rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 3.3. ASEAN and Apparel Trade</u>**

|**Trade Agreement**|Coeffi|cients|Bias cor|rected<sup>1/</sup>|
|---|---|---|---|---|
||β<sup>k</sup>|β<sup>l</sup>|β<sup>k</sup>|β<sup>l</sup>|
|ASEAN - Australia - New Zealand|0.350<br>(0.16)|0.065<br>(0.138)|0.378<br>(0.196)|0.068<br>(0.141)|
|ASEAN - China|0.694<br>(0.146)|0.060<br>(0.138)|0.513<br>(0.314)|0.065<br>(0.142)|
|ASEAN - India|-0.207<br>(0.148)|0.067<br>(0.137)|-0.169<br>(0.235)|0.069<br>(0.141)|
|ASEAN - Japan|0.655<br>(0.244)|0.046<br>(0.141)|0.712<br>(0.349)|0.042<br>(0.144)|
|ASEAN - Korea, Republic of|0.566<br>(0.254)|0.059<br>(0.138)|0.547<br>(0.484)|0.063<br>(0.14)|
|ASEAN Free Trade Area (AFTA)<br>1/ This estimator from Correia, Guimaraes<br>biases arising from country specific time tre<br>able to be computed by the model<br>Note: Standard errors in parenthesis.|0.564<br>(0.145)<br>and Zylkin (201<br>nds and other|0.063<br>(0.138)<br>9) adds robustne<br>parameters in th|-<br>-<br>ss by correcting<br>e model. Cells in|-<br>-<br>the potential<br>blank were not|

To test the robustness of the estimation two different labor-intensive sectors (apparel and furniture) are considered. Table 3.4, therefore, shows the coefficients for the model considering trade in the furniture sector. For the agreements with Australia-New Zealand and Japan, the estimates for trade in apparel are larger than those for the furniture sector.

**<u>Table 3.4. ASEAN and Trade in Furniture</u>**

|**Trade Agreement**|<sup>k</sup><br>Coeffi|<sup>l</sup><br>cients|<sup>k</sup><br>Bias cor|<sup>l</sup><br>rected<sup>1/</sup>|
|---|---|---|---|---|
||β|β|β|β|
|ASEAN - Australia - New Zealand|0.192<br>(0.25)|0.099<br>(0.208)|0.177<br>(0.133)|0.380<br>(0.124)|
|ASEAN - China|0.743<br>(0.239)|0.518<br>(0.2)|0.184<br>(0.134)|0.364<br>(0.126)|
|ASEAN - India|0.551|0.379|0.204|0.364|
||(0.249)|(0.206)|(0.132)|(0.125)|
|ASEAN - Japan|-0.015<br>(0.261)|-0.045<br>(0.22)|0.179<br>(0.135)|0.312<br>(0.128)|
|ASEAN - Korea, Republic of|0.197<br>(0.249)|0.147<br>(0.206)|0.131<br>(0.133)|0.177<br>(0.126)|
|ASEAN Free Trade Area (AFTA)<br>1/ This estimator from Correia, Guimaraes<br>biases arising from country specific time tre<br>Note: Standard errors in parenthesis.|0.646<br>(0.245)<br>and Zylkin (201<br>nds and other|0.742<br>(0.2)<br>9) adds robustne<br>parameters in th|0.145<br>(0.133)<br>ss by correcting<br>e model.|0.311<br>(0.127)<br>the potential|

18 | P a g e
surveys to probe for the dimensions of performance are set out in Appendix 3. Appendix 4 shows how the performance indicators were constructed for the East Caribbean states.

_Accountability_ was measured by using 15 questions to create an indicator covering, inter alia, enforcement of regulations; demonstrated accountability to the public at large, and to civil society and parliament. In assessing the institutional environment, the existence of credible rules was tested in four areas: record management, project evaluation, internal audit and performance appraisal. In measuring performance, the past enforcement of these rules was tested in the same four areas.

Appendix 4 shows how the performance indicators, including that for accountability,. were constructed for the East Caribbean states.

Employee morale was measured by creating an indicator covering employee satisfaction and vertical solitude. Four questions were used to calculate satisfaction indicator as shown in Appendix 4.

Vertical solitude was defined in Section 2.3 above. The indicator for vertical solitude was calculated by first scoring managers' perception of each aspect of the institutional environment and then the general officials' perceptions of the same. Then, the (absolute value) difference of these scores in each aspect was averaged to yield an indicator of vertical solitude.46 The overall performance indicator was calculated by taking the simple arithmetic mean of the results-focus, accountability, and employee morale.

