rafmacalaba/datause-encoder-data huggingface dataset:
tasks are more skill-intensive than those previously performed in the country to which they were o¤shored, but they are less skill-intensive than those in the country that is doing the o¤shoring.

Feenstra and Hanson (1999) test whether their explanation for the increase in the demand for skill, based on more o¤shoring, is consistent with the pattern of increasing wage inequality in the United States. They consider the alternative hypothesis that skill-biased technological change accounted for the observed increase in wage inequality. They proxy for technical change with the fraction of high-technology equipment in each industry’s capital stock and they measure o¤shoring with the intermediate inputs imported by each industry. They use a two-step procedure, ...rst to identify the impact of o¤shoring and high technology investments on productivity and prices, and then to trace through the induced productivity and price changes to calculate production and non-production wages.

Using data for the U.S. manufacturing sector between 1979 and 1990, Feenstra and Hanson (1999) ...nd that 25 percent of the increase in the relative wage of nonproduction workers was explained by o¤shoring and about 30 percent by technological change. They conclude that both o¤shoring and the increased use of high-tech capital are important in explaining the increase in the relative wage of skilled workers. They also examine the impact on real wages as distinct from relative wages, which are the focus of measures of inequality. They ...nd that the real wages of production workers were probably una¤ected by o¤shoring activities while the real wages of non-production workers increased by 1 to 2 percentage points. Sitchinava (2008) updates the Feenstra and Hanson (1999) paper to 1996 and also takes into account the possibility of services o¤shoring. Sitchinava ...nds that most of the increase in the relative wages of nonproduction relative to production workers can be explained by technical change, which is proxied with the share of high-technology equipment in the capital stock.

Adapting Feenstra and Hanson (1999) to measure service o¤shoring, Amiti and Wei (2009) provide evidence for the e¤ects of both service and material o¤shoring on domestic productiv35
**Table 4:  Probit results - child animal-source food intake**<sup>**a**</sup>

|**Mother's Education**|**Parameter**|**Month**|**Parameter**|
|---|---|---|---|
|**(0 Excluded)**|**Estimates**|**(January Excluded)**|**Estimates**|
|**Primary**|**0.153**|**February**|**-0.047**|
||**(4.24)****||**(0.66)**|
|**Middle**|**0.422**|**March**|**0.048**|
||**(10.05)****||**(0.64)**|
|**Secondary**|**0.496**|**April**|**-0.021**|
||**(9.18)****||**(0.27)**|
|**Post-Secondary**|**0.637**|**May**|**-0.045**|
||**(7.02)****||**(0.55)**|
|||**June**|**0.091**|
|**Asset Quintile (Q1 Excluded)**|||**(0.87)**|
|**Q2 **|**0.142**|**July**|**0.176**|
||**(3.09)****||**(2.17)* **|
|**Q3 **|**0.366**|**August**|**0.267**|
||**(6.80)****||**(3.06)****|
|**Q4 **|**0.407**|**September**|**0.361**|
||**(6.42)****||**(3.95)****|
|**Q5 **|**0.415**|**October**|**0.257**|
||**(4.99)****||**(2.82)****|
|||**November**|**0.334**|
|**Log Age **|**-3.331**||**(4.21)****|
||**(8.82)****|**December**|**0.279**|
|**[Log Age]**2**|**3.061**||**(3.34)****|
||**(13.99)****|||
|**[Log Age]**3**|**-0.539**|||
||**(14.12)****|**Constant**|**-3.459**|
||||**(11.39)****|
|**Year (2000 Excluded)**||||
|**2004**|**0.435**|||
||**(7.77)****|||
|**2007**|**0.187**|||
||**(2.76)****|||
|**2011**|**0.108**|||
||**-1.73**|||
|**2014**|**0.145**|||
||**(2.08)* **|||

**a Grid location results excluded for brevity**

**Observations: 13,987 Absolute value of t statistics in parentheses [* significant at 5%; ** significant at 1%]**

9
Yet, even while migrants fare better economically, people migrate for reasons that may not be clearly linked with economic gains, such as moves associated with marriage. In our country of study, Malawi<sup>1</sup> , the largest ethnic group is the Chewa, who historically practice matrilocal patterns of social organization—husbands have typically moved to their wives’ compounds following marriage, which is often in other villages. Over the past century, however, matrilocality has become more irregular. The extent to which the Chewa continue to practice matrilocal forms of relocation has been on the wane in recent decades, with the increasing role of labor migration cited as a potential reason (Phiri 1983). We will return to this topic in our empirical analysis below.

