rafmacalaba/datause-encoder-data huggingface dataset:
# **4.2 Data**

## **4.2.1 Surveys**

_Household phone surveys._ The woman in the household with the most knowledge and decision power, or the primary woman, completed the endline phone survey. Four short rounds of surveys, approximately 30 minutes each, were conducted between 3 and 7 weeks apart to accommodate for the limited duration of a phone survey.<sup>12</sup> Some outcomes were measured in more than one phone survey round, while others were measured only once (Table A1 in the Online Appendix). The baseline and midline surveys were conducted in person with the primary woman (lasting 2 hours approximately) and primary man (lasting 45 minutes approximately).<sup>13</sup> _Market surveys._ A market-level survey was conducted in four districts encompassing the 80 study villages to collect data on food prices. This was used to calculate consumption values from quantities in the household survey. This method replicates the approach used by the Afghan government to calculate national poverty measures before 2021. Surveyors visited the largest market in each district and collected sales prices for all food items found in the household consumption survey. These data were collected by more than one surveyor, and were administered in parallel with the household surveys.

## **4.2.2 Primary Outcomes**

Here we describe the primary outcome measures at endline, collected at the household- and individuallevel from the primary woman. These are described fully in Online Appendix Text 1, where we include the main differences with midline outcome measures, which are mostly due to the need of adjusting the indicators to fit the time limit and other constraints faced during phone surveys.

1. _Consumption proxy._ A proxy for total per capita consumption is estimated using a subset of food items consumed in the last week, and non-food expenditures in the past month or twelve months, depending on the item.<sup>14</sup> We followed a two-step estimation to select the subset of items that best predicted consumption using baseline and midline data. We use district market prices from the market surveys to estimate the value of food consumption. We report the consumption proxy as an index of real values, where per capita consumption for control UP at endline equals 1. This allows for comparison across

> 12The average interval between phone survey rounds one and two was 50 days, while the average interval between survey rounds three and four was about 20 days.

> 13If there was no primary man in the household, the primary woman would complete both portions of the survey, and vice versa.

> 14Our consumption proxy explains 75% and 59% of the variability in consumption including UP and non-UP at baseline and midline, respectively (see Table A2 in Online Appendix Text 2 for further details).

13
18 effective treaties with either Mauritius or countries where the sum of FDI stocks exceeds 2 times the country’s GDP in 2016.<sup>34</sup> Observations on FDI are more complete than on corporate income tax revenues. Among countries that have concluded a treaty with Mauritius, the average logarithmic inward stock of FDI is at around 0.43, while it is only -0.65 among countries not having concluded such a treaty. Similarly, when not controlling for any other aspects, CIT revenues tend to be larger in countries that have concluded a treaty with Mauritius (-1.83) than those that have not (- 2.57).

|**Table 2. Descriptive Statis**<br> <br>|**tics**<br> <br>|||
|---|---|---|---|
|Variable<br>Observations|<br>Min|Mean|Max|
|Tax variables||||
|Mauritius treaty<br>1219|0|0.15|1|
|Non-mauritius treaty<br>1219|0|2.22|15|
|Hub-treaty<br>1219|0|0.27|3|
|Non-hub treaty<br>1219|0|2.1|13|
|CIT rate (in %)<br>1218|10|35.83|70|
|∑α��,�τ�,�,�<br>�<br>1219|-0.34|-0.08|0.11|
|∑α��,�η�,�,�<br>�<br>1219|-0.59|-0.18|0.02|
|�<br>�<br>∑η�,�,��τ�,�,��τ�,��<br>�<br>1218|0|0|0.01|
|�<br>�<br>∑�τ�,�,��τ�,��<br>�<br>�<br>1218|0|0.01|0.02|
|Key control variables||||
|Log Imports<br>1219|-2.87|0.39|4.51|
|Log Exports<br>1219|-4.59|-0.03|4.63|
|Log Gdp<br>1219|-2.4|1.41|6.34|
|Gdp growth<br>1219|-0.96|0.06|0.78|
|Log Population<br>1214|12.68|15.73|19.04|
|Inflation<br>1209|-1.88|10.76|55.21|
|Exchange rate<br>1207|0.26|376.1|1662.69|
|Dependent variables||||
|Log Fdi<br>1196|-18.42|-0.48|4.55|
|Log Cit<br>632|-9.6|-2.43|2.88|
|Log Fdi with Mauritius DTT<br>182|-2.67|0.43|3.46|
|Log Cit with Mauritius DTT<br>119|-4.02|-1.83|0.14|
|Log Fdi without Mauritius DTT<br>1014|-18.42|-0.65|4.55|
|Log Cit without Mauritius DTT<br>513|-9.6|-2.57|2.88|

