rafmacalaba/data-use-mentions-extended huggingface dataset:
13)||
|Year 2014|0.150***|‐0.0142|0.155***|0.147***||0.113***||
||<br>(7.68)|<br>(‐0.57)|<br>(8.51)|<br>(6.99)||<br>(3.13)||
|_cons|<br> <br>8.024***|<br>6.487***|<br>6.849***|<br>7.513***|6.677***|<br>7.580***|7.225***|
||(35.15)|(25.99)|(32.66)|(30.26)|(20.11)|(18.23)|(13.23)|
|N|<br>27698|3811|11025|19748|6492|7950|2715|
|R‐sq|0.432|0.204|0.365|0.399|0.409|0.316|0.305|

Notes: Table indicates coefficients estimated from ‘Pooled’ Cross‐section model using Ordinary Least Squares. * 0.10 ** 0.05 *** 0.01 significance level. Values in parentheses indicate standard errors corrected for cluster correlation at commune‐level. _Source: Authors’ calculation based on data from Brauer et al, 2015, Hansen et al., 2013, Vu et al., 2014b, Harmonized World Soil Database, Climate Research Unit, Bangalore et al., 2016, Winsemius et al., 2015, and VHLSS 2010, 2012 and 2014._ 16
# **1 Introduction**

This chapter surveys the broad features of and developments in the use of trade policy across countries, within countries, and over time. Our goal is to describe and, whenever possible, quantify the extent of cross-sectional heterogeneity in applied commercial policy across countries, their economic sectors, and their trading partners, over time.<sup>1</sup> We construct a relatively comprehensive picture of trade policy for 31 economies that represented 83 percent of the world’s population and 91 percent of the world’s GDP in 2013. Our main conclusion is that substantial trade policy barriers remain as an important feature of the world economy today.

As a starting point, we must define what we mean by a trade policy ‘barrier.’ As we will illustrate, there is an ever-increasing set of policy instruments that both researchers and trade negotiators would like to better understand because they are perceived to have a trade-restricting effect. Until recently, researchers have had little information about border policy instruments. Indeed, as Anderson and van Wincoop noted: “[t]he grossly incomplete and inaccurate information on policy barriers available to researchers is a scandal and a puzzle” (Anderson and van Wincoop 2004, p. 693). The good news is that research no longer needs to operate behind a veil of ignorance created by the lack of information on border barriers; product-level data on many of the important trade policy instruments imposed at the border are now routinely being made available for most economies in the world.<sup>2</sup> The picture of the empirical landscape of trade policy that emerges is complex, with substantial variation along some dimensions and little variation along others. We frame our assessment of this trade policy variation by centering our analysis around five main questions:

1. Do some countries have more liberal trading regimes than others?

2. Within countries, which industries receive the most import protection?

3. How do trade policies change over time?

4. Do countries
We use the 90-day recall periods to construct quarterly estimates of each of these outcomes. For the intensive margin outcomes, we use the survey date and the variables indicating which months the indicated person worked to calculate the percentage of the month that is applicable for each individual. This is then used to calculate the months and hours worked during that 90-day period.<sup>10</sup> Using the 12-month recall data we create similar quarterly measures to match the dates of the shorter recall.<sup>11</sup> Figure 1 provides a timeline of the four rounds of the surveys, showing the survey dates, the months covered in each recall period, along with the agricultural calendar for maize, the main staple crop in Malawi. We note that there was a delay between the third and fourth (final) survey. This has the effect that some of the time covered in the 90-day recall asked in the Round 1 interview is not well covered by the 12-month recall questions asked at the endline. As such, we observe very low levels of work in Quarter 1 as measured in the 12-month recall data. In particular, we were concerned that including all four rounds of short recall from the four surveys would result in artificially low estimates of work in Quarter 1 for the long recall group. Additionally, these delays resulted in capturing labor data for May and June—the harvest months for the primary agricultural season, in both Round 1 and Round 4.

