rafmacalaba/datause-encoder-data huggingface dataset:
quality of work. Another limitation is that the data exclude self-employed individuals and some substantial portion of migrants. Nevertheless, this study suggests that higher wages paid by foreign firms to unskilled women relative to alternative jobs in the informal sector may be among the factors which drew women disproportionately toward foreign job opportunities in Vietnam.

# **Reference**

Almeida, R. (2007), ‘The Labor Market Effects of Foreign Owned Firms,’ _Journal of International Economics_ , **72** , 75-96.

Anderson, J. B. and D. Dimon. (1999), ‘Formal Sector Job Growth and Women’s Labor Sector Participation: the Case of Mexico,’ _The Quarterly Review of Economics and Finance_ , **39** , 169191.

Andrews, M., L. Bellmann, T. Schank and R. Upward. (2009), ‘The Takeover and Selection Effects of Foreign-owned Establishments: an Analysis Using Linked Employer-Employee Data,’ _Weltwirtschaftliches Archiv_ , **145** , 293-317.

Athukorala, P. and T. Q. Tien. (2012), ‘Foreign Direct Investment in Industrial Transition: the Experience of Vietnam,’ _Journal of the Asia Pacific Economy,_ **17** , 3, 446-463 _._ Brassard, C. (2004), ‘Wage and Labour Regulation in Vietnam within the Poverty Reduction Agenda,’ National University of Singapore.

Braunstein, E. and M. Brenner. (2007), ‘Foreign Direct Investment and Gendered Wages in Urban China,’ _Feminist Economics_ , **13** , 3-4, 213-237.

Conyon, M., S. Girma, S. Thompson and P. Wright. (2002), ‘The Productivity and Wage Effects of Foreign Acquisition in the United Kingdom,’ _Journal of Industrial Economics_ , **50** , 85-102.

Fan, C. S. and H. Lui. (2003), ‘Structural Change and the Narrowing Gender Gap in Wages: Theory and Evidence from Hong Kong,’ _Labour Economics_ , **10** , 609-626.

Fu, X. and V. N. Balasubramanyam. (2005), ‘Exports, Foreign Direct Investment and Employment: the Case of China,’ _The World Econom_ y, **28** , 4, 607-625.

Fukase, E. (2013a), ‘Export Liberalization, Job Creation and the Skill Premium: Evidence from the U.S.-Vietnam Bilateral Trade Agreement (BTA),’ _World Development_ , **41** , 1, 317-337.

Fukase, E. (2013b), ‘Foreign Job Opportunities and Internal Migration in Vietnam,’ _Policy Research Working Paper_ , forthcoming, the World Bank.

25
grandfather bargaining power: if the maternal grandfather passes away early, then family influence on parental marriage decision-making “changes” and a child’s parents are less likely to be in a consanguineous union.

What do these findings mean for public policy? First, the negative effects of consanguinity go beyond selected severe disabilities and thus there is reason to be concerned about such high prevalence of consanguineous marriages in the developing world. Second, if left-alone, the prevalence of consanguinity is likely to decrease very slowly. With urbanization, increased educational attainment, increased non-land wealth and improvements in both the business and public order environments one can expect to see some decreases in consanguinity, but the effects of these changes may be gradual and relatively modest. Indeed, discussing trends in consanguineous marriages, Saggar and Bittles (2008) also come out as somewhat pessimistic that this practice will end on its own over time, observing that “it could be argued that the ongoing wide-spread popularity of consanguinity makes a rapid decline in its prevalence improbable. In many developing countries, strenuous official efforts are being made to lessen the appeal of close-kin unions, although with no apparent appreciation or acknowledgement of the balancing social and economic benefits.” Outright prohibition of consanguinity through legislation, perhaps through prohibition of first-cousin marriage given its stronger linkage to poor development outcomes (the practice in Western countries varies: cousin marriages are legally banned in 24 out of 50 states of the United States of America but they are not prohibited under UK law). In Pakistan and many other developing countries, the state’s ability to enforce such legislation might be limited. In fact, negative externalities to such policy cannot be ruled out: for example some might avoid formalizing or reporting their marital status, others might avoid getting national ID cards (which is a requirement for establishing Bank accounts, voting, driving, getting a cell phone among others).

