rafmacalaba/datause-encoder-data huggingface dataset:
**Table** **<u>5.</u>** <u>OLS Model of Beliefs (BELIEF1) about Experimental Group Members’ Contributions.</u>

||Model 1|Model 2|
|---|---|---|
|HILL|-16.904|-15.078|
||(1.17)|(1.08)|
|AGE|0.300|0.288|
||(1.02)|(1.04)|
|FEMALE|19.256|18.913|
||(2.12)**|(2.04)*|
|DALIT|8.404|9.565|
||(0.81)|(0.90)|
|FAMILY|4.293|4.259|
||(2.70)**|(2.75)**|
|MAXEDUC|-2.838|-2.829|
||(2.40)**|(2.30)**|
|EDUC_RESP|1.262|1.502|
||(1.18)|(1.40)|
|LAND_HA||9.227|
|||(2.29)**|
|FOREST_ROP||-2.107|
|||(1.94)*|
|OXEN||0.507|
|||(0.10)|
|BUFFALO||-1.609|
|||(0.44)|
|CONSTANT|87.352|83.928|
||(3.23)***|(3.17)***|
|_R_<sup>2</sup>|0.09|0.10|

N=327 in all models; * _p_ <0.1; ** _p_ <0.05; *** _p_ <0.01; robust t-statistics and errors clustered at the forest user group level 32
**Table 12: Antidumping Measures by Affected Country**

|||1987-1994|||1995-2001|||2002-2011||
|---|---|---|---|---|---|---|---|---|---|
||AD|World Exp|Ratio of|AD|World Exp|Ratio of|AD|World Exp|Ratio of|
|Affected Country/ Group|Msre(%)|Share(%)|Rad|Msre(%)|Share(%)|Rad|Msre(%)|Share(%)|Rad|
|WTO Members|74|88|0.8|63|89|0.7|93|98|0.9|
|Developed countries|41|66|0.6|33|68|0.5|23|62|0.4|
|Developing countries|33|22|1.5|30|21|1.4|70|36|1.9|
|Developing co. excl. China|..|..|..|..|..|..|39|27|1.5|
|Non-WTO members|26|12|2.2|37|11|3.4|7|2|3.5|
|**Memo Items**||||||||||
|China **|..|..|..|17|3|5.7|31|9|3.4|
|Chinese Taipei **|..|..|..|5|2|2.5|6|1|6.0|
|Former FSU|..|..|..|10|2|5.0|5|3|1.7|
|TOTAL: All countries|100|100||100|100||100|100||

Notes: .. = data not available.

**  China and Chinese Taipei became WTO members after 2001 accordingly.

Rad = the ratio of share in total antidumping investigations relative to the share in world exports.

Sources: WTO antidumping database and World Bank WDI database (trade data).

The Table also shows the very large incidence of anti-dumping actions taken against China, Chinese Taipei and non-WTO members, especially so called 'non-market economies' mostly countries other than the Baltics that emerged from the former Soviet Union, in particular Russia, Ukraine, Belarus and Kazakhstan which have seen their exports being targeted for anti-dumping measures at a far greater rate than their share in world trade. Indeed, controlling for the value of total exports over the last three decades anti-dumping measures were at least four times more likely to be directed against a product from a non-market economy which is not a WTO Member than a product from a developed market economy. These conclusions are entirely consistent with the Bown’s findings (Bown, 2013, p21) regarding the changing use of trade remedies against different countries over time. He shows a decreasing but still substantial use of trade remedies against countries like Ukraine, Russia and Kazakhstan between 2001 and 2011 and an increasing use against China and Korea.

54
absence of a protective benefit against harassment is 70 percent of willingness to pay when there is a protective benefit. What explains the fixed-amenity value of the reserved space even when it is not effectively reserved?

First, we show that the “high-level” users, who we showed in Sections 5.4 and 6.4) report the highest level of harassment and have the highest willingness to pay to ride in the reserved space, also ascribe the largest fixed-amenity value to the reserved space. Figure A10 examines how demand for the reserved space by these groups varies by the _de facto_ effectiveness of the reservation rule. High-level users of the space have not only higher demand overall, but also demonstrate a higher demand for the reserved space even when there is effectively no difference between the reserved and public spaces in terms of presence of men and, therefore, protection from harassment.

