rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 9309

# **Abstract**

Total energy expenditures for the Indian population between 1983 and 2012 are estimated to shed light on the debate concerning falling measured caloric intake during the period (Deaton and Drèze 2009). Anthropometric, time-use, and detailed employment surveys are used to estimate the separate components of total energy expenditure related to metabolism and physical activity levels. Despite a significant drop in adult physical activity levels, total energy expenditures are flat overall between 1983 and 2012. Rising metabolic requirements due to increases in weight dampened the effect of falling activity levels on total energy expenditure. In addition, the 10% decline in the population share of children in the period raised average total energy expenditures considerably as children have much lower metabolic requirements and activity levels than adults..

This paper is a product of the Knowledge and Strategy Team, 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 nick.li@utoronto.ca.

_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
appear to be motivating many countries and voters to contribute more (given that some of the richest OECD donors do not contribute the most, as measured by share of GDP).<sup>5</sup> Moving to the more nudging indicator may well be worth the loss in precision.

A similar argument might be made for carbon dioxide emissions, which while not an MDG target, has been suggested as a target for MDGs 2.0. Thousand metric tons of carbon dioxide equivalent emissions, per capita or per dollar of national GDP, is a difficult-to-grasp indicator. Even measures of global average temperature may not be intuitive – an increase of two degrees centigrade in the average temperature of the world does not seem like much to the untrained ear. Rather, the contributions of an average person in a country to the rise in sea levels, in millimeters, over a defined time period, would be a more palpable target. That indicator can be visualized. It also embeds within it the causal narrative between carbon emissions and some of its potentially catastrophic consequences. It is true that estimating current and future sea levels as a function of carbon emissions is an ongoing area of research, and the estimates would invariably have a confidence interval around them. But again, if the goal is nudging, and if the confidence intervals are not too large, this would be an acceptable cost for a motivational device.

For fair trade, which is another element of the “global partnership for development” (MDG 8), an indicator might be the total cost of each country’s tariff and non-tariff barriers on the income of the poorest countries. This could be easily comparable among rich countries, and the numerical benchmark of zero – no tariffs and non-tariff barriers on the poorest countries – is easily understood. It is also consistent with the readily grasped “do no harm” principle (more on that below).

The health targets in the MDGs were to reduce by two-thirds the under-five mortality rate and by three quarters in the maternal mortality ratio. These are complex indicators, and the numerical quantities associated with good outcomes on these measures, are, for most people, not intuitive. For instance, it is

> 5 The share of GNI target may well have played a role in motivating an increase in ODA in some countries, such as the Norway, Sweden, United Kingdom, and Ireland, in the period from 2000 to 2011. But it is not clear, yet, whether public support for increases in ODA has consolidated. For instance, Hudson and VanHeerde-Hudson (2012) find that OECD public support for development aid, as measured by Eurobarometer surveys, rapidly fell back to 1990s levels after peaking in run up to the 2005 Gleneagles Summit.

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# **1. Introduction**

Investigating differences in human capital accumulation between males and females has been the focus of much research over the years. Gender featured in two of the eight Millennium Development Goals: to achieve universal primary education for all boys and girls (Goal 2) and to promote gender equality and empower women, with a focus on ending gender disparity in primary and secondary education (Goal 3). These goals were to be achieved by 2015, but gender disparities in these areas still exist in many countries around the world. Perhaps unsurprisingly, gender equality continues to feature prominently in the Sustainable Development Goals. Gender equality—particularly in education—can help to reduce child mortality, improve health outcomes, improve the productivity of communities and countries, and help them to improve their financial position and minimize poverty (OECD 2015, World Bank 2011).

Research has shown that gender differences in educational achievement emerge in the early years of school and can persist into adulthood (Anderson 2008). There is also a growing body of evidence suggesting that non-cognitive skills observed in early childhood affect academic performance and labor market outcomes in later years (Cunha and Heckman 2008, Cunha et al. 2010). As a result, there is considerable interest in understanding the extent to which gender gaps exist in cognitive and non-cognitive skills in the early years, and what factors may explain these observed gender gaps. However, research from developing countries on this topic is still relatively sparse.

In this paper, we focus on rural Indonesia and investigate gender differences across an array of developmental domains for children in the first few grades of primary school. We also contribute to the literature on gender gaps by examining whether early schooling and parenting practices account for this observed gender gap and, if so, to what extent. Our rich data set allows us to measure not only the quantity of early schooling but also its quality. In addition, the data set also contains information on the parenting practices children experience. Furthermore, this paper implements an extension of the Oaxaca-Blinder decomposition method that takes into account moderating factors to explore the extent to which early schooling and parenting practices contribute to the gender gaps we observe in the first few years of primary school.

