rafmacalaba/data-use-mentions-extended huggingface dataset:
MINIMUM WAGES IN CAMBODIA 20 <!-- Start of picture text --> All<br>Garments Other sectors<br>.1 .2 .3 .4 .5 .6 .7 .8 .9 .1 .2 .3 .4 .5 .6 .7 .8 .9<br>Quantiles of monthly wage distribution<br>Point estimates 95% CI<br>Female<br>Garments Other sectors<br>.1 .2 .3 .4 .5 .6 .7 .8 .9 .1 .2 .3 .4 .5 .6 .7 .8 .9<br>Quantiles of monthly wage distribution<br>Point estimates 95% CI<br>.08<br>.06<br>.04<br>.02<br>0<br>-.02<br>Quantile treatment effects -.04<br>-.06<br>.05<br>0<br>-.05<br>-.1<br>Quantile treatment effects<br>-.15<br><!-- End of picture text --> Figure 6. Quantile treatment effect of minimum wage hikes on monthly wage earnings _Source_ : Author’s calculations from Cambodia SES 2007-2015.

_Note_ : The figure plots the estimate of _α_ 2 + _β_ 2 (left panel), and _α_ 2 (right panel) from quantile treatment effect estimation of Equation (6) for monthly wages. Quantiles are indicated by the horizontal axis. Sample restricted to working age population, and those observations surveyed within 8 quarters of any of the minimum wage hikes. In addition to controls mentioned in the notes for Table 1, the estimates also control for garment sector specific time trend for each location, and garment sector specific global recession shock.
**Table 6.5: Dispersion of Income 1987-1991**

||Accra|Other<br>Towns|Rural|
|---|---|---|---|
|GLSSI(1982)||||
|Standard||||
|deviation||||
|Household|287.430|274.250|354.073|
|Income||||
|Income per|82.945|77.588|107.690|
|capita||||
|Incomeper<br>.AEU|113.517|100.035|127.928|
|Income per|187.696|188.929|199.129|
|Earner||||
|GLSS3(1921||||
|Standard<br>deviation||||
|Household|622.456|633.112|475.932|
|Income||||
|Income per|235.581|199.547|172.763|
|capita||||
|Income per<br>AEU|266.569|230.138|183.478|
|Income per|575.250|571.772|404.939|
|Earner||||

_Source: GLSSl and GLSS3_ Reference must be made at this point to the findings in the Extended Poverty Study of the World Bank. The poverty line in this case was defined as two-thirds of mean household expenditure per capita in 1988 for the country as a whole, and expressed in constant May 1992 Accra prices. The strong conclusion of this work was that between 1988 and 1992 "poverty (as represented by both the headcount and poverty-gap ratios) declined steeply between 1988 and 1992 in all areas except Accra." (World Bank 1995, p.42). This trend is reconcilable with the evidence of widening income differential between Accra and the rural areas only if the degree of inequality in the distribution
-8 -

What has happened to the access to education by provinces in other years? Similar household surveys are not available, but enrollment rates by provinces, ranked by per capita income from poor to rich, indicate that the importance of income as a correlate of school enrollment has not changed, the richer the province the higher the enrollment rate in post-primary education.

**(d) Social selectivity.** Chinese educational system suffers from social selectivity. Disparities in drop-out rates at the secondary education level are quite pronounced . Male-female differences in primary education are modest but very significant in secondary education. Drop-out rates also widely vary according to geographic location and level of education: high in poor provinces and secondary education, and vice versa. In secondary education, in rural areas, more females tend to drop out of school than do males (Table 3). The reason is that due to obvious economic difficulties, females from poor families leave the schools earlier to start working at home or to help their families. Inadequate learning environment in poorer provinces is another reason for early exit from school system.

