rafmacalaba/datause-encoder-data huggingface dataset:
Mongolia, Iraq, Chad and Algeria are also poised to be significantly impacted as their fossil fuel exports range between one-fifth and one-third of their GDPs (Figure 3).

**Figure 3 Top 10 developing countries according to economic exposure of fossil fuels to the EU, US, and China policies**

<!-- Start of picture text --> Economic exposure (LHS) Sector exposure (RHS)<br>60 100<br>50 80<br>40<br>60<br>30<br>40<br>20<br>10 20<br>0 0<br>Libya Azerbaijan Angola Congo, Rep. Equatorial Mongolia Iraq Chad Algeria Gabon<br>Guinea<br>Percent<br><!-- End of picture text --> _Source:_ Authors’ calculation based on 2022 data from the World Bank’s WITS.

_Note:_ Fossil fuels (coal, oil, and gas) data are collected based on four HS digit codes (2701, 2709, 2710, and 2711). Sector and economic exposure are export of fossil fuels to three economies divided by exports of fossil fuels to the world and by GDP, respectively.

**The EU CS3D regulation will increase costs for exporters in fossil fuel sectors.** Countries involved in fossil fuel sectors, even if not exporting directly to the EU, will face higher costs when participating in oil and chemical supply chains for the EU market, due to the new requirements related to information gathering and reporting. Figure 4 shows the GVC exposure to the EU for petroleum, chemical, and non-metallic mineral products,<sup>18</sup> considering both direct and indirect exports based on the GVC exposure index developed by Arvis et al. (forthcoming). Niger has the most exposed sector, with about one-third of its direct and indirect exports of petroleum, chemical and non-metallic mineral products focused on the EU, followed by Bosnia and Herzegovina, and Tunisia.

**Figure 4 Developing countries with largest GVC exposure to the EU: Petroleum, Chemical and NonMetallic Mineral Products (percent of domestic products output)**

<!-- Start of picture text --> 35<br>Direct export to EU Indirect export to EU<br> 30<br> 25<br> 20<br> 15<br> 10<br> 5<br> -<br>Niger Bosnia and Tunisia Libya Belarus North Morocco Albania Türkiye Sri Lanka<br>Herzegovina Macedonia<br><!-- End of picture text --> _Source:_ Arvis et al. (forthcoming) and authors’ calculations.

> 18 GVC exposure data is not disaggregated at the fossil fuel level.

14
**Table 4: Correlation of country-level energy subsidies and firm-level adoption of energy efficiency measures**

||(1)|(2)|
|---|---|---|
||Energy Efficiency|Energy Efficiency|
|Petroleum subsidies (average previous 5 years)|3.091***||
||(0.274)||
|Electricity subsidies (average previous 5 years)|0.215***||
||(0.0131)||
|Petroleum subsidies (lagged)||0.602***|
|||(0.190)|
|Electricity subsidies (lagged)||0.275***|
|||(0.0187)|
|Micro|-0.300***|-0.449***|
||(0.0344)|(0.0339)|
|Medium|1.003***|1.040***|
||(0.0394)|(0.0390)|
|Large|1.188***|1.181***|
||(0.0678)|(0.0679)|
|Age (log)|0.287***|0.279***|
||(0.0227)|(0.0235)|
|Agriculture|0.0340|-0.0498|
||(0.0618)|(0.0647)|
|Construction|0.232***|0.165**|
||(0.0802)|(0.0801)|
|Retail|-0.162***|-0.164***|
||(0.0381)|(0.0375)|
|Transportation|0.0695|0.0113|
||(0.0803)|(0.0792)|
|Accommodation|0.138*|0.155**|
||(0.0714)|(0.0690)|
|Restaurants|-0.0186|-0.0413|
||(0.0557)|(0.0540)|
|IT|0.535***|0.513***|
||(0.0929)|(0.0933)|
|Financial|0.741***|0.751***|
||(0.0828)|(0.0836)|
|Education|-0.0861|-0.232**|
||(0.0911)|(0.0925)|
|Health|0.267**|0.196*|
||(0.119)|(0.117)|
|Other Services|-0.0513|-0.0858**|
||(0.0416)|(0.0413)|
|Constant|-1.230***|-1.029***|
||(0.0774)|(0.0750)|
|Observatons|13,706|13,706|
|Pseudo R2|0.3142|0.3027|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Weighted by country sample size. Controls for GDP per capita; Omitted variables: small and manufacturing 22
Table 7. More Complex Dynamic of Investment and Timing of Tax Response

