rafmacalaba/datause-encoder-data huggingface dataset:
conclude that much of the degradation has occurred in the biomass rich north, with less occurring in the central and southern regions which had less forests in them.

Table 5 presents evidence of the dependence of households on biomass for energy based on the household data. As indicated in Table 5, 99% of poor households use fuelwood as cooking fuel. Though the non-poor consume significantly more fuelwood as compared with the poor, proportionately more poor households collect fuelwood. A large majority of the households (55%) collect fuelwood from unfarmed community areas. Less than 10% collect from their own lands. On average, a Malawian household spends the equivalent of MK 2558 per year per, per capita on fuelwood.<sup>20</sup> That is, about 12 percent of total per capita annual consumption expenditure is spent on fuelwood.

The household heads were asked the time it takes for a one way walk to the location of fuelwood collection from their respective houses. We found no significant differences between the poor and non-poor households in terms of time taken for a one way walk to the location of fuelwood collection.

On average active women spent 1 hour and 30 minutes to collect fuelwood. The time spent in collection is slightly lower in the northern region but otherwise there is little difference in collection time between regions and rich and poor households. Eighty-four percent of all the active individuals who collected fuelwood were women.

Figure 3 combines household data with biomass estimates by overlaying district level biomass availability on district level poverty estimates. There is considerable variation in biomass at the district level. Furthermore, at the district level there is no one-to-one connection between poverty and biomass. While the south faces considerable biomass scarcity and poverty, even in the south there are districts that are much better off than others.

# <u>Household Welfare and Biomass Use</u>

> trees/forest; under our assumptions (namely the 20% threshold) some of this land area is classified as forest resulting in an increase of the forested area with respect to the 1993 estimates.

> 20 These numbers are self-reported.  Since most Malawians collect and do not buy fuelwood, we expect that households used local prices to estimate the value of the fuelwood they use.

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that higher levels of past inputs (and therefore of current human capital) yield higher returns to current investment in human capital.

While there is substantial empirical evidence that intensive early education interventions targeted specifically to disadvantaged children lead to significant benefits (see, among others, Lee et al. 1990, Barnett, 1993, Barnett, 1995, Currie and Thomas 1995, Reynolds 1998, Karoly et al., 1998, Danzinger and Waldfogel, 2000; Currie, 2001, Garces et al. 2002, Blau and Currie 2004, and Schweinhart 2005), much less is known about the benefits of expanding pre-primary education for the population as a whole. Cascio (2004) finds that the expansion of kindergarten financing in the late 60s and early 70s in the Southern and Western States of the US reduced subsequent grade repetition relative to Northern States. Using data from the Early Childhood Longitudinal Study, Magnuson et al. (2005) find that pre-primary education in the US is associated with higher reading and mathematics skills at primary school entry, but that these effects dissipate by the end of first grade. They also find that pre-primary education in the US is associated with higher levels of behavioral problems, especially when pre-kindergartens are not located in public schools. Exploiting a natural experiment, Berlinski et al. (2006) find a positive effect of pre-primary school attendance on third grade standardized Spanish and Mathematics test scores in Argentina. They also find that pre-primary school attendance positively affects primary school pupils' behavioral outcomes such as attention, effort, class participation, and discipline.

A major challenge in identifying the causal effect of pre-primary school attendance on later school outcomes is non-random selection into early education across households. Positive selection, whereby parents whose children attend pre-primary school possess characteristics that promote better school performance, would result in a spurious positive correlation between preschool and later academic outcomes. Indeed, since children are not randomly selected into pre-primary education, selection based on parental heterogeneity is most likely to be non-ignorable in identifying the effect of pre-primary education on subsequent school progression. In order to 3
# **5. Conclusions**

Education plays an important role in wage differentials in the Philippines. A large part of inequality stems from the difference in wages between the less educated and the better educated. At the national level, education accounts for about 30 percent of the difference in wages. It accounts for a higher percentage of the difference between female workers than between male workers. Across regions, education accounts for 20-30 percent of the difference in wages between NCR and Visayas and between NCR and Mindanao; across sectors, it accounts for 6 percent, 14 percent, and 33 percent of the difference in wages in the agricultural, manufacturing, and services sectors, respectively.

Returns to education increase with years of schooling – workers with elementary, secondary, and tertiary education earn 10 percent, 40 percent, and 100 percent, respectively, more than those with no education. Tertiary education is often a prerequisite for high-paid jobs. The majority of the least educated clustered in low-paid services jobs and in the agricultural sector.

Efforts to improve education to increase the supply of highly educated people are important for economic efficiency enhancement and growth acceleration. They are important not only for longterm growth, but also for helping to translate growth into more equal opportunities for the children of the current generation.

# **References**

Asian Development Bank. (2007). Asian Economic Outlook 2007. Manila, Philippines.