Although studies of migration and remittances in Sub-Saharan Africa are numerous, few have focused specifically on the population of young adults. One exception is McKenzie (2007), whose research demonstrates that a large share of migrants is constituted by young people; he concludes that their reasons for and experiences related to migration differ from migrants who are slightly older or much older. However, McKenzie’s work considers international migration only, which currently, at least in Malawi, is a relatively rare form of migration compared to internal moves. Sander and Maimbo (2003) characterize migration in Sub-Saharan Africa as unique, since most people on the continent move intra-regionally and within-country, rather than overseas.

Although during Malawi’s colonial era international migration was common, following independence in 1964, President Kamuzu Banda began to discourage international migration,

> 1 Malawi is an impoverished small country in southern Africa. Its population of 13 million is 85% rural (NSO, 2008), with most people living from subsistence farming supplemented by small-scale income-generating opportunities that are typically more available to men than they are to women. Agriculture accounts for 34% of its GDP, largely generated from smallholder farmers, with its main products being tobacco, tea, coffee, and cotton. The country is poor even by African standards: in 2007 GNI per capita was $250 compare to an average of $952 for the Sub-Saharan region (World Bank 2010).

4
1- 0

|||||||COUN|TABLE<br>TRY PERF|2<br>ORHANC|ES||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Rate <br>1970-80|of Crowt<br>1981-84|h0()<br>1986-88|Domestic<br>1970-80 1|Saving t<br>981-84 1|o CDP<br>986-08|Inve<br>1970-80|stment<br>to<br>1981-84|CDP<br>1986-89|Ratio of<br>1970-80 1|Exports<br>981-84|toGDP<br>1986-88|
|EIALavege<br>:Wdibn<br>-1.t<br>q..ertil<br>-3 dQ.atri<br>*|4.6<br>(2)<br>S.6<br>2.5<br>6.3<br> <br>|1.3<br>(3)<br>0.6<br>-0.6<br>2.7<br> <br>|4.2(1)<br>3.9'<br>2.3<br>4.1<br>|17.4<br>(1)<br>16.8<br>13.9<br>20.S<br>|14.8 (1)<br>14.4<br>10.7<br>20.1<br>|17.2<br>311)<br>19.3<br>10.9<br>2a.0<br>|22.5<br>(2)<br>23.5<br>20.2<br>25.9|19.9(1)<br>20.2<br>16.1<br>23.7|18.6<br>(1)<br>18.6<br>14.9<br>23.7|24.2<br>(2)<br>20.8<br>i8.4<br>29.8|251 (2)<br>22.5<br>145<br>35.0|28.3<br>(1)<br>23.2<br>17.6<br>37.0|
|OAL averag<br>ead.<br>an<br>-3a<br>tq.OrtiI<br>-34<br>q..a,til<br>**_4AL_**<br>|3.9<br>(3)<br>3.6<br>2.7<br>4.9<br> <br> <br> <br>|2.3<br>(2)<br>1.9<br>0.6<br>3.9<br>|3.0(2)<br>3.3<br>1.2<br>3.8<br> <br>|1402° (3)<br>142<br>4.7<br>21.6<br>|12.7(2)<br>12.1<br>1.3<br>21.6|13.3 (3)<br>13.<br>4.1<br>20.7|22.3<br>(3)<br>217<br>15.0<br>26.9|22.0<br>(2)<br>19.4<br>15.3<br>26.7|20.3<br>(3)<br>18.4<br>13.8<br>24.9|22.2<br>(3)<br>19. 1<br>12.8<br>28.8|24.4<br>(3)<br>22.3<br>12.8<br>27.9|23.6<br>(3)<br>22.3<br>12.5<br>32.2|
|**_~_**<br>***.eragw**<br>-SeLq..arLif<br>-3r,<br>q9..al&t|<br> <br>5.5<br>(1)<br>4.6<br>3.6<br>7.0|3.1(1)<br>**_2.7_**<br>-0.7<br>5 7|**_2.7_**<br>(3)<br>2.2<br>0.a<br>4.1|16.7(2)<br>6.2<br>11.0<br>20.1|14.0(3)<br>14.1<br>6.2<br>21.3|14.4 (2)<br>12.2<br>6.2<br>21.3|23.4<br>(1)<br>22.3<br>27.9|24.1<br>(3)<br>23.5<br>l8.5<br>18.<br>28.0|20.0<br>(2)<br>20.0<br>13.6<br>24 .2|26.3<br>(1)<br>23.2<br>35.4<br>32.3|26.1<br>(1)<br>24.2<br>13.8<br>31.1|<br>24.6<br>(2)<br>20.8<br>12 1<br>34.6|

