rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 10170

# **Abstract**

Most welfare measures, including food expenditure and diet quality, are based on household aggregates and assume an equal or equitable distribution of resources among members within the household. But it is unlikely that resources are distributed equally or equitably within most households. As such, individual food expenditure and diet quality measures, rather than household aggregations, may paint a more accurate picture of intra-household welfare. This paper assesses the disparity between household and individual measurement of food expenditure and diet quality in the Philippines using data from 2013. It finds evidence of intra-household inequality for food expenditure and for diet quality. In particular, for the consumption of starchy staples, meat, fish, and legumes, women and children do not meet the recommended consumption, even within households that, in aggregate, are able to meet the recommended consumption. However, intra-household inequality is not observed under circumstances in which no one in the household meets recommended consumption, as is the case for many food categories in our analysis.

This paper is a product of the Agriculture and Food Global Practice. 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 fdizon@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
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE The additional capital investment is recouped in less than four years through savings in variable cost expenditures. This is equivalent to a return on investment of 28 percent. Annual savings are 11.5 percent of the annualized cost of expansion.

**Table 3.7. Rate of return on power trade at country level**

|Country|Price gain<br>(US$/kWh)|Net power<br>trade<br>(TWh)|Annual<br>benefits<br>(US$ million per<br>annum)|One-time<br>investment<br>(US$ million)|Rate of<br>return (%)|
|---|---|---|---|---|---|
|Exporter||||||
|Cameroon|0.04|6.7|322|2,680|12|
|Importer||||||
|Chad|0.04|0.1|4|0||
|Congo, Rep. of|0.02|5.84|117|106|110|
|Equatorial Guinea|0.02|0.01|0|15||
|Gabon|<0.01|1.2|0|2||
|_Source:_AICD calculations.||||||

The rates of return for importers and exporters vary Gabon <0.01 1.2 0 2 _Source:_ AICD calculations. at the country level. Based on limited evidence, regional power integration offers large and promising rates of return for power exporters and importers alike. A one-time investment of $2.6 billion to develop additional hydropower potential and interconnectors in Cameroon can result in a return of 12 percent on investment. Similarly, on the importer side, a one-time investment of $106 million in interconnectors in the Republic of Congo could result in a return on the order of 110 percent every year (table 3.7).

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**Table 1.2 Uganda 2006, special funds in infrastructure**

|**Fund**|**Administrating**<br>**authority**|**Funding sources**|**Objectives**|
|---|---|---|---|
|Rural Electrification Fund|Rural<br>Electrification<br>Agency|5% levy of transmission bulk<br>purchases by generation<br>companies; World Bank;<br>government|Capital subsidies to private rural<br>generation companies; tariff subsidies<br>to rural distribution companies.|
|Credit Support Facility||World Bank, government|Refinancing facility for long-term<br>private lenders to rural electrification<br>projects; partial riskguarantees.|
|Tariff Stabilisation Fund|UECL<br>(transmission<br>company)|Tariff levy on cost of generation|Smoothing of electricity tariff increase<br>until Bujugali comes on stream.|
|Rural Communications Fund|Uganda<br>Communications<br>Commission|1% levy on gross revenue from<br>telecommunications and postal<br>services providers (universal<br>service levy)|Support for rural communication;<br>provision of at least one public<br>telephone per 5,000 people at the<br>subcounty level; ensure Internet<br>access at every district headquarters.|
|_Source:_World Bank 2006b.||||

# Collecting readily available relevant data

## **Private participation in infrastructure (template 3)**

Indicative levels and trends of private participation in infrastructure according to various private participation modalities and arrangements can be gleaned from the World Bank‘s private participation in infrastructure database (http://ppi.worldbank.org/). One template should be completed for each country.

Template 4 contains a list of transactions in infrastructure services involving private participation. The source is the World Bank database on private participation in infrastructure (PPI), available online. The PPI database is undoubtedly the most consistent and comprehensive cross-country documentation of private transactions in infrastructure sector in developing countries. When analyzing the results of the exercise, the PPI data provide perspective on the relative importance of the public sector vis-à-vis the potential and current activities of the private sector. This is a great example of how tapping secondary sources in the pre-field stage leverages the SPI exercise.