(e) **Affordablity and** Quality. Private costs incurred by the poor are high enough to prevent them from attending schools. The main components of out-of-pocket educational expenditure are tuition, textbooks, stationaries and contribution to schools (Table 9, Annex 1). The figure 6 shows that as a percentage of non-food household expenditure, the poorest quintile spend three times more on education (14.2%) than those in the top quintile (5.5%). The full private cost of school attendance would raise the cost-nonfood expenditure ratio--a measure of affordability--further if the opportunity cost of time in school is considered. For rural area, total private educational expenditures are examined by provinces, ranked by per capita income from poor to rich (Fig 7), and similar conclusion holds--that the prices faced by families in poorest rural provinces are high enough to create an
6 FERNANDES-KLENOW-MELESHCHUK-PIEROLA-RODR ́ıGUEZ-CLARE tains annual export flows (in current values) disaggregated by firm, destination and Harmonized System (HS) 6-digit product. Oil exports are excluded from the customs data due to lack of accurate firm-level data for many of the oil-exporting countries. For most countries total non-oil exports in the EDD are close to total non-oil exports reported in COMTRADE/WITS. More than 100 statistics from the EDD are publicly available at the origin-year, origin-product-year, origindestination-year, or origin-product-destination-year levels. These include average exports per firm as well as the number of exporting firms.

For the descriptive analysis in this section as well as for the regression and simulation work in the sections that follow we focus on a _core sample_ that consists of 50 countries (49 from the EDD and China) for which we have the firm-level data.<sup>6</sup> However, to use the most comprehensive sample of countries available we rely for the motivating plots below on an _extended sample_ that includes the 59 origin countries from the EDD plus China. Both samples cover a subset of years from 2003 and 2013 — see Table 1 and Table I1 in the Online Appendix.

We focus on EDD statistics based on products belonging only to the manufacturing sector. Specifically, relying on a concordance across the ISIC rev. 3 classification and the HS 6-digit classification, we consider only exports of HS 6-digit products that correspond to ISIC manufacturing sub-sectors 15-37. Using these data we calculate variants of average exports per firm, number of exporting firms, and total exports at the origin-destination-year level or at the originproduct-destination-year level. The product disaggregations that we use are HS 2-digit for the extended sample and HS 2-digit, HS 4-digit, or HS 6-digit for the core sample.

# **Importance of the intensive margin**

Let _Xij_ , _Nij_ and _xij ≡ Xij/Nij_ denote total exports, total number of exporting firms, and average exports per firm from country _i_ to country _j_ , respectively.<sup>7</sup> In Figure 1 we
|
|Rainfall - 3o Princ. Component|.01638063|.0044276|.01515498|.00688489|
|Employment rate in 1991|.17582282|.14548762|.21597567|.23001887|
|crit. fn. test of overid. restrictions|----|----|6.5507803|6.5507803|
|Dependent Variable:<br>Delta ln Employment|Spatial GMM<br>Est.<br>(Cutoff = 1)|Spatial GMM<br>SE<br>(Cutoff = 1|Spatial GMM<br>Est.<br>(Cutoff = 2)|Spatial GMM<br>SE<br>(Cutoff = 2)|
|Intercept|-.94565964|.24317868|-.8953951|.27096564|
|ln Wage in 1991|.1860382|.02520692|.17956887|.02802359|
|ln 5 to 15 over 15 to 55 yrs Ratio|<br>.2456395|.06085758|.2379445|.06769622|
|Proportion of Natives in 1991|.31289083|.07724686|.30829117|.08909906|
|Delta ln Market Potential|.19815785|.05295998|.19621347|.06306989|
|ln Teacher Qualification in 1991|-.00740787|.00443293|-.00709898|.00504883|
|Homicides in 1991|-18.404268|18.498964|-26.86664|28.508017|
|Employment in Farming in 1991|-.6742841|.31386852|-.72451919|.35913906|
|Bank Dummy|.00197269|.0137091|.00300001|.01441328|
|ln Population in 1991|-.02758414|.01119957|-.02459399|.01372423|
|Mean Temperature|-.00145903|.00495593|-.00197288|.00596594|
|Total Rainfall|.00001497|.00002414|.00002119|.00002897|
|Rainfall - 1o Princ. Component|.00662112
6 productivity,<sup>8</sup> nontradable) services in urban areas, consistent with the emergence of consumption cities following Africa’s resource boom (Gollin, Jedwab, Vollrath, 2016), and premature deindustrialization (Rodrik, 2016). Nonetheless, a glance at the national accounts would suggest that huge gains can still be reaped from an accelerated reallocation of labor out of agriculture. Also using macro data, Gollin, Lagakos, and Waugh (2014b) estimate that non-agricultural labor is 6 times more productive than agricultural labor in Africa, relative to 4.5 times in other developing countries, 3.4 times in middle income countries, and 2.2 in high income countries.