Second, we show that attitudes and perceived social norms around women’s choice of space are correlated with women’s demand for the reserved space. We showed in Section 4.3 that men and women commuters tend to associate women riding in the public space as more open to advances. Table 4 uses these same platform survey and IAT data to relate women’s demand for the reserved space with their perceived risk of harassment and their first- and second-order beliefs about women’s choice of space. Specifically, we regress a dummy for usually riding in the reserved space (which corresponds to being a high-level user in our experimental sample) on the following measures of the respondent’s first- and second-order beliefs: the respondent’s IAT D-score associating the choice of the public space with sexual openness (column 1); the explicit measure of the respondent’s _first-order_ belief that women who choose the public space are provoking harassment (column 2); and her _second order_ belief about how other women (column 3) and men (column 4) commuters would answer this question.

We observe a small, imprecisely estimated positive relationship between women’s own (first-order) beliefs about women’s behavior and their choice of the reserved space (Table 4, columns 1-2). However, women’s _second-order beliefs_ strongly predict women’s takeup of the 28
the labor market in a substantial way by increasing rigidities, but it also influences the informal sector, by strong lighthouse effects (the “Efeito Farol”). This last remark was also noted by Neri, Gonzaga and Camargo (2000).

Carpio and Pabon (2014) found that the evidence of the effects of the minimum wage on employment, poverty and inequality is mixed. In some cases, the measure leads to layoffs of low productivity workers, but in some others, the effects were minimal due to “low compliance with the law and because large segments of people work outside the reach of labor law”. They also mention that “moderate changes in the minimum wage level, aligned with key economic variable, can limit the negative impacts and lead to positive impacts from minimum wages” and that when setting minimum wage “it is best to have a clear formula that avoids excessive rigidity (...) and it is easy for all stakeholders to understand and apply”. Using a transparent adjustment process (with a pre-set formula) in the Republic of Korea is found not to have any negative employment effects, since the absence of discretion the policy makers have in setting minimum wages allowed for better planning of future labor costs for the firms.

The literature on the minimum wage policy implies that the impact depends on many aspects, such as the degree of competition in labor and product markets, the methodological evaluations used (Card and Krueger, 1994; Neumark, 2013), the time span, the category of people affected (teens, women), the sectors where the employees work, the magnitude by which the minimum wage increased (de Linde Leonard et all, 2014) or the workers’ and firms’ policy expectations (Pinoli, 2010) and whether or not minimum wages are indexed to inflation (Strain and Brummund 2016; Tilly, 2017).

On the methodological evaluations, Card and Kruger (1994) found that cross sectional studies comparing regions are more likely to have negligible or positive effects of the minimum wage increase on the employment side than the time series studies or the panel data studies. More recently, Neumark et al. (2013) showed however that adding state specific time trends to data will no longer show negative effects on employment. Improvements in this regard have been seen in recent years also for panel data studies, when appropriate controls are put in place.

As for Romania, an IMF study (2016) made a preliminary assessment of the minimum wage increases that started in 2013. The findings were mixed. On the one hand, the study finds that minimum wage increases reduced inequality, narrowed the gap between low and high pay, and boosted consumer spending. On the other hand, the impact on reducing poverty was inconclusive, 5
River Delta, the differences being statistically significant in each case. The rural Southeast and "Other urban" areas are significantly less poor than the rural Red River Delta, but the difference between the two is not statistically significant. The ninth region, Hanoi and Ho Chi Minh City, is significantly less poor than any of the other eight regions (see Tables 4 and 5).

Combining the _stratum-level_ regression models with the Census data yields results similar to those based on the urban-rural regression models, as shown in the last two columns of Table 4. Again, the poverty estimates are less extreme than the VLSS estimates and the standard errors are somewhat lower. One notable difference is that the standard errors of the poverty estimates based on the stratum-level regression models are higher, often two to three times higher, than those based on the urban-rural regression models.

# **4.2 Provincial poverty estimates**

One of the main advantages of using Census data is that they allow us to generate reliable estimates of poverty for smaller geographic units, such as provinces or districts, which would be difficult or impossible to estimate with a household sample survey such as the VLSS'<sup>3</sup> . Tables 6 shows the estimated provincial poverty rates, along with the standard errors of the estimates, based on the urban-rural regression models (the corresponding results from the stratum-level regressions are given in Annex 2). Figure 1 shows the geographic distribution of poverty at the provincial level, also based on the urban-rural regression models.