Our analysis is based on data collected as part of an impact evaluation of an intervention, which expanded access to preschool services in parts of rural Indonesia. As part of this study we collected measures of cognitive and non-cognitive skills of children in the early grades of primary 2
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Figure 8: Range of the time-varying impact of the expected public debt-to-GDP ratio on the interest rate during 2006H2-2018H2 (Baseline) <!-- Start of picture text --> IT<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>AUSCAN CZE DEN JPN KORNORSWECHEGBRUSABGRCHNCOLHUNPOL TUR IND IDN MYSMEXPER PHL ZAF THL<br>PSTR<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>AUSCAN CZE DEN JPN KORNORSWECHEGBRUSABGRCHNCOLHUNPOL TUR IND IDN MYSMEXPER PHL ZAF THL<br>GPSTR<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>AUSCAN CZE DEN JPN KORNORSWECHEGBRUSABGRCHNCOLHUNPOL TUR IND IDN MYSMEXPER PHL ZAF THL<br>basis points<br>basis points<br>basis points<br><!-- End of picture text --> Notes: The range is the difference between the maximum and minimum point estimate during 2006H2-2018H2. AUS:Australia, CAN:Canada, CZE:the Czech Republic, DEN:Denmark, JPN:Japan, KOR:Korea, NOR:Norway, SWE:Sweden, CHE:Switzerland, GBR:the United Kingdom, USA:the United States, BGR:Bulgaria, CHN:China,COL:Columbia, HUN:Hungary, POL:Poland, TUR:Turkey, IND:India,IDN:Indonesia, MYS: Malaysia, MEX:Mexico, PER:Peru, PHL:the Philippines, ZAF:South Africa, THL:Thailand 31
|Agriculture|0.236|3.864|0.082||
|---|---|---|---|---|
|Services|0,000|0.625|0.203|-|
|_Zimbabwe_<br>Industry|F (1, 22)<br>0.036|X2...Hy(2)<br>1.414|F.h(l, 21)<br>0.000|F,.ea(12,10)<br>0.440|
|Agriculture|0.707|0.000|2.908|_0.556_|
|Services|0.083|0.525|0.242|0.667|
|_Ghana_|FK(1, 10)|%**DiW(2)**|Fh(1, 9)||
|Industry|0.211|2.9|0.001|--|
|Agriculture|0.066|1.864|0.679|--|
|Services|0.557|0.947|0.863|--|
|**_Vector analysis_**|||||
|Cote d'Ivoire|F=(9,17)<br>1.345|2 nomi(6)<br>4.22|--||
|Zimbabwe|Fr(9, 43)<br>1.782|X2<br>DODty6)<br>1.17|F.ch(72, 33)<br>0.430||
|Ghana|F,,(9, 14)<br>0.740|x<sup>2</sup><br>uty(6)<br>6.630|--||

_Note:_ -- implies that the test statistic could not be computed. _Source:_ Authors' calculations.

**Table 3. Eigenvalues and Log-Likelihoods**

||CMte d'Ivoire||Zimbabwe||Ghana||
|---|---|---|---|---|---|---|
|Rank|Eigenvalue|Log-<br>likelihood|Eigenvalue|Log-<br>likelihood|Eigenvalue|Log-<br>likelihood|
|0||245.458||210.334||231.872|
|1|0.618|258.919|0.523|221.797|0.374|238.188|
|2|0.354|265.030|0.248|226.217|0.207|241.315|
|3|0.007|265.132|0.197|229.626|0.028|241.700|

_Source:_ Authors' calculations.

**Table 4. Eigenvalues and Log-Likelihoods for Ghana, Sub-sectoral Specification**

|Rank|Eigenvalue|Log-likelihood|
|---|---|---|
|0||383.189|
|1|0.841|410.730|
|2|0.806|435.318|
|3|0.570|447.959|
|4|0.358|454.613|
|_5_|0.220|458.340|
|6|0.119|460.247|

_Source:_ Authors' calculations.

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predicted values from the auto-regressive OLS are added to the monthly median to impute missing prices.<sup>20</sup> The evolution of the median monthly maize wholesale price across all markets for each country is depicted in Figure 1. The series display high volatility, especially in the second half of the 2000s. Visual inspection further suggests that there is some annual regularity (i.e. seasonality) in these price series, with the seasonal pattern in Uganda (which is above the equator) often the reverse of this in Tanzania (below the equator)—when the median monthly price peaks in Tanzania, it tends to bottom out in Uganda, and vice versa. Seasonal price movements in Malawi (also below the equator) are similar to those in Tanzania.