Yet measuring the potential gains from intersectoral labor reallocation is wrought with measurement challenges, and the national accounts and macro data routinely overstate the prospective gains from leaving agriculture. When adjusting their macro estimates for sectoral differences in human capital and hours worked, the agricultural labor productivity gap declines by half, to 3.3 for SSA and 2.2 for developing countries (Gollin, Lagakos and Waugh, 2014b). Also correcting for differences in human capital, but using micro income data and indirect proxies for differences in sectoral time use shares instead, Vollrath (2013) finds that the misallocation of labor effort between sectors explains only 12 percent of cross-country income variation, compared to 30 percent when using unadjusted macro data from the national accounts.

Important new work by McCullough (2016) using household income and individual hourly labor input data by sector collected through nationally representative household surveys in four countries under the World Bank-led Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMSISA) initiative points in the same direction. Once one accounts for inter-sectoral differences in time worked, which is particularly important due to the seasonal nature of agricultural work as well as the often-diversified income portfolios of rural households, the productivity differentials commonly found in less detailed data sets nearly disappear. The powerful implications are that what is commonly understood as an intersectoral labor
in living standards into the future. This is the population that constitutes Nepal’s growing vulnerable class.

This point is supported by evidence from other sources of data. Gallup World Survey, for example, also asks respondents similar questions about their perceived position in an 11-step Cantril ladder denoting various parts of the economic distribution within each country. Respondents are asked about current position as well as positions five years before the survey and expected position five years hence. Based on responses to these three questions, Gallup defines households as thriving, struggling or suffering. Thriving households have strong current life situation and have positive views about the next 5 years. Struggling households have wellbeing that is moderate but inconsistent while suffering households have well-being that is at high risk, they report poor ratings of their current life and also poor outlook for the future. Based on cross-country analysis of these categories, Gallup has found thriving respondents to have fewer health problems, less worry and stress, sadness and anger and more happiness, enjoyment, interest and respect. Struggling households on the other hand report higher daily stress, and worry about money than thriving 16
approach. Section 5 presents more detail on the household panel survey and the RRMIMP. Section 6 presents the results on the average short- and long-term effects of road investment, and Section 7 presents the distributional impacts. Section 8 concludes.

# **2. Short- term versus long-term welfare impacts of rural roads: What do we know?**

The mechanisms tying road development to income gains and poverty reduction are highly complex and necessarily country- and context-specific. Improved roads and infrastructure can help reduce poverty by lowering transport and other input costs (Jacoby 2000; BIDS 2004) and expand earnings opportunities through easier access to markets and technology (BIDS 2004, 2009; Lokshin and Yemtsov 2005; Fan, Hazell, and Thorat 2000). The poor may benefit from these changes; for example, Khandker, Bakht, and Koolwal (2009) find positive short-term impacts on poorer households’ consumption from the RRMIMP project. But greater commercialization, rising land values, and shifts in growth across local farm and non-farm sectors may hamper economic opportunities for very poor households, particularly where the initial level of community development is low (see e.g., Narayana, Parikh, and Srinivasan 1988). Overall, initial area characteristics are likely to condition the nature of impacts, including distributional outcomes (Binswanger, Khandker, and Rosenzweig 1993; Jalan and Ravallion 1998).

The timing of road impacts, which can also depend on initial area characteristics, has not been closely examined in the literature. A number of studies have examined the short-term impacts of rural road development, controlling for potential selection bias due to unobserved heterogeneity within project and control areas, and/or differences in pre-program local-area endowments across project and control areas (see, e.g., Jacoby and Minten 2008; Khandker, Bakht, and Koolwal 2009; Lokshin and Yemtsov 2005; Jalan and Ravallion 1998). One recent exception that compares shortterm and long-term impacts is a study of a rural road rehabilitation project in Vietnam by van de 5
line for people with less schooling. However, schooling attainments depend on individual personality as well as opportunities. For example, there is evidence that more conscientious people acquire more schooling.<sup>29</sup> At the same time, more conscientious people tend to delay gratification in other aspects of their life, which may well mean that they report lower current subjective welfare at given current income than do others. Then a regression for subjective welfare ignoring the personality differences will tend to lead one to underestimate the required gradient in poverty lines needed to compensate for differences in schooling opportunities.