46 This formula was adopted from suggestions by Jeff Rinne of PRMPS.

21
borrow from them, the relatively poor. Therefore, the financial structure is in effect asking the poor people to subsidize the investment of rich people or large corporations. This exacerbates the skewed income disparity and concentration of income in large corporations in China.

105. Third, the low royalty levy on natural resources boosts corporate savings. In particular, due to the scarcity of natural resources in China, their prices are relatively high. However, the Chinese government imposes little royalty levy on these resources. Domestic and foreign petroleum companies are exempt from royalty taxes on crude oil for levels of production less than 1 million tons. For crude oil production above this threshold, taxes range from 4 percent to 12.5 percent depending on the level of production. Royalty rates for natural gas are much lower: Companies are exempt from taxes for natural gas production less than 2 billion cubic meters, and pay taxes at rates of 1 to 3 percent for larger volumes.<sup>80</sup> To put this into perspective, the United States requires payment of royalties at a rate of 12.5 percent for onshore crude oil and natural gas production, and 16.7 percent for offshore production, regardless of the amount of the resource being extracted.<sup>81</sup> It is clear that state-owned and private mining companies in China benefit greatly from the low royalty payments. Thus, this also leads to a transfer of wealth to a small group of people.

106. Fourth, monopoly power in financial institutions and in the telecommunications industry makes these industries extremely profitable, with only a limited number of people having access to these monopoly rents. For example, the top five banks in China have experienced a 536 percent increase in profits in the past 7 years, recording RMB 545 billion in profits in 2010. Notably, the Agricultural Bank of China enjoyed an impressive 4,838 percent increase in profits during this period (Figure 50). In the telecommunications sector, China Mobile has had a 24fold increase in profits during the past decade (Figure 51), and maintains a market share of 69.3 percent.<sup>82</sup> Since the ̳ `抓大放小` ‘- reforms<sup>83</sup> SOE‘s have not been required to pay out dividends to their shareholders. With industrial profits increasing to trillions of RMB (Figure 52), corporate savings have increased as well. These are the four most important sources explaining the high rate of savings in China.

> 80 These rates are stipulated in the “Provisional Regulations on the Payment by Sino-Foreign Cooperative Joint Ventures of Royalties for the Exploitation of Onshore Oil Resources,” updated last in 1995. 81 American Petroleum Institute, “The Facts about the U.S. Royalty Collection System.” June 6, 2011.

> 82 China Mobile Limited Annual Report 2010, pg 3.

> 83 A broad term meaning “grasping the big, letting go of the small” which refers to the reforms described above.

67
poverty, _P_ . Real GDP per worker (in thousand PPP US dollars) is taken from WDI. The annual change rate of each variable _x_ , _dx_ / _x_ , is given by (ln _xt_ − ln _xt_ −t )/ t , where _t-τ_ and _t_ are the initial and the final year of the period, respectively. The number of floods during the spell is used to control for possible differences in migration patterns to metropoles and the missing middle in response to natural hazards.<sup>17</sup> The metropolitan share of the population, _sU_ , is represented by the share (in %) of the population living in cities with one million or more taken from the United Nations’ _World Urbanization Prospects (UNWUP)_ . To check for robustness, the 750,000 cut-off will also be used.<sup>_18_</sup> In the UNWUP, the population data are available every five years. The data for other years are interpolated, assuming a constant growth rate during each 5-year period. Two sources of data are used to calculate the share (in %) of people in agriculture, _sA_ : FAO’s database and the World Bank’s _World Development Indicators_ (WDI). The coverage of FAO’s database is larger than that of WDI, and the FAO data are used whenever they are available. The share of the population engaged in non-farm activities located in the intermediate space or the “missing middle”, _sN_ , is defined as the residual, i.e. _sN_ ,=100- _sU-sA_ . Given the (deliberately) narrow definition of urban areas (i.e. only the mega cities), _sN_ includes people living and employed in secondary towns as well as those engaged in off-farm employment in rural villages.

It is worth highlighting that the study differs herein conceptually from the literature, which typically deploys either a spatial (rural-urban) or an occupational (agriculture-nonagriculture) dichotomy. Here it is the combined effect of occupational and spatial mobility that is considered, allowing each of them to occur separately or jointly. As highlighted by Beegle, De Weerdt, and Dercon (2012), both occupational and spatial mobility matter for poverty reduction. Yet, in the absence of individual tracking data, separating them becomes difficult, as some people in rural areas are already mainly engaged in rural nonfarm

> 17 http://www.emdat.be/, June 2009.