||(1)|(2)||(3)|(4)|(5)|
|---|---|---|---|---|---|---|
|Rate Diferential|2.811***<br>(0.726)||Rate Diferential|||2.139**<br>(0.868)|
|Lagged I/K|0.286***|0.286***|Rate Diferential (t-1)|1.920**||0.457|
||(0.0335)|(0.0335)||(0.867)||(1.303)|
|Non-Host part||2.790***<br>(0.823)|Rate Diferential (t+1)||4.348***<br>(0.811)|2.666***<br>(0.676)|
|Host part||-2.825***<br>(0.671)|||||
|Observations|9,637|9,637||9,637|9,612|7,430|
|Pseudo R<sup>2</sup>|0.173|0.173||0.158|0.163|0.152|

_Notes:_ The dependent variable is _It/Kt−_ 1, where _I_ is FAI flow and _K_ is FAI stock. Estimation is by PPML. In columns (1) and (2) the baseline specification is augmented with the first-lag of the _I/K_ ratio (that is, FAI flow at time _t −_ 1 scaled by FAI stock at time _t −_ 2). In columns (3)-(5) we augment the baseline specification with leads and lags of the CIT differential term. Host-year fixed effects and parent fixed effects are included, and parent country macroeconomic variables as well as bilateral gravity variables are included as control variables. Standard errors, in parentheses, are clustered by host. ***, ** and * indicate significance at the 1%, 5% and 10% levels respectively.

55
and older plants increase labor productivity by 16.4%.<sup>17</sup> Older plants may be growing or gaining higher rents à la de Loecker et al., while shedding some labor, relative to younger plants that hire as they expand.

# _iii._ **_The role of initial plant capabilities/characteristics_**

Table 5 splits the regressions presented in Table 4 by plant position above or below median values of four pre-GQ levels of plant capabilities: skill intensity defined as the average plant wage per employee; capital intensity measured as the ratio of the value of tangible assets to total revenue; labor productivity; and TFPR.

The impact of proximity on exit follows the same decreasing gradient with age found previously although surprisingly, there appears little relationship with initial plant capabilities. Except for capital intensity for young plants, in general more able plants do not seem to survive better with proximity than those less so.

However, there are important differential effects on output and plant size that are consistent with findings in the aggregate data, that again suggest two types of adjustments for young and old plants. These adjustments indicate that exit may be a longer-term process as noted in Figure 1. With GQ proximity, young plants with above median skill intensity show a 10% increase for output compared to those below which show a negative but insignificant response. Older plants show the same pattern but where skill now mitigates contraction: output in below median skills plants fell by 11.8% and those with above median skills showing no significant effects.

A similar pattern emerges with capital intensity which is consistent with the industry characteristic effects discussed earlier: output in young plants with above median capital intensity increases by 13%, while those below the median contract by 9%; older plants with above median capital intensity show no effect while those below show a contraction of 8.5%. More sophisticated young plants grow with proximity to the highway network, while sophisticated older plants, contract less. Plant size (employment) also follows the same pattern, albeit with less of an age “twist”.<sup>18</sup>

> 17 The only significant coefficient in the census scheme suggests this gradient as well with young firms, again an insignificant 4.3% and older firms with a significant 14.4% (Annex table 4).