Even with similar personalities (or statistically ignorable differences) different people may apply different criteria for scaling their welfare. They have different ideas about what it means to be ―rich‖ or ―poor,‖ or what it means to be ―satisfied‖ or not with one’s life. Again latent heterogeneity in factors that are essentially irrelevant to welfare but influence responses to subjective welfare questions can cast doubt on the implied interpersonal comparisons of welfare from subjective data. If such heterogeneity was purely random then it would not invalidate inferences from such regressions (at least for linear models). Thus it has been argued in the literature that, while inter-personal welfare comparisons are invalidated by heterogeneous scales, the regressions are likely to be robust to such heterogeneity.<sup>30</sup> However, that claim is of doubtful veracity. As already noted, it is a well-established idea that people assess their welfare relative to some ―comparison group‖ such as neighbors or coworkers. This argument has emphasized relativist welfare comparisons. It has also been argued that reference groups play an important role in expectations formation.<sup>31</sup> It is a small step from these ideas to the proposition that survey respondents answer questions with reference to their immediate experiences and that this may well be highly localized in some relevant social or geographic dimensions. People will answer subjective questions relative to their personal ―frame-of-reference,‖ which depends on
2016 (the NTS, 2013).

With the variety of supporting measures for taxpayers, the NTS ramped up its capability to combat tax evasions and frauds as well. The NTS launched an ETI early warning system (EWS) to combat invoice seller fraud and identify input tax credit frauds in January 2012 following the internal development from 2009 to 2011.<sup>50</sup> The EWS was developed through identifying fraud types, configuring analysis processes, and structuring early warning typology with series of expert and user consultations and pilot testing in 13 regional tax offices for two months. The EWS provides profiles of suspicious transactions using information from ETIs, corporate income tax filings, tax delinquency records, and other sources to help tax examiners decide on further actions. The EWS is used to not only issue early warnings for tax frauds, but also conduct risk assessment and verify VAT refund claims.

One key function of the EWS is to monitor ETI issuing in real time and produce an alarm when invoice seller fraud is identified even before VAT is filed. The EWS analyzes monthly ETI transactions and taxpayers’ filing information within 15 days

> 50 The NTS developed EWS as one component of ETI systems. Development cost for EWS was approximately KRW700 million (USD630,000) for three years from 2009 to 2011.

25
sales prices for all food items found in the household consumption survey. These instruments were administered in parallel with the household surveys.

Primary Outcome Measures We generate primary outcome measures as follows:

1. **Consumption** : Total value of food for the last 7 days, and non-food expenditures in the past month, where non-food expenditures includes personal and household items, education and medical expenses, household repairs, social expenses (e.g. weddings, funerals and other ceremonies), and temptation goods. Non-food item values are estimated by household respondents. Food values use the district market price from the market survey where it exists, or the median market price from the surveys conducted in the remaining districts otherwise.

2. **Assets** : An index generated using principal component analysis of number and type of productive and household assets (excluding land/property), a proxy for wealth following Filmer & Pritchett (2001).

3. **Financial inclusion index** : A standardized index combining the following variables: (i) primary woman knows different places to save; (ii) anyone in the household has a 15
, there is no significant difference in the mean level of sovereignty across regime types in our data and no significant correlation of regime type and conflict over sovereignty (see results in the supplement).  The effect of other covariates (e.g. population) on sovereignty is slightly different across regime types.  Regime type is coded on the basis the Polity IV database (Marshall and Jaggers 2000), taking a five-year average for the period from 1995-2000.

> 41 The destabilizing effects of bursts of democracy have been discussed extensively since Tocqueville (1856). Gurr (2000) links stable democracies to lower levels of ethnopolitical conflict.

