Electricity: Average End-User Prices (US cents/k Wh) - . . _, ve<br>(Residential/Non-Energy: Residential) aa 6 6 reeled a<br>Energy: Electric Power Transmission and Distibution Losses<br>(% of total output) 3.0 14.0 19.0 14.0 Outperform<br>ICT: Cost of Phone Call to the US 5 5 OF 335<br>(US cents’/S minutes) a a 7 Masala<br>ICT: Cost of Cellular Local Call 25 5 2 9<br>(US cents/3 off-peak minutes) a . 2 peste<br>Deviation of between -10% and +10%o from Benchmark<br>Enerty: Energy Use Per PPP GDP 4 .<br>(kg of. oil equivalent/1000a PPP dollars, Constant 2000) 257 240 3o4 275 AA ver aage<br>Water:(%7 ofer population)SGAccesstoo TmprovedLmproved WaterWater SourcesSource: 87 03 64 80 Average<br>Water: Households Using Piped or Well Water as Main Source of<br>Drinking Water = (% of households)- .5 a 7. 79 at)Avera<br>ICT: Cellular Teledensity - -<br>(celhilar subscribers/1000" people) 3o4 381 73 29® AA ver aage<br>Deviation of -10% or more from Benchmark<br>Energy: aeaead to Electricity Network 66 87 15 60 Underperform<br>(% of Population)<br>Energy: Households Reporting Access to Electricity 6s 74 33 45 Underperform<br>(%o of households)<br>a Acces to Improved Sanitation 67 86 37 64 Underperform<br>(%o of population)<br>ICT: Mamlines Teledensity<br>(Mainlines subscribers/1O00. people) 107 261 31 213 Undi eafi<br>ICT: Cast of Local Phone Call 15 9 10 Pa Underperform<br>(US cents/3 minutes) ~ . ¥<br>ICT: Phone Faults 48 18 57 37 Underperform<br>(reported faults/100 maimlimes) ¥<br>Te Cite Road Density in Terms of Population 61 02 33 6.7 Underperform<br>(voad-km‘1000 people)<br>Transport: Road Density in Terms of Land EG, 1076 156 841 Underperform<br>(road-kimv/'1000 sq km) a ; ~ ¥<br>Transport: Rail Lines Density in Terms of Land<br>(vail-km’1000 sq km) . 17 31 4 23 Underperform¥<br>Transport: Travel Time to Work in Main Cities 35 20 a4 31 Underperform<br>(minutes/one-way work trip) ~ 7 7 7<br>Transport: Paved Roads a1 57 a5 =n Underperform<br>(% of total roads) 7 - ~~ ~<br><!-- End of picture text --> Note: Data used isfrou the latest observation available between 1997 and 2045, Source: Estache, Antonio and Ana Goicoechea (2005). “A ‘Research’ Database on Infrastructure Economic Performance,” Policy Research Paper No. 3643, Infrastructure Network, Office of the Vice President, June 2005.
could use. The consequence of this smaller sample size is lower statistical power for detecting effects on firm outcomes like sales and profits than originally anticipated.

We conducted a stratified random assignment, where firms were randomly assigned within each of the seven<sup>4</sup> induction batches to one of four treatment groups (described further below) and a control group. Random assignment was done by the authors using Stata and was done privately. This resulted in a sample of approximately 150 firms in each of the five groups (152 insourcing, 150 outsourcing, 152 training, 149 consulting, and 149 control firms).

Table 1 provides pre-specified balance checks on a set of baseline variables, showing balance on observables across our five groups. It also provides context for the types of entrepreneurs and firms in our experiment. The entrepreneurs have an average age of 38 years and 44% are female. They are highly educated, with 87% having completed undergraduate education and 48% having a postgraduate degree. Their firms are heterogeneous in terms of sector, with half of them in light manufacturing (e.g. paint manufacturing, garment making, processed food), and the remainder relatively equally distributed across the other four GEM sectors – 15% in ICT (e.g. software development); 13% in hospitality and tourism (e.g. catering, travel agencies); 12% in entertainment (e.g. TV and film production); and 12% in construction (e.g. architects). These are all relatively young existing enterprises, with an average firm age of 4.3 years. Most firms are formal (86% registered with the Corporate Affairs Commission). They have between 2 and 15 fulltime workers at baseline, with a mean of 4.3 paid staff. Average sales in the past month were US$3,265 – although the standard deviation is twice this (US$6,653) and the 10-90 percentile range is US$246-7,397.<sup>5</sup> At baseline only 4 percent of firms have used an HR service provider in the last 12 months; 34% currently had someone other than the entrepreneur doing their finance/accounting; and 20% currently had someone other than the entrepreneur in charge of their marketing/sales.