The results indicate that Lai Chau, located at the extreme northwest corner of Vietnam, is the poorest province, with over three-quarters of its population living below the poverty line. The next five poorest provinces (Ha Giang, Son La, Cao Bang, Lao Cai, and Lang Son) are all provinces in the Northern Uplands on the northern border with China or the western border with

> **13** There are three factors that complicate using the VLSS for estimating provincial poverty. First, three provinces are not included in the VLSS sample. Second, in the remaining provinces, the sample size is small: most provinces have less than 100 households and some have as few as 32. Third, the sample (and hence the sampling weights) are not designed to produce precise estimates at the provincial level. For example, the proportion of urban households in each province is not accurate, even after applying sampling weights. 22
- Acemoglu, D. and Dell, M. (2010). Productivity differences between and within countries. _American Economic Journal: Macroeconomics_ , 2(1):169–88.

- Acemoglu, D., Naidu, S., Restrepo, P., and Robinson, J. (2013). Democracy, redistribution and inequality. National Bureau of Economic Research, Working Paper 19746.

- Acemoglu, D. and Robinson, J. (1998). Why did the west extend the franchise? democracy, inequality, and growth in historical perspective. _Quarterly Journal of Economics_ , 115(4):1167– 69.

- Acemoglu, D. and Robinson, J. (2002). The political economy of the kuznets curve. _Review of Development Economics_ , 6(2):183–203.

- Alesina, A. and Rodrik, D. Distributive politics and economic growth. _The Quarterly Journal of Economics_ , 109(2):465–490.

- Amendola, A., Easaw, J., and Savoia, A. (2013). Inequality in developing economies: the role of institutional development. _Public Choice_ , 155(1):43–60.

- Ansell, B. (2008). Traders, teachers, and tyrants: Democracy, globalization, and public investment in education. _International Organization_ , 62(2):289–322.

- Atkinson, A. and Brandolini, A. (2001). Promise and pitfalls in the use of ’secondary’ data-sets: Income inequality in oecd countries as a case study. _Journal of Economic Literature_ , 39(3):771– 799.

- Bardhan, P. (2005). _Scarcity, conflict and cooperation: Essays in political and institutional economics of development_ , chapter History, Institutions, and Underdevelopment, pages 1–27. Massachusetts: MIT Press.

- Baum, M. and Lake, D. (2001). The invisible hand of democracy: Political control and the provision of public services. _Comparative Political Studies_ , 34(6):587–621.

- Baum, M. and Lake, D. (2003). The political economy of growth: Democracy an human capital. _American Journal of Political Science_ , 47:333–334.

- Bethell, L. (1997). _Historia de Am ́erica Latina: Pol ́ıtica y Sociedad desde 1930_ . Barcelona: Cambridge University Press.

- Billor, N., Hadi, A., and Velleman, P. (2000). Bacon: Blocked adaptive computationally efficient outlier nominators. _Computational Statistics and Data Analysis_ , 34(3):279–298.

- Brown, D. and Hunter, W. (2004). Democracy and human capital formation education spending in latin america, 1980 to 1997. _Comparative Political Studies_ , 37(7):842–864.

- Chong, A. (2004). Inequality, democracy, and persistence: Is there a political kuznets curve?

21
Table 13: Impacts on sanitation loan uptake and toilet uptake by official’s report on delays in subsidy disbursement

||(1)<br>Sanitation loan|(2)<br>Sanitation loan|(3)<br>Toilet|(4)<br>Toilet|
|---|---|---|---|---|
|Credit|0.242<sup>_∗∗∗_</sup>||0.149<sup>_∗∗_</sup>||
||(0.0698)||(0.0713)||
|Credit - Small delay||0.125<sup>_∗∗_</sup>||0.267<sup>_∗∗_</sup>|
|||(0.0485)||(0.105)|
|Credit - Large delay||0.337<sup>_∗∗∗_</sup>||0.0549|
|||(0.104)||(0.0937)|
|Large delay||-0.0358||0.195<sup>_∗_</sup>|
|||(0.0604)||(0.100)|
|Household covariates|Yes|Yes|Yes|Yes|
|~~F~~<br>~~i~~nteraction||0.0629||0.119|
|controlmean|0.0153||0.280||
|controlmea~~n~~<br>~~n~~odelay||||0.218|
|controlmea~~n~~<br>~~d~~elay||||0.353|
|N|462|462|462|462|

