rafmacalaba/datause-encoder-data huggingface dataset:
and central regions, exploiting<sup>regionalvariationinthewages24.Thefinalsample consists ofa total</sup> of 1272 households with<sup>707householdsfrom thenortheastregionand565households fromthe central</sup> region.

Figure 2 in page 39 displays the<sup>occupationalmapgeneratedusingtheestimatedparameters.For</sup> the SES dataset, observations<sup>in1981seemtobelessconstrainedthanthosein1976,naturallyasthe</sup> country was growing and wealth<sup>washigher. For theTownsend-Thaidataset, thecentralregionappears</sup> to be less credit constrained than<sup>theNortheast,reflectingperhapsthefactthatthecentralregionis</sup> more prosperous.

Table 1 reports the estimated<sup>parametersas wellasthe standarderrors25.The parametery forboth</sup> datasets was found by multiplying<sup>anestimate of thesubsistencelevelfromthe data by the scaling factor</sup> estimated. For the SES data,<sup>weusedthemeanincomeof farmersin1976whichamountedto19,274</sup> baht. Analogously, we used the<sup>average incomeofworkersintheNortheastregionwithoutaccessto</sup> credit as reported in the Townsend-Thai<sup>data,or10,727 baht.The wageforthetwotimeperiodsinthe</sup> model units at the estimated scaling<sup>factorswere</sup><sup>**_W_**</sup> **_7 6_**<sup>=0.048and</sup><sup>_w8l_=0.053for theSESdatasetand</sup> **WNE** = 0 016 and wc = 0.037<sup>forthetwo regionsinthe Townsend-Thaidataset.Themaximizedvalue</sup> of the likelihood function obtained<sup>using theSES data was -8,233.92whereastheTownsend-Thaidataset</sup> yielded a value of -616.92.

Table 1: MLE Results

||SES|<br>|Townsend<br>|-Thai<br>|
|---|---|---|---|---|
|||SE|Cffii|SE|
||Coefficient|.|oecent||
|_ScalmngFactor_|||||
|**sa**|1.4236|0.00881|1.4338|0.03978|
|_SubsistenceLevel_|||||
|-y|0.02744|0.00119|0.01538|0.00408|
|_F7:redCostDtstrib_|_uton_||||
|m|-05933|005801|0.00559|0.17056|
|_Technology_|||||
|ca|0.54561|0.06711|0.97545|0.00191<br>|
|_1B_|0.39064|0.09028|0.0033|0.00013<br>|
|p|0.03384|0.00364|0.00966|0.00692<br>|
|or|0.1021|0.02484|0.00432<br>|0.00157<br>|
||0.2582|0.03523|0.12905|0.04146|
|NumberofObs.|24,43|3|1,27<br>|2<br>|
|Log-Likelihood|-8,233.|92|-616.9|2|

The parameter value and standard<sup>errorreportedare multipliedbya factor of</sup><sup>**106.**</sup>

> 24 Unfortunately, estimating a model that, features a unique wage by exploiting the geographical variation m the wage observed **in** the data is a contradiction.<sup>Of coursecostlymigrationcould beintroducedbutwedo nottakethatexplicit</sup> approach here. We draw some<sup>confidencefromthe factthattheseare secondarydata andwearecomparingits estimates</sup> to those from the SES dataset,<sup>with its temporalvariationin wagesconsistentwiththeestimatedmodel.</sup>

> 2 5Note that e > **_"u76,_** C > **_W8i_** and ( > _wNE,_ C > _wC_ and p >0 , **_a_** _>_ **0** for both datasets as required **in** Footnote 9

13
-10

-

developing countries is 2xtraordinarily had on a range of quantitative measures, especially when compared with achievements in developed countries. But how does this compare with the situation thirty years ago? Has there been any progress? Again both reports are broadly agreed in the answers to the e questions. They conclude that, by and large, during the 1960s and 1970s th poor improved their position along che dimensions of consumption and of social indicators. In the 1980s, howevec, the picture is mixed, depending on the dimension considered and, more importantly, on where the poor lived. In particular, in the 1980s Sub-Saharan Africa experienced a decline in consumption per capita while all other regions of the developing world experienced an increase.