**Template 3 Private participation in infrastructure**

|**Name of**<br>**project**<br>_BOT=Build Ow_|**Modality**<br>**(concession, BOT,**<br>**management**<br>**contract, and so on)**<br>_ned and Transfer_|**Sector**|**Subsector**|**Contract**<br>**period**|**Investment year**<br>**or financial**<br>**closure year**|**Value of**<br>**investment**<br>**commitment**<br>**(US$ million)**|
|---|---|---|---|---|---|---|

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# ANNEX 5. Attrition

A simple comparison of attrition between the treatment and control group participants suggests that there were no significant differences in the rates of attrition between the two groups, that is, being in the treatment group did not significantly increase or decrease the likelihood of attrition in subsequent rounds.

Table 10a: Comparison of Attrition

|(1)<br>(2)<br>VARIABLES<br>5 weeks<br>3 months|
|---|
|Treatment<br>‐0.00949<br>‐0.00971|
|[0.0418]<br>[0.0455]|
|Constant<br>0.120***<br>0.145***|
|[0.0296]<br>[0.0323]|
|Observations<br>235<br>235|
|Adjusted R‐squared<br>‐0.004<br>‐0.004|
|Standard errors in brackets|
|*** p<0.01, ** p<0.05, * p<0.1|
|Source: Saraf, 2019; World Bank Group, 2018|

Analysis of differential attrition (which assesses if attritors in the treatment group have different baseline characteristics compared to attritors in the control group) shows that attritors in both groups are similar across key demographic variables‐ gender, education, ERKF funding round, household income (50,000 PKR or above), age, and number of dependents. The exceptions are marital status (i.e. whether married or not) and household income at the 5‐week mark. Attritors in the treatment group were less likely to be married (difference of 38% percentage points; p=0.0087) and had much higher household income relative to those in the control group. Similarly, non‐attritors in the treatment group had lower income relative to those in the control group. However, all difference across the two groups becomes statistically insignificant at the 3 months mark.

Table 10b: Differential Attrition

||Attriti<br>|on=1<br>|Attri<br>|tion=0<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|VARIABLES|5 weeks|3 months|5 weeks|3 months|
|ERKF Round|‐0.1648|0.2096|0.0561|‐0.0026|
||[0.3219]|[0.2400]|[0.4144]|[0.9703]|
|Male|‐0.0549|0.0662|0.0284|0.0090|
||[0.7562]|[0.5238]|[0.1831]|[0.7575]|
|Education|0.2418|‐0.1213|0.5809|0.6703|
||[0.8779]|[0.9380|[0.3043]|[0.2394]|
|Married|0.3846***|0.0074|‐0.0211|0.0276|
||[0.0087]|[0.9504]|[0.6082]|[0.5222]|

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We consider two versions: including only HS 6-digit product-country fixed effects in column (3) of Table 6 or including firm fixed effects in addition to product-country fixed effects in column (4) of Table 6.<sup>49</sup> Our estimates indicate that reductions in median customs clearance time from 2007 to 2012 have a significant negative effect on import growth over the long term but only when we do not include the firm fixed effects. This suggests that there is important variation across firms (within product-country pairs), which pins down the estimates in column (3), but that there is not sufficient evidence once we control for variation across firms in column (4). If identification comes from variation within firms across HS 6-digit product-origin countries the effect is negative but insignificant and moreover the IV strategy does not work so well because the link between reductions in physical inspections and customs clearance time in the first stage is also insignificant.