- Balisacan, A.M. and N. Fuwa. (2004). “Going Beyond Cross-Country Averages: Growth, Inequality and Poverty Reduction in the Philippines”, World Development, Vol. 32, Issue 11, November 2004, pp. 1891-1907.

- Bourguignon, F., M. Fournier, and M. Gurgand. (2005). “Distribution, development, and education in Taiwan, 1979-94.” in: F. Bourguignon et al. (eds.): The Microeconomics of Income Distribution Dynamics in East Asia and Latin America, pp.313-356. World Bank, Washington D.C.

- Chiswick, B.R. (1997). “Interpreting the Coefficients of Schooling in the Human Capital Earnings Function”, Policy Research Working Paper, No. 1790. World Bank, Washington D.C.

Greene, W.H. (2004). Econometrics, Prentice Hall, Upper Saddle River, New Jersey.

- Fields, G.S. (2003). “Accounting for Income Inequality and Its Change: A New Method, with Application to the Distribution of Earnings in the United States”, Research in Labor Economics.

- Fournier, M. (2005). “Exploring Information from Path Dependency in Oaxaca-Blinder Decomposition Procedures”, Applied Economics Letters, 12, pp. 669-672.

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Table 4-2 shows the main results of our preferred specifications for both countries. A number of variables primarily relating to labor supply issues that factored into early jobs-at-risk discussions were not statistically significant determinants of the actual level of PFPS job losses in Jordan or Georgia. First, “essentialness” (E ) was not significant in any stage of the estimation; nor was the share working-from-home ( ). Moreover, EE none of the sector-level task characteristics such as face-to-face, physical labor, or ICT intensity were EEIIssh significant in any specifications of the job loss equation (and so were removed from the final version).

|_Table 4-2: Job Loss Equation Estimation Results, Jordan and Georgia_<br>Dependent variable: jobs lost / permanent jobs (more positive means<br>morejobs lost)<br>|<br>Estimated Coe<br>inparentheses|fficient (standard errors<br>)|
|---|---|---|
||Jordan|Georgia|
|Percent change in sales same month lastyear/since COVID(Q)<br>|-0.434**|-0.430*|
||(0.187)|(0.253)|
|Started or increased online or deliveryactivities(1=yes) (A)<br>|1.401|-4.824|
||(4.737)|(18.14)|
|Faced financial issues(1=yes) (F)<br>|-1.707|-7.004|
||(7.748)|(17.03)|
|Percentage of firm workers workingremotely (percentage) (EIsh)|-0.0660|0.00284|
||(0.104)|(0.168)|
|Received other(non-wage subsidy) policysupport(1=yes)|0.451|6.048|
||(7.646)|(11.62)|
|Received wage subsidies(1=yes)|-7.357*|-8.203|
||(4.456)|(22.23)|
|Percent of workers onpermanent contracts in Dec18|0.0615|0.204|
||(0.146)|(0.269)|
|Inverse Mills ratio|22.02~|n.s.|
||(13.63)||
|Residuals from sales equation|0.417**|n.s.|
||(0.204)||
|Constant|-24.36|-15.28|
||(18.52)|(22.48)|
|Observations|564|424|
|Adjusted R-squared|0.082|0.019|
|*** p<0.01, ** p<0.05, * p<0.1; ~=significant in some bootstrap trials.<br>Note: n.s. indicates that the variable was omitted due to a lack of statistical sign<br>Standard errors are bootstrapped for Jordan.|ificance.||

Our results suggest that consumer demand conditions and breakdowns in the supply chain were the most decisive determinants of job loss in these countries. For every 1 percent decline in sales, firms reduced their permanent workforce by approximately 0.4 percent, all else equal. Despite the similar coefficients on key determinants of job loss, the ranking of factors by the magnitude of their average impact differs for the two countries (see Figure 6). In particular, in Georgia, the unexplained determinants of sales contributed positively to firms’ employment levels, and the greatest negative impact on average came through infection risk, whereas the largest adverse impact was through unexplained declines in sales for Jordan. However, infection risk did not have a separate augmenting effect on job losses in Georgia, a finding that aligns with Georgian high-frequency phone survey data showing very few workers claiming to have stopped working in order to 19
children younger than 5 years in the household, and height-for-age Z-score. The effects on child cognition of demographic controls in the table are in the expected direction. Z-scores of children living in urban areas are 0.33 standard deviation higher than those of children living in urban areas. In addition, children’s nutritional status also affects performance in the PPVT. The coefficients for wealth are positive, statistically significant, and among the highest, which is in line with research that points to socio-economic status gradients of cognition as measured by vocabulary (Schady 2011; Paxson and Schady 2007; Fernald et al. 2011; Naudeau et al. 2011; Schady et al 2015).