The UDR presents a detailed analysis of household survey data for 11 countries, mainly in Asia and Latin America, for periods ranging from l; to 24 years. The overall conclusion is that the percentage of people in consumption poverty in those continents has declined dramatically. However, because of rising population, the decline in the total numbers of the poor has been more modest. In a country like India, for example, between 1972 and 1983 it estimated that the headcount index fell from 54% to 43%, but the number of poor increased slightly, from 311.4 million to 315.0 million. Among the most dramatic poverty declines is that for Indonesia where the headcount index fell from 58% in 1970 to 17% in 1987, and the number of poor also fell, from 67.9 million to 30.0 million. Progress in poverty reduction was slowed down in the 1980s, and reversed for some countries in Latin America. Data availabilit, precludes both reports from providing extensive discussion ef income/consumption poverty in Sub-Saharan Africa. The HDR quotes an ILO
9 GTAP commodity level based on the elasticity of substitution in consumption among imported goods from different sources (Hertel et al. 2003).

The GTAP-AGR model introduces a number of modifications to the way agriculture is handled in the standard GTAP model, based on recent econometric studies. First, it incorporates a region-specific elasticity of land transformation amongst agriculture uses. While land is specific to agriculture in the GTAP model, the new parameters in GTAP-AGR make land less responsive within the agricultural sector to changes in relative agriculture prices. Second, GTAP-AGR incorporates region-specific labor and capital supply elasticities in constant elasticity of transformation functions that allocate their use between agricultural and non-agricultural sectors. The limited mobility of labor allows for wage differentials between agriculture and non-agricultural sectors, and capital too is allowed to receive return differentials between agricultural and non-agricultural activities. These supply elasticities are based on estimates from the OECD (2001). Third, the GTAP-AGR model also allows for substitution among farmowned and purchased inputs, and between the two, by calibrating each sector’s constant elasticity of substitution cost function to the region-specific Allen elasticities of substitution provided by OECD estimates. Fourth, the livestock production function is modified to capture more realistic substitution possibilities in feed demand, by modeling the substitution possibilities for feedstuffs as an additional CES nest in the sector’s cost function. This livestock production function is parameterized based on a three-stage model describing the behavior of European livestock producers, composite feed mixers, and grain producers (Surry 1990). Finally, the GTAP-AGR consumer demand system is re-specified assuming separability of food from non-food
# 1. Introduction

A gap in earnings between women and men in the labor force is a common empirical feature of the labor market in countries around the world. The gender wage gap has been documented by economists for over half a century. While its magnitude has diminished in that time, it has seldom disappeared. To date, every country has some degree of wage inequality. Even in Iceland, ranked first in the World Economic Forum’s Global Gender Gap Index 2016, women’s earnings are on average approximately 79% that of men’s. In the country with the lowest ranking, Yemen, that figure is 63%.

One factor frequently cited to explain this gap was the difference in levels of education between male and female workers. Indeed, under the Millennium Development Goal on promoting gender equality and empowering women one target was to “(e)liminate gender disparity in primary and secondary education, preferably by 2005, and in all levels of education by no later than 2015.” Over the long term, perhaps in part as a result of such directed efforts, the global trend has been a narrowing of the education gap between men and women, and even a reversal in some countries including Vietnam. Figure 1 shows that male and female enrollment rates in primary and secondary education are similar, with female enrollment exceeding male enrollment at the secondary level in East Asia and Latin America.

