rafmacalaba/data-use-mentions-extended huggingface dataset:
2 their productivity; see French, King and Minami (1985) and Hartley, Nerlove and Peters (1985) for two recent examples.

Econometric equations for replanting and new planting will be derived fros. a neo-classical cost of adjustmen. ivestment model modifled to allow for "vintage effects", viz., differences In the productivity of trees in different age-cohorts. This extends previous analyses. Using available data on new planting and replanting since 1966 and average oroductivity at different ages, and using the concepts described In Akiyama and Trivedi (1987b), it has been possible to separate the potential output attributable to pre-1968 and post-1966 planted area and to construct measures of productivity on pre-1966 area<sup>1</sup> . These measures play an essential role in explaining replanting behavior.

A fundamental difference between new planting and replanting decisions arises **from** differences in the cost of adjustment. Whereas both Involve Installation costs, replanting involves uprooting which results in a (temporary) revenue loss whose magnitude depends upon the current and future expected prices and on the average productivity of the uprooted trees. Consequently high and rising prices and productivity tend to encourage new planting and to discourage replanting.

The broad conclusion of the study is that the growth in Brazilian new planting and replanting was a response to high and rising cocoa prices and to zero or negative real interest rates that prevailed for most of our sample period. That is, the evidence is consistent with the behavior implied by the theoretical neo-classical investment model. However, in contrast to 1 This calculation, sometimes referred to as the vintage matrix method for calculating potential output is described in detail in Akiyama and Trivedi (1987a, 1987b).
propping up the returns to public higher education. Evidence from the recently completed Higher Education Graduate Tracer Study (World Bank 1997) suggests that change is occurring rapidly. For example, more and more university graduates are taking positions in the private sector, in contrast to the situation just three to four years ago, when hardly any graduates did so.

It is important to monitor future earnings and labor market trends. In fact it would be desirable to update these estimates as soon as more recent earnings data are available. The Higher Education Graduate Tracer Study conducted in 1996-97 suggests that the labor market is changing rapidly. Future updates of this analysis, based on more recent data, could not only provide more robust estimates, but also provide evidence on whether the impact of labor market reforms is increasing over time, as suggested in this paper. The Vietnamese Government is currently reassessing pricing policy. It has already begun to increase cost recovery at the higher education level. Tuition fees were introduced in 1993 and the current policy is to increase cost recovery, which is likely to reduce the gap between social and private rates of return. If costs could be reduced-for example by better utilization of teachers and by reducing unit costs in higher education through encouraging mergers to achieve economies of scale-this could reduce costs and therefore increase the rate of return.

24
# **Figure 2: Trends in Sectoral Shares, for Employment and Value-Added, 1990–2018**

# **Sectoral share of employment**

# **b. Subsectors within industry**

# **a. Broad sectors**

<!-- Start of picture text --> 60 20<br>50 15<br>40<br>10<br>30<br>5<br>20<br>10 0<br>1990 1994 1998 2002 2006 2010 2014 2018<br>0 Year<br>1990 1994 1998 2002 2006 2010 2014 2018 Industry Manufacturing Mining Public Utility Construction<br>Year<br>Agriculture Industry Services<br>Broad sectors d. Subsectors within industry<br>40<br>50<br>40 30<br>30 20<br>20 10<br>10 0<br>1990 1994 1998 2002 2006 2010 2014 2018<br>0 Year<br>1990 1994 1998 2002 2006 2010 2014 2018 Industry Manufacturing Mining Public Utility Construction<br>Year<br>Agriculture Industry Services<br>Share (percent)<br>Share (percent)<br>Share (percent)<br>Share (perent)<br><!-- End of picture text -->

# **Sectoral share of value-added**

# **c. Broad sectors**

_Source_ : Authors’ estimates based on statistics for Ghana from the Economic Transformation Database.

_Note_ : Agriculture includes forestry and fisheries; industry includes mining, manufacturing, public utilities, and construction; and services include wholesale and retail trade, transportation and storage, financial and real estate activities, government services, and private services. Almost 95 percent of industrial employment is in mining and manufacturing.

30
optional module based on the WGSS. Some countries have changed questions in the module (Casebolt 2020).