_Notes_ : Sample restricted to matched SBM subsidy eligible MF client households without a toilet at survey baseline; GPs with no subsidy disbursements are dropped from the sample (no such GPs in control sites); We only keep GPs where there were delays of either 36 months (Large delay) or 13 months (Small delay); The dependent variable in (1)-(2) is an indicator equal to one if the MF client had taken a sanitation loan by August 2017; The dependent variable in (3)-(4) is an indicator equal to one if a toilet was observed by the interviewer during endline survey in August 2017; SL stands for sanitation loan treatment arm; Robust standard errors clustered at the village level are shown in parentheses; *, **, *** indicates significance at the 10, 5 and 1 percent level; The F interaction stat shows the p-value of the F-test of equality between the coefficient estimate of SL - Small delay and the coefficient estimate of SL - Large delay; Covariates: Indicator for presence of a child aged 02 at baseline, ratio between number of sampled clients and village size, indicator for whether there is any uncertainty around the matching quality, number of subsidy applications made in the GP since survey baseline (as per SBM official report), indicator for whether or not the Sarpanch has been in office for less than 2 years and an indicator for whether or not the Sarpanch had been in politics prior to becoming a sarpanch; Strata and interviewer fixed effects included; Toilet uptake comes from household survey; Sanitation loan uptake comes from administrative data.

39
# 1.1. African exports

Easterly and Reshef (2010) documented the recent success of African countries in enhancing remarkably their exporting capacities. Data from the Direction of Trade Statistics (DOTS, IMF) indicate a steady increase in the annual average rate of African export growth, with an acceleration over the last decade. African exports have grown annually on average by 2.6 percent in the 1980s, 8 percent in the 1990s, and 15 percent in the 2000s. Despite this significant increase, African exports (totaling US$450 billion in 2010) still represent a small share of global exports. Indeed, in 2010, Africa provided only 3.5 percent of global exports.

The destinations of African exports have shifted over time with the share of African exports headed for OECD countries decreasing from 86 percent in the 1980s to 60 percent in 2010 (see Appendix A). In parallel, the share of African exports to emerging countries has risen from about 4 percent in 1980 to 25 percent in 2010. The level of intra-African trade is rather limited but has grown significantly, expanding by 11 percent every year on average over the period 1980-2010. As a result, in 2010, African countries were exporting 11 percent of their products within Africa.

A large share of African exports is composed of primary commodities that are homogeneous products. In 2000, according to Rauch’s classification (1999), about 60 percent of African exports were goods traded on organized exchanges (classified as homogenous goods), 20 percent were products with reference prices (classified as intermediate), and only 20 percent of exports could be considered as differentiated products. This pattern especially applies to African exports in destination to non-African countries. For intra-African trade, however, the types of traded goods are slightly different, with a smaller share of homogeneous traded goods and about 35 percent of the exports being differentiated products.

# **2. REVIEW OF THE LITERATURE**

Results of the empirical literature examining the migration-trade nexus provide strong evidence in support of a pro-trade effect of migration. Most empirical papers testing the relationship between migration and trade have, however, focused on single-anchor developed countries and few considered global bilateral data sets. Few papers have focused on the specific pro-export effect in the context of developing countries.

5
_Data on informal workers_ _Labour Force Survey (SAKERNAS)_ SAKERNAS is a nationally representative household survey designed to collect information on the labor market activities of working-age individuals in selected households. Since its inception in 1976, the survey has undergone several changes to its frequency (Vaccaro et al., 2022). Since 2015, however, SAKERNAS has been conducted every February and August of each year. The February round providing provincial-level estimates now covers approximately 75,000 households, while the August round providing district-level estimates now covers 300,000 households (Vaccaro et al., 2022). Since 2011, SAKERNAS has used a rotating panel design whereby 75 percent of households are retained for two consecutive survey rounds. Households in the sampling frame can be interviewed a maximum of 4 times (Vaccaro et al., 2022).

