rafmacalaba/datause-encoder-data huggingface dataset:
Table 7: OLS regression of impacts under Monopolistic Competition, on initial characteristics of cells and changes in trade costs

||Real Income Growth|Primary GVA Growth|Manufacturing GVA<br>Growth|
|---|---|---|---|
||(1)|(2)|(3)|
|Share of Employment|0.00612|‐0.0565**|0.164***|
|in Manufacturing|(0.00643)|(0.0219)|(0.0464)|
|Decline Export|0.901***|‐0.496*|3.932***|
|Trade Cost|(0.0830)|(0.252)|(0.598)|
|Decline Import|0.301***|0.414***|‐0.750***|
|Trade Cost|(0.0220)|(0.0672)|(0.159)|
|Constant|‐0.268|4.261***|‐8.272***|
||(0.307)|(0.963)|(2.211)|
|Observations|52|49|52|
|Adjusted_R_<sup>2</sup>|0.898|0.617|0.622|

OLS regression with all variables expressed as percentages. Standard errors in parentheses * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01 Table 8: OLS regression of impacts under Monopolistic Competition, on initial characteristics of cells and changes in trade costs

||Real Income Growth|Primary GVA Growth|Manufacturing GVA<br>Growth|
|---|---|---|---|
||(1)|(2)|(3)|
|Share of Employment|0.00635|‐0.0632***|0.182***|
|in Manufacturing|(0.00680)|(0.0226)|(0.0466)|
|Decline Export|0.940***|‐1.501**|5.116***|
|Trade Cost with Region|(0.251)|(0.738)|(1.719)|
|Decline Export|0.892***|‐0.281|2.205**|
|Trade Cost with RoW|(0.148)|(0.443)|(1.016)|
|Decline Import|0.268|1.286**|‐0.647|
|Trade Cost with Region|(0.195)|(0.572)|(1.332)|
|Decline Import|0.306***|0.280**|‐0.628**|
|Trade Cost with RoW|(0.0379)|(0.113)|(0.259)|
|Constant|‐0.273|4.424***|‐8.808***|
||(0.317)|(0.972)|(2.168)|
|Observations|52|49|52|
|Adjusted_R_<sup>2</sup>|0.894|0.620|0.645|

OLS regression with all variables expressed as percentages. Standard errors in parentheses * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01.

19
ranging from heavy reliance on subsistence agriculture with little earned income, to complete reliance on wage labor. This hypothesis is supported by the significantly lower consumption inequality of 0.43, which is roughly equal to that in urban areas. International research shows that the more unequally income is distributed the less effective economic growth is in reducing poverty (Lustig et al. 2001).

High return rural nonfarm occupations in Argentina were mostly taken up by the comparatively better off, although the poor have also participated to some extent (see Verner 2006). Therefore, it is likely that the impact has not been equalizing, and the rural nonfarm sector has contributed in some measure to the worsening of rural income distribution. Private transfers (and to a much lesser extent public transfers) have definitely helped the poor more than other groups, and have therefore had an equalizing impact (see Verner 2006).

The problem of poverty and inequality in rural Argentina largely reflects disparities in opportunities. The distribution of key productive assets—jobs, human capital, physical assets, financial assets, and social capital—is highly unequal both among and between provinces. These disparities are greatest between the poor and nonpoor, but also manifest themselves differently by geographic area. In addition, access to services is unequal.

|Table4.4: Gini|Index in Di|spersedRural|Areas o|f Argentina,|2003|
|---|---|---|---|---|---|
||Mendoza|Santiago<br>del<br>Estero|Chaco|Santa Fe|Total|
|Income|0.50|0.48|0.79|0.76|0.74|
|Consumption|0.37|0.37|0.48|0.35|0.43|
|Source: Own calcul|ation based on|RHS 2003.||||

# **5. Poverty Profile**

After counting the rural poor, we need to know who they are, where they live, and what they do. Comparing average levels of poverty for different categories is useful for learning which population groups are falling behind or catching up in terms of poverty. This is useful for the design of policies: for example, we would like to know whether more or less educated people are more likely to be poor in rural Argentina. Unfortunately, data do not allow for analyzing how the relative odds of being poor have evolved for these groups since data are only available for 2003. The poverty profile constructed is based on data from the RHS (Table 5.1). In the following the indigent poverty line and consumption poverty are referred to in the text. The main questions addressed are: (1) who are the poor? (2) what are the characteristics of poor households? (3) where do they live? and (4) where do they work?