We next explore whether these negative impacts hold three years after the first measure of vocabulary. Using data of child vocabulary at the age of 8, we estimate the model presented in equations (2) and (3). The main results are presented in Table IV. Our estimations show that maternal depression has no effect on child vocabulary at the age of 8. Not only the estimated coefficients are not statistically significant, but also their sizes are very small – that is, the vocabulary of children whose mothers’ experienced mental health problems when they were 1 year old caught up with the vocabulary of children whose mothers did not suffer mental health problems. These results suggest that the effect of exposure to maternal depression during early childhood need not undermine language development permanently and exposure to rich vocabulary environments later on during childhood can compensate earlier developmental gaps. For our sample, it is possible that the convergence in vocabulary development is explained by the fact that by the time they reached age 8, all children had had exposure to formal education opportunities (99.9% of children in our sample), which may have a compensatory effect on children’s vocabulary development. Still, given that early vocabulary constitutes a foundational skill that facilitates the development of other cognitive skills (and potentially socio-emotional skills as well), based on our results we cannot rule out the possibility that exposure to maternal depression during early life undermines cognitive development and academic achievement.

# **_B. Heterogeneity by household characteristics_**

We explore heterogeneous effects by a number of maternal characteristics that have been identified in the literature as moderators of the effect of mental health, using our main model and all measures of mental health.

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one of B’s papers is known to have had any influence, while both of A’s have demonstrated influence. Under this third condition, A’s record is deemed to have higher total influence.<sup>16</sup> To test robustness under all three conditions (and some technical conditions given in Ravallion and Wagstaff, 2010), one first graphs (on the vertical axis) the number of publications receiving more than the corresponding number of citations (this time on the horizontal axis) ; this is the inverted citation curve. Next one calculates the area under this curve at each value of citations. If the area under A’s inverted citation curve is greater than the area under B’s for each citation level, A’s publication record has more influence than B’s for any stable influence function that is increasing in citations but displays diminishing marginal returns to citations in terms of the influence they generate. In this case, we say that A’s publication record secondorder dominates B’s for this sub-set of influence functions. Citation curve dominance can thus be considered a robust indicator of which publication record has had greater aggregate scholarly influence even when that influence cannot be directly observed.

We can construct similar tests for average influence. If one normalizes the standard citation curve by the number of publications (so that the horizontal axis gives percentiles of total publications), then dominance in the normalized curve implies that the higher curve has higher influence per publication for any influence function. Similarly, if the normalized curves intersect, one may still be able to make a robust comparison for the subset of influence functions that display diminishing marginal influence; we simply compare for each level of citations the areas under the normalized version of the inverted citation curve (Ravallion and Wagstaff, 2010).

# **_Citations of World Bank-authored publications_**

Our interest is in citations of all Bank publications, not just journal articles for which SSCI and SCOPUS provide citation data. We were also interested in citations in a broad range of documents, including scholarly journals not indexed by SSCI and SCOPUS, and publications other than scholarly journals, such as books, working papers, technical reports, dissertations, and conference proceedings. Most of these are at best partially covered (more often not even covered at all). While citations in such documents may not matter much for university-based authors, they are an important indicator of the degree to which the Bank and other aid agencies achieve the broader influence they strive for. So, in addition to presenting data on citations to Bank

> 16   Ravallion and Wagstaff (2010) discuss the arguments for and against this third condition.  One measure in the literature does not satisfy this condition, though Ravallion and Wagstaff argue against that measure.

15
<!-- Start of picture text --> 10<br>Chart 3b: Characteristics component of the gender gap in<br>8<br>Mathematics score: 2003 vs. 2007<br>6<br>4<br>G<br>2<br>e<br>n 0<br>d Math-2007<br>5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95<br>-2<br>e Math-2003<br>r -4<br>-6<br>G<br>a -8<br>p -10<br>Percentile<br>35<br>Chart 3c: Coefficients component of the gender gap in<br>30 Mathematics score: 2003 vs. 2007<br>25<br>G<br>20<br>e<br>n<br>15 Math-2007<br>d<br>e Math-2003<br>10<br>r<br>5<br>G<br>a 0<br>p 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95<br>Percentile<br><!-- End of picture text --> 15
Policy Research Working Paper 10430

# **Abstract**

This paper estimates the effect of voice and accountability, which captures transparent electoral processes, free media, and freedom of expression, on access to sanitation services in developing countries. The core argument is that voice and accountability increases the visibility of sanitation as a public good and raises awareness of its benefits; hence, increasing its supply and demand. The analysis utilizes data from 73 developing countries and an instrumental variable approach to identify the causal effect of voice and accountability on access to, and use of, sanitation. The paper also employs a novel _instrument-free_ _estimator_ as both an alternative estimator for the analysis and an empirical strategy to formally assess the validity of the instrument in a just-identified model—a previously untestable just-identifying exclusion restriction. The paper finds robust evidence that voice and accountability increase access to sanitation and help close the persistent rural- _urban inequality_ in access to sanitation. The results suggest that key tenets of democracy such a freedom of speech, free media, and power of electoral incentives are not a luxury of the rich—they are relevant to the world’s poor as they can shape the demand and distribution of sanitation services.