_Figure 1: Gender gaps in enrollment around the world_ <!-- Start of picture text --> Educational enrollment rates<br>Primary, male. Primary, female. Secondary, male. Secondary, female.<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>EAP ECA LAC MENA SAR AFR World<br>Net Enrollment in %<br><!-- End of picture text --> _Source: World Development Indicators (WDI) 2014. The latest available date was used for every region._ The persistence of the gender wage gap stands at odds with the narrowing of the education gap. Figure 2 makes this point more strongly ‐ for a set of countries in East Asia, not including Vietnam, which we explore in this study, controlling for levels of education actually increases the magnitude of the gender 2
<!-- Start of picture text --> 90 Projected Growth of Indicator 2<br>86<br>80 81<br>70 62<br>63 68<br>57<br>60<br>48<br>50<br>Actual<br>40<br>Last 3 Years<br>30<br>Last 5 Years<br>20 Last 10 Years<br>10<br>0<br>1992-2001 1993-2002 1994-2003 1995-2004 1996-2005 1997-2006 1998-2007 1999-2008 2000-2009 2001-2010 2002-2011 2003-2012 2004-2013 2005-2014 2006-2015 2007-2016 2008-2017 2009-2018 2010-2019 2011-2020 2012-2021 2013-2022 2014-2023 2015-2024 2016-2025 2017-2026<br><!-- End of picture text --> Although all projections assume the pace of growth in an indicator to be linear for the future as well, in reality the pace of growth can be very different. In fact, there are many reasons for the pace of growth to be non-linear. For example, recent improvements in the technology for data collection will certainly reduce the burden of data collection by NSOs. As shown earlier, there is a clear lag of nearly two years between data collection and dissemination of poverty data. The use of Computer Assisted Personal Interview (CAPI) has the potential to cut this lag significantly. Currently, very few countries use CAPI for data collection, and as more countries adopt CAPI, the pace of growth in both indicators of data availability could increase. Needless to say, the current delays between data collection and final dissemination are often due to weak capacity or bureaucratic inertia, and the success of CAPI would depend on making progress on those limitations.

There is also a possibility that the pace of growth declines. Some countries that can easily increase the frequency of poverty data, e.g., ECA countries, might have increased it already. The remaining countries may not have increased data collection simply because for them it is not easy to do so. For example, many countries under extreme data deprivation are small island countries that face much higher survey implementation costs per capita. Improving the frequency of poverty data in these countries is much more challenging than in other countries. Similarly, many fragile and conflict prone countries face real challenges in collecting data.

The complexity of projecting the pace of growth in both indicators can be seen from how significantly the future trends differ based on what reference period is chosen. Also, the relationship between the reference period and the pace of growth is not the same for the two indicators. Therefore, all projections should not be seen as predictions but rather seen as a means to set aspirational targets.

13
An analysis of the two waves of the UBI sample surveys‐namely, UBI 2002 and UBI 2009‐ replicates the same patterns of shifts in employment shares as we read from UBR 2001 and UBR 2010, showing a shift in employment from the traded sector to the non‐traded activities, SMEs and larger firms to micro enterprise and from startups and up and coming businesses to established firms, all on scales comparable to the corresponding shifts as observed in the UBR.

This is very important because UBI 2002 and UBI 2009 also show that the 2000s were indeed a decade of fairly rapid growth in labor productivity across all industries economywide. The structural transformation that the economy underwent over the decade as captured by the UBR censuses was thus accompanied by large gains in productivity. However, UBI data also suggest that the observed structural change probably held back the growth of aggregate labor productivity rather than fuel it and, in that sense, may not have been growth enhancing after all.

This is because UBI data show that non‐traded services, which gained 7 percentage points in employment share, had lower value added per worker on average than the traded sector, which had lost in employment share by that much. They also show that the tendency of this to reduce aggregate labor productivity was reinforced by the informalization of employment involved in the employment shifts toward microenterprises as well as by the consolidation of employment in established businesses. As one would expect labor productivity was far lower in microenterprises than in SMEs and lager establishments per UBI 2002 as well as UBI 2009. Moreover, both waves of UBI, show that labor productivity was significantly lower on average in established firms than in younger firms including startups economywide, and within broad sectors and within most single digit ISIC industries,.

The rise in aggregate labor productivity that accompanied the structural transformation of Uganda’s non‐subsistence economy in the 2000s cannot thus be explained in terms of the economies of scale or life cycle effects associated with the reallocation of labor across the size and age distributions of employers. The authors assess the merits of other possible explanations of the rise in aggregate labor productivity during the decade in a companion paper also based on the UBI data.