Source: World Bank Data

**(1)** The rate of growth Is calculated with data In constant local currency. Tho ratios are calculated wIth **data** in current local currency. Figures in parenthesis **indicate** the ranking for **the** respective variable. A (1) indicates the best performance for the respoctive policy indicator.
Policy Research Working Paper 6551

# **Abstract**

Energy intensity has declined significantly in four Chinese industries—pulp and paper; cement; iron and steel; and aluminum. While previous studies have identified technological change within an industry to be an important influence on energy intensity, few have examined how industry-specific policies and market factors also affect industry-level intensity. This paper employs unique firm-level data from China’s most energy-intensive large and medium-size industrial enterprises in each of these four industries over a sixyear period from 1999 to 2004. It empirically examines how China’s energy-saving programs, liberalization of domestic markets, openness to the world economy, and other policies, contribute to the decline in energy intensity in these industries. The results suggest that rising energy costs are a significant contributor to the decline in energy intensity in all four industries. China’s industrial policies targeting scale economies— for example, “grasping the large, letting go off the small”—also seem to have contributed to reductions in energy intensity in these four industries. However, the results also suggest that trade openness and technology development led to declines in energy intensity in only one or two of these industries. Finally, the analysis finds that energy intensities vary among firms with different ownership types and regional locations.

This paper is a product of the Environment and Energy Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The World Bank co-author, Michael Toman, may be contacted at mtoman@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
Policy Research Working Paper 6510

# **Abstract**

Using data from a field experiment in 500 villages, this paper studies how local institutions affect the quality of governance, as measured by aid distribution outcomes. In villages where elected councils exist and manage distributions, aid targeting improves. However, if the distribution is not clearly assigned to either the council or customary leaders, the creation of elected councils increases embezzlement and makes decision-making less inclusive. Requiring that women manage the distribution jointly with customary leaders also increases embezzlement. Thus, while elected councils can improve governance, overlapping mandates between new and existing institutions may result in increased rent-seeking.

This paper is a product of the Office of the Chief Economist, East Asia and the Pacific Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at abeath@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 4. Civil Wars, 1960-2006**

_These figures show the percentage of countries that had a new civil war in a given year._

||**Non oil**<br>**producers**|**Oil**<br>**producers**|**Difference**|
|---|---|---|---|
|_Overall:_||||
|**AllStates and Periods**|2.8|3.9|1.0**|
|_By Income:_||||
|**Low Income (below $5000)**|3.8|6.8|3.0***|
|**High Income (above $5000)**|1.2|1.4|0.2|
|_By Period:_||||
|**1960-1989**|2.4|2.7|0.3|
|**1990-2006**|3.6|5.3|1.7**|

*** significant at 1%, ** significant at 5%, * significant at 10%, in a one-tailed t-test.

_Source:_ calculated from data in Gleditsch et al. (2002).

