rafmacalaba/datause-encoder-data huggingface dataset:
# Results

Our results for Turkey, presented in Figure 1. show that there are indeed considerable benefits from uniformity of incentives in export subsidies and the external regime as a whole.

If uniformity is limited to the tariff regime<sup>20</sup> then benefits are obtained but they are relatively modest. Nonetheless, the result verifies the intuition of practitioners: the actual pattern of import taxation departs significantly from Ramsey optimal import taxes.2<sup>1</sup> When uniformity of external incentives is extended to include export subsidies<sup>2</sup> , the benefits of uniformity increase dramatically and exceed one percent of GDP. This is because there is great dispersion in export subsidy rates, with many sectors receiving no incentive **and** others receiving large incentives to export (see Table 1). When we add a uniform VAT and production subsidies23 to uniform external taxation we increase the benefits further, consistent with the fact that all goods are taxable and labor supply is perfectly inelastic in our model.

We demonstrate in the next section that the principal distortion remaining in the external regime of Turkey is the _dispersion_ of the incentives rather than the _level_ of the incentives.

## 3.2 Across-the-Board Liberalization

# Background

A number of developing countnes, including Turkey, have progressively liberalized their import regimes. Quantitative restraints are virtually elimunated and tariffs have been lowered, but some export subsidies remain. Often the sectors with high tariffs are the same ones that have high export subsidies. Although these countries stand ready to reduce tariffs further, in such a situation does it enhance welfare to continue to reduce tariffs across-theboard while leaving export subsidies in place? Conversely, would it be beneficial to increase export subsidies as an offset to the anti-export bias of the tariff, as a second-best measure to reducing the tariff? We first consider the

> 3D The uniform tariff is set at 8.115%, which is the benchmark import-weighted average tariff. It is applied to all sectors, irrespective of their trade status in the benchmark. Vintually identical results obtain if one applies it only to those sectors with benchmnark imports or import tariffs. The same generalization applies to the other unifornity packages considered below.

21 If it did not depart from the Ramsey optimal tax structure then uniformity would, by definition, imply welfare losses.

22 The uniforn level of export subsidies is 7.399%, which is again based on trade-weighted benchmark data.

3 The uniform VAT is 6.520% and the uniform production subsidy is 0.730%.

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manufactured export performance (the source of much dynamism in East Asia’s economies since the 1960s) and the non-tradable sector in the rural economy (which includes a wide array of local agro-processing) to explain subsequent reductions in poverty. This model, drawing on Mellor’s earlier work in India (Mellor, 1976) and more recently in Egypt (Mellor, 2000), explains why countries with substantial agricultural sectors that experienced rapid growth from labor-intensive manufactured exports had such good records of overall economic growth and poverty reduction.

# **Connecting the rural economy to poverty reduction: the Asian experience**

Most of the four Asian country studies for the World Bank’s recent study of propoor growth bear out the importance of agricultural growth for poverty reduction (Besley and Cord, 2006). For example, the Indonesian case study (Timmer, 2006) argues that conscious policy stimulus to agriculture was the key to the country’s 30-year record of rapid, pro-poor growth (from 1967 to 1997), and that the model of smallholder agricultural development used by Indonesia is quite general. The Indonesian model is explicitly set in the broader historical literature on the role of agriculture and economic development that has been generated by successful countries not burdened with highly skewed land distributions as a starting point for their development (Johnston and Mellor, 1961; Hayami and Ruttan, 1985, Timmer, 1988, 2002).

However, the Bangladesh case study (B. Sen, Mujeri and Shahabuddin, 2006) and the Vietnam case study (Klump, 2006) each argue that agriculture played a large and crucial role in poverty reduction, but for highly idiosyncratic reasons based on unique initial conditions and domestic institutions. Thus they argue that agriculture was important to pro-poor growth in both countries, but that the role cannot be generalized to other countries.

In a separate study, Ravallion and Chen (2004) report that nearly all of the remarkable reduction in poverty in China between 1980 and 2001 was the result of agricultural growth specifically and diversified rural economic growth more broadly. They can find very little impact from growth in urban industrial and service sectors on reductions in the headcount poverty index (or the poverty gap or squared poverty gap).

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the relative prices of capital and labor. If labor is to some degree substitutable with ICT capital (e.g., Autor and Dorn, 2013; Karabarbounis and Neiman, 2014), then low-income countries will likely opt for lower ICT capital stocks, as labor is relatively cheaper in these countries.

