rafmacalaba/datause-encoder-data huggingface dataset:
For urban areas, energy consumption as a share of total expenditure, represented by the line in the graph, accounts for about 9% in the lower deciles but above 10% for the three higher deciles. For rural households, it is about 8% in the lower deciles, and reaches about 12% in the top decile.

**Figure 2**

<!-- Start of picture text --> Energy consumption (KgOE) and share of energy on expenditure<br>(urban)<br>100% 13.0%<br>12.0%<br>80% Natural Gas<br>11.0%<br>60% LPG<br>10.0%<br>40% Electricity<br>9.0%<br>Gasoline<br>20%<br>8.0%<br>Energy %<br>0% 7.0%<br>0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1<br>Deciles<br>% energy expenditure Energy / total expenditure<br><!-- End of picture text --> Source: own calculations using ENIGH 2010 In urban areas, the lower deciles consume more units of electricity, followed by LPG, gasoline, and natural gas, while the upper income levels consume more units of gasoline, followed by electricity, LPG, and natural gas. In rural areas, households in lower deciles consume more units of electricity, LPG, and gasoline, while upper income households use more units of gasoline, electricity, and LPG, in that order.<sup>4</sup> Since the KgOE measure allows for comparison of the same unit but for different energy consumption, Figures 4 and 5 show the units that average households in each decile consume in urban and rural areas.

> 4 Indirect consumption of gasoline or diesel through public services is not addressed here, since the payment is for the service (transportation, for example) and not for the raw material (gasoline).

13
Figure 4. Initial Poverty Levels (2006) and Poverty Reduction (2006-2013) <!-- Start of picture text --> TRA2<br>TRB1<br>TRA1<br>TR10<br>TR41<br>TR42<br>TRB2<br>TR90<br>TR51 TR72<br>TR52 TR71<br>TR33 TR81<br>TR21 TR82<br>TR32<br>TR31 TR22<br>TR63 TRC3 TRC2<br>TR83<br>TR62<br>TR61<br>TRC1<br>2 2.5 3 3.5 4<br>Log initial poverty rate ($5/day PPP)<br> Low Initial Poverty High Initial Poverty<br>5<br>0<br>-5<br>-10<br> Declining Poverty Increasing Poverty Annualized growth in the poverty rate 2006-2013 ($5/day PPP)<br><!-- End of picture text --> _Notes_ : Initial poverty rates are in 2006. Poverty line is $5/day PPP per capita, OLS imputed income poverty, spatially deflated. _Source_ : LFS, SILC 2006-2013; authors’ calculations.

# **<u>Poverty Gap</u>**

Fluctuations in Turkey’s poverty rate explain only a portion of the entire poverty story as the headcount rate shows only the number of people living under a certain amount of income or consumption level. Supporting this headcount rate with the poverty gap index is valuable for obtaining a more complete picture of poverty in Turkey. The poverty gap measures the depth of poverty by taking into account the distance between the poverty line and the observed level of income/consumption of the poor. As the poverty gap increases, people under the poverty line have to increase their income/consumption at proportionally higher rate in order to escape poverty.

The relationship between moderate poverty rate and poverty gap is analogous to the previously explained trend between moderate and extreme poverty rates (Figure 17). The moderate poverty gap and headcount ratio both increase with the impact of the financial crisis but recover in the immediate aftermath. However, after 2011, the poverty gap starts to increase despite a continued decline in the headcount ratio. This divergence, which can also be seen between moderate and extreme poverty, suggests that the distance between the poverty line and people who are in moderate poverty is increasing despite a fall in the ratio of people living under $5 PPP a day. As a result, there is a net increase in the moderate poverty gap between 2006 and 2013 despite a simultaneous poverty reduction.

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average for the earlier cohorts and for entrepreneurs with primary education, it has been small even for the well-educated recent firm founders.