# **2.3 Details of the Interventions and Take-up**

Implementation of the program occurred through a project implementation unit set up under the Nigerian Federal Ministry of Industry, Trade and Investment. As such the interventions reflect

> 4 There were seven induction batches from Lagos and Abuja, plus one firm from Abuja that attended an induction workshop held in a different state and was assigned to training. This firm is effectively in its own stratum. 5 We use an exchange rate of US$1 = 365 Naira for exchange rate conversions in this paper.

7
areas, the least wealthy members of society may still be most vulnerable to the negative impacts of air pollution.

We rely on spatially resolved estimates of wealth and PM2 _._ 5 that are constructed by model predictions from observed inputs, particularly from satellite imagery. While both sets of predictions are trained using data collected on the ground for areas in which that data exists, they are useful precisely because data does not exist everywhere. In areas without ground collected data their accuracy cannot be verified. We confirm that our results are robust to using alternative measures of wealth and pollution (see Section SI-1.4) for the areas in which that data exists but these are also the areas where the algorithms would be expected to be most accurate. It is by construction uncertain how accurate the wealth and PM2 _._ 5 predictions are in areas for which we have no ground data. If, for example, wealth is systematically underestimated in less polluted areas, or pollution levels are overestimated in wealthy areas, then our approach would overestimate the degree of progressivity in the pollution-wealth relationship. Conversely, if the wealth data systematically underestimates wealth in polluted areas or pollution is underestimated in poorer areas then our approach will underestimate the progressivity.

Understanding the distribution of ambient air pollution concentrations across wealth levels in LMICs is important for policymakers. The common perception that the least wealthy members of society typically live in the most polluted areas is not consistent with the empirical evidence presented here. Our results indicate the relationship around the world differs from that found in high income countries and varies across countries. The substantial heterogeneity we document highlights that the benefits of reducing ambient air pollution will accrue to different populations in different settings. Finally, our results emphasize that ambient air pollution is a problem in precisely the places that are engines of economic growth in most countries.<sup>24</sup> Failure to address the challenges of ambient air pollution in these settings is likely holding back economic growth.<sup>35</sup> This work leaves open several important questions. In particular, more research is needed to understand if there is a consistent driver of the relationship between wealth and pollution across cities or whether these relationships are explained by idiosyncratic conditions. The answers will inform our understanding of the pollution burden around the world and how cities can be designed to maximize the economic opportunities they offer while minimizing the negative consequences of the pollution they generate.

12
<!-- Start of picture text --> Resids vs. linear pred.<br><!-- End of picture text --> <!-- Start of picture text --> °<br>3 3 é .<br>2 “ é $ : Sak %<br>s 3 S 2 o§ 2 (& “9 6<br>© 4 2 © Gh si SG oh Sees eee<br>BOS “9 Oe ge 0%<br>5 mo 606 o 89%<br>3s ° °<br>2-<br>99 °<br>0.2 -0.1 0.0 0.1 0.2 6 7 8 9 10 11<br>theoretical quantiles linear predictor<br>Histogram of residuals Response vs. Fitted Values<br>=) — Pa<br>@o2° = R ge<br>> o S5<br>2 o £ ® ow”<br>cy © ° ll<br>fos) Fas<br>0.3 -02 -01 00 01 02 03 6 7 8 9 10 11<br>Residuals Fitted Values<br><!-- End of picture text -->
indicators for education level, a rural or urban location, and regional (division) effects. We then take the estimated returns to self-employment, _α_ ˆ _a_<sup>_SE_=1</sup> , for each age _a_ , to compute In this expression, _aret_ denotes the age at the time when a migrant returns home. We evaluate _r_ 4 only on those who were not self-employed before migration. Thus it measures how much the transition into self-employment after return contributes to earnings gains until retirement, which is assumed to be at 60 years of age. In our sample, the majority (65%) of return migrants who were economically active throughout, but were not self-employed or employers prior to migration, do become self-employed after return. Different from other contexts, where returns to experience lead to steep age profiles that would also generate a gain in incomes, Figure A3 shows that income profiles are virtually flat in our setting. Instead, the investment of savings from abroad into creating businesses leads to an average excess income after return as high as 2.2 times the cost of migration. When these long-term returns are taken into account, in addition to the returns over the migration episode itself, temporary migration becomes yet more profitable.