23
Diaspora effects in international migration: key questions and methodological issues.

Michel Beine<sup>a</sup> , Fr ́ed ́eric Docquier<sup>b</sup> and Caglar Ozden<sup>c</sup>

> aUniversity of Luxemburg and CES-Ifo

b FNRS and IRES, Universit ́e Catholique de Louvain b DECRG, The World Bank November 9 2010 Abstract This paper reviews the existing literature on the impact of migrants networks on the patterns of international migration. It covers the theoretical channels at stake in the global effect of the networks. It identifies the key issues, namely the impact on size, selection and concentration of the migration flows. The paper also reviews the empirical hurdles that the researchers face in assessing the importance of networks. The key issues concern the choice of micro vs a macro approach, the definition of a network, the access to suitable data and the adoption of econometric methods accounting for the main features of those data. Finally, the paper reports a set of estimation outcomes reflecting the main findings of the macro approach.

1
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41
Policy Research Working Paper 9527

# **Abstract**

Theory suggests several ways in which exporting may benefit women’s employment. However, the empirical evidence is mixed and limited, especially for developing countries. This paper uses firm-level survey data for 91 developing countries to estimate the relationship between exporting and the share of women workers at the firm. The analysis pays close attention to endogeneity concerns. First, it proxies a given firms’ exports by the average exports of all other firms in the same country-year-industry cell. Second, it exploits the repeated cross-section nature of the data and analyzes how changes over time in exporting activity are associated with changes in the share of women workers. The strategy is more immune to endogeneity problems than pure cross-section regressions. Third, it tests several mechanism or mediating factors as predicted by the theory through which exporting impacts women’s employment prospects. The predictions are confirmed in the data, an unlikely scenario if exports were a mere proxy for other correlated drivers of women’s employment. The results show a large, positive impact of higher exports on the share of women workers. A conservative estimate is that for each percentage point increase in the ratio of exports to total sales, the share of women workers increases by 0.16 percentage point. Consistent with the theoretical predictions, this positive relationship is much larger (more positive) in industries that rely more on women workers, in country-industry pairs where competitive pressure is largely from international markets in comparison to less competitive domestic markets, when social attitudes and labor laws are more favorable toward women’s work, and when the law and order situation is more business friendly.

This paper is a product of the Global Indicators Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at mamin@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 8096

# **Abstract**

This paper uses nationally representative panel data and a combination of econometric approaches, to explore linkages between rural non-farm activities (wage and self-employment) and household welfare in rural Malawi. The paper analyzes the average treatment effects and distributional effects on participants’ welfare indicators, such as households’ per capita consumption expenditures. Then it investigates the effects of non-farm activities on the use of agricultural inputs, one channel through which non-farm employment might improve the welfare of rural households. Although participation in non-farm activities is not randomly assigned in the data, the identification strategy relies on fixed effects and correlated random effects estimation methods, dealing effectively with time invariant heterogeneity, coupled with geographical covariate adjustments, controlling for time varying differences in local market conditions and employment opportunities. The results suggest that non-farm wage employment and non-farm self-employment are welfare improving and poverty reducing. However, households at the lower tail of the wealth distribution benefit significantly less from participation than the wealthiest. Although the results support the promotion of the rural non-farm economy for poverty reduction purposes, they indicate that targeted interventions that improve poor households’ access to high-return non-farm opportunities are likely to lead to bigger successes in curbing rural poverty.

