rafmacalaba/datause-encoder-data huggingface dataset:
- 19 -

# Table 7: RESULTS FROM NET MIGRATION REGRESSIONS

|Independent<br>D<br>Variables|ependent Variables||
|---|---|---|
|(A)<br>MIGPT|(B)<br>MIGPT|(C)<br>MIGPT|
|CONSTANT<br>-9.536<br>(1.676)***|-9.578<br>(1.757)***|-7.732<br>(1.394)***|
|DC80<br>454.722<br>(129.550)***|||
|HINV81|0.025<br>(0.009)***||
|HHDIFF||-0.362<br>(0.132)***|
|WACEINDEX85<br>0.062<br>(0.019)***|0.067<br>(0.020)***||
|WAGEDIFF||0.0020<br>(0.0004)***|
|Adjusted R2<br>0.41|0.35|0.31|
|Standard errors in parentheses<br>*<br>=Significant at 90% level<br>=Significant at 95% level|||
|=Significant at 99% level|||

In sum, although our model of migration flows in Poland is a simple one, explaining only a small part of the variation in interregional migration, it suggests that housing shortages influence net migration patterns in a manner consistent with our model: households appear to move in response to wage differentials but are deterred from doing so when housing shortages are great.

# C. The Wage Rate and Housing Market Disequilibrium

The above sections have shown that the housing market has been in persistent disequilibrium, and that the interregional labor supply is depressed by the housing disequilibrium. Are workers then compensated for housin3 cost differential? The analysis of the most direct link between the housing and labor markets in our model indicates that they are. Higher average wages do tend to be associated with greater housing shortages.
- 48 -

# T-ble7Z: Rates of Growth in NonfarEmployment by Locality Size Sierra Leone, 1974-1980 (percent compound growth)

# Locality Size

# Activity

2,000-20,000 20,000-250,000 250,000+

|Repairs<br>Radio|15.0<br>22.0|5.2<br>19.2|15.0<br>b/|
|---|---|---|---|
|Motor vehicle<br>|17.0<br>|2.4<br>|_20.0_<br>|
|Watch|a/|a/|13.0|
|Food Processing|14.0|33.0|21.0|
|Bakery|3.4|14.0|32.0|
|Other|39.0|b/|a/|
|Woodwork|0.5|8.8|7.6|
|Carving|0.0|b/|24.0|
|Carpentry<br>|-0.6<br>|8.6<br>|3.6<br>|
|Other|b/|a/|0.0|
|Clothing|0.7|1.8|5.0|
|Tailoring|0.0|3.8|4.9|
|Tie Dyeing|-3.0|-8.5|a/|
|Shoemaking|-4.3|16.0|6.2|
|Other|a/|a/|0.0|
|Metalwork|-5.8|9.4|10.0|
|Welding|0.0|23.0|6.6|
|<br>Blacksmithing<br>|-5.5<br>|1.8<br>|22.0<br>|
|Goldsmithing|a/|a/|a/|
|<br>Other|0.0|0.0|0.0|
|Other Manufacturing|a/|7.2|_/|

m/ Greater than zero but less than one percent. k/ Value for 1974 is zero.

Source: Chuta and Liedholm (1982), pp. 104-105.
# **Hub-Periphery Development Pattern and Inclusive Growth: Case Study of Guangdong Province**<sup>*****</sup>

**Xubei Luo** The World Bank

## **Nong Zhu**

INRS-UCS, University of Quebec

**JEL classification:** J61; O11; R12.

> * The authors would like to thank Ehtisham Ahmad, Ahmad Ahsan, Shahrokh Fardoust, Chorching Goh, Sanjeev Gupta, Khalid Ikram, John Litwack, Teresa Ter-Minassian, Meili Niu, Kezhou Xiao, Chunlin Zhang, and participants at the conference of Financing Local Investments within a Sustainable Development Strategy for P.R. China (organized by Asia Research Center, London School of Economics & Sun Yat-Sen University, January, 2015) for valuable comments _._
**Table 6. How does FDI firms and sectors differ from domestic firms and sectors**

