rafmacalaba/datause-encoder-data huggingface dataset:
-3-

developed countries (the United States has collected census data for nearly 200 years) an.i even in some developed countries as well (e.g. Sri Lanka undertook a household expenditure survey in 1953 and a census in 1901). However, for most developing countries data of this type were hard to come by, and the data that were available suffered from a variety of problems, such as non-representative samples, doubtful reliability, and very long time lags between data collection and the production of published reports.

The international community undertook several programs to improve data collection efforts at the household level in developing countries. One of the most important is the United Nations Household Ourvey Capability Program (UNHSCP), which began in the late 19?0's. An early description is given in UNSO (1980). In contrast, data colle. Lon at the household level has never been done at the IMF and before the 1980's was undertaken only for special purposes at the World Bank (e.g. an urban household survey in Bogota, Colombia, in 1973).

Parallel to these developments, in the early 1970's there was an increasing recognition, both inside and outside the World Bank, that development efforts needed to pay more attention to the scope and nature of poverty in the Third World. Robert MacNamara, the president of the World Bank form 1968 to 1981, announced a major change in the focus of World Bank activities in Nairobi, Kenya, in September, 1973: the World Bank would promote projects designed to directly improve the living conditions of the poor. To support this work, the World Bank's Development Research Center, under the direction of Hollis Chenery, turned much of its attention to questions of poverty and inequality. This increased emphasis on the poor brought out the need for better data at tht household level, and it eventually Led to a new
attributable to both increasing precipitation and glacier melting due to temperature increases. Our review shows that, overall, glaciers are losing mass in both eastern and western Himalaya, but not in the Karakorum region where some glaciers have positive mass balance.

While several glacier lake outburst floods (GLOFs) have occurred historically in the region, threats appear to have increased due to the increasing number of expanding lakes and the formation of new ones. Literature suggests 25-45 ha/year growth of glacial lakes in the Nepal and Bhutan from 1990-2009, which far exceeds the growth rate of the western India, which averaged at 4 ha/year. Several future climate projections suggest a warmer region, generally agreeing with observed trends in terms of seasonality (winter projected to be warmer), and higher temperature change at high altitude (Tibetan Plateau and high Himalayan region seeing more temperature rise). GCMs/ RCMs project increased monsoon precipitation, reduced winter precipitation, highly variable seasonal precipitation patterns, and increased frequency and magnitudes of daily precipitation and temperature extremes. A high-resolution RCM indicates an increase in frequency of heavy precipitation events (days with ≥10 mm rain) during monsoon in the whole Himalayan region.

Earlier literature was dominated by climate change sensitivity analysis and impact studies particularly with respect to temperature increase. Simulations of temperature increase while keeping precipitation unchanged suggest reduced glacier extent, increased snow and glacier melt, initial increase but eventual decline in streamflow, and increased threat of GLOFs. The literature shows a higher sensitivity to climate change of Karakorum glaciers than Himalayan glaciers, with meltwater contribution to streamflow higher in the former due to aridity. While in the long run deglaciation is a concern for streamflow under projected climate change, the severity will be largely determined by future precipitation. Despite inherent uncertainties of future precipitation scenarios, as well as data and modeling uncertainties, the existing studies point towards higher monsoonal streamflow and thus an increased threat from floods in the future. Other potential negative impacts of potential climate change include increased surface runoff in wet periods and less streamflow in dry seasons. Depending on the area-altitude curve and future precipitation, long-term impacts on dry season streamflow could be significant, particularly for small, upland, glaciated catchments. In small basins with significant glaciated area, the threat of GLOFs depends on local topography, geologic, and glaciologic conditions.