# **4.2 Borrowing for Migration**

A large fraction of migrants finance part or all of their expenses and fees through loans. A distinctive feature of the formal and strongly regulated migration relative to entrepreneurial investment is that migrants have verifiable employment contracts and a higher probability of repaying their loans. Indeed, migrants cannot migrate to the GCC or East Asian countries without an employment contract with a specific employer. As a result, earnings from overseas are easily verifiable by the lender, as opposed to mostly informal earnings from self-employment. Regarding the relative riskiness of migration loans versus business startup loans, we compare the fraction of migrants who were expelled from their overseas employment before they generated enough income to cover migration costs to the fraction of microenterprises that exit their markets before generating enough profits to cover initial investment costs. According to the BRMS data, the share of temporary migrants whose total earnings overseas are lower than the total migration costs they incur is 11.9%. In contrast, the estimated share of Bangladeshi microentrepreneurs who do not generate sufficient profits over the lifespan of their enterprise to cover initial investment costs is 60.2%.<sup>11</sup> Based on this

> 11We use the average annual survival rate of microenterprises from McKenzie and Paffhausen (2019), and average net incomes generated by household enterprises in Bangladesh from the HIES 2016/2017 to compute

17
where αζ is the scaling factor and σ<sup>H</sup> is the elasticity of substitution between present consumption and household savings. ζ and S are derived from the first order condition of maximizing utility under budget constraint, I = ς .p ς + S p. S , as follows: where p ζ and pS are prices of present consumption and savings. I is the full income of households. In the same manner, other demand and price variables in the household model are derived through the different levels of the nested structure shown in Figure 3.

# _2.2.2. Incorporation of the DSM into the model_

The DSM program is incorporated into the model while modeling household behavior. This is because the DSM program considered here refers to electrical appliances in the household sector. It is also possible to include electrical appliances in other sectors, such as manufacturing and service sectors. This could be a further expansion of the study because the end-use demand for electricity in the manufacturing sector differs significantly from the household sector<sup>9</sup> .

Modeling demand side options in a general equilibrium framework is often constrained by data limitations. For example, an analysis of the substitution of incandescent lamps by compact fluorescent lamps would require detailed information on the industries producing these appliances (e.g., labor, capital and material inputs). In other words, we need an input-output table (I/O table) that treats the lamp industry as a separate sector. However, in the existing I/O tables of Thailand, information is available only at an aggregated electrical appliances/machinery industry level. Because of this limitation, we incorporate the DSM

> 9 While most of the electricity demand in the manufacturing sector is for motive power (i.e., electrical motors), household demands for electricity is for lighting, air-conditioning and refrigeration etc.

10
# **Annex II: Works cited**

East African Community. http://www.eac.int/about/overview, accessed June 12, 2017.

PWC (April 2015). East Africa Private Equity & Venture Capital Asssociation (EAVCA) - 2015/2016 Budget Submission. Nairobi, Kenya.

Emerging Markets Private Equity Association (EMPEA) (April 2011). EM PE Annual Fundraising and Investment Review. Washington, DC.

Emerging Markets Private Equity Association (EMPEA) (March 2016). 2015 Annual Fundraising and Investment Review: Private Capital in Emerging Markets. Washington, DC.