Simple dissemination of information to the public about the possible negative results of consanguineous marriages may provide an easier means of reducing their incidence. Furthermore, information interventions (even those about future intentions) are feasible to implement at relatively low cost (e.g. Leibman and Luttmer, 2015). A large share of the adult population, 72.4 percent in the LSS survey, report that they are not aware of negative consequences from consanguinity. As consanguinity is more prevalent among low-income households (with the exception of rural landholding families), such information campaigns might target the poor, who are the targets of new social safety net programs. Indeed, pairing an information intervention with social insurance schemes aiming to reduce exposure to earnings risk may 16
provide a discussion base to revisit the policy agenda on the improvement of the accessibility to markets, the improvement of the business environment to ease the creation and development of new firms, and a well-managed urbanization process to tap in the economic potentiality of cities.

The remaining of the paper is organized as follows. Section 2 presents a simple theoretical framework and the empirical approach chosen to assess the impact of accessibility, localization and urbanization economies on Turkish sectors’ productivity. Section 3 presents and comments the empirical results, while Section 4 explores some policy implications of these results. Section 5 concludes the paper.

# <u>2) Theoretical framework and empirical approach</u>

Many recent papers in the flourishing New Economic Geography literature have revisited the interaction between agglomeration economies and productivity using various proxy and estimation techniques. For instance, Ciccone and Hall (1996) and Ciccone (2002) estimated the relation between labor productivity and employment density. Cingano and Shivardi (2004) use a panel of plant-level data across Italian cities to estimate the longrun impact of city employment on firms’ productivity. Rice and al. (2006) use travel time within Britain NUTS3 regions as the measure of proximity and estimate the impact of the underlying spatial variation of earnings on firms’ productivity. Ottaviano and Pinelli (2006) assess the impact of market potential on Finnish firms’ productivity using a basic new economic geography model highlighting market accessibility and demand linkages.

_Theoretical framework_ Following Lall and al. (2004), this paper makes the prior assumption that agglomeration economies impact on firms’ productivity through three channels: (i) market accessibility, (ii) intra-industry localization economies, and (iii) inter-industry urbanization economies. In the tradition of the early eighties papers presented in the introduction, we consider the following production function of a representative firm: where _Y_<sup>~</sup> ( _K i_ ) is the firms’ own constant return to scale technology for a vector of inputs K, _g_ () . is a Hick’s neutral external shift factor whose arguments are accessibility ( _S_ ), intra-industry localization economies ( _L_ ), and inter-industry urbanization economies ( _U_ ). ~ Since _Y_ () . is a constant return to scale function, we can aggregate over firms and use sector-location observations.

~ Equation (1) may be rewritten as _y s_ = _Ys_ / _N s_ = _g_ ( _S_ , _L_ , _U_ ) . _Y_ ( _k s_ ) where _Ns_ is labor inputs in sector _s_ and _ks_ is the vector of ratios of remaining factors to _Ns_ . It directly links sectoral 5
Table S2.2: EFFECTS ON FOOD TRANSFERS OF ULTRA-POOR HOUSEHOLDS

||Transfer|s received|Transfe|rs given|
|---|---|---|---|---|
||(Yes=1)|(No.)|(Yes=1)|(No.)|
||(1)|(2)|(3)|(4)|
|Treatment|0.013|0.037|0.048*|0.152*|
||(0.010)|(0.065)|(0.027)|(0.078)|
|Treatment_×_Endline|0.000|0.120|0.041|0.153|
||(0.017)|(0.098)|(0.040)|(0.126)|
|Mean in control|0.936|2.340|0.560|1.284|
|Adjusted R-squared|0.069|0.170|0.260|0.315|
|Observations|13464|13464|13453|13464|
|_Panel B:_||_Within-vil_|_lage Transfe_|_rs_|
|Treatment|0.015|0.059|0.045|0.147*|
||(0.011)|(0.063)|(0.027)|(0.075)|
|Treatment_×_Endline|0.010|0.155|0.046|0.160|
||(0.021)|(0.099)|(0.040)|(0.123)|
|Mean in control|0.907|2.199|0.547|1.236|
|Adjusted R-squared|0.078|0.194|0.259|0.318|
|Observations|13464|13464|13464|13464|
|_Panel C:_||_Outside-vil_|_lage Transfe_|_rs_|
|Treatment|-0.012|-0.017|-0.001|0.003|
||(0.014)|(0.019)|(0.006)|(0.008)|
|Treatment_×_Endline|-0.031|-0.034|-0.004|-0.007|
||(0.022)|(0.031)|(0.010)|(0.014)|
|Mean in control|0.107|0.141|0.037|0.047|
|Adjusted R-squared|0.066|0.074|0.037|0.036|
|Observations|13464|13464|13464|13464|