# **References**

Bartelsman, E., J. Haltiwanger, F. and S. Scarpetta .2009. “Cross country differences in productivity: the role of allocation and selection,” NBER Working Paper 32
Table 10c: Impacts of PROSPERA on School Enrollment: 6<sup>th</sup> Graders Enrolled in Indigenous Primary Schools in 2008 Nearest-Neighbor Matching models

|||Enrollment<br>||
|---|---|---|---|
|Variables|7thgrade 2009-2010|9thgrade 2011-2012|12thgrade 2014-2015|
||Propensityscore matchingw|ith exact matchingon age a|nd state|
|Girls|0.097***|0.089***|0.039**|
||(0.0199)|(0.019)|(0.018)|
|Observations|4,93|4,931|4,931|
|Boys|0.085***|0.127***|0.081***|
||(0.018)|(0.018)|(0.017)|
|Observations|4,751|4,751|4,751|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Variables which enter the propensity score include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, calculator, student teacher ratio, whether the school has a computer, whether the school has rooms with more than one grade, average teacher education levels and marginality index components. Community level variables include the margination index, proportion illiterate, proportion of households without piped water, proportion without electricity and proportion without drainage.

Turning to type of school, Table 11a through 11c shows effects on type of secondary school enrolled.

Table 11a: Impacts of PROSPERA on Type of Secondary School 6<sup>th</sup> Graders Enrolled in Indigenous Primary Schools in 2008

||State Fixed Effects<br>|||
|---|---|---|---|
||Tele-sec|General|Technical|
|Variables|(1)|(2)|(3)|
|PROSPERA beneficiary 2008|0.108***|-0.004|-0.105***|
||(0.015)|(0.008)|(0.016)|
|Female beneficiary|0.002|-0.014|0.014|
||(0.021)|(0.011)|(0.022)|
|Female|-0.009|0.014|-0.007|
||(0.019)|(0.01)|(0.019)|
|Constant|0.595***|0.103**|0.265***|
||(0.082)|(0.043)|(0.086)|
|Observations|9,253|9,253|9,253|
|R-squared|0.245|0.122|0.244|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Control variables include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, calculator, student teacher ratio, whether the school has a computer, whether the school has rooms with more than one grade, average teacher education levels and marginality index components. Community level variables include the margination index, proportion illiterate, proportion of households without piped water, proportion without electricity and proportion without drainage.

29
> negative sign and statistically significant with lower significance for the _Quality of Governance_ . Column (3) shows that _External Conflict_ has a significant positive effect on centralization, which is also expected as we argued above in section 4.2.  When we include both institutional variables and _external conflict_ in the same regression in column (4), we see that _External Conflict_ still has a positive and significant effect while democratic environment loses its significance and the coefficient of quality of governance turns positive.  Hence, external conflict is the only robust factor among the institutional and conflict variables. Similar to the implications of the theoretical model of Alesina and Spolaore (2003), we find that _Democratic Environment_ loses its importance when _External Conflict_ is added to the analysis. Finally, _Trade Openness_ is significant only in regressions (3) and (4). With a positive and significant coefficient, results for openness in columns (3) and (4) support Rodrik’s argument.

While results in Table 8 are interesting, these regressions are open to the criticism that the dependent variable, central government expenditure as share of GDP, may just be capturing the size of central government instead of centralization in the government sector. To address this, we ran a second set of regressions with a decentralization indicator derived from the World Bank’s Database of Political Institutions (DPI). Since our new dependent variable is an indicator variable where higher numbers indicate greater decentralization, ordered logit regressions are run as described in section 4.2. Results from those regressions are shown in Table 9. We get similar results to the ones in Table 8. We find that the size variables, _GDP per capita_ and _Population_ have the expected positive sign and they are statistically significant in most of the regressions. The coefficient of the _Share of Urban Population_ is negative and significant in all regressions, 18
to be less attractive.<sup>7</sup> The power of correlation analysis could be reduced as a result of these distortions.