26
# Tables

## <u>Table 1. Distribution of spells by regions</u>

||**Pa**|**nel A: All spel**<br>|**ls**<br>|**Panel B:**<br>**hig**|**Spells with po**<br>**her than 2 per**<br>|**verty rates**<br>**cent**<br>|
|---|---|---|---|---|---|---|
||Number of<br>spells|Share of<br>spells,<br>unweighted|Share of<br>spells,<br>weighted|Number<br>of spells|Share of<br>spells,<br>unweighted|Share of<br>spells,<br>weighted|
|East Asia and Pacific|126|8|12|69|12|10|
|Europe and Central Asia|884|55|35|109|19|20|
|Latin America and Caribbean|362|23|15|278|48|22|
|Middle East and North Africa|72|4|9|11|2|6|
|North America|46|3|1|0|0|0|
|South Asia|31|2|4|28|5|6|
|Sub-Saharan Africa|83|5|24|80|14|36|
|Total|1,604|100|100|575|100|100|

<u>Notes: Panel A shows the regional distribution of all 1,604 consecutive spells, while Panel B focuses on the 575 spells where</u> both start and end-year poverty rates are above two percent.

18
two. To know the actual tariff, where specific tariffs are specified, we must calculate their ad valorem equivalents. This is a non-trivial task, and some previous _unpublished_ efforts have simply ignored the specific tariffs, resulting in an underestimate of the actual tariff rates; and, more importantly, (2) until recently, tariff line data on the value and quantity of imports have not been available, and the available more aggregated data were not available electronically.<sup>4</sup> Consequently, earlier calculations were necessarily based on aggregates of tariff lines. Thus, these studies were simply not capable of assessing the tariff rates except at somewhat aggregate levels, and since the calculations were based on averages, the results were imprecise.

In section II we discuss our key results. We calculate average tariffs and standard deviations based on an unweighted basis and also an import trade weighted basis. In section III we discuss the data set. We discuss methodology in section IV. The key results are presented in tables 1-8. We have a one-page note on technical details in the calculations following the tables. In Appendix A, we present tables with more detailed results, namely of 2-digit industry calculations and the tariff lines with tariff rates above 50%. In Appendix B, we provide a second set of parallel tables we call the “estimated collected tariff rates.” These tables reflect the fact that imports from CIS countries enter tariff free with some notable exceptions, so the collected tariff rate is lower than the MFN rate.<sup>5</sup> As we discuss the results, we evaluate the trends in the tariff structure based on the view, elaborated by Tarr (2002), that low and uniform tariffs are preferable to high and diverse tariffs. This paper is methodological and descriptive; so the reader interested in a discussion of tariff policy should consult Tarr (2002) and as applied to Russia Tarr (1999).<sup>6</sup>

> 4 The previous data available were the annual hard copy reports of the Russian Customs Committee. These reports aggregate information from the tariff line level, so that information is reported on about 1700 aggregated product codes out of about 11,000 tariff lines. Previously, we manually entered these data in order to perform the calculations that were possible with those data.

> 5 In addition, personal imports and private imports enter tariff free. Since we do not have data on personal and private imports we do not adjust our computations of the estimated collected tariff rates for these imports. Thus the actual collected tariffs are less than our estimate.

> 6  For other discussions of trade policy in Russia, the reader should consult Michalopoulos and Tarr (1994. 1996, 1997), Tarr (1993, 1994) and Tarr and Thomson (2004).

4
is robustly correlated with lnvestment.28 Regressions (6) and (7) on Table 2 Include the variables of the Barro-Wolf data set that are highly correlated with the initial level of income- ('able 2): the enrollment ratios for primary and secondary education in 1960 (PR1IH60 and SEC60, respectively) and the dumy variables forAfrica and Latin America. Not surprisingly all the variables in the regression become Insignificant, with the exception of the export share, as a result of multicolinearity. In contrast with what one might expect on the basis of life-cycle theories of savings, population-related variables (the growth rate of population, measures of the age structure, mortality rates) are insignificant when included in regression (3).

Table 3 reports similar results for a set of regressions in which the share of investment Is used as a proxy for the savings rate. These regressions are presented to show that the results that we reported are not likely to be an artifact of measurement error in the savings rate series.

# VI. CONCLUSION

The new growth literature has Identified several mechanisms which can potentially explain differences In rates of growth across countries. Unfortunately most of these mechanisms fall to generate different rates of expansion in the presence of International capital markets. Existing models predict that a libe-alization of capital flows would be followed by "capital flight" from stagnated countries, where the rate of return Is low, to fast growing countries. As a result, the growth rates of consumption and of GNP would be equalized around the world.

