rafmacalaba/datause-encoder-data huggingface dataset:
Because the total rent on resource extraction is part of GNI, the effect of resource discovery is to increase net income, _aNNI_ , by decreasing the depletion share of total rent in the years subsequent to the discovery.

**Figure 1. Depletion share of total rent vs. resource lifetime,** **_r_ = 4%**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>0 10 20 30 40 50 60 70 80 90<br>Years of resource remaining (R/P ratio)<br>Source: authors<br>Depletion percent of total rent<br><!-- End of picture text --> By using the schedule of resource depletion shares shown in Figure 1 for a 4% discount rate, we can construct an example of how a large resource discovery would affect the net national income of a country. Assume that a country has 10 years of reserves and that total rent on extraction (assumed to be constant) is equal to 40% of _aNNI_ in the base year. Now assume that the country discovers an additional 15 years of resource reserves, bringing the total to 25 years. Before the resource discovery the depletion share of total rent was 84.4%, while after the discovery this drops to 65.0%. Given the assumed share of rents in _aNNI_ and the calculated drop in the depletion share, the net effect of the resource discovery is therefore to increase _aNNI_ by about 8%. In contrast, the purely Hicksian approach to discoveries (expression 26) would increase income by roughly 12% – this would be an incorrect measure of income because it includes capital gains.

To make the accounting approach explicit, assume that a resource discovery of quantity _D_ is made in year _t_ . Assuming that the quantity extracted remains at a constant level _R_ up to the point of exhaustion of the new larger stock, we can define the new stock as S<sup>∗</sup> = S+ D and the new value of this stock as N<sup>∗</sup> > N, while the pre-discovery unit value of depletion is given by _n_ . Under the assumption of constant unit rent and constant extraction level _R_ , Figure 1 indicates that the new unit value of depletion would satisfy, 9
cooperate settle in an area and several generations later their descendants formalize collective action as a CF could reasonably be considered an exogenous treatment.

We test whether 1990 NDVI adequately adjusts for baseline carbon levels by running forest level regressions of average carbon per hectare on the 1990 forest NDVI. We then examine whether forests that are governed by each of the three types of collective action have total carbon that is statistically different from the predicted values. For example, carbon above the full-sample prediction might suggest that forests having a particular governance form (after adjusting for the 1990 NDVI) also have more carbon than the overall sample. Using t tests for differences in means, we find that no collective action institutions systematically have excess carbon after controlling for 1990 NDVI. We do find, however, that NCFs for which leaders cannot identify a user group formation year (presumably open access) have significantly less carbon than predicted by the 1990 NDVI. On average, these 28 forests have 52 tons per hectare less carbon than predicted by the regression, which suggests that lack of collective action reduces carbon sequestration.

To complement our OLS estimates of the effects of collective action on our dependent variables, we estimate average treatment effects using propensity score matching based on observables. Though not addressing unobservable factors affecting the probability of treatment (e.g. the existence of a strong leader), matching on observables is a potential way to appropriately construct a counterfactual (i.e. what would have happened to a forest or plot had it not become a CF) and is often used for such observational data (Rosenbaum and Rubin, 1983).

Propensity scores estimate the probability that an observation is in the treated group using a probit model. Explanatory variables in our probit models utilize the environmental and community variables in Table 4 and it is on this basis that CF treated and control NCFs are

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Policy Research Working Paper 8491

# **Abstract**

Preferential trade agreements have become deeper over time, often encompassing policy areas that go beyond traditional trade policy, such as investment, competition, and intellectual property rights protection. In the literature, a prominent argument why countries sign “deep” agreements is to promote and facilitate the operation of global value chains. This paper exploits a new data set on the content of trade agreements and data on trade in value added and in parts and components, to quantify the impact of the depth of trade agreements on bilateral cross-border production linkages. The results show that adding a policy area to a trade agreement increases the domestic value added of intermediates (forward global value chain linkages) and the foreign value added of intermediates (backward global value chain linkages) by 0.48 and 0.38 percent, respectively. At the sectoral level, the positive impact of deep trade agreements is higher for higher value-added industries, suggesting that deep agreements help countries to integrate in industries with higher levels of value added. For a larger sample of countries and years, the results confirm that an additional provision in a trade agreement increases bilateral trade in parts and components by 0.3 percent. The content of trade agreements also matters for global value chain integration, but the impact varies by income group. Provisions outside the current mandate of the World Trade Organization (investment and competition policy) drive the effect of trade agreements on North-South trade in parts and components. Provisions under the current World Trade Organization mandate (tariff reduction and customs facilitation) drive the effect of trade agreements on South-South trade in parts and components.