**Table 6. Effect of customs clearance time on imports across periods and over the longer**

||**te**|**rm**|||
|---|---|---|---|---|
||Dependent variable is<br>import value at firm<br>country‐|first‐difference in log<br>‐HS6 product‐origin<br>year level|Dependent variable is l<br>import value at firm‐<br>country|ong‐difference in log<br>HS6 product‐origin<br>level|
||_Clearance time m_|_easured in days_|_Clearance time me_|_asured in days_|
||_2007‐2009 period_|_2010‐2012 period_|_2007‐2012_|_period_|
||IV esti|mation|IV estim|ation|
||(1)|(2)|(3)|(4)|
|First‐diff. in median clearance time|‐0.522***<br>(0.180)|‐1.370***<br>(0.239)|||
|Long‐diff. in median clearance time|||‐4.769**|‐9.962|
||||(2.106)|(10.379)|
|Firm*year fixed effects|Yes|Yes|||
|HS6 product*origin country*year fixed effects|Yes|Yes|||
|Firm fixed effects|||No|Yes|
|HS6 product*origin country fixed effects|||Yes|Yes|
|Observations|73,700|126,581|14,556|14,556|
|R‐squared|0.442|0.286|‐1.59|‐4.568|
|F‐statistic from first‐stage regression|216|196|11|2|
|First‐stage coefficient on first‐diff. in median|||||
|allocation to red channel|0.094***<br>(0.006)|0.057***<br>(0.004)|||
|First‐stage coefficient on long‐diff. in median|||||
|allocation to red channel|||0.038***|0.018|
||||(0.011)|(0.012)|

Notes: Robust standard errors in parentheses. *** indicates significance at the 1% confidence level.

> 49 This second type of specification is what would correspond closely to the specifications in first-differences in Table 5 and would indicate a possibly more sophisticated risk model.

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- [9] Imbs, J and R. Warziarg (2003) "Stages of Diversi fi cation" American Economic Review, 93(1): 63-86.

- [10] Kehoe, T. and K. Ruhl (2002) “How Important is the New Goods Margin in International Trade?” Federal Reserve Bank of Minnesota.

- [11] Hausmann, R. and D. Rodrik (2003) "Economic Development as Self-Discovery" Journal of Development Economics, 72(2): 603-633.

- [12] Rodrik, D. (2006) "What’s So Special About China’s Exports?"NBER Working Paper 11947.

- [13] Schott, P. (2006) "The Relative Sophistication of China’s Exports" NBER Working Paper 12173.

- [14] Zhu Chun, S. and D. Tre fl er (2005). “Trade and Inequality in Developing Countries: A General Equilibrium Analysis", Journal of International Economics 65, 21-48.

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7 Coleman Report found little association between the quality of schools and student achievement on standardized tests.' RIecent research by Card and Krueger (1992) indicates, however, that a large part of the significant variation in the rate of return to education in the U.S. is explained by differences in the quality of schooling. For example, the return is higher for individuals whu attended schools with lower pupil/teacher ratios and with higher relative teacher salaries. They also find that the return is linked to higher education among teachers.

An important policy issue in development economies arises in the context of the length of compulsory schooling. Angrist and Krueger (1991) have undertaken some work on this in the context of the U.S. Their results point in the direction of compulsory schooling laws leading to increases in educational attainment. They do not, however, answer the question: do compulsory schooling laws benefit society? They suggest an answer to this question requires research into the social and private costs of education.

Whether omitted variables disrupt rate of return calculations or not, there is the separate problem that they do not allow the identification of external effects on economic performance. We therefore now turn to macroeconomic studies which should, in principle, show any such effects.

_2.2 Macroeconomic Evience for Links_ **_between Education_** _and Economic Growth_ The approach summarized by Psacharopoulos (1985) has the drawback that it fails to reflect all externalities arising from higher education and, conversely that it is susceptible to the biases mentioned above. An alternative means of identifying the effects of education is to use a regression equation to explain cross-country differences in economic growth, or variations in growth rates in the same country in different periods.

Behrman (1987) finds a negative but insignificant correlation between the change in literacy and economic growth and Dasgupta and Weale (1992) come to a similar conclusion. But it is questionable whether literacy, even if a good indicator of educational attainment, is measured reliably and this, on its own, should not be taken as evidence against a positive link between education and economic performance.