# 3 l The Garments Industry and ':s Exporters

Colombia's gannents sector has shown periodic bursts of dynamism, closely tied to its export performance. Output grew rapidly during the 1970s, stagnated in the early 1980s, and did well again over 1984-90. The trajectory of factory employment has been rather different after rising by about 70 percent from 1971 to 1980, it fluctuated with no net increase over the 1980s. Labour productivity increases made a major contribution to output growth during both the 1970s and the 1980s. Although it was somewhat faster in the former than in the latter (39 percent between 1973-80 vs. 29 percent between 1980-1987), in this recent period it has accounted for all of the output growth that did occur in the factory sector.

As in other countries, there is a heavy representation of both small and medium factories and of very small, family-sized establishmentsY The production of garments is a low-wage, labour-intensive process. In 1990 average remuneration was only about half that of manufacurirng as a whole. About 80 percent of garment workers were women in 1987, compared with 30.5 percent in manufacturing as a whole. The average level of fixed assets per plant was 9.5 million pesos (under US $40,000 at 1987 exchange rates) versus 101 million in manufituring and 170 miflion in textiles (Arango, 1992, 162); a small plant might get startd for under US$20,000?

It is also a sector with marked export orientation This is an industry which has "been there" befiore?° Exports fell from US $113.6 million in 1980 to $40 million in 1983 befbre following the trend of world trade back up to $449.2 million in 1991, 14 percent of manufactured exports (Arango, 1992, 178). Since 1988, garment exports have systematically exceeded those of textiles, once Colombia's main man d export After 20 years in the market, Colombia is now achieving some recognition as a reliable garment supplier. Arango (1992,146) estimates that, at perhaps two-thirds that of the Asian NICs, laboir productivity is high enough to make the industry competitive internationally, given a wage rate (about US $0.75 per hour) that While DANE data show 1053 plants wit to or more workers the Chamber of Commerce uses a figure of 6000 plants in toAl and the industry association (ASOCONFECCIONES) says there are 12000, a figure consistent with the EoDnomic Census data of 1990.

9The reported output/capital ratio is twice as high in garments as in manu(tchtring as a whole, and the capital/labour ratio is only one-seventh as high as in 1987, down fiom one-fourth in 1973 (Arango, 1992, 74). Whereas the capital/labour ratio was 59 percent as high in the small as the large in 1973, this ratio was up to 89 percent by 1987.

'°For a discussion of the factors in this decline, see Morawetz (1981).

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# TABLE VI

CONSUMPTION LEVELS AND RATIOS TO ACCUMULATED CONSUMPTION BY TREATMENT STATUS

||Treatment|Control|Raw Difference|Adjusted Difference|
|---|---|---|---|---|
||Consump<br>|tion Levels (m<sup>3</sup>)<br>||<br>|
|Period -5|456.378|450.914|5.464**|3.922|
||(1.469)|(1.714)|(2.230)|(4.423)|
|Period -4|464.935|456.953|7.982***|-1.592|
||(1.927)|(1.414)|(2.360)|(5.062)|
|Period -3|243.048|242.274|0.774|-7.062*|
||(1.576)|(1.391)|(2.076)|(3.526)|
|Period -2|110.743|107.277|3.466***|2.760|
||(0.970)|(0.809)|(1.247)|(2.933)|
|Period -1|96.448|96.001|0.447|6.835|
||(1.293)|(0.940)|(1.579)|(4.075)|
|Period 0|140.970|138.733|2.237|-0.630|
||(1.046)|(1.008)|(1.435)|(3.080)|
|Ratio of Con<br>|<br>sumption in a Period and A<br>|<br>nnual Accumulate<br>|<br>d Consumption by Pe<br>|<br>riod 0<br>|
|Period -5|0.302|0.303|0.000|0.003|
||(0.001)|(0.001)|(0.002)|(0.003)|
|Period -4|0.308|0.307|0.001|-0.001|
||(0.001)|(0.001)|(0.001)|(0.003)|
|Period -3|0.161|0.163|-0.002|-0.004|
||(0.001)|(0.001)|(0.001)|(0.002)|
|Period -2|0.073|0.072|0.001|0.002|
||(0.001)|(0.001)|(0.001)|(.002)|
|Period -1|0.064|0.064|-0.001|0.005|
||(0.001)|(0.001)|(0.001)|(0.003)|
|Period 0|0.093|0.093|0.000|0.000|
||(0.001)|(0.001)|(0.001)|(0.002)|