Private Equity International (October 28, 2016). AfricInvest to launch evergreen financial sector fund. https://relationshipscience.com/News/story/africinvest-to-launch-evergreen-financial-sector-f8506349?feed=Org|861545&type=-1, accessed June 10, 2017.

African Private Equity and Venture Capital Association (January 16, 2017). AfricInvest and Bpifrance launch French African Fund. http://www.avca-africa.org/newsroom/member-news/2017/africinvest-andbpifrance-launch-french-african-fund/, accessed June 9, 2017.

MEDA website. http://meda.org/fr/about-meda, accessed June 11, 2017.

Holman, Kelly (September 1, 2015). Rise of the high net worth investors. Private Equity International. https://www.privateequityinternational.com/magazine/2015-09-01/rise-of-the-high-net-worth-investor/, accessed June 5, 2017.

Africa Business Central (May 1, 2015) New York pension fund to invest up to $5.4B in Africa. http://www.africanbusinesscentral.com/2015/05/01/new-york-pension-fund-to-invest-up-to-5-4b-inafrica/, accessed June 1, 2017.

Why US-based fund placed a big bet on TransCentury. Business Daily. http://www.businessdailyafrica.com/corporate/Why-US-based-fund-placed-a-big-bet-onTransCentury/539550-3914274-yp255t/index.html, accessed June 9, 2017.

Kenya Insurance Regulatory Authority (November 2015). Guidelines to the Insurance Industry on Management of Investment.

http://www.ira.go.ke/attachments/article/221/Draft%20Investment%20Guidelines.pdf, accessed June 1, 2017.

KPMG and EAVCA (June 2015). Private Equity Industry Survey of East Africa for the period 2007 to 2014.

Business Daily (October 28, 2015). Huduma centres push Kenya up in ease of doing business index. http://www.businessdailyafrica.com/news/It-is-easier-to-do-business-in-Kenya-World-Bank/5395462933230-blmhcx/index.html, accessed June 11, 2017.

Njini, Felix (May 11, 2017). Kenya's Largest Bank Expects Rate-Cap Removal in Second Half. https://www.bloomberg.com/news/articles/2017-05-11/kenya-s-largest-bank-sees-rate-cap-beingremoved-in-second-half, accessed June 11, 2017.

52
# **Figure 16**

<!-- Start of picture text --> United States<br>Federal-wide cocaine seizures<br>(kg.)<br>200000<br>180000<br>160000<br>140000<br>120000<br>100000<br>80000<br>60000<br>40000<br>20000<br>0<br>19891990199119921993199419951996199719981999200020012002200320042005<br><!-- End of picture text --> Source: ONDCP and DEA.

60
and re-timing provisions (Column 6) also play a role: each accounts for about 15% of the ETR difference on average. Finally, Column 9 controls for all our explanatory variables at once: 83% of the coefficient on the top 1% dummy is explained but 17% remains unexplained with our method.

This is because we only control for tax incentives with dummy variables, instead of actual amounts. Taking into account the amounts, i.e. fully decomposing the STR-ETR difference into the different drivers, is not possible with our methodology and with the requirement that tax expenditure concepts are harmonized across countries. This is because some tax expenditures are deducted from the tax base while others are deducted from the tax liability, and these concepts interact. For instance, the importance of losses in explaining low ETRs depends on the extent to which a firm used special deductions to lower its tax base. We do not engage in a full accounting decomposition of the ETR, as this would require a country-specific exercise, and distract from our focus on the general ETR-firm-size pattern and its comparison across countries.

**Discussion** We would ideally like to categorize and study tax expenditures by their intended rationale, but doing this transparently with tax return data is challenging: some provisions have generic names, while others have precise names but refer to laws stating multiple objectives. Frequently, line items on tax returns concern several tax provisions and objectives. To try to specify the intent and importance of specific tax provisions, we draw, in addition to the tax return forms, on each country’s tax expenditure reports. Table A.7 lists for each country the availability of each type of provision (based on the tax form): claims for tax credits (the most important tax provision in explaining the ETR drop at the top) and exempt income are available in 8 of the 13 sample countries. The countries where neither is available are the ones with flatter ETR-firm-size profiles: the low tax rate Balkan countries (Albania and Montenegro), and Ethiopia.