Figure 2 summarizes FDI and CIT revenue for countries that concluded a DTT with Mauritius (Treated) and a synthetic control group which is chosen to resemble these countries before treaty-conclusion. Annex 2 provides details on our matching approach. The horizontal axis depicts years relative to the treaty with Mauritius becoming effective (Time 0). The graph suggests that both FDI and CIT have followed similar trends across these groups before treaties with Mauritius became effective, supporting the common-trend assumption. Despite selecting a control group which resembles treated countries, CIT revenue is noticeably smaller among control group members before the intervention, underscoring the importance to test treaty-effects in a difference in difference framework.

34 Malta; Cyprus; Hong Kong SAR, China; Luxembourg; Singapore; Ireland; Switzerland; the Netherlands; Barbados; and the Seychelles.
and Venezuela, Aitken et al. (1996), also fi nd higher wages for foreign fi rms. With competitive labor markets, there is no reason for a positive foreign wage premium unless workers or fi rms differ in some unobservable characteristics. In most of the fi rm level data sets used in the empirical literature, worker level information such as education, experience and tenure of the workforce is not collected. The assumption in this work is that omitting these characteristics does not generate signi fi cant biases in the estimation of the differences between domestic and foreign fi rms. Lipsey and Sjoholm (2003) control for the worker’s education and other worker level characteristics but they still fi nd a foreign wage premium in a cross section of Indonesian fi rms. Conyon et al. (2002) and Lipsey and Sjoholm (2002) try to overcome the problem of the unobservable fi rm characteristics that are correlated to the fi rm nationality by analyzing the impact on average wages of a foreign acquisition. Conyon et al. (2002) fi nd that average wages increase by 3.4 percent following a foreign acquisition but they do not control for any workforce characteristics. Lipsey and Sjoholm (2002) fi nd that average wages of blue and white collar workers increase by 10 percent and 21percent respectively. Both fi nd evidence that part of the cross section wage premium in manufacturing is due to a causal effect of foreign investment on wages. An alternative explanation for the foreign wage premium is based on imperfect labor markets. For example, if foreigners incur in higher search costs they may pay more to workers in order to discourage turnover. If foreign fi rms offer more training, have less power with unions or are more likely to adhere to minimum wages, they may also pay higher wages. Moreover, foreigners could pay higher wages simply because they have higher pro fi tability than domestic fi rms (rent sharing theories<sup>2</sup> ).

In this paper I analyze the Portuguese case which combines two important features. First, Portugal had a permissive legal framework for the operation of foreign fi rms that translated into generous amounts of foreign direct investment (FDI) in the late 1980s and 1990s<sup>3</sup> . Before becoming an European Union member in 1986, the amount of FDI in Portugal never 2Rent sharing theories argue that the labor market is not competitive as empirically wages are correlated with measures of the fi rm or industry “ability to pay”. See e.g. Blanch fl ower et al. (1996) for evidence on the US manufacturing.

> 3The importance of European Union and OECD as sources of FDI is clear: in 1992, 76% of the FDI came from the EU and 89% from OECD countries.