While these mechanisms are plausible, we find evidence in support of a much simpler explanation based on cross-country differences in industrial composition. Heterogeneity in industrial composition may imply that production in developing countries is relatively less intensive in ICT capital. For example, if agriculture has a larger share in output in developing countries and agriculture is inherently less ICT-intensive than other industries, one would expect there to be relatively less ICT in developing countries. While the first two explanations would imply that, in a given industry, production in developing countries relies less on ICT, our findings suggest that, after controlling for industrial composition, the strong association between income per capita and relative ICT abundance breaks down.

To illustrate this point, we use a simple theoretical framework in which ICT and non-ICT capital intensities differ by industry. Since we are interested in gauging the degree to which industrial composition alone can explain differences in ICT abundance, we assume that sector specific ICT intensities are the same across countries and calibrate these based on data from the US. We then use sector-specific value added for a wide range of countries to predict the aggregate ICT capital share. The exercise suggests that after accounting for the portion of relative ICT abundance that is predicted by cross-country heterogeneity in industrial composition, there is no longer a systematic relationship between GDP per capita and the share of ICT out of total capital. This implies that differences in specialization can fully account for the strong correlation between income per capita and relative ICT abundance.

As an additional piece of evidence for this result we further document that there is no systematic relationship between a proxy for ICT abundance and income per capita _within_ industries. More precisely, while it is well documented that capital labor ratios within the same industry vary widely across countries (e.g., Davis and Weinstein, 2001), we do not find a systematic relationship between income per capita and ICT spending as a fraction of the capital stock.

In sum, our results suggest that the variation across countries in ICT abundance is predomi3
efficiency or sales. This is not only unrealistic, but also would have invalidated the moment conditions needed to identify the coefficients of the production function, as the productivity shock would not have been orthogonal to factor choices. In other words, if TFPR is a function of digitization, the PF elasticities will be biased. The sign of the bias is ambiguous, depending on whether digitization is factor-augmenting or factor-saving. If business digitization is factor-augmenting, then TFPR would be underestimated. By contrast, if TFPR is factor-saving, TFPR will be overestimated.

There are important reasons to make TFPR a function of business digitization. Using email to connect with clients and suppliers or having a business website to gain online presence can affect TFPR through various channels. On the demand-side of the market for an enterprise’s goods and services, reductions in search and transaction costs affect firm profitability at the extensive and intensive margins, by facilitating access to new clients or expanding the volume of transactions online. Dynamically, the scale-up of the demand for a firm’s products or services increases profits, allowing it to pay the fixed cost of investing in TFPR-enhancing activities like innovation, managerial upgrading, or technology adoption (Bustos 2011). On the supply-side, using email to connect with suppliers helps improve production efficiency, by enlarging the potential set of input providers in non-relationship specific investments. Alternatively, it reduces the number of suppliers in relationship-specific investments but enlarges the fraction of repeated interactions, thus addressing contract incompleteness and guaranteeing access to specific assets needed to produce more sophisticated goods (Aral, Bakos, and Brynjolfsson 2018). Because adoption of digital business solutions is not exogenous, we lagged the corresponding variables used to estimate their effects on TFPR.

Since WBES data are not census data, a key question is whether to do weighted estimations by using sampling weights to estimate the coefficients of the production function and TFPR. Following Cameron and Trivedi (2005), sampling schemes such as stratification lead to the conditional density of any variable in the sample differing from that in the population.

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nicipalities, but also small and remote municipalities with no connections. These functional territories seem to be consistent with the LLM assumption that local trade and technological shocks do not spill-over to other areas through labor migration (see Table 20).

**Other data** : We use data from EUKLEMS on adoption of Information Technology (IT) and Communication Technology (CT) by sector and time for the US to estimate a measure of exposure of Mexican LLMs to such technologies in the US, for a robustness test. We use data on the degree of offshorability and routine task intensity of Mexican occupations from Mahutga et al. (2018). We use data the susceptibility of automation of occupations from Artuc et al (2018). Data on fixed assets, machinery and value added per worker by LLM come from the publicly available tabulates of the Mexican Economic Censuses for 2003 and 2013.

# **4.2 Descriptive Statistics**

Figure 1 shows the evolution of the stock of robots per thousand workers. Automation in the US, Europe and Mexico increased almost every year since they entered the sample. According to this measure, automation in Europe is almost twice the level of the US. Accordingly, Mexico’s automation is almost half that of the US. However, it increased at a fast pace since 2011 narrowing the initial gap. In contrast, automation in Brazil is much lower and did not experience significant changes since 2004.

