rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 2: Logit regression of commune participation in RTP1</u>**

||Coeff.|s.e.|
|---|---|---|
|Terrain: Coast|_Re_|_ference_|
|Mountains|-0.56|1.06|
|Upland|0.04|0.82|
|Plains|-1.28|1.17|
|Province: Tra Vinh|_Re_|_ference_|
|Lao Cai|1.80|1.08*|
|Thai Nguyen|2.15|1.30*|
|Nghe An|3.19|1.32**|
|Binh Thuan|2.69|1.41*|
|Kon Tum|4.04|1.22**|
|Population (log)|1.16|0.53**|
|Population density (log)|0.33|0.34|
|Minority population share|1.60|0.96*|
|National road passes through commune|-2.06|0.44**|
|Railway passes through commune without stop|1.63|0.65**|
|Waterway passes through commune|0.66|0.75<br>|
|Distance to province center (km) (log)|-0.51|0.28*|
|Commune has a passenger transport service|-0.92|0.41**|
|Share of households engaged in non-agricultural activities|-3.02|2.16|
|Share of population working in government|-0.34|0.30|
|Share of population working in private enterprises|-0.29|0.13**|
|Share of population working in state enterprises|0.29|0.69|
|Share of crop land|1.71|1.67|
|Share of perennial crop land|2.91|3.50|
|Land rental market exists in commune|0.77|0.49|
|Number of production organizations|0.02|0.07|
|Commune has a radio broadcasting station|-0.53|0.47|
|Commune has a market|0.18|0.42|
|Agricultural crop land adversely affected by natural disaster (1996)|0.49|0.52|
|Commune has an agricultural bank|-1.15|0.61*|
|Number of official credit sources|-0.32|0.21|
|Enrollment rate for children age 6 to 15|-3.10|1.25**|
|Commune has a lower secondary school|0.03|0.60|
|Predicted consumption per capita (log)|1.35|1.09|
|Share of households owning motorcycles|0.06|0.04|
|Road density (commune and district level roads)|-16.68|9.46*|
|Share of earth and car impassable roads in total road km|1.17|0.75|
|Constant|-18.69|8.27**|
|Number of Observations||200|
|Pseudo R<sup>2</sup>||0.25|

Note: (i) The dependent variable equals one for project communes and zero for non-project communes. (ii) Standard errors are clustered at the district level of which there are 29. (iii) ** and * indicate significance levels of 5 and 10%. (iv) Average commune household consumption is predicted using a consumption model calibrated to the 1998 VLSS.

24
||(2.30)|(2.44)|(3.63)*|(4.27)|
|---|---|---|---|---|
|Firms’ age|0.07|-0.02|0.08|0.50|
||(0.05)|(0.08)|(0.08)|(0.17)***|
|Firms’ status|1.53<br>|1.30<br>|3.07<br>|0.56<br>|
||(1.11)|(0.87)|(1.86)|(0.85)|
|Gender|0.64|-0.12|8.95|-6.63|
||(1.65)|(1.39)|(6.09)|(4.61)|
|Log(Labor(t-3)+ 1)|-0.31|0.41|1.55|-5.11|
||(0.88)|(0.74)|(2.23)|(1.88)**|
|Log(Output(t-3)+1)|-0.84|-0.89|-0.88|-0.30|
||(0.60)|(0.69)|(0.50)*|(0.24)|
|Transport|0.42|0.23|0.40|2.30|
||(0.41)|(0.49)|(1.63)|(1.79)|
|Exchange rate growth(t-1)|0.57|0.01|0.49|0.60|
||(0.06)***|(0.01)|(0.08)***|(0.06)***|
|Trade openness|0.46|-0.42|0.60|1.30|
||(0.14)***|(0.15)**|(0.24)**|(0.23)***|
|Size|3.78||||
||(1.71)**||||
|Generator use|-0.86|-1.20|-3.04|-0.85|
||(1.56)|(1.05)|(3.13)|(4.01)|
|Electricity losses(t-1)|0.17|-0.97|-0.66|1.40|
||(0.10)|(0.22)***|(0.14)***|(0.30)***|
|GDP per capita(t-1)|-0.01|0.01|-0.01|-0.02|
||(0.00)***|(0.00)**|(0.00)**|(0.01)**|
|GDP growth(t-1)|0.42|1.02|0.49|-1.42|
||(0.14)***|(0.16)***|(0.31)|(0.23)***|
|Rule of law growth(t-1)|-0.26|-0.60|-0.52|0.70|
||(0.05)***|(0.09)***|(0.10)***|(0.05)***|
|R2|0.23|0.23|0.29|0.45|
|_N_|3,629|2,335|980|314|
|# Countries<br>|39|39|39|39|
|- Fixed Effects: Country,Year|Yes|Yes|Yes|Yes|