> 18 <u>http://esa.un.org/unup/. April, 2008.  In the urban economics literature, urban concentration is often</u> measured by urban primacy—the share of the urban population living in the largest city. This is adequate when most data points are small countries. The approach is too limited when the spells cover many large countries, as is the case here (Henderson, 2010).  They often have more than one metro area.

12
**Silverman,** B.W., "On thE Estimation of a Probability Density Function by the Maximum Penalized Likelihood Method," _Annals of Statistics,_ 1982, _10,_ 795-810.

- , "Spline Smoothing: The Equivalent Variable Kernel Method," _Annals of Statistics,_ 1984, 12, 898-916.

- **Takayama, Takashi and George G. Judge,** _Spatial and Temporal Price and Allocation Models,_ Amsterdam: North-Holland, 1971.

- **Timmer, C. Peter,** _Getting Prices Right: The Scope and Limits of Agricultural Price Policy,_ Ithaca: Cornell University Press, 1986.

- **Venables, Anthony J. and Nuno Limao,** "Geographical Disadvantage: a von Thunen Model of International Specialization," 1999. (mimeograph).

- **von Thunen, Johann Heinrich,** _Isolated State: an English Edition of Der Isolie0te Staat,_ Oxford: Pergamon Pre3s, 1966.

- **Yang, Dennis** T., "Education and Off-Farm Work," _Economic Development and Cultural Change,_ 1997, 45 (3), 613-632.

40
Latin America’s indigenous people earn less and complete fewer years of schooling than Latin America’s non-indigenous people do. The two deficits are linked: indigenous people generally receive lower returns to schooling than non-indigenous people do, partly because indigenous children enter school with disadvantaged backgrounds, partly because indigenous students learn in lower-quality schools that reinforce that disadvantage and partly because indigenous adults may face discrimination in the labor market (Psacharopoulos and Patrinos 1994).

Indigenous people in Latin America, who number between 22 and 34 million, are no homogenous group. Approximately 30 percent of Peruvians in urban areas are indigenous. In Peru’s urban areas, most indigenous people speak both Spanish and an indigenous language, while in Peru’s rural areas, few indigenous people speak Spanish. In Guatemala, approximately 40 percent of the population is indigenous and speaks one of over 20 Mayan languages. Three of four indigenous people in Guatemala live in rural areas and about half of employed indigenous people work in agriculture (Shapiro 2006). In absolute terms, Mexico has the largest indigenous population in Latin America, with the states of Oaxaca, Yucatan, Puebla, Veracruz and Chiapas registering the highest indigenous population density (Gonzalez 1994). According to the 1990 census, 23 percent of the indigenous people in Mexico spoke Nahuatl, 14 percent Mayan, and 7 percent spoke Mixteco and Zapoteco. Based on various censuses, the indigenous population has, however, been declining. By the year 2000, the indigenous language speaking population was about 7 percent, down from 8 percent in the 1990s, 10 percent in 1950 and 14 percent in 1930.

2
# _2.3. What sectors are driving the fall of China/India-LA RCAs correlation?_

The fact that the specialization pattern of LA is moving away from the specialization pattern of China and India may be due to various forces. On the one hand, LA may be specializing in a few products (concentrating its exports) where China’s presence is not very strong or declining. On the other hand, LA may be diversifying its export bundle into new sectors. To ascertain what is driving the evolution of the correlation of RCAs, Figures 4 to 8 also show the evolution of an export concentration Herfindhal index (higher values indicate a more concentrated export bundle), where the right hand side vertical axis provides the scale. The evidence suggests that LA as a whole has been moving towards higher concentration of its export bundle throughout the period. This trend is mainly driven by a strong move towards concentration of exports by the group of Andean countries since the mid 1990s and some mild trend towards concentration in the Southern Cone. In contrast, Central America has shown some strong diversification of its export bundle, and Mexico some mild diversification. During the same period China moved towards a more concentrated export bundle, in particular since the mid 1990s, while India has shown some diversification. Overall this suggests that the explanation behind the falling correlation between LA and China is that LA and China are moving towards more specialization but in a different set of products. In the case of India, the trend would also be explained by the diversification of India’s export bundle.

