rafmacalaba/datause-encoder-data huggingface dataset:
**Table 5. Decomposition of the total change in the**<sup>**crop sector productivity in Punjab during 1971-94**</sup>

_percent per year_

||||_System_<br>|||
|---|---|---|---|---|---|
||**_Wheat-_**<br>**_mixed_**|**_Wheat-_**<br>**_cotton_**|**_Wheat-_**<br>**_rice_**|**_Wheat-_**<br>**_mungbean_**|**_OveraU_**<br>**_Punjab_**|
|_Soil and water quality deterioration_||||||
|Water electroconductivity|-0.0241|0.0033|-0.0109|0.0013|-0.0073|
|Soil phosphorous|-0.0268|-0.0642|-0.0868|-0.0043|-0.0487|
|Soil organic matter|-0.1547|-0.109|-0.2123|-0.0655|-0.1374|
|Total soil soluble salts|-0.0567|0.0009|-0.0583|0.0001|-0.0265|
|Other factorsa|-0.3060|0.4670|-0.9250|-0.5570|-0.3140|
|Total|-0.5682|0.2982|-1.2929|-0.6257|-0.5343|
|_Technological Change_||||||
|CI &MVb|0.3499|0.7268|0.3394|0.5271|0.4970|
|_Public investment_||||||
|Roads and literacy|0.4176|0.4984|0.3714|0.4643|0.4434|
|Net effects|0.1992|1.5234|-0.5821|0.3658|0.4061|

Note: The rate of productivity change in each production<sup>system was evaluated by multiplying the negative of the coefficient with the</sup> system-level rate of per annum change (in percentage)<sup>in each factor.</sup> Based on the system-specific trend coefficients evaluated<sup>at regional mean values of the other variables used in the cost function .</sup>

**bCI** = cropping intensity, MV = percent area in modem<sup>varieties of wheat.</sup>

_Source: Authors' own calculation_ The deterioration in agro-ecosystem health,<sup>depicted by the declining trend in the resource stock</sup> variables discussed earlier, is in itself a cause for concern.<sup>This is more so when at least some of</sup> this deterioration seems to relate to intensification,<sup>especially increased use of fertilizer and</sup> water (table 6).14 We also estimated the long-term<sup>negative effect of modern inputs on</sup> productivity through their effect on degradation of the<sup>resource stock. On average, a 1% increase</sup> in tubewell water and fertilizer reduced productivity<sup>over the period by 0.011 and 0.021%</sup> through their effects on the quality of the resource<sup>stock, offsetting some of their positive</sup> production effects.

> 14 Some of these effects may be indirect. For example,<sup>use of fertilizer and water may deplete soil phosphorous and</sup> organic matter by encouraging higher cropping intensity.

21
quantitative restrictions (QRs). The implications of omitting the 2003 US QRs on apparel products are discussed below. The tariff data include detailed information on preference eligibility by product and program.

# **4.  US Tariff Preferences on Non-Agricultural Products**<sup>13</sup>

Figure 1 shows US imports of non-agricultural products in 2003 from beneficiary countries by tariff treatment. The CBERA countries are split into those eligible for CBTPA and those eligible for CBERAonly, since the CBTPA includes apparel preferences and has broader non-apparel coverage. Countries which are exclusively eligible for the GSP are split into two groups: GSP-only and GSP-LDC.

The first striking feature of figure 1 is the high overall use of the regional preference programs. In 2003, the US imported about $19.6 billion of non-agricultural products from CBTPA countries, 50% of which was apparel. Nearly half of these imports entered the US under the CBTPA program. Similarly, of the $9.8 billion the US imported from ATPA countries (11% of which was apparel), nearly 60% entered under the ATPA program. AGOA countries accounted for $19.1 billion of US non-agricultural imports, 8% of which was apparel. About 68% of these imports entered the US under the AGOA preference program. Only the CBERA-only beneficiaries made little use of regional preferences. Of the $1.8 billion US non-agricultural imports from these countries, only 7% entered under CBERA.

The second striking feature of figure 1 is the low use of GSP. Only 1% of US imports from CBPTA countries and from CBERA-only countries came in under the alternative GSP program. US imports from ATPA and AGOA beneficiaries under GSP were only 3% and 4%, respectively. Even the non-LDCs who are beneficiaries of GSP alone made little use of the program. Only 15% of the $113.8 billion of non-agricultural imports from the GSP non-LDC beneficiaries entered the US under GSP. In Customs value to estimate the AVE. 4) If there is no US import trade for the given year or the previous year, the Office of Tariff Affairs and Trade Agreements of the USITC is asked to provide an estimated AVE.