> 18Young firms with above median skills are 7.4% larger and those below are 6% (insignificant), very similar to older firms. Those above average in labor productivity show increases of 9.2% and those below, 6.9%. A similar pattern appears, although again, more mutedly, for mature firms for skills, capital intensity and labor productivity. Similar effects appear for labor productivity for small firms (capital intensity), mature firms (skills) although signs flip for above and below average labor productivity suggest perhaps mechanical mean reversion effects dominating. Similarly, TFPR for small and mature firms shows the same pattern for capital intensity differences, and perhaps mean reversion effects for labor productivity.

20
They tote Compaq iPAQ handheld computers loaded with Elektra's rich database, which includes customers' credit histories and even names of neighbors who might help track down delinquent debtors.<sup>21</sup> "The difference between Elektra and more established banks is that Elektra has a much better collection system," said Joaquin Lopez-Doriga, an analyst with Deutsche-Ixe. "If you want to address the market that Elektra is addressing, you have to know it very well. That's very labor intensive."<sup>22</sup> The bank began operations by offering savings accounts that could be opened with as little as $5. In the first month, 157,000 accounts had been opened. By the end of December 2002, more than 250,000 accounts had been opened.

In December 2002, Banco Azteca received authorization to make loans, and has begun offering consumer financing identical to the installment plans of the Grupo Elektra retailers and has also begun offering $500 personal loans not tied to appliance purchases. Elektra's old consumer financing operation closed down as its borrowers paid off their loans, which usually run for 53 weeks.

After less then a year of operations, Banko Azteca “has seen rapid success as it taps Mexico's vast informal economy, thanks to Elektra's long record of serving the working class.”<sup>23</sup> Below are several anecdotal accounts from the press, on the typical customers of Banco Azteca.

# **Banco Azteca bikers reach out to Mexico's poor.**

By Lorraine Orlandi, 21 September 2003, Reuters News, (c) 2003 Reuters Limited Loan officers make house calls to investigate potential customers and to ensure they make payments once a loan is approved. Sometimes they repossess items that are resold at Elektra's Bodega de Remates discount stores, typically at prices higher than the amount due on the loan.

On a recent afternoon Azteca loan officer Manuel Monroy hopped on a Honda 125 to visit Blanca Guevara at home in a working-class neighborhood of Mexico City. The 48-year-old widow had applied to buy a sewing machine for 2,174 pesos (about $210) on credit at Elektra the day before.

Guevara said she could pay 41 pesos a week over 53 weeks, after a 141-peso down payment. But as an off-the-books house cleaner, she had no proof of income.

Once her mother-in-law, who owns their house, agreed to back the loan, Monroy looked over property and identification papers and recorded the information on a hand-held computer.

> 21 Business Week, 13 January 2003 22 Reuters News, 21 September 2003 23

> Reuters News, 21 September 2003

18
# **3. Nutritional Status of Children in Kagera**

The nutritional status of a child is usually assessed with three indicators: stunting (low height for age), wasting (low weight for height) and underweight (low weight for age). All indicators are expressed in “z-scores” which are derived by comparing the child’s height and weight with that of a “reference” group of well nourished children defined by the US National Center for Health Statistics (NCHS) (World Health Organization (WHO) 1995). More specifically the stunting z-score is the difference (expressed in standard deviations (SD)) of a child’s height for age from the median height of children of the same age and sex in the reference population. The z-score is thus the conversion of the child’s height into a standardized unit. Relatively short children have negative z score; tall children a positive z score. The commonly used cut-off point to identify severely malnourished children is a measurement of 2 SD below the median of the “reference” group. Thus, a child is “stunted” if his/her z-score- is below or equal to –2.

Table 1 reports the descriptive statistics related to the sample. On average, 28 percent of children aged 0-5 are moderately underweight (i.e. their weight-for-age is more than two standard deviations below the median of the international “reference” population). Nearly 40 percent of the children are stunted but only 6 percent are malnourished according to the weight for height indicator. This is a general pattern; low height for age or low weight for age reflect long term cumulative deprivation while low weight for height indicates an acute crisis (Alderman, 2000). The former two measures being cumulative generally reveal more children outside of normal ranges than does the latter, which typically only shows high values during periods of acute food shortage. The WHO recommends stunting as a reliable measure of overall social deprivation (WHO, 1986), whereas the MDG nutritional indicator is defined as weight for age. In the remainder of the paper we concentrate on both stunting and being underweight.