3
# **Dependent Variable: Sensitivity analysis: LN Total cost of tax compliance (-10% across the board)**

||Coeff.|Std. Error|Sig.|
|---|---|---|---|
|(Constant)|6.510|0.297|0.0%|
|LN full-time employees|0.128|0.030|0.0%|
|LN of q55 after imputed values were introduced (imputed "no answer")|0.166|0.023|0.0%|
|Share of activities completely outsourced|0.285|0.086|0.1%|
|Share of activities partially outsourced|0.730|0.103|0.0%|
|Guateng provincial dummy|0.183|0.067|0.6%|
|Western Cape provincial dummy|-0.148|0.078|5.8%|
|sole proprietor dummy|-0.323|0.094|0.1%|
|close corporation dummy|-0.162|0.072|2.4%|
|Manufacturing|-0.184|0.089|3.8%|
|VAT|0.204|0.114|7.4%|
|PAYE|0.271|0.099|0.6%|
|Adj. R2|0.295|||
|Mean of the dependent variable|9.626|||

**Dependent Variable: Sensitivity analysis: LN Total cost of tax compliance (-20% across the board)**

||Coeff.|Std. Error|Sig.|
|---|---|---|---|
|(Constant)|6.386|0.298|0.0%|
|LN full-time employees|0.128|0.030|0.0%|
|LN of q55 after imputed values were introduced (imputed "no answer")|0.166|0.023|0.0%|
|Share of activities completely outsourced|0.386|0.086|0.0%|
|Share of activities partially outsourced|0.787|0.103|0.0%|
|Guateng provincial dummy|0.185|0.067|0.6%|
|Western Cape provincial dummy|-0.146|0.078|6.3%|
|sole proprietor dummy|-0.326|0.094|0.1%|
|close corporation dummy|-0.166|0.072|2.1%|
|Manufacturing|-0.181|0.089|4.2%|
|VAT|0.210|0.114|6.6%|
|PAYE|0.265|0.099|0.8%|
|Adj. R2|0.298|||
|Mean of the dependent variable|9.545|||

**Dependent Variable: Sensitivity analysis: LN Total cost of tax compliance (-30% across the board)**

||Coeff.|Std. Error|Sig.|
|---|---|---|---|
|(Constant)|6.245|0.299|0.0%|
|LN full-time employees|0.129|0.030|0.0%|
|LN of q55 after imputed values were introduced (imputed "no answer")|0.167|0.023|0.0%|
|Share of activities completely outsourced|0.502|0.087|0.0%|
|Share of activities partially outsourced|0.855|0.104|0.0%|
|Guateng provincial dummy|0.187|0.067|0.5%|
|Western Cape provincial dummy|-0.144|0.079|6.8%|
|sole proprietor dummy|-0.329|0.095|0.1%|
|close corporation dummy|-0.171|0.072|1.8%|
|Manufacturing|-0.178|0.089|4.6%|

39
|**Table 7. Co**<br>_2010-2014_|**mpa**|**rative Statistics, Pr**|**oduct Conc**|**entration**|||
|---|---|---|---|---|---|---|
||||SSA<br>Leader|World Excl.<br>SSA(75th<br>percentile)|MIC<br>Leader|LIC<br>Leader|
||7|SSA Average|2.71%|2.42%|3.47%|2.37%|
|Gth|14|SSA oil RR|2.42%|3.89%|4.94%|3.85%|
|row|0.0|SSA non-oil RR|3.47%|3.60%|4.66%|3.56%|
||-|SSA non-RR|2.37%|2.02%|3.07%|1.97%|
|||SSA Average|2.76%|2.46%|3.54%|2.42%|
|Citl|015|SSA oil RR|4.26%|3.97%|5.04%|3.93%|
|apa|-0.|SSA non-oil RR|3.97%|3.68%|4.75%|3.64%|
|||SSA non-RR|2.35%|2.06%|3.13%|2.02%|
||8|SSA Average|1.84%|1.64%|2.35%|1.61%|
|TFP|099|SSA oil RR|2.84%|2.64%|3.35%|2.61%|
||.0|SSA non-oil RR|2.64%|2.45%|3.16%|2.42%|
||-0|SSA non-RR|1.57%|1.37%|2.08%|1.34%|