According to Figure 2 and Figure 3, automation has been primarily driven by the automotive sector, both in Mexico and the US. Other leading sectors include computers, plastics, basic metals, pharmaceuticals and machinery. Sectors that adopted robots at a higher pace in the US and Mexico also experienced a higher increase in exports from Mexico to the US, although the correlation is rather weak (Figure 2 and Figure 4).

There is substantial variation across regions in terms of exposure to US and local automation (see Figure 6). As expected, when weighting by exports, exposure to US and local automation are highly correlated (Figure 5). Such correlation becomes substantially weaker 13
them to improve their well-being in the long-term. These patterns of diversification are also consistent with long-term TUP impacts in more stable settings: in Bangladesh 7 and 11 years after the asset transfer (Bandiera et al., 2017; Balboni et al., 2021), as well as in India 10 years after the asset transfer (Banerjee et al., 2021). However, the type and dynamics of diversification depend on the context. Early in the program, all treatment households are able to take on higher-return activities, mostly through an increase in labor participation of the household members with idle time, particularly women. While livestock earnings continue to be significantly higher for treatment households than control by year 7 both in Bangladesh and India, this pattern changes by year 10 in India, when the main source of earnings shifts away from livestock towards wage income, including remittances, due to the migration of household members to areas with better economic conditions (Banerjee et al., 2021). In Bangladesh, diversification into livestock and agricultural businesses continues in the longer term. It is important to keep in mind that both Bangladesh and India did not experience deep economic or system-wide crises during the evaluation period. On the contrary, India’s 10 years of the study period correspond to a rapid expansion in economic growth, wages and income.<sup>34</sup> These differences, highlight the potential role of local conditions for the patterns of diversification, but also emphasize the important role of diversification regardless of the local conditions.

These results support the ability of the TUP to create longer-term sustained impacts in Afghanistan, one of the most fragile settings in the world, including through diversification within livestock as well as non-livestock activities and income sources, consistent with previous results from more stable settings.

# **7 Benchmarking TUP Households’ Socioeconomic Conditions against Those of Non-Ultra-Poor Households**

How do the improvements in UP treatment households’ outcomes influence their economic and social standing relative to the rest of the population in their communities? To answer this question, we leverage data collected at baseline, midline and endline on a random representative sample of non-UP households (excluding the census of UP that was randomized into treatment and control) across the 80 villages in the study. Bedoya et al. (2019) show that (i) all households, including non-UP, are vulnerable; and (ii) UP households participating in this study were considerably worse off than non-UP households at baseline across all dimensions studied, including consumption, poverty, food security, assets, and psychological well-being. Here, we extend this analysis by exploring where UP treatment households have been able to catch up with non-UP households across multiple socioeconomic dimensions. Table 10 shows how socioeconomic gaps

> 34The control group doubled their consumption between baseline and the 10-year follow-up, while earnings multiplied by 3 between the 18-month and the 10-year follow-ups (Banerjee et al., 2021).

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impacts of the huge investments taking place in developing modern biofuels are not well understood, and hence more controversial, whereas several prominent works on traditional biomass already exist (Smith 1987 and 2003; Ravindranath and Hall 1995; Barnes and Floor 1996; and Bailis, Ezzati and Kammen 2005).

The rest of the chapter is organized as follows. Section 2 provides some basic statistics on global energy use and the share of biofuels. Section 3 provides a historical perspective on biofuel use. Section 4 describes the various biofuel technologies in use today. Section 5 summarizes the findings of several studies that estimate the future potential of biofuels. Section 6 describes cutting edge research in biofuel technologies. Section 7 concludes the chapter.

# **1.3 Global energy situation and the share of bioenergy**

The global energy production in 2004 was about 440 quadrillion Btu<sup>2</sup> (11000 mtoe<sup>3</sup> ) (EIA 2007) (figure 1). In terms of end-use consumption, transportation, and electricity accounted for 21% and 30%, respectively (Watson, Xinyowera, and Moss 1996). In terms of sources of energy, about 80% of the supply was comprised of crude oil, coal, and natural gas while the contribution of renewable energy sources was about 13% (figure 2). In terms of the sources of renewable energy, about 80% of the supply was comprised of combustible renewables like wood, dung, charcoal, and agricultural wastes while hydro, wind, solar, tidal, and geothermal contributed the rest (figure 2). Combustible renewables and waste are consumed mainly in non-OECD countries while hydro and other modern renewables are consumed largely in OECD countries (Figure 3). Overall Africa, nonOECD (Organization for Economic and Development) Asia, and China combined for 67% of the global renewable energy (figure 4). We can also infer that renewable energy in developing countries is comprised almost entirely of traditional biomass where as in the developed countries it is comprised largely of modern renewables like solar, wind, and hydro (figure 3 and 4). From an end-use energy perspective, 58% of the renewable energy is consumed by the residential, commercial, and public sector (figure 5). We can

> 2 Btu –British thermal unit 3 mtoe – million tonnes of oil equivalent

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and related welfare measures (such as inequality measures). Furthermore, more validation of this method is needed.<sup>26</sup> We return to more discussion with the illustrative example below.