The resulting pool included 734 data sets and 1,297 data set-years and censuses from 133 countries in East Asia/Pacific (22 countries), Europe & Central Asia (23 countries), Latin America & Caribbean (24 countries), Middle East and North Africa (12 countries), South Asia (8 countries), and Sub-Saharan Africa (44 countries).<sup>2</sup> Each of the 1,297 data set-years was screened for disability questions. The full list is in Appendix 1. We conducted a systematic assessment of the survey questionnaires to figure out which ones included any of the disability questions described in Section 1. Each questionnaire was searched looking for several disability-related words noted below in italics: _disability, impairment ( blind, deaf, dumb, mental, physical), limited in usual activit_ (y/ies _), limited in the amount of work or type of work or activities related to work_ , ADLs ( _walking, dressing, bathing, eating, walking, toileting, urination and defecations) and questions related to limitations in IADLs (housework, shopping, cooking, managing money, and taking medicine, phone_ ), _difficulty in seeing, hearing, walking, concentrating, self-care, communicating_ .

> 2 The data sets that were reviewed are listed in this document.

9
These laws redistributed revenue within states according to an algorithm handed down by the federal government, and thus exogenous to any one municipality’s investment decisions. The rules changed slightly each year, but always identify two things: how much each municipality has to pay into its state’s education fund (always a set fraction of its revenue), and how much of the fund is given to each municipality (equal to its share of total public school students in the state, with students at different grade levels weighted differently each year). Given enrollment and initial revenue data for all municipalities, the algorithm delivers a “new” revenue to each municipality, which is lower for net losers and higher for net winners from the reform.

Of course, the reforms almost certainly induced endogenous revenue and enrollment responses (Hoxby 2001; Gordon 2004). A good instrument should encapsulate the credibly exogenous variation in revenue generated by the law, but _exclude_ variation due to municipalities’ own actions. To do this, I simulate the revenue each municipality would have each year if the current year algorithm were applied to 1997 (pre-reform) enrollment and revenue data. I then instrument for actual (endogenous) revenue with the simulated instrument, which allows me to test how different municipalities differentially spend an exogenous shock to revenue, all else equal. I instrument for the interaction of per capita revenue with covariates using the interaction of simulated per capita revenue with those covariates.<sup>31</sup> See Appendix D for further details.

The first stage equations state that per capita revenue and its interactions with per capita income and inequality are a function of simulated per capita revenue, _sit_ and its interactions with per capita income and inequality:

> 31In a similar vein, Mart ́ınez-Fritscher _et al._ (2010) use commodity price shocks in Brazil during 1889-1930 as an instrument for state capacity to spend on publicly-provided goods. They find that education expenditures increased during this period in response to positive shocks to the prices of commodities produced
1999, community-led WASH programs have been implemented in at least 60 countries, and 15 countries have incorporated them into national policy [11,12].

Despite this broad adoption, the health effects of community-led WASH interventions – and of many WASH interventions in general – remain poorly understood. The accumulation of evidence has accelerated in recent years, but the length of the causal chain from the intervention to the outcome, and the vast design space for WASH interventions (which can incorporate behavior change campaigns, infrastructure, institutions, and/or new technologies), means that many fundamental questions remain unanswered. A meta-analysis of 13 randomized WASH trials found no effect on child length-for-age, but a meta-analysis of 124 WASH studies (randomized and observational) found a protective effect against diarrhea [11,13–24]. There is a great deal of heterogeneity in effect size across studies, likely due to variation in intervention components and intensity, and to the influence of contextual factors such as baseline exposure to fecal matter.

To our knowledge, this is the first trial of an intervention that combines the creation of new institutions, funding for new or improved infrastructure, and a behavior change campaign, all within a locally-led process of targeting and implementation. We study this complex intervention as it is implemented at scale, providing a realistic estimate of effectiveness for policy makers in similar contexts. Our follow-up period is unusually long (3.6 years), enabling us to address the question of sustainability. We also provide evidence in a conflict-affected setting, where WASH interventions have rarely been evaluated using experimental designs.
# **Fixed capital**