||**D**|Panel A:<br>**omestic fir**|domestic f<br>**ms**|irms VS F|DI firms<br>**FDI firms**||||
|---|---|---|---|---|---|---|---|---|
||<br>**(i.e., fore**<br>|<br>**ign owners**<br>|<br>**hip <0.1)**<br>|**(i.e., fore**<br>|<br>**ign owners**<br>|<br>**hip > =0.1)**<br>|Difference|diff_se|
|Var|Obs.|Mean|SD.|Obs.|Mean|SD.|||
|Capital/Labor ratio|32717|0.109|2.445|3529|0.316|6.248|0.207***|0.054|
|lnL3|87071|3.054|1.366|9324|3.954|1.611|0.900***|0.015|
|TFP|29777|-0.162|1.339|3238|0.151|1.414|0.313***|0.025|
|lnLP|76658|9.481|1.530|8120|10.033|1.656|0.553***|0.018|
|Skilled workers’share|47350|0.689|0.310|4851|0.672|0.322|-0.017***|0.005|
|||Panel B: F|DI-heavy s|ector VS o|ther sectors||||
||FR<br>|Nckt <=me<br>|dian<br>|F<br>F<br>|RNckt > med<br>DI-heavy se<br>|ian:<br>ctor<br>|Difference|diff_se|
|Var|Obs.|Mean|SD.|Obs.|Mean|SD.|||
|Capital/Labor ratio|15795|0.058|0.794|16649|0.159|3.338|0.101***|0.027|
|lnL3|45468|3.053|1.371|40885|3.055|1.361|0.002|0.009|
|TFP|14341|-0.217|1.338|15187|-0.106|1.337|0.111***|0.016|
|lnLP|40866|9.354|1.509|35232|9.632|1.541|0.278***|0.011|
|Skilled workers’share|25344|0.699|0.294|21549|0.675|0.327|-0.024***|0.003|

Notes: *, **, *** represent statistical significance at the 10, 5, and 1 percent levels.

Capital/Labor ratio equals to K/L, and K is replacement costs of land and machine (million dollar), L is number of labors.

Skilled workers’ share is the ratio of skilled workers / (skilled workers + unskilled workers) In panel A, we define foreign-owned firms to be those with ownership being 10% and more.

In panel B, we delete FDI firms with foreign ownership being 10% and more.

28
**Figure 3.1: Graphical illustration of deriving Learning‐Adjusted Years of Schooling**

<!-- Start of picture text --> A<br>D<br>C<br>B<br>Years of schooling<br>Country A Country B<br>800<br>700<br>600<br>500<br>400<br>Test score<br>300<br>200<br>100<br>0<br>0 1 2 3 4 5 6 7 8 9 10 11 12<br><!-- End of picture text -->

# **_3.2 Years of schooling versus years of learning: when does learning begin?_**

The measure of relative learning productivity (defined in Equation 3) depends not only on the numerator, but also on the denominator (years of learning preceding the test). In the example above, we equate years of learning to years of schooling, but of course learning does not wait until children begin primary school. Every child acquires some language, mathematical concepts, reasoning skills, and socioemotional skills before arriving at school, and some systems may be better than others at fostering that acquisition. One indicator of this comes from the Multiple Indicator Cluster Surveys (MICS), which include assessments of whether young children between the ages of 3 and 6 can recognize 10 letters of the alphabet and whether they recognize the numbers 1 to 10.<sup>7</sup> While clearly not a complete measure of pre-school “learning,” the results suggest that even as early as age 3—three years before most of them will start school—some children are already beginning to acquire these basic academic skills, and that the

> 7 <u>http://mics.unicef.org</u>

11
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# **Figure 1. Evolution of Financial Integration**

## **b. Quinn**

<!-- Start of picture text --> a. Chinn-Ito<br>1 1<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>0 0<br>1961 1971 1981 1991 2001 2011 1961 1971 1981 1991 2001 2011<br><!-- End of picture text --> Notes: The figures show country averages for each year. The solid (blue) lines show averages for Advanced Economies, the dashed (red) lines show averages for developing economies and the dotted (orange) lines show averages for all countries in our sample. The vertical axis shows restrictions in each of the respective categories. A zero (0) shows that there are restrictions in place and one (1) suggests that the country is liberalized. “Chinn-Ito” and “Quinn” refers to the Chinn-Ito and Quinn measure of financial integration, respectively.