**Source:** Author’s analysis based on original survey data.

**Notes:** The table reports ITT estimates based on specification (2) estimated with OLS. The sample includes observations from ultra-poor households surveyed at the midline and endline surveys. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). *** (**) (*) indicates significance at the 1% (5%) (10%) level. The dependent variable in column 1 is a dummy variable (DV) equal to 1 if the respondent’s household ever receives any food transfers from other households The dependent variable in Panel A, column 1 is a dummy variable (DV) equal to 1 if the respondent’s household ever receives any food transfers; in column 2 it is the number of households (capped at 3) that the respondent’s household receives food transfers from; in column 3 it is a DV equal to 1 if the respondent’s household ever gives food transfers to other households; in column 4 it is the number of households (capped at 3) that the respondent’s households gives food transfers to. All dependent variables in Panel B (C) are identical to those in Panel A, except they refer to within (outside) village food transfers.

13
Policy ReseaRch WoRking PaPeR 4587

# **Abstract**

This paper examines the determinants of child nutritional status in seven provinces of China during the 1990s, focusing specifically on the role of two areas of public policy, namely health system reforms and the one child policy. The empirical relationship between income and nutritional status, and the extent to which that relationship is mediated by access to quality healthcare and being an only-child, is investigated using ordinary least squares, random effects, fixed effects, and instrumental variables models. In the preferred model – a fixed effects model where income is instrumented – the author find that being an only-child increases heightfor-age z-scores by 0.119 of a standard deviation. The magnitude of this effect is found to be largely gender and income neutral. By contrast, access to quality healthcare and income is not found to be significantly associated with improved nutritional status in the preferred model. Data are drawn from four waves of the China Health and Nutrition Survey.

This paper—a product of the Human Development Network, Health, Nutrition and Population Department—is part of a larger effort in the department to understand the effects of socioeconomic status and policy change on health status. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at cbredenkamp@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table A.2: **Quadratic CEFs of Exposed and Comparison Cohorts**

**Panel A: Dependent Variable: Years of Schooling of Children**

||**Son**|**s**|**Daughte**|**rs**|
|---|---|---|---|---|
||**Comparison**|**Exposed**|**Comparison**|**Exposed**|
||(1)|(2)|(3)|(4)|
|Father’s Edu|0.532***|0.388***|0.627***|0.530***|
||(0.02294)|(0.01835)|(0.02688)|(0.02390)|
|Father’s Edu Sq|0.002|0.007***|0.000|0.001|
||(0.00139)|(0.00110)|(0.00173)|(0.00140)|
|Constant|5.044***|6.837***|3.651***|5.957***|
||(0.09773)|(0.08767)|(0.09058)|(0.09836)|
|R2|0.260|0.267|0.334|0.330|
|Observations|119825|1079989|132141|716209|

**Panel B: Dependent Variable: Primary Completion of Children**

||**Son**|**s**|**Daught**|**ers**|
|---|---|---|---|---|
||**Comparison**|**Exposed**|**Comparison**|**Exposed**|
||(1)|(2)|(3)|(4)|
|Father’s Edu|0.059***|0.028***|0.082***|0.040***|
||(0.00220)|(0.00136)|(0.00262)|(0.00197)|
|Father’s Edu Sq|-0.003***|-0.001***|-0.004***|-0.002***|
||(0.00012)|(0.00006)|(0.00013)|(0.00009)|
|Constant|0.663***|0.837***|0.530***|0.771***|
||(0.01075)|(0.00720)|(0.01282)|(0.01004)|
|R2|0.120|0.059|0.188|0.094|
|Observations|119825|1079989|132141|716209|

Notes: Robust standard errors are 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). 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. Comparison cohorts are children born between 1957 and 1962, and exposed cohorts are children born between 1968 to 1972. Father’s Edu represents father’s years of schooling, which was calculated based on the education level completed. Data source: Indonesia’s full count census 2000.

54
because Ethiopia in 2007 passed important forestry legislation that has improved management (Beyene et al., forthcoming; Mekonnen and Bluffstone, 2014), the default value was set at 88%. This is the value used in this paper.

# **3. Data, Empirical Methods and Identification Strategy**

This section discusses our sampling and data collection methods. It also presents our randomized experimental treatments, strategy for identifying fuelwood savings and method for converting fuelwood savings to CO2 emissions reductions. These are discussed in the following three subsections.