The analysis of the names of individual tax provisions yields tentative lessons. Overwhelmingly, the rationale concerns foreign direct investments, job creation and local economic development. These types of tax expenditures are often targeted at firms in special economic zones (SEZs). Indeed, the largest single provision listed in tax expenditure reports in Costa Rica, the Dominican Republic, Ecuador, and Honduras concerns tax credits and income exemptions for firms in SEZs; in Costa Rica, it represents 0.95% of GDP.<sup>18</sup> Tax provisions for firms in SEZ also appear to play a

> 18Not all firms in SEZ are required to file corporate taxes (e.g. Honduras). Thus, some firms might be absent from our dataset altogether. Including these firms in the analysis would likely strengthen our results.

13
|Rainfall stress|Number of 20-day periods with < 40mm of|0.29|-0.90|1.60|6531|
|---|---|---|---|---|---|
||rain|||||
|_Other independent_<br>|_variables_<br>|||||
|Used CSA|Used min till, hybrid maize seed or fertilizer<br>(yes = 1)|0.82|0.00|1.00|6531|
|Accessed credit<br>(yes =1)|Accessed credit from any source|0.18|0.00|1.00|6531|
|Crop diversity|If Simpsons crop diversity index > median<br>(yes =1)|0.45|0.00|1.00|6529|
|Asset index|Asset index from PCA|0.10|-2.56|23.91|6524|
|femalehead|Female head of household (yes = 1)|0.23|0.00|1.00|6531|
|ageh|Age of household head (years)|52.01|19.00|105.00|6531|
|eduhead|Education of household head (years)|6.04|0.00|18.00|6531|
|dep_rat|(Number of members aged < 14 + =>65)/#<br>aged 15 – 64|162.46|0.00|1300.00|6319|
|mem0_5|# of members 0 – 5 years old|0.51|0.00|7.00|6531|
|mem5_10|# of members 5 – 10 years old|0.96|0.00|7.00|6531|
|mem10_15|# of members 10 – 15 years old|1.14|0.00|7.00|6531|
|mem15_64|# of members 15 – 64 years old|3.65|0.00|15.00|6531|
|mem_65|# of members 6 5 years and older|2.08|0.00|23.00|6531|
|farmsize|Farm size (ha)|5.19|0.00|517.10|6520|
|dmarket|Distance to market (km)|24.32|0.00|258.00|6531|
|_Instrumental varia_<br>|_bles_<br>|||||
|CSA-advice|Received advice on aspects of CSA|0.90|0.00|1.00|6531|
|div-advice|Received advice on crop diversity|0.74|0.00|1.00|6531|
|Mem-loan|Member of a loan/creditgroup|0.13|0.00|1.00|6531|

# 5. Results

- 5.1. What climate shocks do farmers face in Zambia and do these vary by locality, household type and socio-economic status?

This is based on the 2019 wave of the RALS survey which includes retrospective questions on whether households experienced any droughts or floods during the 2015/2016, 2016/2017, 2017/2018 and 2018/2019 seasons. Over these four agricultural seasons, droughts were the most prevalent climate shock at the national level. The worst droughts—affecting nearly 63 percent (or 1,041,217) farm households—occurred during the 2018/2019 agricultural season (Tables 2 and S2). The worst floods—affecting about 12 percent or about 202,257 farm households— occurred during the 2017/2018 agricultural season in Zambia (Tables 2 and S1). The Southern and Western Provinces were the worst affected by droughts, with 99 percent and 95 percent of households respectively, reporting to have had at least one field crop affected by droughts during the 2018/2019 season. In terms of gender, 11.3 percent of female-headed households and 12.3 percent of male-headed households experienced floods in the 2018/2019 season (Figure S7). In general, and on average (except for the 2017/2018 season), male household heads are more likely than are female household heads to selfreport exposure to floods and droughts over the other four seasons captured in this study (Table 2 and Figure S7). Households owning farms between 5-10 hectares (ha) were more 12