The other reason for the lack of correlation could be that the time period under consideration is relatively too short. Since share prices can be very volatile in a short timespan, market capitalization can also be very volatile. Thus the relationship between financial deepening and stock market development is unlikely to be stable in the short term. This would suggest that it is necessary to use a longer time period for the data series in order for the short-term fluctuations to be canceled out. However, Figu.e 13 shows that MCY in the United States had very large cyclical movements over a period of more than 60 years. The correlations between MCY and the indicators of financial depth in USA were unlikely to be high even for this 60 year data series<sup>8</sup> . It is thus difficult to form a conclusive view.

# VI. Concluding Remarks

Results from correlation analysis on cross-sectional data for the period 1978-1990 show that there is a significantly positive correlation between the indicator of stock market development, Market Capitalization as a proportion of GDP, and indicators of financial depth. Breakdown of the time period shows that the correlations are strongest during the late 1980s, which may be explained by the fact that there was a substantial increase in foreign investment in that period into the equity markets of those economies which had better developed financial infrastructure.

7 Although this argument is plausible in theory, it is not supported by all country experiences. Careful examination of the figures depicting the movements of financial variables over time reveals that the directions of movements of these variables were sometimes opposite to what this argument would predict. This could be the result of other mechanisms which offset the effects of inflation but we have identified no consistent pattern.

> **8It** can be seen from Figure 12 that the correlations in the period 19511991 were very poor in the United States.

12
|Table1<br>|:Distributionof Private <br>|EducationStudents,1997/98<br> <br>|
|---|---|---|
|Level|Number of Students|Distribution by<br>Education Level<br>% of Students in<br>Private Schools|
|Primary|213,634|49.4<br>11.8|
|Secondary|191,663|44.3<br>36.0|
|Tertiary (Professional)|27,980|6.3<br>100.0|
|Total|432,277|100.0<br>17.7|

Source: INS, Statistical, Demographic and Social Directory (ASDS), 2002 [NOT IN REFS] Public support to the private education sector grew constantly throughout the 1990s, with spending increasing from $25.7 million in 1993 to $38.8 million in 2001. At the same time, the number of recipients of public support in the private education sector increased from 116,210 in 1993 to 223,244 in 2001, an increase of 92 percent. For example, in 1999, at the primary level close to half of the pupils in private schools benefited form public support, compared to about one-third of the students at the secondary level and about two-thirds of the higher technical education students.

Private schools can be either secular ( _laic_ ) or religious ( _confessionnel_ ). Religious schools were the pioneers in private education in Côte d’Ivoire. At the primary level, religious schools dominate the private school market, while Catholic schools dominate the private religious sector at that level. The situation is reversed at the secondary level, where secular schools dominate the private school market. In total, there were 776 private primary schools and 294 private secondary schools in 1998-99. About 50 percent were secular and the rest religious. The majority of primary religious schools were Catholic (278 of 386, or 72 percent). On the other hand, most of the private secondary schools were secular (257 of 294, or 87 percent). About 78 percent of the private secondary schools were Catholic. About 60 percent were in Abidjan (Ministry of Education 1999).

7
countries. The only exception is a new study of farmers in Africa (Kurukulasuriya and Mendelsohn 2006). This paper follows the approach taken in the African paper but explores the choices of farmers in Latin America.

The theoretical choice model is developed in the next section. Section 3 discusses how data were collected from over 2000 farmers in seven countries across Latin America. Section 4 discusses the estimation procedure and the empirical results. Three climate change scenarios from Atmospheric Oceanic General Circulation Models (AOGCM’s) are then examined in Section 5. The paper concludes with a summary of results and policy implications.

# **2. Theory**

In this paper, farmers are assumed to maximize their profits. Farmers choose the desired species to yield the highest net profit. Hence, the probability that a crop is chosen depends on the profitability of that crop. We assume that farmer _i_ ’s profit in choosing crop _j (j=1, 2,..., J)_ is where _K_ is a vector of exogenous characteristics of the farm and _S_ is a vector of characteristics of the farmer _._ For example, _K_ could include climate, soils, and price variables and S could include the age of the farmer and family size. The profit function is composed of two components: the observable component _V_ and an error term, ε. The error term is unknown to the researcher, but may be known to the farmer. The farmer will choose the crop that gives him the highest profit. When farmers select multiple crops, the crop choice is defined as the single crop with the greatest net revenue. Alternatively, we could have 3