_Notes:_ Oil producers are here defined as countries with over USD 100 dollars per capita in oil income for 2008 (using 2000 general prices). Data are drawn from US Energy Information Administration (EIA). Price data from BP statistical yearbooks.

**Figure 5. Annual Conflict Rates in Oil and Non-Oil Producers**

<!-- Start of picture text --> 10<br>9.1<br>9<br>8<br>7 6.8<br>6<br>5.3<br>5<br>4.2<br>4 3.9 3.6 3.8<br>3 2.8<br>2<br>1<br>0<br>All Post Cold War Low-income Post-Cold War & Low-<br>Income<br>Non-oil states Oil states<br><!-- End of picture text --> _Source:_ calculated from conflict data in Gleditsch et al. (2002).

_Notes:_ The figures represent the annual conflict rate for oil-producing states (black bars) and non-oil states (gray bars). The data are for 1960-2006, except the ̳Post-Cold War‘ period, which is 1992-2006. Low income countries are those with incomes below $5000 per capita. All of the differences between oil and non-oil states are statistically significant in t-tests.

2
that can be considered before the COVID-19 pandemic. The Google mobility data contains information on various categories of places such as: i) retail and recreation, ii) grocery and pharmacy, iii) parks, iv) workplaces, v) transit stations, and vi) residential areas. In the school closure analysis, we include control variables for mobility around retail and recreation, and workplaces since they are the most relevant for business outcomes.<sup>19</sup> The final main piece of data we use provides us with a measurement of social norms around gender equality. In December 2019, prior to the start of the COVID-19 pandemic, the standard bi-annual Future of Business survey collected data on individuals’ personal beliefs and norms towards gender equality across 109 countries (based on measures from Bicchieri (2016)). The survey elicited individual beliefs on gender equality by asking how much the survey respondent agreed with the following statement: do you think “men and women should have equal opportunities” using a four-point scale: strongly disagree, disagree, agree and strongly agree. The individual belief variable was coded as 0/1 where 1 is the respondent agrees or strongly agrees with equal opportunities; and 0 if respondent does not agree with equal opportunities. These beliefs are then aggregated at the country level to give a measure of the share (percentage) of the country who agree with gender equality in opportunities among the study sample.<sup>20</sup> A low share of agreement indicates more conservative views towards gender equality within a country and a higher share of agreement indicates more liberal or progressive views towards gender equality within a country. See Appendix D for a full description of the norms constructs used in this paper.

> 19Retail and recreation index covers visits to restaurants, cafes, shopping centers, theme parks, museums, libraries, and movie theaters. For example, during wave I of the Future of Business survey, on May 23rd 2020, globally, retail and recreation has a mean of -38, suggesting a 38 percentage point drop relative to pre-pandemic baseline levels for the 50 countries in the sample. In wave 6 of the FoB survey, on October 28th 2020, retail and recreation has a mean of -21, suggesting a 21 percentage points drop relative to pre-pandemic baseline levels.

> 20The analysis for this paper includes countries with at least 100 male and female observations.

12
- [34] Martin, S. (2000). The theory of contestable markets. Purdue University. Retrieved from http://www.krannert.purdue.edu/faculty/smartin/aie2/contestbk.pdf.

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25
Annual real GDP figures in local currency were converted into dollars using the market exchange rate. Both GDP and exchange rate data are from the _International Financial Statistics_ of the IMF. Skilled labor abundance is defined as the sum of occupational categories 0/1 (professional, technical, and kindred workers) and 2 (administrative workers) in employment in each country, divided by total employment. These figures are compiled from annual surveys reported in the _Yearbook of Labor Statistics_ published by the International Labor Organization. In cases where some annual figures were missing, the skilled-labor ratios were taken to equal the period averages for each country. Our skill difference variable is the relative skill endowment of the parent country less that of the host country.