This paper is a product of the Macroeconomics, Trade and Investment 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 jfrancois1@worldbank.org_and ckouame 1@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
convergence, both with and without dummies for the different island groups, by estimating the convergence model provided by Barro and Sala-i-Martin (1991, 2004): γ i, t, t+T = β0 + β(ln yit – ln yi*) + εit (1) where γ i, t, t+T is a per capita RGDP growth rate of region i between time t and t+T, yit is region i’s per capita RGDP at time t and yi* region i’s steady state value, β is the coefficient that represents convergence rate and εit the error term. We extend this to incorporate possible spatial divergence by including the weighted growth of the neighboring districts as outlined above.

Second, we explore relative convergence, adding a set of variables representing the initial conditions of each time period. As noted above, these include measures of human and physical capital (population size, level of education, quality of pre-existing infrastructure); location and geography (whether on or off Java, the extent of urbanization, the topography of the region and its distance from major cities); economic structure (the share of different sectors, and the presence of terms-of-trade effects); and socio-cultural institutions (religious and ethno-linguistic fragmentation).

Third, we take into account the possibility of unobserved heterogeneity determining growth by estimating a fixed-effects panel estimate of annual growth. By definition, this approach excludes variables that are invariant over time.

# **5.1 CONVERGENCE OR DIVERGENCE?**

## **OLS estimates of absolute convergence**

Table 5 shows the OLS results testing for absolute convergence during the three time periods. There is little evidence for absolute convergence before the economic crisis. However, neighborhood effects do appear to have an influence, with the weighted average growth of the neighbors of each district having a significant positive effect on the growth rate of that district. The same conclusion holds even when island dummies are added to take into account possible different growth trajectories in different parts of the Indonesian archipelago.

In the immediate post-crisis period, there does appear to be strong absolute income convergence, but this probably simply reflects the fact that better-off areas of Indonesia were harder hit by the economic crisis. The evidence for spatial divergence disappears, as does income convergence, when island dummies are included.

During the decentralization era, the pattern of growth changes again. There is now evidence for absolute income convergence, but the growth of neighboring areas is no longer significantly associated with district level growth. The island dummies also indicate that growth in Sumatra was higher in the post-decentralization period, whilst at the same time growth in Nusa Tenggara and Maluku fell significantly behind that of the rest of the country (Java is the excluded island).

17
example, if two trucks showed up at a location with a flatbed full of cinder blocks, consumers would be indifferent as to which supplier they used. Second, we chose only industries where the number of observations was large enough to be statistically meaningful. To generate consistent measures of quantities across different industries, each product is measured in terms of its weight in kilograms.

Table 1 presents the descriptive statistics of these industries. As can be seen in the table, large productivity dispersion exists within each industry. For example, a plant at the 90<sup>th</sup> percentile (in terms of TFPR) of its industry distribution produces between 1.9 and 2.8 times more than a plant at the 10<sup>th</sup> percentile _with the same level of inputs_ . These estimates are well within the range of those estimated for other countries. Syverson (2011), for example, estimates the ratio of 90<sup>th</sup> to 10<sup>th</sup> percentiles of TFPR at 1.92 for 4‐digit industries in the United States. Similar to other studies, we find that the estimated within‐industry productivity dispersion is larger when it is estimated by TFPQ rather than TFPR.

Finally, Table 1 reports the average (annual) entry rates and exit rates for each industry. We define a plant’s entry date as the year when it was established (as reported by its owner or manager). On average, entry rates vary between 2% and 9% per year. Similarly, we define a plant’s exit date as the last year that it was observed in the census. On average, exit rates vary between 15% and 21%. We should point out that substantial effort was made to ensure that our exit variable reflects a “true” exit from the market rather than a situation in which a plant was not interviewed during a given year. In the panel, there are cases when some firms are not interviewed in one or two years and then reappear in later years. These firms are not included in our estimated exit rates. To ensure that this is the case, we drop the last two years of the census (2010 and 2011) to check that firms which exited in 2009 do not reappear in either 2010 or 2011. In our sample, there are no cases where a firm is not observed for more than two years and then reappears. Our estimated exit rates are similar to those which have been reported for other manufacturing industries in Africa.

A well‐known empirical finding is that firms in larger cities are more productive than those in smaller cities (see Combes and Gobillon, 2015 for a recent survey of this literature). While this 17