A significant part of the new growth literature has been 28Romer (1990b) reports results similar to those of this equation for the investment share. He found that per capita real Income In 1960 is the only variable that has explanatory power for the Investment share after one controls for the influence of the share of exports in GDP.

35
34

**Table 7:  Proportion and Distribution of Remittance-Receiving Households by Decile Group, Ranked by Predicted Per Capita Household Expenditure, Excluding Remittances**

|Rank|Households<br>receiving<br>internal<br>remittances<br>in given<br>decile (from<br>Ghana)|Distribution<br>of<br>households<br>receiving<br>internal<br>remittances<br>(from<br>Ghana)|Internal<br>remittances<br>as percent of<br>total per<br>capita<br>household<br>expenditure<br>(including<br>remittances)<br>Observed|Households<br>receiving<br>international<br>remittances<br>in given<br>decile (from<br>African or<br>other<br>countries)|Distribution<br>of<br>households<br>receiving<br>international<br>remittances<br>(from<br>African or<br>other<br>countries)|International<br>remittances<br>as a percent<br>of total per<br>capita<br>household<br>expenditure<br>(including<br>remittances)<br>Observed|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|(decile)|(percent)|(percent)|(percent)|(percent)|(percent)|(percent)|
|Lowest<br>10|48.42|25.19|8.00|1.00|2.86|11.27|
|Second<br>10|40.86|14.84|57.22|1.43|2.86|8.67|
|Third 10|<br>37.85|12.77|11.27|2.56|4.76|62.18|
|Fourth<br>10|36.83|11.82|15.60|3.49|6.19|21.75|
|Fifth 10|33.52|10.18|19.67|4.83|8.10|28.34|
|Sixth 10|27.84|8.46|17.12|9.66|16.19|26.51|
|Seventh<br>10|21.57|6.38|19.06|6.41|10.48|17.64|
|Eighth<br>10|19.71|5.87|19.58|8.12|13.33|40.74|
|Ninth 10|<br>11.90|3.62|21.20|12.18|20.48|28.42|
|Top10|2.84|0.86|35.62|8.81|14.76|22.93|
|Total||100.00|||100.00||

Notes: Households ranked into decile groups on the basis of predicted per capita household expenditure (excluding remittances). Predicted household expenditure is based on equation (10) in text. However, data on remittance reception and the amount of remittances received is based on observations. Columns (1) and (4) show the proportion of households receiving remittances in the given deciles. Columns (2) and (5) show the distribution of remittances by deciles. Columns (3) and (6) show remittances as a fraction of household expenditure (including remittances).

Source: 2005/06 Ghana GLSS 5 Survey (sub-sample).
revisions to incorporate some refinements developed by Rausser and Love. The grain supply com-onent uses logit equations to represent participation in the feed grain program following the spirit of the work by Chambers and Foster (1983) and later empiricized by Rausser and Love. The acreage equations in particular depart signiticantly from previous econometric practice and incorporate important aspects of the structural relationships among import, at program and market variables in the spirit of the intuitive and conceptual framework developed by Gardner (1988) and LiL.s (1988). The crop supply models are estimated using annual data while the crop demand models and meat supply and demand models are estimated using quarterly data.

The CroR Supply Structure The basic form of the acreage equations is as follows. First, acreage in a market free of government programs is assumed to follow (1) Af - Af(Wn,x,,Af'_l) where

- Af - free market acreage of the crop in question

- anticipated short-run profit per acre from production of the crop in question with free market price

- =a -anticipated short-run profit per acre from production of competing crop(s)

Af 1 - lagged free market acreage (to represent production fixities, etc.). Profit per acr_ is defined by price times yield less per acre production cost, e.g., (2) 7r n P. Y, - C where

- P. market price

- Ya- expected yield

2
8 We chose December 31 because it often is used to represent annual statistics and because data from other countries participating in the same IDB project used this date.

- 9 The official statistics were obtained from Instituto Mexicano del Seguro Social (2001).

- 10 Most of the work in the literature focuses on job flows due to data constraints.