25
A product-source country specific inventory-usage ratio is obtained by exploiting the fact that more storable goods are traded less frequently.<sup>3</sup> We validate the measure of consumption by demonstrating that (1) it eliminates the anticipatory slumps documented for NAFTA’s phased-out goods, and (2) it is effective in replicating the actual consumption response in Monte Carlo simulations of a <u>(</u> _<u>s, s</u>_ <u>)</u> ordering model.

With our measure of consumed imports in hand, we examine how the anticipatory dynamics lead to deviations between the trade-flow and the trade-consumption elasticities using annual data. Consistent with the smooth behavior of consumed imports around the tariff changes, the annual or short-run trade-consumption elasticity is smaller

> 3This relationship emanates from the existence of per shipment ordering costs that lead firms to trade off the frequency of orders against the costs of holding inventories.

2
differences in the quality of parent-child interactions during early childhood are likely to explain part of the observed gender gaps in children’s cognitive and non-cognitive skills in the early years.

In light of the literature reviewed above, in this paper, we investigate gender differences across an array of cognitive and non-cognitive skills for children in the first few grades of primary school, and examine the extent to which early schooling experiences and parenting practices account for this observed gender gap in rural Indonesia.

# **3. Country context**

Indonesia has the fourth largest education system in the world with over 50 million students, 2.6 million teachers, and more than 250,000 schools. In 2012, the government expenditure on education was 4% of its GDP. Net enrollment rates in primary, secondary, and tertiary education are 92%, 77% and 32% respectively (World Bank 2015). There are no significant differences in primary and secondary education enrollment rates between girls and boys (Suryadarma 2015).

However, results of educational achievement data during primary and secondary schooling show some evidence of gender gaps. Girls significantly outperform boys in reading in the fourth grade (Mullis et al. 2012) and by age 15, this female advantage is equivalent to approximately 10 additional months of schooling (OECD 2015). In contrast, results in mathematics are mixed. Using a longitudinal household survey from Indonesia, Suryadarma (2015) finds that girls score 0.08 SD higher in numeracy tests than boys at age 11 and this gap increases to 0.19 SD when the sample of children are 18 years-old in 2007. In contrast, results from the most recent round of PISA show that the difference between boys and girls in mathematics at around age 15 is small in magnitude and not statistically significant (OECD 2015). Thus, the existing evidence of educational outcomes of Indonesian children shows mixed evidence of gender gaps during late primary and secondary school. This paper adds to the gender gap literature in Indonesia by focusing on the
Page 2 4. Doctors and teachers paid by line ministries are really accountable to national authorities only, even if on paper they should also be accountable to local government, communities and the end-users of services. While governments have a primary role in setting the rules and responsibilities of service provision through formal institutional arrangements, monitoring and penalty-enforcement cannot be supplied only by governments. Conflicts of interest and the risk of collusion between the monitors and the monitored call for a more participatory approach to accountability - an approach that directly involves service recipients.

5. This is the rationale for shortening the “route of accountability” by establishing robust participatory mechanisms that engage citizens in holding service providers accountable. Under the participatory approach, governments put institutions in place to allow citizens to play an active role in monitoring and sanctioning providers when their performance is unsatisfactory. Yet in most developing and transition countries, these mechanisms are still missing, or where they exist, they work poorly.

# **I.b. Improving Governance through Stronger Accountability in Albania’s Education Sector**

6. The Government of Albania is making rapid progress in strengthening governance across the public sector, particularly in education. Considerable advances have been made in the education sector in particular, with reforms to the service compact and how education services are supplied (represented in the right-hand side of Figure 1). These measures have reduced the room for discretion and collusive practices, increased fairness, and have brought to bear the disciplining forces of the market and greater transparency to improve the performance of the sector. The education sector reforms have been positively assessed, including in a recent World Bank country report (World Bank 2011) which presents data from a specially designed education governance module that was deployed with the 2008 wave of the Albania Living Standards Measurement Survey (LSMS).

7. The Ministry of Education and Science (MoES) is seeking to further improve governance by strengthening parent and community engagement in participatory accountability structures (the lefthand side of Figure 1). The Ministry has implemented