Monthly information on household food and non-food consumption was obtained from the three Living Standard Measurement Studies-Integrated Surveys on Agriculture (LSMS-ISA) household surveys implemented in Malawi (2010-11), Tanzania (2008-09 and 2010-11) and Uganda (2009-10). For a detailed description of these surveys, see www.worldbank.org/lsms-isa. The key issues and concerns related to the construction of the consumption aggregates and how these might bear on the estimation of intra-annual variation in consumption from these cross-sections have already been discussed above. Some further background on the construction of the consumption aggregate is provided below.

The consumption aggregate comprises four main components: food, non-food, durable goods, and housing, with the latter three merged in a non-food sub-aggregate. The food subaggregate is constructed by summing the reported consumption of all food items (between 120 and 150 organized in 11 categories depending on the country) across all household members present during the past seven days, and using the last seven days as the recall period.

> 20 The formal imputation formula writes the same for each commodity separately as (for all locations _i_ and month t): ln pit = ln p������+ ln pt ıt�−ln p������t where ln pıt�−ln p������t is predicted by the pooled OLS regression: ln pit −ln p�= a+ bt 1�ln pit−1 −ln p����������+ bt−1 2�ln pit−2 −ln p����������+ et−2 it and where the last term is a normally and i.i.d residual term. In the small numbers of cases in which all prices are missing for a particular commoditycountry pair, the values are imputed by the STAMP Kalman filter.

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In the large firm sample of the census scheme there are virtually no significant coefficients on any outcome variables in the aggregate (Table A.6), although breaking apart by initial conditions offers some evidence of similar patterns – higher skill and capital intensity are correlated with high output and plant size responsiveness for young and mature plants, and with higher TFPR for mature and old plants (Annex Table A.7).

These results support the expected heightened effect of increased competition and the opportunity tapped by more capable firms. For younger plants, the effect is to expand size, while for the older ones, it is to defend the existing size. The less capable older plants are eventually likely to exit. The relative rise in labor productivity of older plants, despite lower output gains suggests some relative downsizing, but also in line with district-level pseudo panel results, a possible de Loecker et al. (2016) effect.

# **V. Conclusion**

The natural experiment offered by the GQ upgrades allows examining the adjustment process across plants of different age cohorts and characteristics to an increase in competition and access to markets. Young plants drive the previously documented aggregate positive effects of proximity to the GQ network, while older plants show insignificant or negative effects. Younger plants tend to be more capital intensive and engaged in value chains, suggesting an important domestic “offshoring” dynamic along the GQ. Though in the short-run, exit falls for all plant cohorts in districts along the GQ network, the diverging responsiveness over time between young and older plants suggests that in the long-run older plants will contract and possibly exit. Strikingly, the evidence suggests that this process may take decades.

It is also clear that building roads is not sufficient for local industrial development. The responsiveness of young plants is blunted where factor markets are incomplete, or the business climate is distorted. With the exception of the capabilities of plants and the work force, older plants are less sensitive to misallocation in input and output markets, suggesting that over time they have devised ways around them. The fact that older plants respond better to proximity to the GQ in more distorted districts suggests a de Loecker et al effect where they protect their output market while benefiting from cheaper inputs.

Finally, plant and workforce capabilities are critical complements to physical infrastructure, allowing escape from new competition and taking advantage of new markets: young plants with high human capital or capabilities drive the aggregate positive response to connectivity among these plants, while older plants with low human capital or capabilities drive their contractionary tendencies.

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Table 1: **Commercial services, % share of total exports (excluding trade and transport)**

|||1992|1995|1997|2001|2004|2007|
|---|---|---|---|---|---|---|---|
|Gross|Brazil|0.9|1.2|7.6|9.2|6.6|4.9|
||China|12.8|3.9|1.8|2.1|3.1|1.8|
||Korea|3.7|6.0|7.3|6.0|4.6|3.7|
||USA|12.9|14.8|15.1|17.8|18.4|15.9|
|Direct VA|Brazil|2.1|2.5|15.1|18.3|13.7|10.1|
||China|24.5|7.6|2.7|3.3|6.5|3.9|
||Korea|6.5|11.0|13.5|12.3|9.6|8.0|
||USA|18.7|19.0|21.7|24.9|27.0|23.9|
|Total VA|Brazil|21.0|20.2|26.2|30.8|29.8|25.8|
||China|19.8|14.9|8.2|9.7|15.2|13.3|
||Korea|17.1|21.9|25.0|26.9|24.5|22.4|
||USA|26.1|24.3|28.5|30.1|31.4|29.1|

_source: Francois, Manchin, and Tomberger (2012)_ To generate these values, we begin by denoting a representation of intermediate and demands as follows: In equation (1), the term **Y** denotes a final final demand vector, **Z** denotes a gross output vector, and **A** denotes a matrix of intermediate use coefficients. Equation (1) therefore defines final output with respect to intermediate input requirements. With some manipulation we arrive at the Leontief inverse matrix, also known as the multiplier matrix **M** .