## _3.1 Sampling and Data Collection_

The sample sites are chosen from 110 villages examined during a 2012 study conducted by the Environmental Economics Policy Forum for Ethiopia (EEPFE) based at the Ethiopian Development Research Institute. The project collected critical village information in Amhara, Tigray, SNNP and Oromiya Regional States,<sup>2</sup> which we use to assign treatments and test randomization. Out of the 110 sites, we omit 15 sites that were covered during a pilot survey conducted as part of research for this paper. We also remove 14 sites either because the traditional technology deviates from the three-stone technology, which is the national norm or because _injera_ is typically not cooked. The Mirt ICS is designed for baking _injera_ , which is the most important use of cooking energy in Ethiopia. Three Borena District sites where _injera_ baking is not common are therefore omitted from the sample. We also remove all sites (total of 11) from Tigray Regional State, because three-stone traditional stoves are not typically used.

From the 81 remaining sites we select 36 at random using proportionate random sampling based on regional state forest cover. A total of 20% of observations are sampled from Amhara, 50% from Oromiya and about 30% from SNNP regional states. These percentages also approximate the population distribution across the three regions. Forest cover is used as the sampling proportion, because most fuelwood in the sample areas come from forests and forest area provides a key measure of fuelwood supply. All sites have formal or informal forest user groups. It was at this

> 2 The regional state is the most aggregated sub-national jurisdiction.

**9 |** P a g e
Figure 29. Index of World Commodity Prices, 1974-100.

<!-- Start of picture text --> 300<br>280<br>2600<br>240<br>220<br>200 -<br>180/<br>180<br>120 /<br>100-<br>2 0 * .<br>1981 1963 1985 1967 1969 1971 1973 1976 1977 1979 1981 1983 1985 1987<br>Year<br><!-- End of picture text --> <!-- Start of picture text --> Coffee -- Cotton S- ugar<br><!-- End of picture text --> Figure 2b. Index of World Commodity Prices, 1974-100.

<!-- Start of picture text --> 300<br>280<br>260<br>240<br>220<br>200<br>180 -<br>140<br>120<br>100'<br>60 L<br>40-<br>20-<br>0 -- -- L4- I --J- 4 I . L -4 I L41 1 1 4 WI ]<br>1981 1963 1965 1987 1989 1971 1973 1975 1977 1979 1981 1983 1985 1987<br>Year<br><!-- End of picture text --> <!-- Start of picture text -->
- Tea  - Tobacco<br><!-- End of picture text -->

4
<!-- Start of picture text --> 12<br>== Central America<br>= =~ Latin America and the Caribbean<br>10 . |— —East Asia and the Pacific coyenennnsenseeeeset?<br>+sees* Europe and Central Asia ree<br>++eae++ Middle East and North Africa ae<br>g ‘ ==-=-South Asia we -—<br>od we --<br>3 = + =Sub-Saharan Africa en ao<br>2° we ~ eet -se<br>coy wee we -<— or<br>c weet = “ow —--' lee<br>$ 4 -o— ee -_-<br>= a -_—-¢ -———_ stwee. -- ~--<br>no oe - --"-"”<br>e---or — -— we.iSer<br>2 walses<br>—_ + eee<br>Zea cEapanangnnnneeree==: aor toe<br>0<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010<br>Year<br>10 —— Central America<br>g || sceres+ Costa Rica _—<br>— =Panama -_ a<br>; - oa rca<br>- -® -Guatemala 7 eo .<br>— — ElSalvador te<br>—_36 ’‘ — <_ Honduras se= aeeo , a “7<br>z — + Nicaragua a y < a<br>ras — ” — -<br>oO”-8 os —— _— _ = - .* ae = anZz —a--— « -7 «<br>o TT Tan en nen eeeeeeeee Tete eee ge® aeoonr ~e----e"<br>= A@-27 _-e-7"<br>3 = Aw - -_-—--~<br>— _ ee - = - 7 -_——-e~<br>a<br>2 va = -= —_— — o* -*~<br>peewee = ----0"<br>@----@----e----e---<br>1<br>0<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010<br>Year<br><!-- End of picture text -->
# Table 7

# **Regressions of Monthly Earnings (1n) Full** time. Male. Salaried Workers. **Coopleters of Vocational Schools -** Israeli Census. 1983