Table 2 illustrates the nutrition problems for Tanzania. According to the 1999 DHS, 43 percent of children under five are stunted, 6 percent are wasted and 29 percent are underweight. Though we do not suggest that the Kagera data are representative of Tanzania, a comparison between Tables 1 and 2 shows that the nutritional pattern in Kagera is not very different from that found elsewhere in the country.

Figure 1 indicates that the probability of malnutrition declines with income. At low levels of income, more than 50% of children are malnourished. While this declines with 4
<!-- Start of picture text --> A. IPV Often Index<br>Boys, Partner p=.055 / b=-0.398<br>Goal, Partner p=.006 / b=-0.495<br>Boys, No Partner p=.240<br>Goal, No partner p=.622<br>-1 -0.5 0 0.5 1<br><!-- End of picture text --> <!-- Start of picture text --> B. IPV in last year Index<br>Boys, Partner p=.498 / b=-0.103<br>Goal, Partner p=.017 / b=-0.381<br>Boys, No Partner p=.054 / b=-0.090<br>Goal, No Partner p=.945<br>-1 -0.5 0 0.5 1<br><!-- End of picture text -->

**Figure A1** Impact of treatments on IPV, heterogeneity by had partner in past two years at baseline

_Notes._ This figure presents estimates of _β_ 1 and _β_ 2 and from equation 1. Panel A presents estimates for the IPV often index and Panel B presents estimates for the IPV in last year index. The indices are centered on females in the ELA only communities who had a partner in the past two years at baseline and were not assigned to the _Goal_ treatment. Bolded markers are statistically significant at _p <_ 0 _._ 1 _. p_ -values and coefficient estimates are displayed beside each marker. _Source._ Female baseline and endline data, balanced panel. The sample in the top half of each panel is restricted to females who had a partner in the past two years at baseline. The sample in the bottom half of each panel is restricted to females who did not have a partner in the past two years at baseline.

57
competitiveness in banking. Some studies employ the Panzar and Rosse H-statistic to assess the degree of competitiveness in banking (e.g., Claessens and Laeven 2004, Schaeck, Cihak, and Wolfe 2006, Molyneux and Nguyen-Linh 2008). There are also some issues with this proxy for the degree of market power, particularly because it requires that banks be in long-run equilibrium (Shaffer 2004). As indicated above, one study of the effect of concentration on bank loan risk uses the Lerner index (Jimenez, Lopez, and Saurina 2007). No prior study to our knowledge assesses the prevailing market structure comparing the effects of using different measures of competition. In this study, we include multiple measures of competition to check for robustness of the findings.

An important feature of our approach is that we control for possible endogeneity of the measures of the degree of market power. Endogeneity can arise when causality is reversed, i.e., when the degree of market power depends on loan risk, overall bank risk and capitalization levels. If a well-capitalized bank decides to pursue a growth strategy and merges with another bank, it can then increase the bank’s degree of market power. Similarly, if a bank increases its loan portfolio risk and its overall bank risk, then the higher expected return may provide an incentive for the firm to gain a higher degree of market power. To address this potential endogeneity, we turn to instrumental variable techniques, using a GMM estimator. Following Schaeck and Cihak (2007), we employ activity restrictions, banking freedom, and the percent of foreign- and government-owned banks as instruments to explain measures of the degree of market power.

A further issue in testing the theories is the effect of the business environment for banks. For example, banks operating in nations with weak business environments may find it difficult to expand their loan portfolios to take on additional risks. We include data on an index of legal rights, which measures the degree to which collateral and bankruptcy laws facilitate lending in our analysis. We also control for foreign bank ownership in all regressions.