_Notes:_ Means and standard errors in parenthesis. The Raw Difference column reports mean difference between the Treatment and Control groups. The Adjusted Difference column presents the coefficient of regressing the respective variable on a dummy for treatment and a linear term for annual accumulated consumption by Period 0. Standard errors clustered by accumulated consumption in Period 0. *** p<0.01, ** p<0.05, * p<0.1.

31
Impact of Investment Policies on German Direct Investment in Developing Countries: An Empirical Investigation by Andrea Gubitz*

# Table of Contents

|I.|Introduction|1|
|---|---|---|
|II.|The Effect of Host Countries' Policy Measures on FDI<br>Inflows from Germany|4|
||II.1<br>Obstacles to FDI--<br>Some Major Issues<br>II.2 Measuring the Degree of Openness<br>II.3 Influence of the Degree of Openness on German FDI<br>Outflows to Developing Countries|5<br>9<br>13|
|III.|The Effect of the German Policy Instruments on FDI<br>Outflows to Developing Countries|20|
||III.1 The Federal Guarantee Scheme<br>III.2 The Effect of Guarantees on FDI Outflows|21<br>24|
|IV.|Summary and Conclusions|26|
|Annex|es|28|
|Refer|ences|39|
telephone survey did not respond to the telephone survey.<sup>5</sup> Given that we did not know telephone numbers of households in the remaining list of replacement, we could not fill the non-response at this stage.

These non-response rates are certainly not negligible but it is worth noting that they are typical for Serbia and similar to rates observed for other household surveys such as the HBS and LFS where non-responses were also replaced. Further, the attrition rates at the telephone survey are also typical in Serbia LFS telephone surveys, and much lower than the pilots in the L2L study.

In the L2L study, more than 60 percent of households in Peru and around 40 percent of households in Honduras did not respond to telephone interviews after they agreed to participate in the follow-up telephone surveys at the baseline survey. More important, as long as the F2F and CATI samples are balanced after the replacement and attrition, this pilot can serve its objective. Indeed, table 5 shows that these two samples were still balanced ex post. Although he F2F and CATI samples remained balanced, a comparison between the ex-ante check (table 3) and the ex post check (table 5) shows that after the replacement and attrition, the summary statistics did not change much.<sup>6</sup>

**Table 5:** **_Ex-post_ check of the final sample: testing balance of corresponding census variables**

|Census variables|F2F|Ur<br>Phone|ban<br>Diff|P-value|
|---|---|---|---|---|
|Household size|2.80|2.82|-0.02|0.875|
|Highest HH education|||||
|Primary or less|0.079|0.085|-0.006|0.767|
|Vocational school|0.116|0.104|0.013||
|High school|0.322|0.359|-0.037||
|Higher education/university|0.483|0.452|0.031||
|||R|ural||
|Census variables|F2F|Phone|diff|P-value|
|Household size|3.19|3.35|-0.16|0.200|
|Highest HH education|||||
|Primary or less|0.179|0.183|-0.004|0.413|
|Vocational school|0.226|0.223|0.003||
|High school|0.436|0.385|0.051||
|Higher education/university|0.159|0.209|-0.050||

Source: The authors’ estimation using the pilot data.

5 58 percent of households responded to telephone interviews via mobile phones.

6 There is another important aspect of selecting households – selection of respondents. Who responds to interviews likely has impact on the response patterns. In this experiment, the respondents were chosen in the same way as the statistical office does in its household surveys. In the F2F interviews, enumerators of this experiment selected respondents following interviewers tried to meet household heads as the preferred respondent. If interviewers could not see household heads, they tried to see anyone who knew his/her household best. In the CATI interviews, respondents were selected in the same way as the F2F and other SORS's surveys in the pre-interview stage and called the persons at the telephone interview stage. If the preselected members were not available, Interviewers called other available phone numbers, which were collected in the pre-interview stage.