16
age, sex, height and weight from the NFHS on a set of common variables available in the NSS Schedule 10 employment survey. Because age and sex are always observed, this procedure generates additional variation in TEE conditional on age and sex that captures average differences in height and weight associated with socioeconomic status, maternal and childhood nutrition. Appendix Table S.4 reports the variables and coefficients for these regressions, which include individual and household age, education, demographics, school attendance, land holdings, and two-digit primary occupation code (using NCO1968 classification and available concordances for the later NFHS rounds). Separate equations are estimated by sector and sex, pooling the available years of data (which includes 1998 for women and 2015 for both men and women) and including year fixed effects (the omitted category is 2005). In principal one could also use separate predictive equations for height and weight, and then apply the (Henry 2005) equation, but results are similar either way.

Note that these equations can only be estimated for the adult age ranges in the NFHS eligible for height and weight measurement (15-49 for women and 15-54 for men). For individuals outside of this range, the following procedure is adopted:

1. Assume that their age is equal to 15 or 49 (54 for men) and use the predictive equation for REE

2. Scale this REE by the ratio of average REE for that individual’s age and sex relative to the average REE for their “assumed age” used in the predictive equation.

REE is scaled using the age and gender specific mean height and weight reported by the National Nutritional Monitoring Bureau for rural areas of 16 Indian states between 2000-2002 (reported in (Indian Council of Medical Research 2009)), together with the (Henry 2005) equations. Note that (Indian Council of Medical Research 2009) uses the 95th percentile of this same distribution to generate their recommended daily caloric intake, highlighting an important difference between prescriptive estimates of recommended daily caloric intake and the descriptive estimates of population total energy expenditure reported here.

The estimates reported in the text make two additional adjustments. First, because children under 18 are still growing, calories consistent with 20
where MAXp,a,t = maxi{Stdi,p,a,t } and MINp,a,t = mini{Stdi,p,a,t }. _Std_ is country _i_ ’s MRL for each product-pesticide pair in year _t_ . This index will be between zero and one, zero being the least restrictive and one the most restrictive.<sup>7</sup> In the case that a country does not set an MRL for a given product-pesticide (i.e., Stdi,p,a,t is missing) we substitute the missing Stdi,p,a,t with MAXp,a,t. This index combines the number of pesticides restricted as well as the intensity with which they are set into one measure. The second advantage of the index of restrictiveness is that for every product, it includes all pesticides regulated in the world, this contrasts to for example Li and Beghin (2012) which analyze only those product-pesticide pairs regulated by Codex.

Trade data are sourced from the United Nation’s Commodity Trade Statistics Database, and tariff data is taken from the Trade Analysis and Information System developed by the United Nations Conference on Trade. Countries’ gross domestic products are taken from the World Bank World Development Indicators. Finally, gravity variables such as distance, language, colonial relationship are extracted from Centre d`Etudes Prospectives et d'Informations Internationales (CEPII).

# **3 Methodology**

We first aim to measure the effect of food safety standards on exports of agricultural commodities—mainly products that fall under edible vegetables (HS 07) and edible fruits (HS 08).<sup>8</sup> We then explore whether BRICS import policies have a different impact on LDC exports. We use the gravity model of trade which has been the workhorse for policy analysis in trade. The gravity model has been used in studies to test the impact of regional trade blocs, regional trade bias and home country trade bias. The gravity model has also been the most common method to estimate the impact of product standards and food safety standards on trade flows. Moenius (2000) used the gravity model to provide a framework for estimating the effect of product standards on trade flows. Otsuki et al. (2001) used it to estimate the impact of the EU’s new aflatoxin standards on food imports from Africa. The study suggests that the implementation of

> 7 In the case where Std=MAX=MIN, the ratio inside the summation takes a value of 1.