Weighted ordinary least squares regression is the estimation approach, with standard errors clustered at country-level in parentheses. The treatment variable is a dummy variable taking 1 if the company declares not suffering from power outages and 0 otherwise. The dependent variable is the companies' share of exports in total sales. We include control variables (firms' age, firms' status, manager gender, labor and sales three years ago, the quality of transport service, the real exchange rate growth, trade openness index) and covariates (firms' use of generators, firms' size, electricity losses during transmission and transport, GDP per capita, GDP growth, rule of law growth). Country-level covariates are lagged by one year to avoid reverse causality between them and the occurrence of electricity access in the first stage of the entropy balancing method. Country and year fixed effects are introduced in all full estimates. Small companies are those employing fewer than 20 workers, medium-sized companies employ between 20 and 99 workers, while the number of workers in large companies is greater than 99. * _p_ <0.1; ** _p_ <0.05; *** _p_ <0.01.

# **Appendix A. Total number of firms and share of industry in the overall manufacturing sample.**

**Table A.1. Manufacturing industries in the overall sample.**

||**Overall Sample: Manufa**|**cturing sector**|
|---|---|---|
|Industry|ISIC Code 3.1<br>Freq.|Percentage<br>Cum.|

26
# **Table 7b Total and incremental changes in trade (imports plus exports) under different scenarios A1T- C5T** ($ million)

|**A. Alternative FTA**|A1T:|TPP|A2T: US-|EU27|A3T: India|-EU27|A4T: India|-ASEAN|A5T : TPP U<br>India-EU27 In|S-EU27<br>dia-ASEAN|
|---|---|---|---|---|---|---|---|---|---|---|
||US|India|US|India|US|India|US|India|US|India|
|1. Change in Exports ($ million)|15087|-316|27416|-267|-355|22213|-572|17182|41085|35686|
|To Partners|28973||42171|||15301||6539|63443|24656|
|To Non-partners|-13886||-14755|||6912||10643|-22358|11030|
|2. Change in Imports ($ million)|21020|-649|34753|-737|-2766|25631|-748|20044|51926|40593|
|From Partners|23312||41260|||65652||43126|56017|89148|
|From Non-partners|-2292||-6507|||-40021||-23083|-4091|-48556|
|**Total Trade Change 1 + 2 (A)**|**36107**|**-965**|**62169**|**-1004**|**-3121**|**47844**|**-1320**|**37226**|**93011**|**76279**|
|Of which: Change in India-US Trade<sup>a</sup>|-570|-570|-812|-816|-2893|-3051|194|49|-3967|-4223|
|**B. Alternative FTA (A) + India-US**|B1T:|A1T|B2T: A|2T|B3T: A|3T|B4T:|A4T|B5T: A|5T|
||+Indi|a-US|+ India|-US|+ India|-US|+ Indi|a-US|+ India|-US|
||US|India|US|India|US|India|US|India|US|India|
|1 1. Change in Exports ($ million)|24177|8451|36429|8424|7676|29325|8094|25148|48413|42179|
|To Partners|47211|7219|58936|7067|14019|24000|18036|16271|68593|40992|
|To Non-partners|-23034|1232|-22506|1357|-6342|5325|-9942|8877|-20179|1187|
|2. Change in Imports ($ million)|33429|9350|46908|9157|7966|33701|10752|29081|61686|47964|
|From Partners|31521|21774|50092|21697|9310|74594|10140|59217|67215|93807|
|From Non-partners|1909|-12424|-3184|-12540|-1344|-40893|612|-30134|-5529|-45843|
|**Total Trade Change 1 + 2 (B)**|**57606**|**17801**|**83337**|**17581**|**15642**|**63026**|**18846**|**54229**|**110099**|**90143**|
|Of which: Change in India-US Trade<sup>a</sup>|28662|28993|28444|28763|23329|23410|28176|28254|20241|20143|

32
those issues that they had spoken more about in control villages. In other words, it may be that PVP is fundamentally shifing the _content_ of conversation that women engage in — moving them from discussing primarily domestic maters (water collection, education) to issues related to the administration of the program. Table 9 presents the average number of speeches within a village devoted to specific issues. When we look at canonically “women’s” issues, such as water, housing, education, etc., we see a marked decline in the frequency that these issues are discussed. Te two notable exceptions are entitlement requests and animal husbandry, both of which are emphasized by PVP’s livelihoods and social safety net programs. By contrast, for the canonically “male” issues, such as employment, ration, and garbage, we see no discernible difference. Tis suggests that even though women are speaking _more_ ofen in treatment villages, they are speaking specifically about the project activities, whereas the men continue to raise their usual governance concerns.