This paper is a product of the Poverty and Equity Global Practice 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 authors may be contacted at gadjognon@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
# **Challenges to MDG Achievement in Low Income Countries: Lessons from Ghana and Honduras**

## **Maurizio Bussolo and Denis Medvedev**<sup>*****</sup>

_Keywords:_ Millennium Development Goals (MDG), Ghana, Honduras, Simulation Model _JEL codes_ : D58, O57, O15, E61

* Development Prospects Group, World Bank. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors; they do not necessarily reflect the views of the World Bank, its Executive Directors, or the countries they represent. We are grateful to Zeljko Bogetic, Carolina DiazBonilla, Pablo Flores, Rafael De Hoyos, Ulrich Lachler, Hans Lofgren, Lars Moller, Shuo Tan, Hans Timmer, Dominique van der Mensbrugghe, and participants at the PEP (June 12, 2007 in Lima, Peru) and PEGNet (September 6-7, 2007, in Berlin, Germany) conferences for helpful contributions, comments, and suggestions. We are indebted to our counterparts at the Ministry of Finance and Economic Planning (MOFEP) and Bank of Ghana in Accra, Ghana, and our counterparts at the Technical Unit of the Presidency (Unidad de Apoyo Técnico or UNAT) and Government of Honduras in Tegucigalpa, Honduras, for fruitful discussions and sharing of key data. All remaining errors are ours.
To measure each teacher’s support for a given archetype, the questions were coded within the teacher beliefs survey for their relation to that archetype. Depending on their degree of agreement or disagreement with a given belief, teachers were assigned points for a certain archetype. In this way, each teacher received a score indicating their implied adherence to each archetype. These scores were then correlated with the CLASS observation results, as well as with age, teaching experience, and number of students (table 8).

**Table 8: Correlations between Teacher Archetypes and CLASS Results**

||**Transmission**|**Connection**|**Discovery**|
|---|---|---|---|
|**Age **|0.09|0.16|-0.07|
|**Teaching Experience**|0.11|0.25|-0.03|
|**No. of Students**|-0.06|-0.11|0.06|
|**Positive Climate**|-0.34|0.17|0.40|
|**Teacher Sensitivity**|-0.10|0.04|0.12|
|**Regard for Student Perspectives**|-0.20|0.20|0.21|
|**Behavior Management**|-0.15|0.10|0.28|
|**Productivity**|-0.12|0.14|0.10|
|**Negative Climate**|0.00|-0.29|-0.10|
|**Instructional Learning Formats**|0.00|0.07|0.06|
|**Content Understanding**|0.16|0.15|0.16|
|**Analysis and Inquiry**|0.23|0.25|0.16|
|**Quality of Feedback**|-0.07|0.17|0.33|
|**Instructional Dialogue**|0.02|0.30|0.25|
|**Student Engagement**|-0.20|0.12|0.14|
|**Emotional Support**|-0.26|0.16|0.29|
|**Classroom Organization**|-0.11|0.18|0.19|
|**Instructional Support**|0.08|0.23|0.23|

Source: Guangdong teachers’ belief surveys; authors’ calculations.

The more student-centered beliefs (connection and discovery) are more highly correlated with the studentcentered actions, such as Positive Climate and Regard for Student Perspectives. More important, these results are also correlated with higher Instructional Support and Classroom Organization. Indeed, teachers who believe that their role in the classroom is to transmit information to students had a negative correlation with Emotional Support and Classroom Organization. This is important, as it is sometimes stated that while more teacher-centered beliefs may not support a positive classroom climate, they are more effective at ensuring that content is delivered effectively. High correlations between student-centered discovery and connection beliefs and Instructional Support undermines this claim, implying that a student-centered approach is also one that allows for improved Instructional Dialogue and Student Engagement.