_NOTE: The estimations are calculated based on the results from regressions in Table 5 Columns [4] [5] and [6]._ Table 8 presents the potential increase in economic growth, physical capital growth and TFP growth if the gap in export dependence on natural resources gap were to close relative to the four benchmarks mentioned earlier. If the Sub-Saharan Africa region were to match the regional leader’s share of natural resources exports in their overall export basket, growth would accelerate 2.81 percentage points, capital accumulation would be enhanced by 2.26 percentage points and TFP growth would rise by 0.97 percentage points. If we look at the potential gains of the different SSA groups relative to the regional leader, the oil resource rich countries would benefit the most from closing the gap with the benchmarks in terms of growth, capital accumulation, and TFP growth. Furthermore, narrowing the gap in terms of reducing the dependence on natural resources from the SSA average relative to the middle-income leader, which yields the higher benefits than those acquired from the SSA leader, would result in analogous potential growth gains. That is, about 3.9 percentage points per year of growth, 3.14 percentage points of physical capital growth and 1.34 percentage points of TFP growth.

_page 28_
# Table 3. Share of Adults per Household

||Ave<br>|rage of sa<br>|mple<br>|Poor(un<br>|der US$1.<br>|25 a day)<br>|Poor<br>|(under U<br>day)<br>|S$2.5 a<br>|
|---|---|---|---|---|---|---|---|---|---|
||Initial<br>period|Final<br>period|Annual<br>change|Initial<br>period|Final<br>period|Annual<br>change|Initial<br>period|Final<br>period|Annual<br>change|
|**B. Income-Based Poverty**||||||||||
|Argentina,2000–10|72.3|76.2|0.5%|51.5|62.5|2.0%|53.5|57.7|0.8%|
|Brazil,2001–09|71.3|75.7|0.8%|50.9|56.8|1.4%|55.0|55.7|0.2%|
|Chile,2000–09|72.6|78.0|0.8%|57.5|68.4|1.9%|58.8|65.6|1.2%|
|Colombia,2002–10|68.0|71.1|0.6%|62.1|59.6|-0.5%|61.4|59.1|-0.5%|
|Costa Rica,2000–08|67.1|73.3|1.1%|58.1|64.3|1.3%|56.3|60.0|0.8%|
|Ecuador,2003–10|66.2|72.1|1.2%|56.4|61.5|1.2%|56.8|60.4|0.9%|
|Honduras,1999–2009|57.2|63.5|1.1%|48.7|53.5|0.9%|50.3|55.3|1.0%|
|Panama,2001–09|68.1|70.0|0.3%|53.7|52.2|-0.3%|56.0|54.0|-0.5%|
|Paraguay,1999–2010|59.8|68.2|1.2%|46.3|57.2|1.9%|48.6|56.6|1.4%|
|**B. Consumption-Based Pov**<br>|**erty**<br>|||||||||
|Bangladesh,2000–05|60.4|65.3|0.8%|55.4|58.4|0.5%|58.9|63.6|0.8%|
|Ghana,1998–2005|56.1|60.1|1.0%|49.5|51.3|0.5%|52.3|54.2|0.5%|
|Moldova,2001–10|78.8|81.8|0.4%|73.1|68.1|-0.8%|76.4|73.5|-0.4%|
|Nepal,1996–2003|57.9|61.1|0.8%|55.1|55.0|0.0%|57.0|58.6|0.4%|
|Peru,2004–10|68.3|69.2|0.2%|51.5|51.2|-0.1%|55.0|50.8|-1.3%|
|Romania,2001–09|82.2|84.8|0.4%|60.9|||71.9|67.1|-0.9%|
|Thailand,2000–09|74.2|77.8|0.5%|56.9|53.6|-0.7%|61.4|65.6|0.7%|