To address both of these issues, we exclude Quarter 1 from our main analysis and use quarters 2- 4 to improve the comparability of the short and long recall periods. As seen in Figure 1, these three quarters cover the main components of one maize growing season, although there is a small gap between the final two interviews and some seasonal activities just after harvest time may be undercounted and missed in our estimates. This would likely lead to an understatement of long recall losses. We show robustness (and strengthening) of our main results
sup>10</sup> . The median migrant to the Gulf countries and Malaysia spends 2.3 years abroad per migration episode. More than 85 percent of them finance migration by borrowing, mostly from local loan merchants and from friends and relatives<sup>11</sup> . Borrowing from formal financial institutions is, however, quite infrequent with less than 5 percent of the migrants borrowing from banks. Of the remaining 15 percent too, most finance the migration through grants and help from family and friends.

A few key points of international migration from Nepal are worth highlighting, which ties the theoretical framework in the context. The cost of migrating to India and finding a job is much lower than the costs of migrating elsewhere. Interestingly, these costs do not seem to vary by location of origin community or by the education level of the migrants<sup>12</sup> . The returns to migration also seem to follow the same pattern so that migration is quite profitable on average, given the typical duration of migration and the amount of savings while abroad. Jobs, particularly to non-India destinations, are prearranged so that there is no risk of not finding work conditional on migration. The vast majority of the migrants finance their migration by borrowing, but borrowing from formal financial institutions is quite low. Most migrants borrow from local moneylenders against a collateral.

# **Data on migration rates**

The data on migration for this paper comes from three different surveys conducted by the Central Bureau of Statistics (CBS) of Nepal over 2001-2010 period. All three surveys have information on absentee members of the household and because of their unique and circumstantial survey design, forms a panel of nationally representative villages observed thrice during the decade.

> 9For more details, see Shrestha (2017b,a).

> 10Though the earnings in Malaysia seem to be lower than the earnings in the Gulf countries, it is believed that the wages are actually higher in Malaysia. Workers in the Gulf countries tend to work
water. Therefore, we limited our watershed to be at least 50 Kilometers from a large body of water. Due to the spatial resolution of the data, each observation has a diameter of about 30 kilometers. As a result, the basin needs to be greater than several hundred Kilometers in area in order to have numerous satellite observations over the river basin. Finally with regards to the hydrological cycle, the basin must contain natural inter- and intra-annual variability of flow, in order for the data to contain natural variations over the period of record. Surface wetness performs best in areas without high vegetation, yet arid regions can be difficult too, due to the intensity of the water flow (e.g. flash floods) that may not be recorded by the orbit of the satellite.

# **Regression to predict ex-post river flow**

All rivers have extreme events in flow. Correlating historical record of flow with known flood events allows us to examine whether the BWI corresponds with variations in flow rates in near real-time. As an illustrative example, ex-post knowledge of a flood in a basin is used to test the statistical models‘ estimate of runoff. We used both gauge data and meteorological data to validate the model‘s accuracy. Results are reported in the next section.

# **Estimating probabilities of extreme flow**

The probability distribution function (PDF) describes the likelihood for an amount of runoff to occur at a given time. This distribution makes it possible to provide statistics on the likelihood of low and high flow within a treaty basin. All parameters have been estimated, assuming a gamma distribution derived from the sample _L-moments_ (as per Hosking 1990, 1995; Hosking and Wallis 1997) using the ―lmomco‖ package (Asquith 2007) in R (R Development Core Team 2008). _L-moments_ are summary statistics for probability distributions and data samples computed from a linear combination of the ordered data values<sup>7</sup> . It provides measures of Arithmetic mean, _L-scale_ —analogous
|_1156_|_749_|_487_|_688_|_545_|_905_|_573_|_511_|_641_|_610_|
|_Ukraine_|_8500_|_11300_|_6300_|_4700_|_44648_|_4347_|_7027_|_8793_|_8088_|_8131_|_10210_|_10815_|
|_Uzbekistan_|_1257_|_2048_|_1466_|_1280_|_912_|_1106_|_1455_|_2605_|_1274_|_2894_|_1349_|_3251_|
|_Forner Soviet Union_|_67581_|_69431_|_i57957_|_45199_|_62919_|_40197_|_86718_|_73537_|_87235_|_75464_|_77552_|_79941_|

_* Estimate based on data for the first three quarters._ Source: IMF, Direction of Trade Statistics 1994-1998.