Some key questions remain unanswered. For example, in which sectors is LA specializing and what are the sectors in which China and India are specializing? In order to answer these questions we look at the evolution of RCAs between 1990 and 2004 at the 3 digit level of the ISIC for LA, the Andean group, Central America, Mexico, the Southern Cone, China, and India. In order to focus on the observed negative trend in the correlation between RCAs of LA and those of China and India, we identify 6 potential cases which can explain this trend:

1. China/India RCA increases, and LA (or any of the LA sub-groups) RCA remains constant (China/India is specializing in a product where LA comparative

9
Table 9: Multivariate estimations of push vs. pull entry at the district-industry level

||All ind|ustries|Tradable|industries|Non-Tradab|le industries|
|---|---|---|---|---|---|---|
||Full sample|New firms|Full sample|New firms|Full sample|New firms|
||(1)|(2)|(3)|(4)|(5)|(6)|
|||DV: Log|Panel A: U<br>employment in inf|rban areas<br>ormal sector 1-pe|rson firms||
|Log employment in informal sector|0.116***|0.068***|0.137***|0.095***|0.103***|0.049***|
|firms with 2-5 employees|(0.008)|(0.006)|(0.013)|(0.010)|(0.011)|(0.007)|
|Log employment in informal sector|0.033***|0.021***|0.048***|0.039***|0.023**|0.009|
|firms with >5 employees|(0.008)|(0.007)|(0.012)|(0.012)|(0.011)|(0.008)|
|Log employment in organized sector|-0.026***|0.008|-0.033**|0.013|-0.023*|0.003|
|firms with 1-50 employees|(0.010)|(0.008)|(0.016)|(0.013)|(0.013)|(0.009)|
|Log employment in organized sector|-0.008|0.006|-0.008|0.009|-0.008|0.002|
|firms with >50 employees|(0.008)|(0.006)|(0.012)|(0.011)|(0.011)|(0.008)|
|Observations|30,882|30,882|12,449|12,449|18,433|18,433|
||||Panel B: R|ural areas|||
|Log employment in informal sector|0.114***|0.079***|0.084***|0.091***|0.139***|0.072***|
|firms with 2-5 employees|(0.010)|(0.007)|(0.016)|(0.012)|(0.012)|(0.009)|
|Log employment in informal sector|0.011|0.026***|0.009|0.013|0.011|0.028***|
|firms with >5 employees|(0.011)|(0.008)|(0.020)|(0.017)|(0.012)|(0.010)|
|Log employment in organized sector|-0.039***|0.003|-0.068***|0.017|-0.019|0.001|
|firms with 1-50 employees|(0.012)|(0.009)|(0.021)|(0.014)|(0.015)|(0.011)|
|Log employment in organized sector|-0.025***|-0.002|-0.038**|-0.001|-0.013|-0.002|
|firms with >50 employees|(0.010)|(0.007)|(0.017)|(0.012)|(0.012)|(0.009)|
|Observations|27,867|27,867|9,081|9,081|16,961|16,961|

Notes: Estimations include district-industry, industry-year, and state-year fixed effects and control for state-industry-year output trends. Estimations report standard errors clustered at the district-industry level and weight observations by sample weights. District-industry cells with less than 4 observations were dropped from the sample in order to incorporate fixed effects. * significant at 10% level; ** significant at 5% level; *** significant at 1% level.
conditioning variables. Table 12 shows that both approaches return qualitatively and quantitatively similar estimates of the causal impact of the IHC program across all five outcome variables, relative to Table 9.