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/research. The authors may be contacted at nrocha@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
that there was no change, and Milanovic finds an increase of 3 Gini points between 1988 and 1993 followed by a decline of 1 Gini point in the next five years.<sup>26</sup> His most recent (and unpublished) calculations for 2002, show another small increase of about 1 Gini point. Thus, according to Milanovic, there are zig-zags. They are explained, in the early 1990s, by the slow growth of rural incomes in India and China and economic collapse of Eastern Europe both of which contributed to global inequality. When both developments reversed in the next five-year period global inequality decreased. But these are zig-zags caused by specific economic events in large countries, not a trend.

This lack of unanimity on changes, and disagreement on whether there is any trend at all, stem not only from the differences in methodologies but paradoxically also from the very similar results that all authors obtain regarding the overall _level_ of inequality. The reason is as follows. Different methodologies yield similar inequality levels but they do so with quite a lot of noise caused by measurement problems. Mean incomes, whether obtained from surveys or national accounts, are not consistently calculated and key data sources disagree among themselves. The computation of Concept 2 inequality using GDI per capita—a metric on which there is apparently least dispute—from World Bank or Maddison’s data series will differ by several Gini points. This is because, as pointed out by Sutcliffe (2003), Maddison’s data include estimates for a number of war-torn or otherwise “excluded” countries like Sudan, Afghanistan, Somalia, the Congo, Cuba, North Korea etc. that are almost invariably poor and not included in the World Bank database. In addition, Maddison’s growth rates for China are less than the official rates and those used by the World Bank. It is then not surprising that when one superimposes estimates of national distributions on one or another set of GDIs per capita to generate global inequality, the choice of the GDI database will clearly influence the end result.<sup>27</sup>

> 26 World Bank’s 2006 World Development Report uses mean log deviation as the measure of global inequality. It finds that it has decreased between 1994 and 2000  from 0.87 to 0.82 (see World Bank 2005, p.64).

> 27 For example, Concept 2 inequality calculated using World Bank GDI per capita data from 138 countries shows a decrease of some 3 Gini points between 1985 and 2000. The same concept calculated using Maddison’s data over the same period for about 160 countries shows a decline of only 1 Gini point (author’s unpublished calculations).

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The firtst step in iwproving agricutturat support service' projectes fs to develop an understanding ef fenat'ëfarmers,-thoiFr activities cd circumstances, and of the chiracteristics of African households-InfoFmution iss'ræeded"on: ' - househo&d obtlgations by gender since this witt determine the objectives of mon6s and women's - activities' gender rotes in agricutturo, activities. men Ïd women _rMy_ havm comptementa **r** y otes within en activity, or may have separate activities. Gender rotes are _dcnl ic_ and women are becoming - increasingty lnvotved in cash crope production ' - uio has corntrot over resources: the factors of production, the crops and tivestock produced and -'the rincome from sate of produce - types of women farmers. The needs of the women wilt vary depending on whether they are farm managers, partners or laborers Coltect diagnost **i** c nformation by direct questioning and observation of women farmers: - use methodotogies such as: agriculturat catendars, spatiaL maps, seasona **t** abor profites, 'informat surveys, group and community interviews, cornunity interviews, community portraits, household recordkeeping, poticy inventory techniques - for rapid coit effectfve techniques use: rapid rurat survey ceobined with short fietd visits, group interviews with key questions, one-time surveys, and monthly extension meetings Monitor and evatuate suPcort services to women farmers by use of muttipte data cotlection methode which are rapid and cost-effective: - monitoring through gender disaggregation of extension agents' reports: How many men and women contacted? Whose farm/fietds were visited? Who received project benefits? Who adopted and to what extent? - evatuation of the project: Is the project attempting to reach women? Is the project in fact reaching targeted women? Are the targeted women benefitting7 What is the effect of the project on the targeted women?
The timing of a program may, in fact, be used for identification purposes. For example, some programs are implemented in phases, and if the phasing is applied randomly, the random variation in duration can be used for identification purposes in estimating program impact (Rosenzweig and Wolpin 1986 is a seminal article on this point). One example is Mexico’s PROGRESA (Programa de Educación, Salud y Alimentación) which was targeted to the poorest rural communities when it began. The program identified the potential beneficiaries using administrative and census data on measures of poverty (Behrman and Todd 1999). Out of the 506 communities identified for the evaluation sample, about two-thirds were randomly selected to receive the program activities during the first two years of the program starting in mid-1998, while the remaining one-third received the program in the third year, starting in the fall of 2000. The group that received the intervention later has been used as a control group in evaluations of PROGRESA (see, for example, Schultz 2004 or Behrman, Sengupta and Todd 2005).