Table 2 summarizes some studies, ' nh h use macroeconomic data; regression results lining economic growth to education and to other variables are presented in table 3. McMahon (1987) claims to find a return of the order of 20% for investment in education in Africa. This 'A survey of the literature can be found in Hanushek (1986).
The WGI data sources reflect the perceptions of a very diverse group of respondents. Several are surveys of individuals or domestic firms with first-hand knowledge of the governance situation in the country. These include the World Economic Forum’s Global Competitiveness Report, the Institute for Management Development’s World Competitiveness Yearbook, the World Bank / EBRD’s business environment surveys, the Gallup World Poll, Latinobarometro, Afrobarometro, and the AmericasBarometer. We refer to these as "Surveys" in Table 1. We also capture the perceptions of country analysts at the major multilateral development agencies (the European Bank for Reconstruction and Development, the African Development Bank, the Asian Development Bank, and the World Bank), reflecting these individuals’ in-depth experience working on the countries they assess. Together with some expert assessments provided by the United States Department of State and the French Ministry of Finance, Industry and Employment, we classify these as "Public Sector Data Providers" in Table 1. We also have a number of data sources provided by various nongovernmental organizations such as Reporters Without Borders, Freedom House, and the Bertelsmann Foundation. Finally, an important category of data sources for us are commercial business information providers, such as the Economist Intelligence Unit, Global Insight, and Political Risk Services. These last two types of data providers typically base their assessments on a global network of correspondents with extensive experience in the countries they are rating.

The data sources in Table 1 are evenly divided among these four categories. Of the 35 data sources, eight are from commercial business information providers and the remaining categories have nine data sources each. However, an important distinction is that the commercial business information providers typically report data for much larger country samples than our other types of sources. An extreme example is the Global Insight Business Conditions and Risk Indicators, which provides information on 203 countries in each of our six aggregate indicators. Primarily for reasons of cost, household and firm surveys typically have much smaller country coverage. Our largest surveys, the Global Competitiveness Report survey and the Gallup World Poll each cover around 130 countries in 2007, and several regional surveys cover necessarily smaller sets of countries. Some of the expert assessments provided by NGOs and public sector organizations have quite substantial country coverage, but others, particularly regionally-focused ones again have much smaller country coverage.

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conceptual appeal of this measure, used<sup>in its unadjusted form in cross section it shows</sup> no impact.'° This conclusion changes in the panel<sup>context. Natural resource exports have no</sup> significance with the basic conditioning<sup>set, but adding the capital accumulation variables</sup> makes their impact _positive_ and significant<sup>at the 10% level. This again suggests some</sup> depressing impact of resource exports<sup>on physical and human capital accumulation.</sup> Controlling for terms of trade variations<sup>increases both the magnitude and significance of</sup> the variable, suggesting that the fall in resource<sup>prices across this period did predictably</sup> have a depressing impact on growth.<sup>Adding the macro stability variables renders the</sup> resource variable again insignificant,<sup>however, suggesting that it is the association with</sup> unstable macro policies, rather than natural<sup>resources themselves which is driving the</sup> observed correlation. The export Herfindahl has little effect<sup>here, but the intra-industry</sup> trade variable again makes the resource variable<sup>significant and positive.</sup>

# _Export Concentration_

For both measures of concentration,<sup>the cross section and panel results are</sup> somewhat more consistent. With the<sup>basic conditioning set, the export Herfindahl is of</sup> the same order of magnitude and negative<sup>in both regressions although only significant in</sup> cross section. Adding the capital accumulation<sup>variables increases the magnitude and</sup> makes concentration significant at the<sup>1% level.</sup> In cross section the addition of new conditioning variables has limited effect<sup>on the coefficient value or significance. There is</sup> a marginally significant change in value<sup>of the OLS estimate with the introduction of the</sup> resource exports/exports variable. The panel findings, on the other hand,<sup>suggest a</sup> marginally significant positive impact of concentration on capital accumulation. Arguably, the largest impact of concentration,<sup>predictably, is through the terms of trade</sup> variable, whose inclusion reduces the magnitude<sup>and significance of the Herfindahl.</sup>

**10** With the Sach-Warner 1997b data, our<sup>sample of countries yields their results. Hence, the difference in</sup> findings is not due to the sample of<sup>countries.</sup>

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the trends in wage inequality (which are arguably exogenous). We believe that our strategy of matching sectoral tariffs to sectoral wages through two episodes or trade liberalization and one episode of reversal to protection provides a good and valid identification strategy of the effects of trade liberalization on wages and wage inequality.