> 8 The detailed list of the 66 products included in our analysis can be seen in Table 3.

7
xcii DATA AND METHODS Table A3 (continued)

|Country,<br>economy,<br>or<br>territory|Total population,<br>mid-1985|Total fertility,<br>1985-90|
|---|---|---|
|Switzerland|Official<br>est. (UNDY 1988)|Bank est from official<br>CBR (PVSR 10/90,<br>10/91)|
|Syrian<br>Arab Rep.<br>Taiwan,<br>China<br>Tajikistan|U.N. 1992 Rev. (Prelim.)<br>Bank est. based on official<br>year-end<br>est.<br>(Stat. Data Book, 1987)<br>Bank est. from U.S.S.R. 1979 and 1989<br>censuses|U.N. 1992 revision<br>(preLim.)<br>Bank est. from officiai<br>CBR (Taiwan<br>Stat. Data<br>Book, 1987)<br>Bank est. from U.S.S.R. 1989 census report|
|Tanzania<br>Thailand|Bank est. from 1978 and 1988 censuses<br>Official<br>est. (Nat Econ and Soc Dev Board,<br>1985)|From Bank sector report est.<br>U.N. 1990 revision|
|Togo<br>|Bank projection<br>of 1981 census<br> <br>|1987 OHS|
|Tonga|Bank projection<br>from 1984 mini-census|Bank est. based on U.S. Bureau of the Census,<br>1989|
|Trinidad<br>and<br>Tobago|U.N. 1992 revision<br>(prelim.)|Bank est. based on 1987 DHS|
|Tunisia<br>Turkey|Official<br>est. (UNPVSR<br>1/88)<br>Official<br>est. (Social<br>Indicators,<br>Turkey,<br>1990)|Bank est, based on 1988 DHS<br>Bank est. from official<br>CBR (UNPVSR<br>10/91)|
|Turkmenistan|Bank est. from U.S.S.R.<br>1979 and 1989<br>censuses|Bank est. from U.S.S.R.<br>1989 census report|
|Uganda|Bank projection<br>from 1969, 1980,<br>and 1991<br>censuses|1988/89<br>DHS|
|Ukraine|Official<br>est. (UNDY 1989)|Official<br>est. (UNDY 1989)|
|United<br>Arab<br>Emirates|U.N. 1990 revision|U.N. 1990 revision|
|United<br>Kingdom<br>|Eurostat,<br>1991|Eurostat<br>1989, 1991|
|United<br>States of<br>America|Official<br>est. (UNDY 1989)|Official<br>est. (Nat.<br>Center for Health Stat.)|
|Uruguay<br>Uzbekistan|Official<br>est. from 1985 census<br>Bank est. from U.S.S.R.<br>1979 and 1989<br>censuses|Bank est. from official<br>est.<br>Bank est. from U.S.S.R.<br>1989 census report|
|Vanuatu|Bank est. from 1979 and 1989 censuses|Bank est. based on U.S. Bureau of the Census,<br>1989|
|Venezuela<br>|Official<br>est. (UNPVSR<br>1/87)|U.N. 1990 revision|
|Viet Nam|Bank est. from 1989 census|Bank est. from 1989 census|
|Virgin Islands<br>(U.S.)|U.S. Bureau of the Census,<br>1985|Bank est. from official<br>COR (UNPVSR<br>4/90)|
|West Bank|U.S. Bureau of the Census,<br>1989|U.S. Bureau of the Census,<br>1989|
|Western<br>Samoa|Bank est. from 1986 census|Bank est. from 1986 census|
|Yemen,<br>Rep. of|Bank est. from 1988 census<br>Yemen P.D.R.<br>and<br>1986 census for Yemen<br>A.R.|Bank est. based on U.N. 1990 revision|
|Yugoslavia|Official<br>est. (Statistical<br>Office,<br>Belgrade<br>|<br>Bank est. from official<br>est. (Stat.<br>Office,<br> <br>|
||1988)|Belgrade,<br>1988)|
|Zaire<br>|U.N. 1992 revision<br>(prelim.)<br>|<br><br>Bank est.|
|Zambia|Bank projection<br>from 1980 census|From Bank sector report<br>est.|
|Zimbabwe|Bank projection<br>from 1982 census|<br>1988 OHS|
# Table B.4: **Effects of Inpres Schools on Intergenerational Educational Mobility: Comparison Cohorts: 1960-1962**