Michalopoulos &Tarr 1991-1993.

_8_
to the w _ork & study_ Index. In other words, transitions are more common between _work only_ and _Nini,_ than between _work only_ or _Nini_ and _study only_ .

All in all, the history of past decisions regarding time use influences decisions regarding time use in the future, but as students leave school it is harder for them to go back, particularly if they have experienced Nininess.

# _(ii) Quintiles of household income per capita to proxy for youth income,_ ��

As Figure 7 shows, the relationship between quintiles of household income per capita and the probability of being _Nini_ is non-linear for men and inexistent for women aged 15 to 18 years old. Male youths from better-off households (those in the fifth quintile of the distribution of household income per capita) have a 8.1 p.p. lower probability of being _Nini_ compared to those in poor households (those in the first quintile, the comparison group). Quintiles 3 and 4 exhibit lower and similar (according to the confidence intervals) probabilities of being _Nini_ , while the probability for youth in quintile 2 is not statistically different from that of those in quintile 1, creating a non-linearity in the response. For women, although the same pattern is present (see Figure 7), there is not enough statistical confidence to distinguish these patterns from each other or from the first quintile.

Figure 7. Proportional Change in the Probability of Being Nini to HH Income Quintile changes, Base Group is Quintile 1, Youth Aged 15-18 y.o., by Gender (a) Male (b) Female <!-- Start of picture text --> 0.04 0.04<br>0.02 0.02<br>0.00 0.00<br>-0.02 -0.02<br>-0.04 -0.04<br>-0.06 -0.06<br>-0.08 -0.08<
where the gender gap in literacy of the younger generation is insignificant, Banjar Baru is found to have a 2-percentage-point gender gap in literacy favoring females. For the older generation, the gender gap is lower than the national average, with a 3.09 percentage point gap favoring males.<sup>16</sup> In Banjarmasin, the average GPI for NERs is 0.95 and there is a 0.4-percentage point gender gap in adult literacy favoring males for the young generation, while for the older generation the gap is nearly 2.74 percentage points favoring males.

# 3. Findings

We use administrative, survey, and census data, and other studies to reveal several elements of gender disparity and issues of social inclusion; combined with data gathered from the field visits to East Java and South Kalimantan.

## 3.1. Gender Disparity in School Participation

Nationally, Indonesia has achieved gender parity in school participation. There is gender parity for Net Enrollment Rate (NER) at primary level (1.00). Then the gender parity index (GPI) for NER increases with each level of education, so that at senior secondary the GPI for NERs is 1.03 and at tertiary it is 1.15, showing that, relative to the school-age populations for males and females, a higher proportion of schoolage females are in school than school-age males.<sup>17</sup> The significant improvement in school participation has been accumulatively contributed by various government programs in the past decades. The national educational development program started in 1948 with the ‘alleviation of illiteracy’ program. The second stage was launched in 1973 through the presidential instruction on elementary school development assistance (locally known by the _SD Inpres Program_ ). Afterward, the 6-year compulsory education program ( _Wajar_ ) was launched in 1984, which was then followed by the 9-year compulsory education program in 1984, and currently the government is

> 13 BPS (2018).

> 14 Overview of Education and Cultural Data 2017/18
understanding both the influencing factors (institutional and economic) of the structure of decentralization for the road sector and for evaluating empirically the impact of decentralization on road service delivery. The study is also interesting in that it makes a distinction between the three types of road activities mentioned earlier and the desirability of decentralizing their provision.