To end, we use our data to ask whether trade liberalization has had an effect on the wages of young (versus more tenured) workers. This is an interesting question for it may indicate an additional channel by which trade can affect income inequality and poverty. Our findings reveal, however, that there is no evidence that trade protection has affected the experience premium; indeed, the coefficients of the interactions of trade with age (and age squared) are statistically insignificant. In the end, we conclude that trade has had an impact of the skill premium but not on the tenure premium.

# 4 Conclusions

This paper proposed to explore the links between trade liberalization and skill premium by exploring a historical dataset of Argentine trade policy and labor force surveys. To this end, we put together information spanning almost 30 years of recent Argentine history, from 1974 to 2001. This is thus one of the few cases where such a data set is constructed. A similar case is Attanasio, Goldberg, and Pavcnik (2004), who carry out an analysis covering the 1984-1998 period in Colombia. But while they explore one episode of trade liberalization (using 8 labor force surveys from 1984 to 1998), we exploit two episodes of trade reforms separated by a decade of reversion to protection (using 40 surveys from 1974 to 2001).

The 1974-2001 period under study is one of active and fluctuating trade reforms and wage inequality in Argentina. Tariff reforms accelerated in the late 1970s and early 1980s, stagnated during the 1980s, and picked up further momentum during the 1990s. The skill premium, in contrast, increased during the 1970s, declined during the 1980s, and increased again during the 1990s. In addition, the share of labor income in GDP sharply dropped in the late 1970s, and never recovered.

We use these historical trends in tariff liberalization, in wages and in wage inequality 16
(i.e., the perception of corruption). On the imports side, these figures are $17.1 billion and 11.2%, respectively and, the most important single factor is also the improvement in Port Efficiency. Sector wise, greater Textiles and Transport Equipment exports are expected to come after improvements in Port Efficiency, whereas greater exports of Food and Textiles would come from improvements in the Regulatory Environment. On the imports side, greater imports are expected to come mainly from improvements in Port Efficiency, although for imports of Transport Equipment, improvements in the Service Sector Infrastructure showed also to be of importance. It will be useful to explore further which sector specific issues (e.g., risk of spoilage in food trade) are driven by our results, and also to better assess the plausibility of the unexpected negative signs found for some variables.

Our results show that unilateral trade facilitation reforms for the case of Mexico could generate an increment of more than 20% for exports as well as about 11% for imports. This suggests that trade facilitation measures, including estimating the costs that would be involved for Mexico to improve these indicators, should be considered seriously in any discussion about trade policy.

# **Data Appendix**<sup>**18**</sup>

Data come from the World Economic Forum, Global Competitiveness Report, 2001-02 (GCR), IMD Lausanne, World Competitiveness Yearbook 2002 (WCY), and Kaufmann, Kraay and Zoido-Lobaton (2002) (KKZ). All survey data in GCR comes from the World Economic Forum’s Executive Opinion Survey. A total of 4022 firms were surveyed. “In order to provide the basis for a comparative assessment on a global basis, it is essential that we interview a sufficient number of senior business leaders in individual countries and that the sample in each country is not biased in favor of any particular business group. We have taken a number of steps to ensure this.

First, we have asked each of our partner institutes, the organizations that administer the surveys in each country, to start with a comprehensive register of firms. From this, they were asked to choose a sample whose distribution across economic sectors was proportional to the distribution of the country’s labor force across sectors, excluding agriculture. They were then asked to choose firms randomly within these broad sectors (for example, by choosing firms at

> 18 Follows the presentation of WMO(2004)

18