The survey collects a range of information from respondents, including their educational qualifications, labor market activities, and the characteristics of the respondents’ main and secondary job, if any. Over the years, Statistics Indonesia has added new questions to capture emerging topics relevant to the labor market. In 2018, for example, questions related to the Internet and the digital economy were added and questions to identify “dependent contractors” (e.g., gig workers).<sup>7</sup> Likewise, the August 2020 and February 2021 waves of the SAKERNAS asked respondents about the impact of COVID-19 on their employment status, working hours, and earnings (Vaccaro et al., 2022).

Most relevant to informality are the specific questions used to identify informal workers. Consistent with the Proxy 1 and Proxy 2 definitions of informal employment, SAKERNAS regularly collects information on workers’ occupation and employment status. With the introduction of the

> 7 BPS defines dependent contractors as “someone who is bound to an economic unit (whether a business or company, institution, government, or other unit) to produce goods or services that can provide benefits to the economic unit, but is not a laborer or employee of the economic unit. A dependent contractor is also defined as a person who tries to be self-employed or tries to be assisted by a nonpermanent worker, a family worker, or unpaid labor, but does not have full control over their business in areas such as raw materials, capital goods used, price or tariffs for goods, services produced, or market or customer access, these being governed by the binding economic unit.” (Wihardja & Cunningham, 2021, p. 50)

8
-113-

# All - Petroleum Refining and Products

- Shell Oil's Technical Information Dept. in The Hague provided the following conversion factors which we used to convert US output in barrels to metric tons (see also table A11.5 and A11.6):

- light fuel oil: 1000 barrel = 135.945 ton

- heavy fuel oil: 1000 barrel = 144.690 ton

- paving grade asphalt: 1000 barrel = 163.8 ton roofing grade asphalt: 1000 barrel = 168.5 ton miscellaneous asphalt: 1000 barrel = 166.2 ton lubricating and similar oi'ls: 1000 barrel = 141.5 ton

- The following conversion factors were used to convert Brazilian output in kilograms to liters or from tons to cubic metres. They were derived from Kirk and Othmer's Chemical Encyclopedia (1968) (see also table A11.5): benzene: 1 liter = 0.88 kilogram

- toluene: 1 liter = 0.87 kilogram

- xylene: 1 liter = 0.86 kilogram

- Shell Oil's Technical Information Dept. in The Hague provided the following a litional conversion factors for Brazil (see also table A11.5 and A.1.7).

- propane: 1 liter = 0.51 kilogram

- propylene (propene): 1 liter = 0.52 kilogram

- diesel oil: 1 liter = 0.89 kilogram

- lubricating oils: 1 ton = 1.1236 cubic metres

- liquid petroleum gas (LPG): 1 ton = 1.835 cubic metres combustible fuel oil: 1 ton = 1.053 cubic metres

- The following conversion factor was used to convert Mexican output in cubic metres to tons (as provided by Shell Oil's Technical Information Dept. in The Hague, see also table A11.6 and A11.7): asphalt: 1 cubic metre = 1.045 ton

- A14 - Bricks

- Brazilian quantities are converted from square metres to single units using the following conversion factors provided by Vereniging De Nederlandse Baksteenindustrie, De Steeg, Nethevlands (see also table A14.7): ceramic bricks: 1 square metre = 65 single units

- Mexican quantities are converted from tons to single units using the peso unit value for "Other Bricks" of which the quantity was given in single units (see also table A14.6 and A14.7).

- A15 - Iron and Steel

- US figures on quantity and value of shipments are derived from table 6a-2 in the Industry Series, Blast Furnaces, Works, d Rolling and Finishing Mills (MC77-I-33A, Change Sheet, October 1980). This table was originally derived from the Current Industrial Reports, MA-33B, Steel Mill Products _cof_ the 1977 Census of Manufactures.

- - The commodity items "sponge iron" and "pig iron" in the Brazilian industry 178 "Iron and Steel Making and Manufacture of Iron and Steel Products" are excluded from the sample because it wa3 classified elsewhere (in other industries) in Brazil and the USA.

- A17 - Motor Vehicles and Equipment - The figures for 134 items in the Mexican industry 3819 "Manufacture of Other Parts and Acessories for Motor Vehicles" are presented in a consolidated form.

- See separate note on the unit value adjustment for passenger cars.