11 It is worth noting that both the job-creation percentage and the job-destruction percentage rose from 1989 to 1990.

> 12 A concern about Table 6 is the endogenous movement of establishments into different size categories over time. This concern is related to other regression-to-the-mean criticisms discussed in Davis et al. (1996) and Davis and Haltiwanger (1999, pp. 2743-2744).  If we assign firms on the basis of base year size, then our analysis is accurate.  Nevertheless, the results seem robust to the way of allocating firms into size categories.  To address this problem, however, we use the average of the two employment measurements to allocate firms.

13 Recall that the denominator for all percent-change formulae is the sum of current and previousyear’s employment. Since previous-year’s employment is zero by definition for all births, the formula reduces to 200 times current employment divided by current employment or simply 200.

14 See Alvarez and Robertson (2004).

15 We define the border region to consist of the following cities: Mexicali, Tecate, San Luis Río Colorado, Tijuana, Ciudad Acuña, Piedras Negras, Ciudad Juárez, Nogales, and Agua Prieta.

16 Gender differences in risk aversion have been the subject of growing academic debate. See Schubert et al (1999) for a review of an aspect of this debate.

17 We thank a referee for this possibility.

18 An employee is placed in an age category based on his or her age on December 31 of the year listed in the table. For example, if an employee who is 59 on December 31, 1999 is no longer with the establishment on December 31, 2000, we treat this as a separation for the age category 60-65 in the year 2000.

23
place on regional standards but positive and significant when it takes place on international ones. For South-South trade, a negative and significant coefficient is obtained on the interaction term, suggesting that the deflection effects are at play for products affected by a NTM and for which standards are harmonized with the North.

# **Table 4. Product-level Estimations**

|Specification|Poisson maximum likelihood|
|---|---|
|Trade flows|South-South trade<br>North-South trade|
|Dependent variable|Bilateral imports between<br>Southern countries<br>Bilateral imports of the Northern<br>country from the Southern partner|
|Products|HS6 products|
|Years|2004|
|Model|(1)<br>(2)|
|N-S EIA for the importer|-0.79<sup>a</sup><br>(0.28)|
|Product NTM x N-S EIA & stds harm. with|-1.01<sup>a</sup>|
|the North for the exporter|(0.21)|
|Product NTM x N-S EIA with stds harm. &|-0.68<sup>a</sup>|
|promotion of regional stds|(0.18)|
|Product NTM x N-S EIA with stds harm. &<br>promotion of international stds|0.97<sup>a</sup><br>(0.09)|
|Observations|9,944,060<br>873,810|
|Log pseudolikelihood|-1,023.01<br>-1,456.94|
|FE exporter|Yes|
|FE importer|Yes|
|FE dyad|Yes|

_Source:_ Authors’ analysis based on data sources discussed in the text.

_Note_ : The sample for South-South trade is restricted to the sub-sample of Southern exporter countries with an EIA with a Northern partner. Robust standard errors (importer-exporter clustered) in parentheses.<sup>a</sup> , denotes significance at the 1% level.

33
# **Table 2. Descriptive Statistics for Students in Brazil and Ecuador**

**_Based on Individual-Level Data for Students Matched to Surveyed Programs_**

|**Variable**|**Br**|**azil**|**Ecu**|**ador**|
|---|---|---|---|---|
||**Mean**|**Std. Dev.**|**Mean**|**Std. Dev.**|
|**Panel A.Student characteristics**|||||
|Age|24.53|5.65|24.66|3.40|
|Female|0.59|0.49|0.62|0.49|
|Mother's education:|||||
|Less than primary|0.12|0.32|-|-|
|Primary school|0.16|0.36|0.15|0.36|
|High school|0.19|0.39|0.28|0.45|
|Higher education|0.07|0.26|0.52|0.50|
|Unknown|0.46|0.49|0.05|0.22|
|Student has children|-|-|0.27|0.44|
|Socioeconomic index (std)|-|-|0.01|0.97|
|Student administrative variables(PCA Score)|0.01|1.25|0.07|1.32|
|**Panel B. Peer(average) characteristics**|||||
|Age|24.59|1.70|24.66|1.81|
|Percentage of female peers|0.56|0.27|0.62|0.30|
|Percentage of peers by mother's education:|||||
|Less than primary|0.12|0.06|-|-|
|Primary school|0.15|0.06|0.15|0.14|
|High school|0.18|0.07|0.28|0.19|
|Higher education|0.06|0.06|0.52|0.24|
|Unknown|0.49|0.13|0.05|0.06|
|Percentage of peers with children|-|-|0.27|0.18|
|Peers' socioeconomic index (std)|-|-|0.01|0.37|
|Peers' administrative variables(PCA Score)|-0.22|1.57|0.14|1.36|
|**PanelC. Own andpeers’previous labor market outcomes**|||||
|Percent of time employed before graduation/entry|46.32|43.50|19.02|36.24|
|Average peers' percent of time employed before graduation/entry|49.13|13.50|19.02|21.26|
|Employed at least one month before graduation/entry|0.64|0.48|0.26|0.44|
|Pct. ofpeers employed at least one month beforegraduation/entry|0.65|0.18|0.26|0.25|
|**Panel D.Outcomes**|||||
|Average monthly wage conditional on working (USD, PPP)|1,031.40|577.56|876.06|381.36|
|Average monthly wage (USD, PPP)|715.62|678.84|300.29|472.02|
|Percent of time employed after graduation|53.37|43.97|24.01|38.23|
|Employed at least one month aftergraduation|0.70|0.46|0.35|0.48|
|_Number of observations_|7,843||1,239||