One way to regard duration effects is that, given constant dosage or intensity of a treatment, lengthening duration of exposure is akin to increasing intensity and thus the likelihood of greater impact. Two cases show that impact is likely to be underestimated if the evaluation coverage is too short. First, skill development programs are an obvious example of the importance of the duration of program exposure because beneficiaries who attend only part of a training course are less likely to benefit from the course and attain the program goals than those who complete the course. In evaluating the impact of a training course attended by students, Rouse and Krueger (2004) distinguish between students who completed the computer instruction offered through the Fast ForWord Program and those who did not. The authors define completion as a function of the amount of training attended and the actual progress of students toward the next stage of the program as reflected in the percentage of exercises at the current 6
# **Table 5: Probit of choosing teaching by college graduates**

||Redu|ced for|m|Struct|ural fo|rm|
|---|---|---|---|---|---|---|
|Independent<br>variable|Marginal<br>effect||Standard<br>error|Marginal<br>effect||Standard<br>error|
|Age|0.002|***|(0.000)||||
|lnWnt_lnWt||||-0.295|***|(0.013)|
|Dmarried*|0.054|***|(0.004)|0.023|***|(0.005)|
|Dfemale*|0.084|***|(0.004)|0.083|***|(0.004)|
|Drural*|0.175|***|(0.006)|0.117|***|(0.006)|
|D11*|0.023|*|(0.013)|0.024|*|(0.013)|
|D12*|0.001||(0.011)|0.000||(0.011)|
|D13*|-0.014||(0.011)|-0.015||(0.011)|
|D14*|-0.014||(0.014)|-0.013||(0.014)|
|D15*|0.046|**|(0.020)|0.047|**|(0.020)|
|D16*|0.008||(0.015)|0.008||(0.015)|
|D17*|0.043|**|(0.018)|0.044|***|(0.018)|
|D18*|0.030|*|(0.017)|0.027|*|(0.017)|
|D19*|0.047|*|(0.029)|0.055|**|(0.030)|
|D21*|0.012||(0.025)|0.011||(0.025)|
|D31*|-0.081|***|(0.006)|-0.075|***|(0.006)|
|D33*|0.094|***|(0.012)|0.094|***|(0.012)|
|D34*|0.000||(0.010)|0.004||(0.010)|
|D35*|0.052|***|(0.009)|0.051|***|(0.009)|
|D36*|0.009||(0.014)|0.004||(0.013)|
|D51*|-0.012||(0.010)|-0.013||(0.010)|
|D52*|0.114|***|(0.018)|0.113|***|(0.018)|
|D53*|0.035|**|(0.017)|0.027|*|(0.017)|
|D61*|0.022||(0.017)|0.024||(0.017)|
|D62*|-0.021||(0.017)|-0.020||(0.017)|
|D63*|-0.013||(0.013)|-0.008||(0.014)|
|D64*|-0.012||(0.014)|-0.012||(0.014)|
|D71*|-0.030|**|(0.013)|-0.033|**|(0.013)|
|D72*|0.020||(0.016)|0.022||(0.016)|
|D73*|-0.018||(0.010)|-0.016||(0.010)|
|D74*|-0.005||(0.014)|-0.002||(0.014)|
|D75*|0.008||<br>(0.022)|0.007||<br>(0.029)|
|D76*|0.016||<br>(0.030)|0.007||<br>(0.022)|
|D81*|-0.006||(0.016)|-0.008||(0.016)|
|D82*|0.013||(0.024)|0.011||(0.023)|
|D91*|-0.039||(0.024)|-0.050|*|(0.022)|
|D94*|-0.049|***|(0.014)|-0.050|***|(0.014)|

23
this component of RTP3 to have large effects on the labor market or income outcomes of participating households.

Finally, RTP3 followed the decentralized structure of the Government of Vietnam. Although the project was administered and supervised centrally, implementation was carried out by provincial and local government bodies. For instance, while the selection criteria for roads was developed centrally, provincial Departments of Transport (DoTs) were responsible for submitting lists of eligible roads. Provincial DoTs, in turn, invited local government bodies to apply for the project before shortlisting roads based on the eligibility criteria.