# **_Outline of the Paper_**

The paper is organized as follows. Section two introduces the general and extended theoretical frameworks of the study. Section three provides a framework for the analysis of the optimal structure of road service provision. Section four presents the data set that will be used for the analysis. Section five studies the relationship between decentralization and road service delivery. It discusses the impact of decentralization on road performance for various activities. Further, it looks at the dynamics of decentralization for eight countries that have varying degrees of fiscal and functional decentralization for road works. This provides an understanding of the forces that derive decentralization and the potential tradeoffs that governments can make to reach optimal road service levels. The sixth section is a longitudinal analysis of the road provision experience of Korea. Korea has been selected because of its rich experience with decentralization in the road sector. Section seven presents the findings from a case study conducted for Germany. The analysis in this section is different in that it develops both vertical and horizontal measures of decentralization. Finally, based on the analysis conducted in sections three to seven, section eighth will attempt to develop guidelines for optimal provision of road services for various road activities.

# **2. Theoretical Framework**

# **_General_**

Governments have complex structures both horizontally in terms of numerous agents and vertically in terms of different levels of government. In many developing countries, the trend in recent years has been towards increasing decentralization in the direction of granting more authority to lower levels of government. Most interestingly, new levels of government have emerged to address the difficulties faced by central governments. The question of which level of government is the appropriate or desirable provider
_G_1|0.4191|0.3346|
|Change in Gini coefficient:_∆G _|0.0237|0.0123|

Note: Computed from panel data from 1989 and 1997 waves of China Health and Nutrition Survey (CHNS) and the two (and only) waves of the Vietnam Living Standards Survey (VLSS). CHNS is not nationally representative, but the 1993 wave of the VLSS was. Figures for China refer to income, but those for Vietnam refer to consumption. For further details of sampling and variable definitions, see text.

> 7 Liaoning was sampled in the CHNS in 1989, 1991 and 1993, but was dropped in the 1997 round, being replaced by Heilongjiang.  Neither is therefore represented in the 1989-1997 panel.

> 8  The Gini and all the various concentration indices were computed using the convenient covariance method (cf. e.g. Jenkins 1988).

> 9  Gini coefficients are computed on household-level data, but households are weighted by household size.  The Ginis therefore indicate the extent of income inequality _between individuals_ .
_The Global Information and Communications Technology Industry: Where Vietnam Fits in GVCs_ relocated with relative ease. Accordingly, GVCs in the ICT industry are more geographically extensive and dynamic than in any other sector.

Evidence of the importance of the ICT hardware sector in GVCs can be found in statistics on intermediate and final goods trade. Trade in intermediate goods is indicative of GVCs because fragmented production processes require that parts, components, and partially manufactured subassemblies cross borders—sometimes more than once—before finished goods are shipped to final markets (Feenstra 1998; Dean, Fung, and Wang 2007; Brülhart 2008). Data on trade in intermediate and final goods can be found in the World Bank’s MC-GVC database, which in addition to electronic hardware covers the automotive and textile/apparel/footwear industries. As Table 1 shows, world merchandise exports have grown faster than world GDP since from 1988, until 2011, when the current collapse of international trade began to reverse the trend (from 20112014 world GDP slowed to 2.2% per year while world merchandise exports grew at less than 1%). Trade in ICT hardware is the largest of the three sectors covered by the World Bank’s MC-GVC database, and its importance has grown from 11% of total merchandise exports in 1988 to 16% in 2014 (from US$ 69 billion to US$ 2.7 trillion). Intermediate ICT goods exports grew faster than final goods in the period 1988-2011, but slightly more slowly since.

From a GVC perspective, countries with very high levels of intermediate goods imports tend to act as hubs for final assembly and export of finished goods (unless they are large reexporters). Historically, the United States was the top location for electronics manufacturing and export, followed by Japan and Germany. Today, these countries are still in the game, in regard to production (especially of intermediates), ownership of intellectual assets, and orchestration of the GVC, but their importance as traders of ICT goods as China has become the
|85.3|82.1|86.1|84.7|80.9|85.6|
|- Periodicity and Timeliness Indicator|72.2|58.7|75.4|85.3|82.1|86.1|73.3|57.3|77.1|
|Data Transparency||||||||||
|- SDDS Subscription|0.4315|0.4286|0.4321|0.9110|0.9333|0.9066|0.9342|0.9500|0.9311|
|- SDDS Standard Compliance|0.1957|0.0476|0.2248|0.4849|0.2333|0.5344|0.5644|0.3833|0.6000|