**Panel A: Dependent Variable: Primary Completion**

||**Linea**|**r Model**|**Quadra**|**tic Model**|
|---|---|---|---|---|
||**Sons**|**Daughters**|**Sons**|**Daughters**|
||(1)|(2)|(3)|(4)|
|Born 1968-72_×_Inpres|0.207***<br>(0.040)|0.203***<br>(0.040)|0.198***<br>(0.042)|0.190***<br>(0.042)|
|Father’s Edu_×_Born 1968-72_×_Inpres|-0.022***<br>(0.005)|-0.012**<br>(0.006)|-0.038***<br>(0.011)|-0.017*<br>(0.009)|
|Father’s Edu Sq_×_Born 1968-72_×_Inpres|||0.002***<br>(0.001)|0.001**<br>(0.001)|
|R2|0.100|0.159|0.111|0.174|
|Observations|1163653|804747|1163653|804747|
|**Panel B: Dependent**|**Variable: Ye**<br>**Linea**|**ars of School**<br>**r Model**|**ing**<br>**Quadra**|**tic Model**|
||**Sons**|**Daughters**|**Sons**|**Daughters**|
||(1)|(2)|(3)|(4)|
|Born 1968-72_×_Inpres|1.660***<br>(0.384)|0.551<br>(0.467)|1.602***<br>(0.387)|0.498<br>(0.492)|
|Father’s Edu_×_Born 1968-72_×_Inpres|-0.131**<br>(0.051)|0.076<br>(0.061)|-0.247***<br>(0.090)|0.042<br>(0.088)|
|Father’s Edu Sq_×_Born 1968-72_×_Inpres|||0.014**<br>(0.006)|0.005<br>(0.007)|
|R2|0.318|0.401|0.319|0.401|
|Observations|1163653|804747|1163653|804747|

Notes: Robust standard errors in parentheses clustered at the district of birth (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). Quadratic model is adopted following Becker et al. (2015). Sample corresponds to children born between 1960 and 1962, or 1968 to 1972. Covariates include birth district FE, year of birth×1971 enrollment, year of birth×1971 number of children, year of birth×water sanitation program, year of birth dummies, following Duflo (2001). Primary completion takes the value of 1 if the child has completed primary and 0, otherwise. Father’s Edu represents father’s years of schooling, which was calculated based on the education level completed. Family background is measured by father’s years of schooling. The variable Inpres measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. For the sake of parsimony, only intercept, linear, and quadratic terms are reported in this table; full table, with all coefficients, is available upon request. Data sources: Indonesia’s full count census 2000 and Duflo (2001).

67
# **5.4 Equity considerations: are there income and gender disparities in the effects of the policy variables?**

To explore whether the impact on nutritional status of being an only-child and having access to healthcare is more salient for children with certain characteristics, and in particular for children that belong to relatively disadvantaged subgroups, interaction terms are introduced into the models.

There do not appear to be any income or gender disparities in the effect of the only-child variable. While being an only-child was a highly significant variable in all models estimated in this paper, the nutritional status of only-children is not significantly better in higher income households. Also, boys who are onlychildren do not have z-scores that are significantly different from girls who are only-children.