13
Christy et al. 2009; Das 2012; Dethier and Effenberg 2012; Mobarak and Rosenzweig 2013; DiazBonilla et al. 2014).

Our research investigates the role played by laws and regulations for agricultural development. We contribute to the existing literature by employing a new data set that covers regulatory areas that are specifically relevant for agricultural businesses. We propose new measures of quality and efficiency for business regulations in agriculture and analyze their relationship with agricultural productivity.

# **III. Measuring Regulation in Agriculture**

Designing regulations that can pursue social objectives without imposing excessive costs on firms or other undesired economic effects is no easy task. In the agricultural sector, more balanced regulations that foster well-functioning markets while ensuring transparent and strong protections for consumers are needed (Swinnen et al., 2015). Economic research is key in guiding evidencebased policy making towards more effective regulations. This relies critically on the availability of firm-level data as well as on data on the quality and efficiency of regulatory practices. To build our measure of business regulations for agriculture, we use a new data set produced by the World Bank Group (WBG), Enabling the Business of Agriculture (EBA), which provides benchmarks on regulations that impact firms along the agricultural value chain.

## _Data description_

EBA indicators cover 62 economies in their 2017 edition.<sup>**</sup> The data are standardized through case-study assumptions which allow robust comparison of a country’s regulations with those of others. EBA data collection is primarily survey-based and the data are validated through a direct analysis of the relevant legislation. Questionnaires are completed by private and public sector experts as well as civil society organizations in each target country.

The EBA project collects primary data focusing on legal barriers for businesses operating in agriculture attempting to quantify the transaction costs of dealing with government regulations. EBA indicators cover a wide range of regulatory domains that are relevant to firms operating in

> ** See Annex I for the list of countries included in the EBA sample. The 62 countries are chosen to represent all country groups based on income level, geographical position and role of the agricultural sector as defined by World Bank (2007). For full information on EBA data and underlying methodology see World Bank (2016a).

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the Luhansk oblast. These estimates are driven by the (ex ante) perceived welfare shocks by economic agents, which trigger migration decisions, and they should be interpreted as the lower bounds of the welfare impact. When conflicts affect welfare in non-conflict areas significantly or boost mortality significantly,<sup>2</sup> thereby distorting migration numbers, the actual impact can be larger.

To our knowledge, this paper is the first to estimate the economic impact of war by using preference-based approaches inferred from migration patterns. There is a large and growing body of research focusing on the economic and social impact of wars.<sup>3</sup> This includes a strand that uses synthetic control methods to estimate the GDP impact of war, starting with Abadie and Gardeazabal, 2003.<sup>4</sup> There is also a diverse body of research that uses various methods of inference to measure conflict-driven impact on health and experienced well-being (Clark et al., 2020 and Bendavid et al., 2021), on macroeconomic indicators like GDP, investments, and fiscal flows (Edwards, 2014, and Auray and Eyquem, 2019), and on international trade flows (Glick and Taylor, 2010). Among the latter group, Korovkin and Makarin, 2019 estimate the impact of the conflict in Eastern Ukraine on Ukrainian firms’ trade with the Russian Federation between 2013 and 2016 by exploiting the spatial variation of pre-conflict Russian-speaking population as a proxy in a difference-in-difference setting. Unlike our approach, however, these studies limit attention to a subset of welfare components – often pecuniary ones only. In the international trade literature, Artu ̧c et al., 2010 estimate the welfare generated by mobility using labor flows, and Arkolakis et al., 2012 calculate income gains from trade using trade flows. Both studies use inversion equations similar to Hotz and Miller, 1993, that are related to the inversion equation in our approach. Different from them, our method is implemented using partially observed outflow data, instead of stayer data, which may be unobserved or unreliable in conflict environments.

This paper proceeds as follows. Section 2 lays out the discrete-choice framework and the theory of the welfare impact analysis. Section 3 introduces the Eastern Ukraine case study. Section 4 presents the estimates for migration elasticities, welfare implications, and the equivalent income shock from the conflict. Section 5 discusses the key findings and caveats. The last section concludes.