_Sources_ : Own calculations using individual-level administrative data for Brazil and Ecuador. For more details on data sources and variable definitions, see Section 3.3 and Appendix 1.

_Notes_ : In this table, the unit of observation is a program graduate. Statistics are weighted using WBSCPS sampling weights. A higher value of the socioeconomic index indicates a higher socioeconomic status. For a given student, her peers are the other students in her program and cohort; cohorts are defined in Section 3. Means of PCA scores are different than zero due to the use of sampling weights. In Panel C, previous labor market outcomes are pre-graduation (Ecuador) and pre-enrollment (Brazil). Average monthly wage conditional on working corresponds to individuals who are employed at least one month after graduation. For the remaining individuals, average monthly wage equals zero; therefore, the “Average monthly wage” row shows unconditional average monthly wages (which equal zero for individuals who do not work formally at all). Wage statistics trim off the 1<sup>st</sup> and 99<sup>th</sup> percentile. Purchasing power parity (PPP) adjustment of wages uses the 2019 (Ecuador) or 2017 (Brazil) PPP conversion factor.

43
variables are orthogonal to the country ...xed e¤ects, must be satis...ed.<sup>13</sup> We test the validity of moments conditions by using the test of overidentifying restrictions proposed by Hansen and by testing the null hypothesis that the error term is not second order serially correlated. Furthermore, we test the validity of the additional moment conditions associated with the level equation using the Hansen di¤erence test for all GMM instruments.<sup>14</sup> A particular concern related to this method is the risk of instrument proliferation. In fact, if the use of the entire set of instruments in a GMM context gives signi...cant e¢ ciency gains, on the other hand, a large collection of instruments could over...t endogenous variables as well as weaken the Hansen test of the instruments’ joint validity.<sup>15</sup> The instrument proliferation problem is particular important in small samples, but unfortunately there is no formal test to detect it, even if a possible rule of thumb is to keep the number of instruments lower than or equal to the number of groups.<sup>16</sup> In our analysis, we consider the lagged dependent and all the control variables of interest as predetermined, instrumented with "internal instruments", using their own one-period and further lags, according to the speci...cation.

# 2.2.2 Data

Our data set is a ...ve-year unbalanced panel spanning the period between 1980 and 2005, where the start of the date refers to the dependent variable (i.e., t = 1980, t � 1 = 1975). In our sample, we are considering only developing countries, and they enter the panel if they are independent at time t � 1. The data set employed in our analysis is an updated version of that used by Acemoglu et al. (2005) for the democracy indicators (except for economic freedom) and all the control variables. The migration data come from Defoort (2008).

# Democracy

Data on democracy are taken from the Freedom House data set, from the POLITY IV data set, and from the Economic Freedom of the World project (Simon Fraser

> 13For the level equation the following moment conditions are to be satis...ed:

> 14This test is not reported in the tables, but it is available upon request.

> 15See Roodman (2009)

> 16The xtabond2 command, implemented in Stata, gives a warning when instruments exceed the number of groups.

10