The multiplier matrix **M** measures the inputs contained in a unit of final output. In particular, if we assign the sector indexes _i, j_ to the **A** and **M** matrices, then a representative element of the **M** matrix _Mij_ gives the direct and indirect inputs (and thus the sector _i_ receipts) linked to each unit (for example each dollar) of sector _j_ receipts in the data.<sup>8</sup> This implies real production activities measured by value of output. For our purposes, it provides a means to trace, through these income flows, the flow of gross activity and value added from intermediate to final goods and services, ostensibly across borders as well as sectors. Because linkages will vary by industry, each industry will be characterized by different multipliers. To focus on value added, we note first that in terms of gross output values **Z** , some share of this involves value added within each sector. We define **B**<sup>**ˆ**</sup> as the

> 8In multiplier analysis with fixed input coefficients, these values also represent fixed unit input requirements in value terms, though of course in CGE models one can allow for these coefficients to be endogenous.

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# **Table A2. Survey data**

|**Country**|**Market surveys and descriptions**|**Data sources**|
|---|---|---|
|BRA|Expectations for output, inflation, interest rates, and FX rates,<br>based on_Focus Market Readout_forecasts of about 160 banks,<br>asset managers, and other institutions|Central Banco Brazil|
|CHL|Expectations for inflation and interest rates based on monthly<br>surveys of academics, consultants, and executives or advisors of<br>financial institutions and corporations.|Central Bank of Chile|
|HUN|Professional forecasts on future inflation, output, FX rates|Consensus Economics|
|IND|Professional forecasts on future inflation, output|Consensus Economics|
|IDN|Professional forecasts on future inflation, output, FX rates|Consensus Economics|
|MEX|Expectations for output, inflation, interest rates, and FX rates<br>based on monthly surveys of private-sector economists|Bank of Mexico|
|PER|Median expectations for output, inflation, interest rates, and FX<br>rates, based monthly survey of macroeconomic expectations|Central Reserve Bank of<br>Peru|
|PHL|Consumer expectations surveys on future inflation and output<br>growth|Central<br>Bank<br>of<br>Philippines|
|POL|Consumer expectations for future Inflation and output growth|National Central Bank of<br>Poland,<br>Central Statistical Office|
|ROM|Professional forecasts on future inflation and output|Consensus Economics|
|UKR|Professional forecasts on future inflation, output, FX rates|Consensus Economics|
|RUS|Monthly inflation expectations for the next year|Central<br>Bank<br>of<br>the<br>Russian Federation|
|TUR|Monthly survey of expectations for future output, inflation,<br>interest rates, and FX rates,|Central Reserve Bank of<br>Turkey|
|THA|Professional forecasts on future inflation and output|Consensus Economics|
|ZAF|Bureau of Economic Research Inflation expectations for current<br>and next years<br>South African Chamber of Commerce and Industry Business<br>Confidence Index|Bureau<br>of<br>Economic<br>Research,<br>South African Chamber of<br>Commerce and Industry|
|KOR|Monthly consumer survey index of inflation expectations|Bank of Korea|
|SWE|Monthly consumer trend survey of the general economic<br>situation and inflation|National<br>Institute<br>of<br>Economic Research|

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according to the study by the Japan Institute of Labor, the average amount of monthly remittances was about $1,664 -- $1,848, while the study commissioned by the IDB reported a far smaller amount of $725. Thus, accumulation of ad hoc survey results do not seem very useful for understanding _Nikkeijin_ workers in Japan and future prospect of remittances from those workers. Secondly, an econometric study is clearly needed in addition to descriptive analyses. In order to forecast the future remittances, for instance, it would be useful to estimate elasticity of the amount of remittances with respect to, say, the length of stay, and the number of family members in each _Nikkeijin_ household. For these ends, the availability of the _Brazil Nikkei Household Survey_ data described in Section V will certainly help us deepen our understanding of the migration and remittance behavior of _Nikkei_ migrants and their future prospects.

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