> 2Onder et al., 2019 discuss the conditions for analyzing the income equivalent of the mortality driven decreases in statistical life spans, with an application to the Syrian conflict.

> 3For an excellent review of the economic causes and consequences of wars, see Blattman and Miguel, 2010, and for a comprehensive review of recent advances in understanding the long term implications of exposure to war on human behavior, with an emphasis on cooperative social behavior, see Bauer et al., 2016

> 4For a detailed overview of this approach, including the conditions shaping its feasibility, see Abadie, 2021.

4
& Critical Assessment and Plans for Further Work

# 6.1. Sources of Bias

The methodology used in this study contains several possible sources of bias. The imposition of thresholds for reporting pollution to the EPA causes two obvious sampling biases, the net outcome of which is unclear. First there is no record of the cleanest plants, which will tend to move Upper Bound calculations toward overestimates of average sectoral pollutant intensities. In an effort to correct for this bias, the Lower Bound intensities assign all non-reporting facilities a pollution intensity of zero. The second bias arises because there may be a number of small facilities with very high pollutant intensities which do not reach the reporting thresholds. The Lower Bound estimates falsely assign these plants a zero pollution intensity. An attempt was made to avoid both sources of bias by calculating Inter-Quartile Mean estimates of intensities.

The differences between the Upper Bound, Lower Bound and the Inter-Quartile Mean estimates highlight the difficulty of selecting an appropriate level of sectoral aggregation. At the four-digit ISIC level, the confidence interval defined by the Upper and Lower Bound estimates will be wider than if more finely detailed decomposition is used. But the more detailed the data required, the less likely they are to be readily available.

Beyond the unavoidable inaccuracies of estimating pollution intensities at the four-digit level, a further bias may arise out of the standard procedure used to aggregate the 5-digit US-SIC data to 65
entry from the Barth, Caprio, and Levine (2004) database.<sup>12</sup> Furthermore, as described below, we include an assortment of other bank-specific and country-specific controls.

Table V examines the relationship between bank valuation (MARKET-TO-BOOK) and shareholder rights (RIGHTS), cash-flow rights (CF), and the interaction between shareholder rights and cash-flow rights (CF*RIGHTS), while controlling for net loan growth (LOAN GROWTH) and whether the state is the controlling shareholder (STATE).<sup>13</sup> Due to missing observations on LOAN GROWTH, we have a maximum of 42 countries and 213 banks in the Table V regressions.

The Table V regressions provide three key results on the links between bank valuation, bank ownership, and the legal protection of minority shareholders. First, higher levels of cash-flow rights by a controlling shareholder boost bank valuations. This is consistent with theories predicting that the incentives for controlling owners to exploit the benefits of control diminish as cash-flow rights increase. Second, greater legal protection of minority shareholders rights improves the valuation of banks. This is consistent with the view that weak protection of minority shareholders will induce the marginal small investor to pay less for bank equity. Third, greater cash-flow rights by a controlling shareholder is particularly positive for the valuation of banks in countries with weak legal protection of minority shareholder rights. Thus, the interaction term (CF*RIGHTS) enters negatively and highly significantly. Put differently, high levels of cash-flow rights are less important for the valuation of banks in countries with strong minority shareholder rights. While the first two results are consistent with LLSV’s (2002) finding on non-financial corporations, the strong, robust results on the interaction term are consistent with the theory in LLSV (2002) but more robust than their empirical findings for

> 12 Specifically, we include (i) an index of the number of regulatory procedures required to obtain a banking license and (ii) an indicator of the fraction of bank entry applications that the country’s regulatory agency denies. Including these variables does not alter any of this paper’s findings.

> 13 Some suggest that the State controls banks when the State owns more than 50 percent of the shares (LLS, 2002; Barth, et al., 2003). Thus, we conducted the analyses using this definition as a control variable in the regressions. This does not change the results.

21