# n-9798

||Direct Ma|tchings|Wider Ma|tchings|
|---|---|---|---|---|
|Independent|||||
|Variable|Co-efficient|t-statistic|Co-efficient|t-statistic|
|YRS.SCH|0.024|1.51|0.022|1.38|
|EXP|0.025|2.13|0.025|2.06|
|B p2|-0.0007|5.07|-0.0007|4.98|
|EXP*YRS.SCH|0.002|2.37|0.002|2.36|
|Certification:|||||
|P.CERT|0.011|0.44|0.013|0.38|
|S.CERT|0.073|3.10|0.067|2.84|
|BAG|0.112|3.46|0.103|3.23|
|WEEKS (in)|0.302|13.16|0.298|13.05|
|HOURS (in)|0.374|9.66|0.367|0.53|
|ETHNIC|-0.137|11.84|-0.134|11.66|
|Economic Sector:|||||
|IND|0.101|3.08|0.095|2.90|
|ELECT|0.284|6.48|0.285|6.54|
|COMM|0.11|0.30|0.008|0.23|
|FIN|0.133|3.31|0.139|3.49|
|TRANS|0.084|2.36|0.092|2.58|
|PUB|0.019|0.55|0.033|0.95|
|PRIV|-0.047|1.16|-0.054|1.34|
|CONST|-0.005|0.14|0.00002|0.00|
|Subject of Study:|||||
|ELECTRIC|0.055|2.36|0.033|1.43|
|ELECTRON|0.184|6.62|0.158|5.67|
|METAL|0.057|2.88|0.040|2.01|
|AUTO|0.042|3.27|0.043|1.97|
|CLERIC|0.044|1.25|0.016|0.46|
|SEW|-0.053|0.42|-0.054|0.43|
|HOTEL|-0.024|0.41|-0.038|0.65|
|VOC.M.|0.055|4.54|0.114|9.80|
|Intercept|6.771|25.72|6.838|26.07|
|R2|0.155||0.161||

- 31 -
that it is very difficult to distinguish to what extent specific forms of infrastructure support trade as opposed to non-tradable activities.<sup>9</sup> Trends in aid for trade declined in absolute terms through 2002, after which it rose, reflecting renewed donor interest in growth and developments such as the launch of the Doha Development round (Figure 1). Even so, aid for trade has not kept pace with either total development assistance or that portion allocated to particular activities. Multilateral providers of assistance – aid that is channeled through IDA and the regional development banks – on average allocated a far higher proportion of their concessional aid for trade assistance to low-income countries than do bilateral donors. Some 93 percent of every aid for trade dollar goes to low-income countries ($6.6 billion of a total of $7.1 billion in assistance – Figure 2). However, bilateral donors provided 46 percent of their aid for trade to low-income countries. This highlights the importance of multilateral concessional lending for trade – and the urgency from an aid for trade perspective – of successfully completing the replenishment of the International Development Association’s concessional fund for low-income countries (IDA-16).<sup>10</sup>

**Figure 1: Aid for Trade, 1995-2008 (constant, 2008 USD Million)**

<!-- Start of picture text --> ODA Commitments (constant 2008 US$ million)<br>200000<br>150000 Total ODA excluding<br>actions related to<br>100000 debt<br>50000<br>0<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br>ODA excluding actions related to debt Aid for trade<br><!-- End of picture text --> _Source_ : OECD CRS database.

According to the OECD’s most recent comprehensive report on aid for trade, Asia is the largest recipient of aid for trade. Aid to Africa has been closing in year-by-year in second place. In 2007, Asia received US$10.7 billion, over half of which went to Central and South Asia. Although the volume of aid for trade funds destined for Asia remained stable from 2002 to 2007, the region’s share of total aid for trade funds dropped from 50 percent in the 2002-2005 period to 42 percent in 2007. This is due, in part, to Africa’s increasing share of global aid for trade funds. The region received US$9.5 billion in 2007, representing 38 percent of total aid for trade funds, up from 30 percent in the baseline period. Flows to all other regions were significantly smaller.

> 9 It should also be noted that the OECD/WTO numbers exclude development assistance provided outside of the framework of the OECD Development Assistance Committee (DAC), and thus do not cover assistance provided by countries such as China.

> 10 Note: When references are made to CRS data, USD figures are in 2008 constant terms, whereas statistics attributed to OECD/WTO (2009) are in 2006 constant terms.

6
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35