**Table 3: Regressions of height-for-age z-scores on only-child interaction terms**

||Gender inte<br>|raction<br>|Income int<br>|eraction<br>|
|---|---|---|---|---|
||OLS|FE|OLS|FE|
|Sex (Male=1)|-0.024|-0.002|-0.029|-0.002|
||(0.042)|(0.037)|(0.036)|(0.033)|
|Log real per adult income (yuan)|0.064|0.021|0.052|0.024|
||(0.024)***|(0.022)|(0.027)*|(0.024)|
|Index child is only-child|0.336|0.102|-0.048|0.206|
||(0.061)***|(0.061)*|(0.367)|(0.348)|
|Interaction terms:|||||
|Sex*Only-child|-0.019|0.003|||
||(0.073)|(0.072)|||
|Income*Only-child|||0.051|-0.014|
||||(0.05)|(0.048)|
|Observations|6199|6199|6199|6199|
|No. of community fixed effects||176||176|
|Robust standard errors, clustered at the|community level,|in parenthes|es||

Source. China Health and Nutrition Survey, 1991-2000.

Note. Only select coefficients shown; other variables are the same as in the full pooled OLS and FE models (see Table 2).

*significant at 10%; **significant at 5%; ***significant at 1%.

The interaction of healthcare variables and income also produces insignificant coefficients (see Table A1 in the Appendix). This implies that the extent to which the availability, quality and cost of healthcare will affect child nutritional status does not depend on the amount of income that the household has available to purchase that care. Distance to the healthcare facility (as measured by traveling time) is the only healthcare variable for which a statistically significant gender effect is observed (see Table 4). Since longer traveling times are associated with poorer nutritional status (i.e. lower height-for-age z-scores), the positive coefficient on the interaction term indicates that the association is somewhat weaker for boys than for girls.

13
**c) Destination** : Destination countries in our cross-country raw dataset follow UN’s guidelines (International Merchandise Trade Measures: Concepts and Definitions, p. 60) which recommend that countries of origin record their destinations as defined by the destination countries themselves. For instance, Bermuda (UK); Hong Kong SAR, China; and Macau (China) are all considered distinct destinations although they are not independent countries. Therefore, each country has a potential set of 247 destinations as of end-2011.<sup>11</sup>

The first cleaning operation applied to the country of destination variable relates to the use of special trade regimes by some countries –Bulgaria, Costa Rica, Colombia, Ecuador, Egypt, Jordan, Kuwait, Lebanon, Morocco, Peru, Turkey, Yemen.<sup>12</sup> Customs in these countries record the sales from inland to their own free zones/customs warehouses as exports which results in a larger set of potential destinations. For example, in Colombia’s dataset, the Free Zone “Zona Franca Bogota” appears as a separate destination. Since there is a lack of uniformity across countries in the definition of the special trade regime, we do not consider this type of transactions as exports and hence we drop the observations related to sales to free zones from our dataset.<sup>13</sup> This operation had a minor effect on total export volumes as sales to free zones/customs warehouses are negligible in most cases.<sup>14</sup> The second operation applied to the country of destination variable accounts for the changes in name that some statistical territories have undergone over time due to spatial

> 11 See http://unstats.un.org/unsd/methods/m49/m49alpha.htm for the current potential set of destinations.

> 12 The “special trade regime” considers transactions where the goods are sold from the domestic territory _only_ to both third countries and free zones/customs warehouses of the origin country as exports. In contrast, the “General trade regime” considers transactions where goods are sold from any national territory ( _including free zones_ ) to third countries only as exports (see p. 32 of United Nations, 2008).

> 13 See p. 34 of United Nations (1998).

> 14 The export volumes of Turkey, the country most affected by this operation, drop by 2-3 percent.

8
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Note: Large-firm wage premium is estimated at the country-year level in a Mincer regression including fixed effects for occupation and industry