<mark>Source: Author’s calculations with data from SEDLAC (CEDLAS and the World Bank), RIGA, and National Household Surveys.</mark> 27
<!-- Start of picture text --> Figure 3: Increasing Patenting in Specific Technological Fields, varying by Developing Regions<br>High Income Countries EAP<br>3000 20<br>18 1991-2000<br>2500<br>16<br>2000 14 2001-2010<br>12<br>1500<br>10<br>1000 8<br>6<br>500<br>4<br>0 2<br>0<br>S Asia ECA<br>8<br>8<br>7<br>7<br>6<br>6<br>5<br>5<br>4 4<br>3 3<br>2 2<br>1 1<br>0 0<br>Source: USPTO patent grants in green technology areas<br><!-- End of picture text --> Figure 3 highlights that most developing regions ̳cluster‘ in specific green technologies, with the clusters varying by region.<sup>7</sup> In general, wind, solar and fuel injection for engines are major patenting areas common to both high-income and developing countries. But the EAP region is specialized in wind, lighting, solar, geothermal, ocean energy and waste-to-energy technologies, with no or almost no patenting in carbon capture and sequestration, fuel injection, methane destruction, biomass and hydropower. Developing countries have also moved into new technology areas in the last decade. For example, fuel injection, biomass and cement are now major technology areas for S Asia whereas they had no patents in these areas before 2000. Similarly, lighting is a major new area in EAP, and lighting, fuel injection and waste-to-energy for ECA.

> 7 We omit LAC, SSA and MENA regions due to their low levels of green patents.

6
Table 6: Determinants of probability of transition into the high-productivity convergence Club 1: Logit model marginal effects – covariates average value, 1990-2000

||_EMDE m_|_Dependent_<br>_embership o_|_variable:_<br>_f convergen_|_ce club 1_||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Years of education|0.07**|0.06**|0.05*|0.05*|0.05**|
|Economic complexity index|0.29***|0.29***|0.27***|0.26***|0.25***|
|Initial productivity|-0.18***|-0.18***|-0.19***|-0.16***|-0.17***|
|Trade openness|||-0.10|||
|WGI: Government efectiveness|||0.12***||0.06|
|Investment in percent of GDP||||1.87**|1.76*|
|FDI in percent of GDP|||0.05*|0.06**|0.05*|
|Observations (All)|54|54|54|54|54|
|Pseudo R<sup>2</sup>|0.32|0.33|0.41|0.42|0.46|
|_Note:_|||_∗_p_<_0.1|; _∗∗_p_<_0.05;|_∗∗∗_p_<_0.01|

Note: Marginal effects of a one unit increase in each variable on the probability of an EMDE joining convergence ‘Club 1’ relative to other EMDEs. Derived from a logit model, with standard errors calculated using the delta-method. Average years of schooling for males and females from Barro and Lee (2015). Economic complexity index of Hidalgo and Hausmann (2009). Exports and imports as a percent of GDP. Government effectiveness survey from the World Bank’s Worldwide Governance indicators, defined as: perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies. Gross fixed capital formation and FDI are measured in percent of GDP.

# **F Productivity measurement: PPP versus market exchange rates**

In the analysis of productivity growth and differentials in this chapter, cross-country comparisons are made using productivity measured in dollars at 2010 prices and exchange rates. Often, studies of convergence have used cross-country comparisons of income per capita calculated at purchasingpower parity (PPP). This box addresses the question of how PPP and market-exchange rate measurement of productivity differs conceptually, and how the different measurements affect statistical tests of convergence.

Purchasing power parity calculates the rate at which the currency of one country would have to be converted into another to buy the same assortment of goods and services. Market exchange rates are the rates at which goods and services are actually traded in international markets. Since PPP reflects that goods and services that are not traded internationally tend to be cheaper in lowerincome countries, the purchasing power of lower-income country currencies tends to be higher at PPP exchange rates than at market exchange rates.