This paper uses Bankscope data on 8,235 banks in 23 developed nations to test these alternative views.<sup>3</sup> We include three proxies for financial stability, including a measure of overall bank risk (the Z-index), a measure of loan risk (the ratio of nonperforming loans to total

> 3 The list of countries and the number of banks are included in the Appendix.

3
# **Table A2: Senegal: % of current widows, by age group**

||||||||**A**|**ge grou**|**p**||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Year**|[10-14]|[15-19]|[20-24]|[25-29]|[30-34]|[35-39]|[40-44]|[45-49]|[50-54]|[55-59]|[60-64]|[65-69]|[70+]|[70-74]|[75+]|_Sources_|
|1960-1961||0.2|1.0|1.6|1.6|3.2|8.1|12.6|24.5|36.6|54.9|63.2||76.2|83.7|ED|
|1970-1971|0.1|0.1|0.5|0.9|1.7|2.7|5.0|10.4|18.5|28.6|40.2|49.1||63.0|75.9|ED|
|1978||0.3|0.5|0.9|1.4|2.4|2.0|1.9||||||||WFS|
|1986||0.3|0.4|1.2|0.8|1.0|4.3|4.1||||||||DHS|
|1988|0.0|0.3|0.5|0.9|1.7|3.8|7.2|13.5|19.7|32.0|40.5|54.0|64.1|||Census|
|1992-1993||0.0|0.8|0.7|0.9|2.4|1.9|3.5||||||||DHS|
|1997||0.4|0.2|0.6|0.7|1.9|2.1|3.6||||||||DHS|
|1999||0.1|0.5|0.8|1.0|1.6|2.9|6.0||||||||DHS|
|2000||0.4|0.9|0.9|1.5|2.7|6.1|7.5|16.5|24.0|39.1|50.2||65.9|80.4|MICS|
|2002|0.2|0.3|0.5|0.8|1.2|1.7|3.2|5.1|10.1|14.6|24.7|29.6||42.0|51.5|Census|
|2005||0.1|0.1|1.2|1.0|1.8|3.8|4.3||||||||DHS|
|2010-2011|0.0|0.1|0.2|0.8|1.1|2.3|3.3|5.3|14.0|20.3|38.2|44.6||64.7|80.4|DHS-MICS|
|2012-2013||0.1|0.1|0.8|1.2|1.9|1.8|7.0|12.6|24.3|35.3|53.0||60.2|77.2|DHS|
|2012-2014||0.1|0.2|0.7|1.0|2.5|1.7|6.5|12.5|23.0|37.2|53.5||58.2|79.1|DHS|
|2014||0.0|0.4|0.5|0.9|3.1|1.7|6.0|12.5|21.8|39.2|54.2||56.0|81.2|DHS|

Note: ED : Enquête Démographique ; WFS : World Fertility Survey ; DHS : Demographic and Health Surveys; MICS: Multiple Indicator Cluster Surveys Source : United Nations. Department of Economic and Social Affairs. Population Division (2015). World Marriage Data 2015 (POP/DB/Marr/Rev2015).

49
# **Table 4:  Parametric Hazard Regression: Heterogeneity of Commodity Price Shocks**