7
experiences – eliminating the possibility that higher income leads individuals to do things that raise their test scores.<sup>24</sup> Nevertheless, this fact alone does not solve all of the interpretative issues.

For our analysis, the most important issue is the source of any test score differences. It is natural to believe that schools have an influence on tests, but clearly other factors also enter. The extensive investigations of the determinants of achievement differences indicate that parents, peers, neighborhoods, and other factors enter along with school factors in determining achievement (see Hanushek (2002b)). Thus, most importantly, it is inappropriate to interpret test scores as simply reflecting school quality or school policy.

We return to a discussion of possible school policies below, but at this point it is sufficient to note that skills – however formed – have a systematic impact on earnings. Thus, if we can find approaches that increase skills reliably, the available evidence strongly indicates that individual earnings and productivity will also increase.<sup>25</sup> The other side of this issue is also important, however. Using just quantity of schooling in the earnings analyses assumes that formal schooling is the only source of skill development. But, if a variety of other inputs such as families or peers is also important in the formation of human capital, simple years of schooling is subject to this additional source of systematic measurement error.

# **_2.6 Income Distribution_**

One implication of the impact of cognitive skills on individual earnings is that the distribution of those skills in the economy will have a direct effect on the distribution of income. Cognitive skills by themselves do not of course determine the full distribution, because other factors such as labor market institutions, taxes, and the like enter. But the importance of skills is becoming increasingly evident.

Very suggestive evidence on the impact of skills on the income distribution comes from Nickell (2004). Nickell, using the IALS data, considers how differences in the distribution of incomes across countries are affected by the distribution of skills and by institutional factors including unionization and

> 24 The IALS sampling raises the concern that tests change with age, because of continual learning or simple age depreciation of skills and knowledge. An investigation of this by Hanushek and Zhang (2006) suggests that these are not large concerns in the estimation of the earnings functions.

> 25 There could be other things that simultaneously affect both scores and earnings, such that scores are simply a proxy for some other important factor. Available evidence gives no reason to suspect that such factors are important.

17
- 17 -

Table 1: Proportion of Less-Developed Countries Offeiing Instruction in Selected Subjects of the Primary School Curriculum by World Region*

|Region:|LAC|Asia|SSA|EMENA|
|---|---|---|---|---|
|(|N=25)|(N=18)|(N=29)|(N=22)|
|Combined Lang.|100.0|100.0|100.0|100.0|
|National Lang.|72.0|94.4|48.3|100.0|
|Local Lang.|0.0|5.6|20.7|4.6|
|Foreign Lang.|12.0|22.2|6.9|59.1|
|Official Lang.|28.0|44.4|89.7|4.5|
|Mathemitics|100.0|100.0|100.0|100.0|
|Science|100.0|94.4|100.0|100.0|
|Comb. Social Sci.|100.0|88.9|100.0|100.0|
|Social Studies|88.0|66.7|51.7|63.6|
|History|32.0|16.7|55.2|54.6|
|Geography|32.0|16.7|55.2|50.0|
|Civics|20.0|27.8|44.8|27.3|
|Comb.Moral/Rel.|<br>60.0|94.4|82.8|86.4|
|ReLigion|48.0|27.8|62.1|68.2|
|Moral Ed.|32.0|38.9|27.6|27.3|
|Aesthetic Ed.|92.0|94.4|96.6|95.5|
|Music|72.0|72.2|89.7|77.3|
|Art|76.0|83.3|96.6|95.5|
|Physical Ed.|88.0|94.4|100.0|95.5|
|Hygiene|52.0|55.6|31.0|31.8|
|Comb.Voc.Ed.|72.0|55.6|89.7|68.2|
|Manual|56.0|38.9|44.8|40.9|
|Agriculture|32.0|16.7|55.2|45.5|
|Domestic Science|32.0|0.0|55.2|31.8|
|Voc.Ed.|4.0|5.6|0.0|9.1|
|Business|4.0|0.0|3.5|4.6|
|Other|28.0|61.1|31.0|40.9|