# _Illustrative Example 3_

We start with checking Assumptions IV.1 and IV.2 before discussing estimation results. Since all the 2010, 2012, and 2014 rounds of the VHLSS share the same sampling frame based on the 2009 Population and Housing Census, and their questionnaire design remains almost identical,<sup>27</sup> Assumption IV.1 for a similar survey design is satisfied. We can test for Assumption IV.2 for the period 2010-2012 using the decomposition provided in Equation (10).

We start first with building the estimation models on a cumulative basis for illustration purposes, with later models sequentially adding more variables to earlier models. Model 1 is the most parsimonious model and consists of household size, household heads’ age, gender, highest completed levels of schooling, a dummy variable indicating whether the head belongs to the ethnic majority group, and a dummy variable indicating urban residence. Model 2 adds to Model 1 the household demographics such as the shares of household members in the age ranges 0-14, 15-24, and 25-59 (with the reference group being those 60 years old and older), a dummy variable indicating whether the head worked in the past 12 months. Model 3 adds to Model 2 asset variables, the construction materials for the house’s roof and walls, and the type of water and toilet the household has access to, which are the same as those employed in our earlier analysis of wealth indexes. Full model specifications are provided in Appendix 3, Table 3.2.

> 26 This practically implies that SWIFT has to rely more heavily on the assumption of constant parameters than most other previous studies. As yet, there appear to be no published validation studies besides those offered in Yoshida _et al._ (2015).

> 27 The VHLSSs have a rotating module that collect more detailed data on certain topic in each survey round. For example, the 2014 VHLSS collects more data on land access and ownership. But more importantly for our purposes, the core modules that collect data on household demographics, education, assets, and house materials remain the same over these survey rounds.

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possibility by performing additional estimations in which we control for alternative measures of culture.

First, Global Findex reveals that religious belief is one of the top reasons for voluntary financial exclusion, with 6 percent of respondents citing it. By the same token, Demirgüc-Kunt et al. (2014) demonstrate that Muslims are significantly less likely than non-Muslims to own a formal account or save at a formal financial institution, mostly due to the fact that conventional financial products violate the Sharia. Moreover, evidence shows that religion-based financial exclusion is disproportionately more prominent among women (Cicchiello et al., 2021). As our sample does not allow us to control for respondent’s religion, we consider the CIA World Factbook country-level data to create four dummy variables ( _Catholic, Protestant, Muslim and Buddhist_ ), each of which takes the value 1 if the religion represents more than 50% of the population in the country (highest percentage of practicing population) and 0 otherwise. We drop _Buddhist_ to avoid perfect collinearity.

Second, Lu et al. (2021) suggest that a country’s cultural attributes – particularly its level of individualism – are linked to its inhabitants’ financial inclusion. Overall, the effect of culture on the decision-making process of individuals has been widely investigated in the economics and finance literature (Guiso et al., 2006). Therefore, we consider additional cultural dimensions using the six traditional indices provided by Geert Hofstede, namely _Individualism_ , _Power distance_ , _Masculinity, Long-term orientation, Indulgence_ and _Uncertainty avoidance_ . Variables are described in detail in **Appendix A** .

The reason why additional cultural measures and religion where not included in the main estimations is that they may potentially introduce multicollinearity in our model. Indeed, culture has been shown to exert an influence on the development of laws, especially those related to gender (Bu, 2015).

**Table 6** reports the results of the regressions with these additional control variables included. We first add them separately in Columns (1) and (2) and then jointly in Column (3). Interestingly, we find that while the estimated coefficient of _Masculinity_ is significantly negative, the estimated coefficients of other Hofstede cultural dimensions are insignificant. With regard to religion, we observe that the coefficients of the interaction terms of interest remain positive and significant. We observe that Muslim countries are associated with lower financial inclusion in line with Demirgüc-Kunt et al. (2014). More important for our purpose, the inclusion of these additional variables does not change the interpretation of our results, as

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