|VARIABLES|(1)|(2)<br>Hazar|(3)<br>drate|(4)|
|---|---|---|---|---|
|**Micro factors**|||||
|IFI involved|-0.0940|-0.0529|-0.0116|-0.0519|
||(0.112)|(0.111)|(0.111)|(0.127)|
|Priv. share>80% = x|0.438***|0.490***|0.501***|0.349***|
||(0.0443)|(0.0434)|(0.0435)|(0.0520)|
|x * IFI involved|-0.255**|-0.284**|-0.331***|-0.313**|
||(0.123)|(0.122)|(0.122)|(0.139)|
|OGM sector|0.0321|0.0277|0.0440|-0.0966|
||(0.0633)|(0.0633)|(0.0634)|(0.0761)|
|Energy sector|0.195***|0.221***|0.214***|-0.0382|
||(0.0343)|(0.0341)|(0.0342)|(0.0734)|
|**Macro factors**|||||
|Democracy|-0.580***|-0.467***|-0.564***|-0.240***|
||(0.0531)|(0.0574)|(0.0560)|(0.0631)|
|High state fragility||-0.370***|||
|||(0.0761)|||
|Change in TOT = y|0.620***|-0.209***|0.0374||
||(0.175)|(0.0616)|(0.0703)||
|y * Democracy|-0.732***||||
||(0.177)||||
|y * High state fragility||0.702***|||
|||(0.108)|||
|y * Net commodity exporters|||-0.222**||
||||(0.107)||
|Net commodity exporters|||-0.138***||
||||(0.0492)||
|Increase in input cost (avg) = z1||||11.70***|
|||||(0.682)|
|z1 * Energy sector||||2.555**|
|||||(1.036)|
|Commodity windfall revenue (avg) = z2||||-2.826***|
|||||(0.258)|
|z2 * OGM sector||||0.639|
|||||(0.795)|
|Constant|-22.11***|-15.69***|-21.95***|-23.66***|
||(2.638)|(2.696)|(2.731)|(3.427)|
|Observations|4,382|4,379|4,382|3,388|
|Ln(α)|0.575***|0.580***|0.576***|0.851***|
||(0.0128)|(0.0128)|(0.0128)|(0.0146)|

*** p<0.01, ** p<0.05, * p<0.1;  Standard errors in parentheses.  Sample excludes government guaranteed contracts.  Log of income per capita in 2000 and the square term, as well as change in real GDP per capita growth are controlled.

29
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peers’ support for the introduction of gender equal inheritance law. Second-order beliefs were measured before the assignment of treatment which is why I use the full sample. First-order beliefs were measured in the first endline. The average first order-beliefs are thus computed based on the control group.

Figure 3 aggregates the second-order beliefs of female and male respondents and contrasts them with the actual average first-order beliefs. Unsurprisingly, respondents expect men to be less supportive of legal reform than women: the modal belief is that 0 out of 10 men support legal reform. Perceptions about women’s reform support are much more evenly spread. On average, respondents expect 42 percent of women and 19 percent of men to support legal reform. These second-order beliefs imply that the expected gender gap in reform support amounts to 23 percentage points. This is considerably larger than the actual gender gap revealed by first-order beliefs, which I illustrate with line bars in figure 3: 42 percent of women versus 30 percent of men support a reform. The gender gap in first-order beliefs hence amounts to 12 percentage points, barely more than half the gender gap as measured by second-order beliefs. I conclude that respondents tend to overestimate the gender polarization around the introduction of gender equal inheritance law.

What drives this misperception? Average second-order beliefs regarding women’s reform support are surprisingly close to the true mean. The misperception is driven by respondents underestimating male reform support. One possible hypothesis is that women avoid discussing gender equality in inheritance with men, creating information asymmetry by gender. In that case, we would expect that women have a less accurate understanding of men’s reform support than men (and vice versa for women’s reform support, see Gulzar et al., 2020). But not only women underestimate men’s reform support: figure 6 plots second-order beliefs regarding men’s reform support by gender. The distribution is strongly skewed to the left and there are hardly any differences by gender - men tend to underestimate other men’s reform support just as much as women do. Figure 5 suggests that respondents on average hold quite accurate expectations regarding female reform support. Male respondents tend to give more extreme answers. Overall, respondents tend to overestimate the gender polarization around gender equality in inheritance because they _underestimate_ men’s openness to reform.

Figure 4 presents beliefs aggregated by gender as they were used for administering T2. Almost 60 percent of respondents underestimated public reform support and received the corresponding information if allocated to T2. This is in line with Bursztyn et al. (2023) who find that respondents in relatively less gender progressive countries tend to underestimate 22