_Notes. The sovereign bond spread and return comes from Bloomberg. WB data transparency is obtained from the World Bank, Statistical Capacity Index (SCI) while the IMF data transparency is obtained from the IMF, SDDS data._
World Bank, their Managements, or the governments they represent. The authors thank Natalia Volkow and INEGI for access to the Economic Census microdata. All results have been reviewed to ensure that confidential information is not released. Any errors remain our own. a: World Bank Development Research Group. Corresponding author, Phone: +1 (202) 505-0925, 1818 H St NW, Washington, DC, 20433, treed@worldbank.org.; b: International Finance Corporation, c: World Bank; d: World Bank
various existing data, such as TiVA and Eora, from which the labor value added in exports can be extracted, and which can help increase the frequency of the data and improve their quality.

# _<u>Computing the labor value added content of exports</u>_

We work with multiple versions of the GTAP database, benchmarked to 1995, 1997, 2001, 2004, 2007 and 2011. To maintain backward compatibility, we start with the 1995 structure of sectors, and carry this forward in aggregation of more recent iterations of the database. To do so, we aggregate the 57 GTAP sectors into 24 sectors, which also allows ease of exposition of the results. (Table A2 in Appendix 1 provides the concordance between the 24 and 57 sectors.) We also generate the data with the 57 sectors from 1997 onwards. Our database excludes the regional aggregates in GTAP, resulting in 31 countries in 1995 expanding to 120 countries in 2011.

The IO tables in the GTAP data set allow one to exploit a form of social-accounting data – a variation on the social accounting matrix (SAM) where incomes are shown in the rows of the SAM while expenditures are shown in the columns (Hertel, 2013 and McDougall, 2001). The structure of the data provides a comprehensive and consistent record of national income accounting relationships between different sectors of an economy. It is based on a fundamental, general equilibrium principle of economics – every income (receipt) has a corresponding expenditure (outlay). The strength of this framework is that it provides a comprehensive record of the interrelationships of an economy, including intermediate and final demand linkages. For our purposes, it offers the advantage of linking consumption and external trade patterns explicitly to the inter-industry structure of intermediate demand. This allows for a fuller analysis than is possible when working strictly with IO tables.

We also make one adjustment to the data to ensure compatibility of the skilled-unskilled labor split over the years. All the versions of the GTAP data up to 2007 use
method essentially generalizes the idea of an instrumental variables estimator to a situation in which the dependent variable contains non-ignorable errors.

We illustrate the method using high-quality socioeconomic survey data for Russia in 2002. We first show that applying standard methods from past research to these data suggests little sign of an economic gradient in the self-assessed health of Russian adults. This might not be too surprising if one was talking about Russia some 20 years earlier, at which time it might be argued that a reasonably well-funded universal health care system meant that the quality of health care varied little with income. However, the lack of a gradient in data for Russia around 2000 is surprising. Worsening public health services, increasing reliance on the market for health care, and rising poverty and inequality, would lead one to expect an emerging economic gradient in health status. Psychological adaptation to the stresses of the economic transition and health crisis could well entail that the structure of errors in _SAH_ data for Russia hides the true gradient. One can expect that it will be the poor who are least able to protect their health care during Russia’s health crisis, and they may well adapt to the situation by downplaying the health risks they are facing, at least for some time. Then the true economic gradient will be attenuated in the raw data on _SAH_ . It may well be difficult to identify the gradient in this setting by standard empirical methods, casting doubt on numerous claims found in the literature.<sup>6</sup> Applying our preferred estimation method, we show that much stronger signs of the gradient emerge from a more nuanced empirical model — emphasizing non-income factors relevant to perceived economic welfare at the micro level, and allowing for heterogeneity in _SAH_ data. This model reveals a marked tendency for the poorest to be less healthy, with dampened health gains as economic welfare rises. In short, while there is little or no income effect evident

> 6 For example, Gavrilova et al. (2002) and Brainerd and Cutler (2005) argue