*Key for Regions: LAC = Latin America and the Caribbean

EMENA = Southern Europe, Middle East and North Africa SSA = Sub-Saharan Africa
DHS data. Further, while they may not predict stunting accurately, the impact of a drought may vary across wealth quintiles. Figure 3-a shows the distribution of HAZ scores for the lowest quintile, contingent on a drought. Compare this with Figure 3-b, which shows the drought-contingent HAZ distributions for the top four wealth quintiles. First, the density close to the stunting threshold is higher for the lowest wealth quintile. Second, the shift leftward appears to be more pronounced for the lowest wealth quintile. Tables 7 and 8 Table 7 starts with the regression reported in Table 5 (specification 1) and estimates this regression for different sub-samples of wealth quintiles and countries. Since sample sizes are limited, we group the second and third lowest quintiles and the wealthiest two quintiles. For all the five countries taken together, the impacts are muted for the top two wealth quintiles. The top two quintiles exhibit a 1 percentage point increase in stunting, considerably smaller than the 6 percentage points impact for the bottom quintile. In Mozambique, Malawi (for the season after birth) and Tanzania, the differences in impacts across wealth quintiles are large. The magnitudes of the drought impact for the lowest quintile is more than double the impact for the wealthiest two quintiles. However, in Zimbabwe and Zambia, the drought impacts are comparable across wealth quintiles.

It may be that in Tanzania, Malawi and Mozambique, children in the wealthier quintiles have z-scores significantly above the stunting threshold, and are still vulnerable to drought shocks, even though they do not fall below the threshold. Figure 2 shows that the z-scores for children in the wealthiest quintile in these three countries are, in fact, lower than the z-scores for the comparable children in Zambia and Zimbabwe. There are smaller proportions of children in the wealthiest quintiles in Zambia and Zimbabwe below the -2 standard deviation cut-off.

Table 8, repeats the exercise reported in Table 7, except using height for age z-scores, rather than stunting outcomes as the dependent variable. The results are very similar. In Mozambique, Malawi (once again for the season after birth) and Tanzania, drought shocks are associated with larger impacts for the poorest wealth quintile compared to the wealthiest. The results are most pronounced for Mozambique — a country with large differences in agro-ecological conditions, wealth and connectivity. For the poorest quintile, a drought shock in the birth season is associated with almost half a standard deviation lower z-score (-0.465 standard deviations). In contrast, the wealthiest quintiles have no decline in z- scores (a 0.041 standard deviation increase that is not significantly different from zero). At the opposite extreme, in Zimbabwe, impacts are close to the national average (-0.29 standard deviations) regardless of wealth status.

We draw three conclusions from this exercise. First, although the data are noisy, we show that poor harvests (either during the birth season or the following season) exert a strong adverse impact on children born to households in the lowest wealth quintile in all 17
48 Table 5 Definition and Labels of Variables Included in Logistic Models

|Variable|Variable labels and values<br>Unit|
|---|---|
|FDI|Neonatal deathBirth<br>-1ifdeath in the first month of life,<br>Ootherwise.|
|SI<br>Birth|Exposed to postneonatal death<br>-1 if survival up to the first month of<br>life and born 1 year before the survey,<br>Ootherwise.|
|DI|Postneonatal deathBirth<br>-1ifdeath between the first and the 12th<br>months of life,<br>ootherwise.|
|Time2|Born between 1977 and 1981<br>Birth<br>-1 if yes,<br>ootherwise.|
|Time 3|Born between 1982 and 1986<br>Birth<br>-1 if yes,<br>O otherwise.|
|Ordl|First it birth order<br>Birth<br>-1 if yes,<br>ootherwise.|
|Ord2|Birth orderis7th or higher<br>Birth<br>-1 if yes,<br>Ootherwise.|
|Male|Sex of child is male<br>Birth<br>-1 if yes,<br>O otherwise.|
|Magebl|Mother's age at birth is<br>Birth<br>below 20 years<br>-1 if yes,<br>O otherwise.|
|Mageb2|Mother's age at birth is<br>Birth<br>35 years or more<br>-1 if yes,<br>0 otherwise.|
**<u>Table 6. Estimated elasticity based on freight carrier fixed effects models</u>**

||Linear||Quadratic||Logarithmi|c|
|---|---|---|---|---|---|---|
|Variable|Elasticity|Std. Err.|Elasticity|Std. Err.|<br>Elasticity|Std. Err.|
|_speed_|0.023|(0.009) **|0.014|<br>(0.009)|<br>-0.014|<br>(0.009)|
|_dist_|-0.065|(0.002) ***|-0.063|<br>(0.002)|***<br>-0.050|<br>(0.002) ***|
|_elev_|0.034|(0.001)***|0.053|<br>(0.001)|***<br>0.050|<br>(0.001)***|

*, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.