The cost of investing in the affiliate country is a simple average of several indices of perceived impediments to investment, reported in the _World Competitiveness Report_ (19861994) and _Global Competitiveness Report_ (1995-2003) of the World Economic Forum. The investment barriers include restrictions on the ability to acquire control in a domestic company, limitations on the ability to employ foreign skilled labor, restraints on negotiating joint ventures, strict controls on hiring and firing practices, market dominance by a small number of enterprises, an absence of fair administration of justice, difficulties in acquiring local bank credit, restrictions on access to local and foreign capital markets, and inadequate protection of intellectual property. The resulting indices are computed on a scale from zero to 100, with a higher number indicating higher investment costs. The institutional barriers are defined as the frequency of bribery and corruption in the host country and are computed on a scale from zero to 100 with zero being the least corrupt and 100 being the most corrupt. Meanwhile, the infrastructure barriers refers to the quality of infrastructure in the host country and includes the quality of railroad, ports, air transport, waterways, roads, electric supply as well as telecoms 16
Table 1: Regression of Determinants of Yield, 2014/15

||**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|Logged Yield<sup>8</sup>|**Sorghum**|**Millet**|**Groundnut**|**Sesame**|
|Poor|−0.118**|−0.013|−0.205**|0.142|
||(0.057)|(0.060)|(0.087)|(0.116)|
|Agricultural loan|0.108|−0.236|0.001|0.219|
||(0.084)|(0.154)|(0.173)|(0.159)|
|Nonagricultural loan|−0.166***|−0.172***|−0.076|−0.118|
||(0.052)|(0.053)|(0.079)|(0.110)|
|**Input Use**|||||
|Fertilizer/pesticide|0.128*|0.172|0.224|−0.057|
||(0.075)|(0.117)|(0.180)|(0.153)|
|Labor|−0.025|0.009|0.132|0.080|
||(0.055)|(0.067)|(0.106)|(0.108)|
|Machine|0.069|0.192***|0.115|0.228*|
||(0.059)|(0.060)|(0.096)|(0.131)|
|Farm repair|0.039|0.070|−0.098|0.076|
||(0.059)|(0.070)|(0.093)|(0.104)|
|Fuel|0.067|−0.035|0.065|0.197|
||(0.107)|(0.187)|(0.251)|(0.196)|
|Other inputs|0.111|−0.248***|0.063|0.215|
||(0.078)|(0.089)|(0.107)|(0.158)|
|Irrigated|0.176|0.378***|−0.047|0.483**|
||(0.112)|(0.110)|(0.359)|(0.203)|
|**Land status**(owned omitted)|||||
|Rented|−0.052|0.088|0.018|−0.100|
||(0.052)|(0.063)|(0.077)|(0.119)|
|Partially owned|−0.030|0.237|−0.006|−0.245|
||(0.124)|(0.210)|(0.431)|(0.402)|
|Communal|−0.087|0.068|−0.047|−0.024|
||(0.088)|(0.102)|(0.119)|(0.190)|
|Area (plot size)|−0.032***|−0.050***|−0.054***|−0.031***|
||(0.003)|(0.005)|(0.009)|(0.004)|
|Household size|0.038***|0.018|0.049***|−0.002|
||(0.011)|(0.012)|(0.017)|(0.023)|
|Constant|−1.069***|0.797***|2.115***|−0.244|
||(0.176)|(0.157)|(0.594)|(0.192)|
|Observations|2257|1692|737|480|
|R-squared|0.373|0.410|0.386|0.334|
|State controls|Yes|Yes|Yes|Yes|
|Education of household head controls|Yes|Yes|Yes|Yes|

_Source:_ Authors’ calculations using NHBPS 2014/15. _Note:_ Standard errors are in parenthesis. ***p < 0.01, **p < 0.05, *p < 0.1.

8 As a log linear regression, coefficients need to be transformed. Each unit increase of the independent variable increases yield by 100 × (1 −e<sup>β</sup> )% , where β is the coefficient from the regression. For example, a coefficient on poor for sorghum of −0.118 means that the poor (assigned a value of poor= 1) have 100 × (1 −e<sup>−0.118</sup> )% = −11.3% difference in yields when compared to the non-poor (assigned a value of poor= 0 ). Note that for coefficients close to 0, these estimates are approximately the same.

45