**<u>Table 12. Estimates of the Impact of the IHC Program on Outcome Variables – Robustness</u>**

|||||Hodg|esLehmann|
|---|---|---|---|---|---|
||Median<br>treatment<br>effect|Mean<br>treatment<br>effect|Mean<br>percentile rank<br>statistic (�)|Estimate|95%<br>confidence<br>interval|
|||**(log**<br>|**) Median clearan**<br>|**ce time**<br>||
|9-months post treatment|-0.097|-0.090|0.428|-0.077|(-0.240, 0.083)|
|Matchallpre-treatment outcomes|-0.098|-0.153|0.436|-0.074|(-0.213, 0.082)|
|Match average||||||
|pre-treatment outcomes|-0.177|-0.247|0.401|-0.161|(-0.346, 0.025)|
|||**(log) Cle**<br>|**arance time– 75**<sup>**t**</sup><br>|<sup>**h**</sup>**percentil**<br>|**e**<br>|
|9-months post treatment|-0.522|-0.440|0.308***|-0.687|(-0.687,-0.065)|
|Matchallpre-treatment outcomes|-0.436|-0.467|0.305***|-0.419|(-0.659,-0.178)|
|Match average<br>pre-treatment outcomes|-0.407|-0.514|0.317***|-0.379|(-0.725,-0.108)|
|||**(log) Cle**<br>|**arance time– 90**<sup>**t**</sup><br>|<sup>**h**</sup>**percentil**<br>|**e**<br>|
|9-months post treatment|-0.207|-0.408|0.379|-0.205|(-0.649, 0.075)|
|Match all pre-treatment outcomes|-0.497|-0.554|0.320***|-0.490|(-0.834,-0.148)|
|Match average<br>pre-treatment outcomes|-0.497|-0.625|0.344**|-0.516|(-0.877,-0.109)|
||||**Inspection rate**|||
|9-months post treatment|-0.008|-0.015|0.420|-0.006|(-0.023, 0.013)|
|Match all pre-treatment outcomes|-0.014|-0.016|0.421|-0.011|(-0.027, 0.006)|
|Match average<br>pre-treatment outcomes|-0.011|-0.010|0.440|-0.006|(-0.025, 0.012)|
||||**(log) Import val**|**ue**||
|9-months post treatment|0.076|0.009|0.562|0.082|(-0.090, 0.255)|
|Match all pre-treatment outcomes|0.044|-0.032|0.529|0.028|(-0.146, 0.171)|
|Match average<br>pre-treatment outcomes|0.054|0.036|0.562|0.097|(-0.084, 0.245)|

Notes: ** and *** indicate significance at the 5% and 1% confidence levels, respectively. The 95% critical values for the mean percentile rank statistic in the case of 15 firms (used with the 9-month treatment period) are 0.354 and 0.646. The 95% critical values for the other exercises (with 21 firms) are 0.377 and 0.623.

Our preferred SCM estimates, which we call the benchmark case, include three pretreatment outcome variables in the conditioning set. The choice of these estimates as the benchmark case is supported by a data-driven metric proposed by Dube and Zipperer. For each estimator we calculate the root mean squared prediction error (RMSPE) for both the pre-treatment period and for the six-month post-treatment windows among the placebo firms. Since the placebos 38
# **1 Introduction**

Tracking poverty at the global, regional, and national levels helps international organizations and the development community identify lagging regions and allocate their resources accordingly (see e.g. Ravallion (2018), Ferreira et al. (2015), Bourguignon (2017)). Poverty is commonly measured by the percentage of the population whose income or consumption expenditure falls below a poverty line. The household income and expenditure data used to estimate poverty come from household surveys that are conducted every 1-5 years by the national statistical agencies. Individual countries use these data to monitor poverty at the national and subnational levels.

Elbers et al. (2007) demonstrates the value of estimating poverty at a more disaggregated level. Specifically, they estimate by how much governments could lower the costs of providing resources to the poor if they had access to a poverty map that provides estimates of poverty down to the district or municipality level. Their simulation study using data from Madagascar, Cambodia, and Ecuador suggests that the gains can be substantial. In all three countries, the reduction in poverty that is achieved with a uniform transfer to all households can often be realized with less than half the budget if the transfers could be targeted on the basis of municipality level estimates of poverty. Unfortunately, collecting data on household income and expenditure is costly and time consuming (Kilic et al. 2017; Fujii and van der Weide, 2020). As a result, survey samples tend to be relatively small, permitting only a limited degree of disaggregation, often not beyond large sub-regions.

Estimating poverty at the small area level requires alternative sources of data, ideally a population census, that are able to provide complete coverage of the population. Elbers, Lanjouw, and Lanjouw (2003), hereafter referred to as ELL, pioneered the small area estimation of poverty by combining household survey with population census data. The proposed approach imputes household income or consumption expenditure (multiple times) for each household into the population census, and then aggregates the multiple imputed values to obtain estimates of poverty at the municipality or district level. The population census arguably provides the richest possible data source for this purpose. It contains a comprehensive set of variables that are highly correlated with household income or consumption (although not, usually, income itself). Specifically, a census typically includes information on demographics, education, employment, housing characteristics, asset ownership, and community services (i.e. local amenities) for all households in the population. In the ELL procedure, a household survey is used to infer the relationship between these variables and household income, taking advantage of the fact that many of these variables are available in both the census and household survey.

2