Transactions in global trade, financial markets, and commodity markets are all conducted at market exchange rates; hence, for aggregating output, market exchange rates (as used by the 38
**Figure 22. Tertiary Education, Age Group 30–34, 2010**

**Figure 23. Projections of Income per Capita, 2050: Benchmark Scenario and EU11 Human Capital Investment Improvement Scenario**

<!-- Start of picture text --> 2.5<br>Tertiary education attainment, % of pop. aged 30- 2050, benchmark<br>34 2050, human capital improvement in EU11<br>40 2.0<br>35<br>30 1.5<br>25<br>20 1.0<br>15<br>10 0.5<br>5<br>0 0.0<br>EU15 North EU15 EU15 South EU11 SEE Eastern 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3<br>Continental Partnership<br>Income per capita in EU11, proportion of EU15<br>Density<br><!-- End of picture text --> _Source:_ International Institute for Applied _Source:_ Crespo Cuaresma, Havettová and Lábaj (2012); World Systems Analysis (IIASA) and Vienna Institute Bank staff calculations of Demography (VID)

**Persistent sizable differences in participation rates by educational attainment can be seen in the EU (Figure 22), and are significantly larger for women.** Currently, the share of EU11 population aged 30-34 with tertiary education is about 21 percent, compared to more than 30 percent in EU15-Continental and EU15-North. Further educational expansion at the tertiary level may to some extent be effective in attenuating the detrimental effects of population aging. The average for individuals with tertiary education in EU11 also hides considerable differences between countries in the region. Certain economies might thus earn significant income growth dividends from using investments in human capital to increase participation.

**EU11 investments in human capital are also expected to significantly accelerate the income convergence process.** In order to quantify their effect on convergence, the previous income projection exercise was repeated assuming that EU11 invests more than EU15 in human capital.<sup>20</sup> The results (Figure 23) indicate that EU11 countries have ample room to speed up income convergence significantly by investing in skills formation. Based on both the benchmark scenario and a scenario implying that EU11 invests more in human capital, the distributions of relative income per capita in 2050 for EU11 compared to EU15 indicate that a virtuous interplay of increased labor market participation and higher income growth is likely to result from further human capital investments. This is particularly true for countries that have larger gaps with EU15 in terms of educational

> 20 Following Crespo Cuaresma, Havettová and Lábaj (2012), we assume that this share will grow at a speed estimated from the historical experience of countries at similar levels of educational attainment while tertiary educational attainment rates in the rest of Europe remain constant. That scenario implies that EU11 would over time build up a positive differential in human capital accumulation with respect to the rest of the EU that would materialize in a difference of 8 percentage points in the tertiary education attainment rate by 2050.

22
- **Government effectiveness** : this variable captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies. Like other institutional variables, it is expected to be positively correlated with our SME financial inclusion index.

- **Control of corruption** : this captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as "capture" of the state by elites and private interests. Therefore, we expect a positive sign for this variable.

_Business environment_

- **Credit information** : this variable is proxied by the depth of credit information index, which measures rules and practices affecting the coverage, scope and accessibility of credit information available through either a credit bureau or a credit registry. The index ranges from 0 to 8, with higher values indicating the availability of more credit information to facilitate lending decisions. Given that reducing information asymmetries between lenders and borrowers is a critical step for improving access to finance by SMEs, we expect this variable to enter our model with a positive sign.

- **Rule of law** : this variable measures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts. These factors contribute to a stable and conducive business environment, facilitating transfer and collateralization of assets, ultimately easing access to finance constraints for SMEs. This variable should enter our model with a positive sign.

- **Tax burden** : this is proxied by a composite measure that reflects marginal tax rates on both personal and corporate income and the overall level of taxation (including direct and indirect taxes imposed by all levels of government) as a share of GDP. A high level of taxation can hamper private investment, increase incentives to informality, and negatively affect access and usage of finance by SMEs. We therefore expect this variable to be negatively correlated with SME financial inclusion.

- **Regulatory efficiency** : this variable captures the extent to which a country’s regulatory and infrastructure environments constrain the efficient operation of businesses. A quantitative score on a 0-100 scale (with 100 indicating the freest business environment) is derived from an array of factors that affect the ease of starting, operating, and closing a business such as access to electricity, business environment risk, regulatory quality, and women’s economic inclusion. This variable is expected to be positively correlated with our dependent variable.

10