# 3. Data

As previously mentioned, we relied on a mixed methods analysis to provide evidence on whether men and women benefit equally from rural road improvements, and to explore reasons why they do or do not. Specifically, we used quantitative data to rigorously evaluate the gender-specific impacts of rural road improvements. Then, we collected qualitative data to understand the mechanisms behind these impacts. For the quantitative analysis, we combined administrative data on the rollout of the RTP3 activities with existing household-level data. We describe all three types of data in greater detail below.

## _Household data_

For household data, we turned to the Vietnam Access to Resources Household Survey (VARHS) implemented by UNU-WIDER in collaboration with the Central Institute for Economic Management (CIEM). The VARHS consists of two questionnaires—a commune and a household questionnaire. The former covers information on the commune, recent economic shocks, infrastructure, access to services, and development programs at the commune level. The household questionnaire, which we used in our analysis, includes modules on household members, production and employment, use of inputs, income, and measures of social and political connectedness.

The VARHS was implemented in 2002, 2006, and every two years since 2008 in 12 provinces in Vietnam. From 2008, the survey included a panel component. We used data from the 2008, 2010, 2012, and 2014 rounds of the survey. As per this data, 99.5 percent of RTP3 road improvements occurred between 2009 and 2014 (Table A.1). Having a panel allows us to use household and individual-level fixed effects, which requires less stringent identification assumptions. The panel component of the VARHS is the main reason we chose it over another household-level data set from the same period: the Vietnam Household Living Standards Survey (VHLSS).

The sample in the VARHS was chosen in 2006 to be representative of the population in rural areas in the sampled provinces.<sup>9</sup> In 2008, this sample was expanded by 1,000 households in five additional provinces<sup>10</sup>

> 9 Đắk Lắk, Đắk Nông, Điện Biên, Hà Tây, Khánh Hòa, Lai Châu, Lâm Đồng, Lào Cai, Long An, Nghệ An, Phú Thọ, and Quảng Nam.

> 10 Lào Cai, Điện Biên, Lai Châu, Đắk Lắk, and Đắk Nông.

7
underestimated the current level of inequality.<sup>24</sup> Gimpelson and Treisman (2015) show that ordinary people have little idea about the levels of inequality, its evolution over time, and their place in the income distribution. Individuals consistently arrive to misperceptions of inequality, regardless of the data source, operationalization, and measurement method. In a survey experiment, Cruces et al. (2013) find systematic biases in perceptions of own income rank: a significant portion of relatively poor individuals place themselves in higher positions than they actually occupy. This evidence suggests that the mean of the error term will not necessarily be zero. Thus, if we use the ‘objective inequality,’ instead of the perceived one as explanatory variable in regressions, the measurement error becomes part of the error term in the regression equation, creating an attenuation bias.

In this section, we test whether unfairness perceptions are positively correlated with social unrest, exploiting the fact that both perceptions and stated activism vary at the individual level.<sup>25</sup> We rely on the following question from Latinobarómetro: _“On a scale from 1 to 10 where 1 means “not at all” and 10 “very”, how willing would you be to demonstrate and protest about...? (a) Higher wages and better working conditions; (b) Improvement in health care and education; (c) Exploitation of natural resources; and (d) To defend democratic rights.”_ Figure 9 shows the simple cross-country correlations between unfairness perceptions at the country level and the average index of the different measures of stated activism in 2015. Visual evidence suggests that unfairness measures tend to be positively correlated with the social unrest measures, although in some cases the correlation is small.

_Figure 9. Perceptions of unfairness (very unfair) and stated activism in LA, 2015 (%)_ <!-- Start of picture text --> (a) Better working conditions (b) To defend democratic rights<br>8 8<br>dom dom<br>nic col chl<br>7 bol hnd 7 nic cri<br>bol<br>pan crimexpry pan hnd mex col chl<br>6 ury gtmper bra 6 ury venarggtmperpry<br>arg bra<br>venslv slv<br>5 5<br>ecu<br>ecu<br>4 4<br>40 50 60 70 80 90 100 40 50 60 70 80 90 100<br>Average index<br><!-- End of picture text --> 24 Perhaps more interestingly, individuals constructed ideal distributions that were far more equitable than even their erroneously low estimates of the actual distribution.

25 Most of previous studies linking inequality and conflict are based on cross-country regressions, and therefore have a notably smaller sample size.

25
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