**<u>Table 7. Freight carrier specific fixed effects models by type of commodity</u>**

|Commodity type:|_cloth_|||_equipme_|_nt_||_food_|||_mineral_|||_goods_|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Coef.|Std.Err.||Coef.|Std.Err.||Coef.|Std.Err.||Coef.|Std.Err.||Coef.|Std.Err.||
|ln_speed_|-0.012|(0.022)||0.028|(0.027)||-0.028|(0.011)|**|0.082|(0.031)|***|-0.108|(0.038)|***|
|ln_dist_|-0.059|(0.005)|***|-0.044|(0.007)|***|-0.042|(0.003)|***|-0.086|(0.008)|***|-0.085|(0.009)|***|
|ln_elev_|0.081|(0.002)|***|0.054|(0.004)|***|0.026|(0.001)|***|0.085|(0.004)|***|0.053|(0.003)|***|
|_D_elev_|-0.077|(0.049)||-0.257|(0.222)||0.013|(0.015)||0.143|(0.319)||-0.220|(0.021)|***|
|_crossborder_|-0.013|(0.007)|**|0.054|(0.005)|***|0.049|(0.002)|***|0.023|(0.007)|***|0.107|(0.009)|***|
|constant|1.434|(0.094)|***|1.772|(0.113)|***|1.349|(0.047)|***|1.238|(0.117)|***|1.969|(0.148)|***|
|Obs.|10,347|||6,565|||23,458|||4,729|||6,035|||
|R-squared|0.8162|||0.8778|||0.8882|||0.9027|||0.9304|||
|F stat.|...|||...|||...|||...|||...|||
|No. of carrier dummy||||||||||||||||
|variables|1581|||1227|||3548|||998|||1593|||

Note: The dependent variable is freight rate in logarithm, ln _p_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.
morbidity of adolescents due to external causes is taken from the Hospital Information System from the Unified Health System ( _Sistema de Informacoes Hospitalares do SUS_ ), managed by the Ministry of Health and fed by information from hospitals at the municipal and state level. It provides information on most hospital admissions in Brazil, as well as the reason the patient was omitted to the hospital. Admissions due to homicide, traffic accidents, suicide, and firearms are classified as “external causes.” The AIDS rates (per 100,000 individuals) were calculated by the Ministry of Health data, which identifies age and state of residence of each AIDS sufferer. Again, the 2000 Census data are used to create the indicator.

Finally, a few indicators are derived from very specific studies. The separate indicator for “homicide rate” and “suicide rate” in Table 1 are distinct from “external causes.” They are derived from the Violence Map IV, which was constructed by UNESCO, the Presidential Special Office for Human Rights, and the Ayrton Senna Institute. The choice was made to include both “external causes” and “homicide rate” in the calculation of our indices since adolescent victims of homicide are not always admitted to hospitals. Similarly with “suicide rates,” since they may be classified as death due to accidental factors rather than as a violent act in itself.

Indicators concerning the use of alcohol, tobacco, marijuana, and cocaine were obtained from the (First) Household Survey on the Use of Psychotropic Drugs in Brazil, from CEBRID (2001). The survey encompassed a sample across the 107 largest cities in Brazil, which included all cities with more than 200,000 inhabitants and all state capitals. The indicators presented by CEBRID are regional indices, so for the purposes of creating the indices in this paper, each state was assigned the value of the region where the state lies.

# _Methodology_

Three indices are calculated in this paper; the indicators included in each index are indicated in Table 1. The Youth Well-Being Index includes only those indicators pertinent to adolescents, principally ages 15-24. Not all indicators span this specific age period, since different institutions use different age cut-offs in the creation of their indicators. The Child-Youth Well-Being Index includes all the indicators in the YWI as 7
# _IV.2 Expenditure models_

This section will present the regression models used for (log) expenditure. There are eight geographical regions in Vietnam. To allow for geographical heterogeneity, we estimate a separate expenditure model for each region.

Our strategy of model selection is forward stepwise regressions. We start with a model including only one explanatory variable but providing the best fit. Then other variables are added one by one to the model to increase the goodness of fit. Thus different regions have different expenditure models. Overall, to avoid over-fitting, we tend to use models that are both relatively small and robust. To examine the sensitivity of the poverty estimates to model specifications, for each region, we compare two different models, which vary mostly in the number of explanatory variables they include. The poverty and inequality estimates from large and small models are found to be very similar. Interestingly, also when we compare standard errors, the differences are rather small. The poverty estimates obtained with the large model tend to come with relatively smaller standard errors. We are inclined to label the estimates from the larger model as more precise. In this paper, we will present the estimation results from the large models.