Table 9: Topic Counts, by Treatment Status

||Avg. Speeches,Control|Avg. Speeches,PVP|t-statistic|pvalue|
|---|---|---|---|---|
|Water|9.3200|6.5000|2.1102|0.0376|
|Housing|5.1400|3.1200|2.5822|0.0115|
|Entitlement Requests|7.1200|9.5000|-1.5262|0.1305|
|Education|1.8000|1.6400|0.3752|0.7083|
|Public Infrastructure|2.2400|2.6800|-0.8833|0.3795|
|NREGA|2.7200|1.8000|1.6389|0.1048|
|Ration Shop|3.6200|3.1000|0.5158|0.6073|
|Garbage|1.4200|1.3600|0.1887|0.8507|
|Voter Lists|2.7600|4.4000|-2.2051|0.0299|
|Sanitary Complex|0.8800|0.6800|0.7922|0.4303|
|SHGs|1.4400|1.2200|0.6521|0.5159|

Whether this is normatively problematic or not remains to be seen. On the one hand, if PVP encourages its members to raise issues that are important and consequential for previously disempowered groups, it may not be worrisome that other citizens have less time to discuss maters relevant to them. On the other hand, if the newly vocal constituency of women created by PVP crowds out discussion that is broadly relevant to other marginalized groups, we may have reason to worry that the _gram sabha_ is no longer dominated by men, but by the project’s participants. Tat is, we should be cautious about programs that so alter the organic processes of 25
Tables 9, 10, and 11), larger firms compensated experience more than small firms did, as micro firm employees earned a 2.6 percent premium for each additional year of experience, while workers in large firms earned a premium of 4.1 percent.

Our results for returns to education increasing with firm size coincide with results from numerous other studies, finding that the wage premium increases with firm size (Lallmand et al,2005). This might be explained by higher productivity in large firms, which has been documented for Egypt (World Bank, 2010).

- D. Informality

_Formal vs. informal labor_ The regression results in Table 7 show that formal labor (i.e., have a contract and contribute to social insurance) enjoyed a higher wage premium (13.2 percent) in 2006 compared with their informal counterparts. In 1998, informal labor had a higher wage premium than formal labor. The increase in the wage premium of informal labor coincided with a similar shift toward formality between 1998 and 2006, as shown in Table 4. The table shows the increasing trend of labor to shift from informality to formality; in 2006, formal labor enjoyed higher wages.

# _Formality and returns to education_

We ran additional, separate OLS regressions for formal and informal labor for 1998 and 2006 (see Tables 12 and 13).

In 1998, informal labor experienced higher returns to education for preparatory, 4-year university, and 5-year university degrees compared with their formal counterparts.

In 2006, formal labor witnessed higher RoRs for all education levels except post-graduate studies. The gap in rates of return between formal and informal labor narrowed as education levels advanced. One possible explanation would be that the professions that graduates with higher education pursue might not entail signing a contract or contributing to social insurance (such as consultants, physicians, etc.). This is further confirmed by the higher wage premium received by informal workers (30 percent) who are trade union (or syndicate) members, compared with their formal counterparts (18 percent).

Although an additional year of experience yielded a very close premium for formal and informal labor in 1998, it rewarded 1 percent more for formal workers compared with their informal counterparts in 2006.

In both 1998 and 2006, there was no wage premium among informal labor working in different sectors of employment; their formal counterparts working in the private sector received the highest wage premium, followed by those working in the public sector.

14
In addition, the impact of foreign presence on markups may vary depending on incumbent firms’ ownership structures. To study differential impacts of FDI presence depending on the ownership structure of incumbent firms, we estimate a modified version of (2) with interaction terms with dummies for foreign ownership ( MNEi,s,t ) and state ownership ( SOEi,s,t ). The results from the resulting regressions are summarized in Table 3. The coefficient on the interaction of FDI presence and MNE status tends to be statistically insignificant. In contrast, interactions between a dummy for SOE and foreign presence tend to have positive and at least marginally statistically significant coefficients. This finding suggests that FDI may introduce competition for both foreign- and domestic-owned private firms but that SOEs may be insulated from these competitive pressures through other mechanisms, e.g., preferential financing, subsidies, or regulatory advantages.