# **4 MOVING FORWARD**

## **4.1 Evaluating results**

While the Emotional Support and Instructional Support domains have a good deal of variation, the Classroom Organization and Student Engagement domains have very high scores across the board, with very little variation. With Classroom Organization, 86 percent of all coded sessions received a 6 or higher 19
This is based on the above mentioned ILO definition. Estimates from other west-African countries range from 27-50% in Nigeria to 54% in Cote d' Ivoire (Kanbur and Grootaert, 1995).

Since the child labor force participation is high in Ghana, we would like to understand the characteristics of the child labor. As seen in Table 12, most of the child labor is concentrated in the rural areas (almost 85%). Almost 28 percent do not have any education ( an increase from 16% in 1987), while almost 52% have primary level education. This also explains the dropout rate from primary to secondary being high. On gender basis, boys contribute more to the child labor pool than females. With regards to occupation and type of industry of work, it is not surprising to find that most of the child labor is engaged in agriculture related activities, and small portion in trade and commerce.

# **3.3 Turnover rates**

An issue related to the absorption of labor is the stability of the labor force. This can be seen from the data on turnover rates for different years estimated from the GLSS data sets. We find that the quit rates have increased during this period, perhaps an indication that the labor market is very dynamic and active. However, the labor market is more stable in 1991, compared to 1987 and 1988. Perhaps the quit rates were kept low by the high wages in the formal sector, while the economic reform program undertaken in the recent years have resulted in a large movement of workers from the formal to informal sector during 1992, as seen in Figure 2. Also, the quit rates are lower for those with higher levels of education (Table 13) and highest for those with no education.

**Fig 2: The estimated quit rates among wage workers in Ghana.**

<!-- Start of picture text --> 12 _ .---------<br>4) 1D , X<br>4) 2<br>0<br>0-1 3-4 6-7 9-<br>yr yr length of service yr 10<br>yr<br>-- x- 1987 1988 -0 _ 1991<br><!-- End of picture text --> Source: Authors' own calculations using GLSS 1,2,3.

**Note:** Quit rates are estimated using the years of experience. The quit rates for those with experience t, to t2 is estimated as the percentage of the labor force with atleast t, experience minus the percentage with atleast **t,** experience divided by the percentage with atleast **t,** experience.

24
considerations seem to dominate on the whole, migrants from a subset of households appear to be target-earners. In households with intermediate values of the foreign wage index, the exchange rate shocks lead to increases in variables associated with household investment, such as vehicle or real estate purchases and entrepreneurial income. These results are consistent with the theoretical prediction that the migrants most likely to be target-earners are those in the middle of the foreign wage distribution: positive exchange rate shocks make target-earners more likely to return home and to invest (because they become more likely to have reached the minimum investment threshold).

The general methodology used in these studies on Philippine migrants – examining the impact of an economic shock experienced by overseas migrants on remittances and the outcomes of family members left behind – can potentially be applied in a variety of different contexts. Studies using a similar methodology can be useful to ascertain whether the results in the Philippine case extend to other contexts, or, if not, what might account for the differences in impacts.

The key requirements for such as study are: 1) an origin country whose migrants are destined to a wide variety of overseas destinations, 2) large and heterogeneous economic shocks in destination areas, 3) data on migrant locations before the shocks, and 4) data on migrant and origin household outcomes after the shocks. Many situations satisfy elements 1) and 2): for example, migrants from India and the other countries of South Asia also are destined for a wide variety of overseas destinations, and regional or global country-level economic shocks (such as the 2008-09 global financial crisis) are often heterogeneous in magnitude across migrant destinations. Migrants from specific countries in Latin America are often destined for a variety of locations across the United States, and hence it can be possible to exploit state-level (and perhaps occupation- or industry-specific) economic shocks experienced by migrants to achieve identification. Antman (2010) provides one example of this approach.

A likely hindrance to future research along these lines among other migrant populations is that there are fewer situations where the requisite survey data (requirements 3 and 4) are available. The Philippine case is unusual, in that the National Statistics Office of the Philippines administers a linked set of high-quality surveys to a nationally-representative household sample 11