It should be noted that several explanatory variables such as assets, education, and employment can be endogenous in expenditure and income equations. Ideally, all explanatory variables should be exogenous. However, if we use only exogenous variables such as demography and GIS variables, the prediction power will be small. Thus, we have to use all available household variables. It is expected that the endogeneity of several variables is not a serious problem in the poverty map exercises, since our objective is to predict expenditure (or income) rather than to estimate the causal effect of explanatory variables on expenditure (or income).<sup>2</sup> Tables A.2 to A.9 in the Appendix present the GLS regressions of the logarithm of per capita expenditure (the large models). The results were obtained using the latest version of the PovMap program (updated in March 2009).<sup>3</sup> The location effect was modeled at the district level. (The latter affects the estimates of the variance-covariance matrix and hence the GLS estimates of the model parameters.)

> 2 Another issue is multicollinearity between explanatory variables. We calculated the variance inflation factor (VIF) for multicollinearity after regressions, and all most the estimates of VIF are below 5. It implies that the multicollinearity is not serious. In addition, we also report the correlation matrix of household explanatory variables in Table A.11 in Appendix.

> 3 The program is developed by researchers of the World Bank. <u>http://iresearch.worldbank.org/PovMap/PovMap2/PovMap2Main.asp</u>

9
– Table A.19 Treatment effects on living standards, shocks, and migration

||||Eq (1)||Eq (2)|||
|---|---|---|---|---|---|---|---|
||(1)<br>Control<br>Mean|(2)<br>Control<br>SD|(3)<br>Treatment|(4)<br>T1:<br>Cash &<br>Training<br>Alone|(5)<br>T2:<br>Cash &<br>Joint<br>Training|(6)<br>p-value<br>T1 = T2|(7)<br>N|
|**Household living standards**<br>||||||||
|Total consumption per capita (in Dinars per day)|8.428|6.472|0.546*<br>|0.771*<br>|0.320<br>|0.316<br>|1,824|
||||(0.313)|(0.398)|(0.372)|[0.353]||
||||[0.145]|[0.120]|[0.419]|||
|Food consumption per capita (in Dinars per day)|3.582|3.004|0.334**|0.399**|0.269|0.532|1,824|
||||(0.145)|(0.183)|(0.174)|[0.506]||
||||[0.081]|[0.095]|[0.192]|||
|Non-food consumption per capita (in Dinars per day)|3.757|2.750|-0.005|-0.010|-0.001|0.964|1,824|
||||(0.129)|(0.162)|(0.152)|[0.730]||
||||[0.730]|[0.730]|[0.738]|||
|Asset index (stock)|0.000|1.000|0.104**|0.128**|0.080|0.509|1,824|
||||(0.049)<br>|(0.065)<br>|(0.057)<br>|[0.503]||
||||[0.099]|[0.117]|[0.225]|||
|Asset index (bought since 2019)|-0.000|1.000|-0.011|0.024|-0.046|0.249|1,824|
||||(0.045)|(0.056)|(0.053)|[0.297]||
||||[0.659]|[0.586]|[0.419]|||
|**Shocks and coping mechanisms**||||||||
|Economic shock|0.016|0.125|0.023***|0.016*|0.029***|0.308|1,824|
||||(0.008)|(0.009)|(0.010)|[0.346]||
||||[0.025]|[0.142]|[0.034]|||
|Other shock|0.250|0.433|-0.015|-0.038|0.009|0.090|1,824|
||||(0.020)|(0.024)|(0.025)|[0.153]||
||||[0.464]|[0.171]|[0.619]|||
|Extreme coping strategy|0.123|0.328|-0.043***|-0.039**|-0.046***|0.718|1,824|
||||(0.014)|(0.017)|(0.017)|[0.613]||
||||[0.025]|[0.079]|[0.034]|||
|Other coping strategy|0.152|0.359|0.000|-0.043**|0.042*|0.000|1,824|
||||(0.017)|(0.019)|(0.022)|[0.008]||
||||[0.738]|[0.081]|[0.120]|||
|**Migration**||||||||
|Household migration (since baseline)|0.094|0.295|-0.028**|-0.026*|-0.030**|0.777|2,000|
||||(0.012)|(0.015)|(0.015)|[0.652]||
||||[0.081]|[0.145]|[0.102]|||
|Individual migration (since 2019)|0.190|0.393|-0.002|-0.009|0.004|0.616|1,824|
||||(0.018)|(0.022)|(0.023)|[0.573]||
||||[0.691]|[0.596]|[0.681]|||
|Expected likelihood of future migration|0.054|0.226|0.035***|0.012|0.057***|0.016|1,824|
||||(0.012)|(0.014)|(0.016)|[0.073]||
||||[0.029]|[0.419]|[0.011]|||

Notes: This table reproduces the results from Table 3 excluding control variables and enumerator fixed effects. Robust standard errors are reported in parentheses. FDR q-values are reported in brackets. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

62