_Table 3. Heterogeneous impact of FDI presence on markups depending on ownership structure_

||(1)|(2)|(3)|
|---|---|---|---|
|VARIABLES|Markup (TL)|Markup (TL)|Markup (TL)|
|FDIs,t−1|-0.275**|-0.268**|-0.283**|
||(0.103)|(0.0960)|(0.103)|
|EXPs,t−1|-0.538***|-0.509***|-0.544***|
||(0.158)|(0.156)|(0.159)|
|FDIs,t−1∗EXPs,t−1|0.574*|0.476|0.590*|
||(0.302)|(0.281)|(0.300)|
|MNEi,s,t|0.167***|0.126***|0.164***|
||(0.0575)|(0.0261)|(0.0572)|
|MNEi,s,t∗FDIs,t−1|-0.0345||-0.0263|
||(0.119)||(0.119)|
|MNEi,s,t∗EXPs,t−1|-0.0143||-0.00719|
||(0.147)||(0.145)|
|MNEi,s,t∗FDIs,t−1*EXPs,t−1|-0.120||-0.136|
||(0.291)||(0.287)|
|SOEi,s,t||-0.0672*|-0.0696**|
|||(0.0345)|(0.0323)|
|SOEi,s,t∗FDIs,t−1||0.140*|0.145*|
|||(0.0753)|(0.0715)|
|SOEi,s,t∗EXPs,t−1||0.174|0.194|
|||(0.133)|(0.119)|
|SOEi,s,t∗FDIs,t−1*EXPs,t−1||-0.233|-0.272|
|||(0.254)|(0.226)|
|Constant|1.336***|1.337***|1.338***|
||(0.0543)|(0.0528)|(0.0549)|
|Firm fixed effects|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|
|Observations|97,823|97,823|97,823|
|R-squared|0.824|0.824|0.824|

Robust standard errors clustered at the industry level in parentheses. Dependent variable displayed at the top of each column: CD denotes markups derived assuming a Cobb-Douglas production function, while TL denotes markups derived assuming a translog production function.

*** p<0.01, ** p<0.05, * p<0.1

12
ultra-poor, participants in both regular and PRIME microcredit programs are slightly better off than non-participants.

Among the ultra-poor households, about 85 percent are food poor, 88 percent are moderately poor, and 70 percent are extremely poor. This means that some ultra-poor households have food poverty, but are not extremely poor.

The three measures of poverty just discussed are objective measures of welfare based on actual consumption data. We have also constructed two subjective measures of welfare based on the extent of reported occasional starvation (i.e., no meal in some days) or meal rationing (skipping a meal or two some days) by households. Starvation is an extreme form of food deprivation, while meal rationing is a more moderate type of deprivation As Table 3 shows, the year-round subjective measure of food deprivation (either starvation or meal rationing) is 94.5 percent for all households, compared to 85.4 percent food poverty among the same households. Although the objective measure of food deprivation indicates less hardship than the subjective measure, it is not inconsistent as the objective measure has been constructed using food intake during the post- _monga_ harvest period while the subjective measure refers to food consumption during both the _monga_ and non- _monga_ periods. But the high correlation between the two measures suggests that most households who are food poor are likely to starve or skip meals at certain times, either on a daily or weekly basis.<sup>14</sup> That is, the majority of the ultra-poor are likely to be food poor any time of the year.

The ultra-poor are also similar in terms of observed characteristics (Table 4). Households have a low level of education and have small or negligible amounts of

> 14 Since actual consumption data is expensive to collect on a regular basis, one can collect these subjective measures of food deprivation at low cost to monitor changes in welfare status following an intervention, such as PRIME.

20
<!-- Start of picture text --> Figure 2A: Sex Ratio at Birth, South Korea, 1981 - 2015<br>1.17<br>1.15<br>1.13<br>1.11<br>1.09<br>1.07<br>1.05<br>1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015<br>Source: Korean Statistical Information Service<br><!-- End of picture text --> <!-- Start of picture text --> Figure 2B: Child Sex Ratio, South Korea, 1960 -2010<br>1.14<br>1.12<br>1.10<br>1.08<br>1.06<br>1.04<br>1960 1966 1970 1975 1980 1985 1990 1995 2000 2005 2010<br>Source: Korean Statistical Information Service<br><!-- End of picture text --> Most experts argue that the Korean turnaround was primarily due to societal level shifts in genderspecific attitudes and behavior. There is limited evidence to suggest that the ban on prenatal sex selection technology was primarily responsible for the normalization of SRBs (Park & Cho 1995), and while the government passed key legislation that undermined patriarchal traditions, these 40
SADC’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE Among the road corridors that serve landlocked countries, one finds significant cost differences. There is a strong negative correlation between lower traffic volumes and cost. As traffic increases, the unit cost of transport (per tonne) decreases (figure 2.5). Indeed, unit costs can vary by a factor of one to three—from $0.05 to $0.15 per tonne-km—depending on the level of traffic along different segments of the corridor. Freight tariffs start to fall once traffic volume exceeds 1,000 vehicles per day.

**Figure 2.5 Relationship between traffic volumes and unit costs of transport**

<!-- Start of picture text --> 2500<br>2000<br>1500<br>1000 y = -5007.1x + 1893.2 Series1<br>R2 = 0.1127<br>Linear (Series1)<br>500<br>0<br>0.00 0.05 0.10 0.15 0.20<br>Costs per tonne<br>Weighted average annual traffic<br><!-- End of picture text --> _Source:_ Derived from preliminary data in Nathan 2010. The analysis is based on data from East and southern Africa.

The costs and times of moving imported goods along various corridors in southern Africa are well aligned with observed traffic patterns. An analysis of the time required for imports to reach a landlocked capital ranged from 400 hours to more than 800 hours (figure 2.6a). Around 50 to 80 percent of the time required can be attributed to inefficient operations at ports (based on information from 2006–07; figure 2.6b). In the cases of Durban and Dar es Salaam, some of the inefficiency can be traced to excessive demand for port services, which slows freight clearance. Imports encounter long dwell times, and ships endure long waits for berths. Beira is a smaller port that has been a victim of poor operations. In both Beira and Walvis Bay, significant efforts are under way to improve standards. Regulatory processes in southern Africa (customs clearance and technical control) also add significant times to moving imports into landlocked capitals.

17
<!-- Start of picture text --> Population density (2012)<br><!-- End of picture text --> <!-- Start of picture text --> —— Data<br><!-- End of picture text --> <!-- Start of picture text --> = ee oe<br>825000.<br><!-- End of picture text --> <!-- Start of picture text --> © 20 000}: Bane <<br>° eee ‘<br><!-- End of picture text --> <!-- Start of picture text --> 10 000 eer og<br><!-- End of picture text --> <!-- Start of picture text --> 0 5 10 15 20 25 30 35 40 45 50 55 60<br>Distance in km<br><!-- End of picture text -->
subset of households’ heads having the same gender, educational attainment and age. The observed income from that neighbor is then imputed to the corresponding observation in the second round, which represents the first-round log income for that household surveyed in the second round: (4) where is actual first-round observed income for the household chosen through the matching process ii ii2 ii1 ΔΔ̃yy = yy −yy� (4) where described above. ii1 yy� Summarizing, the steps to estimate the “unobserved” household per capita income in the first round are the following:

1. Append first and second round of cross-sectional data and create a fused dataset

2. Harmonize the joint distribution of gender and educational attainment in both surveys 3. Take a sample for the statistical learning stage: 80 percent of the fused dataset defined in the first step 4. Estimate the parameters and select the best lambda through cross-validation 5. Calculate the LASSO linear prediction of the first-round log incomes for all households surveyed in the first round as in equation (3) 6. Obtain the LASSO linear fit of the second-round log incomes for all households surveyed in the second round as in equation (3)

- second round as in equation (3)

- 7. For every household head in the second round, obtain the nearest first-round neighbors by minimizing the absolute difference between and for every observation in the first round mm

- that has the same gender, educational attainment and birth year mm2 ii1

- yy� yy� ii

- that has the same gender, educational attainment and birth year

- 8. Randomly select one neighbor from the list of nearest neighbors based on first-round sampling weights ss

- weights

- 9. Take the observed log income of neighbor ( ) chosen in the first round, and assign it to observation surveyed in the second round ( ) ss1

- ss yy

- 10. Estimate movements in-out of poverty and other income dynamics mm1 ss1

- mm yy� = yy

- 11. Repeat 100 times all steps 3 to 10

This process allows for estimating standard errors for the statistics in interest. For every bootstrap sample of the data, a new LASSO is fit and the whole welfare vector is constructed via the improved LASSO PMM process described above. As a result, both point and interval estimates are calculated for every poverty dynamics indicator.

# II.2 Defining the middle-class thresholds

The major challenge regarding the estimation of the LAC region’s vulnerable and middle-class groups is the identification of the lower and upper thresholds. These were defined initially by Lopez-Calva & OrtizJuarez (2014) and Ferreira et al. (2013). These authors focus mainly on defining and computing the lower bound. An individual is defined as vulnerable if the probability of falling back into poverty over a five-year interval is greater than 10 percent. Both references use panel data from three different sets of countries. While Lopez-Calva & Ortiz-Juarez (2014) use Chile, Mexico, and Peru, Ferreira et al. (2013) use Argentina, 7