The data series on fixed capital is based upon national accounts data on fixed investment, which includes structures, equipment, machinery, and the like. The mapping from fixed investment to fixed capital follows the methodology of Ball et al. (1993). The capital stock is represented as a weighted sum of past investments where the sequence of relative efficiencies of capital of different ages serves as the weights. The following function of physical depreciation is chosen to describe the relationship between the efficiency of an asset and its age. Let _L_ be the lifetime of the capital good, and  be a curvature parameter bounded from above by 1 in order to restrict productivity to be nonnegative. Then, The asset is discarded at age _L_ , at which time its relative productivity becomes zero. To analyze this expression, we note that _dsj/dj = L(_  _- 1)/(L -_  _j)_<sup>_2_</sup> _< 0_ , for _0_  _j < L_ , indicating that the productivity falls with age (use). The speed of the change in the depreciation with age depends on the sign of curvature parameter,  : When  is positive but less than unity _d_<sup>_2_</sup> _sj/dj_<sup>_2_</sup> _< 0_ , the physical depreciation accelerates with time (use), and the productivity curve is concave. Conversely, when  is negative, the productivity curve is convex. The lifetime of the asset is taken as a random variable with a normal distribution truncated at two standard deviations on both sides. Figure 1 illustrates the dependence of the productivity paths of buildings and agricultural machinery on the parameters in question.<sup>9</sup> The data sources that we use do not provide any information on the separate components of fixed capital, and so, we choose a single set of parameters.<sup>10</sup
<!-- Start of picture text --> Slope = -0.02 [s.d. = 0.11] Slope = 0.19 [s.d. = 0.11]<br>-3 -2 -1 0 1 2 -2 -1 0 1 2<br>Integrity Index - Average Bureaucrats Risk Aversion - Average Bureaucrats<br>(a) Integrity Index (b) Risk Aversion<br>Slope = 0.11 [s.d. = 0.11] Slope = -0.06 [s.d. = 0.11]<br>-4 -2 0 2 4 -1 0 1 2 3<br>Cognitive index - Average Bureaucrats Choose dominated risk option - Average Bureaucrats<br>(c) Cognitive Index (d) Dominated Risk Option<br>Slope = 0.25 [s.d. = 0.11] Slope = 0.06 [s.d. = 0.11]<br>-2 -1 0 1 2 3 -3 -2 -1 0 1 2<br>Altruism - Average Bureaucrats Non-Cognitive Index - Average Bureaucrats<br>(e) Altruism (f) Overall Non-Cognitive Index<br>Slope = -0.01 [s.d. = 0.11] Slope = -0.23 [s.d. = 0.11]<br>-2 -1 0 1 2 -2 -1 0 1 2 3<br>Big Five Index - Average Bureaucrats PSM Index - Average Bureaucrats<br>(g) Big Five Personality index (h) PSM index<br>2 2<br>1 1<br>0 0<br>-1 -1<br>-2 -2<br>-3 -3<br>2 2<br>1 1<br>0 0<br>-1 -1
Figure A12: Share of Informal Consumption for Food and Non Food Goods <!-- Start of picture text --> (a) Informal Food (b) Informal Non-Food<br>BI CD BF BJ<br>NE TD RWTZ SNKMCMBO CG PE<br>MZ ST EC BI CD<br>MA DO<br>PG NE BJ<br>BF<br>RS TD TZ<br>CL RW ST SN<br>TN MX<br>CO KMCM PG<br>ME CG<br>SZ<br>MZ BO TN<br>UY<br>CRBR<br>MA<br>ZA SZ DOECCORSPE ME CR MX BR<br>CL<br>ZA UY<br>5 6 7 8 9 10 5 6 7 8 9 10<br>Log GDP per capita, Constant 2010 USD Log GDP per capita, Constant 2010 USD<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>Share of Food Consumption in Inf. Sector<br>0 0<br>Share of Non Food Consumption in Inf. Sector<br><!-- End of picture text --> Panel (a) shows the share of food consumption which occurs in the informal sector. Panel (b) shows the share of non food consumption which occurs in the informal sector. For each good, this is constructed by taking total informal consumption of the good and dividing it by the total consumption of that good.

63
UNCTAD on export volumes. Years are calendar years. The global volume index is calculated as the weighted average of countries’ volume indices with weights equivalent to countries’ (value-based) share in global merchandise exports.

- India’s export basket is broad based. The share of services exports in 2016 was approximately 36 percent of total exports, core merchandise exports (i.e. non-oil non-gold exports) accounted for about half of all exports, oil exports accounted for 10 percent, and gold exports accounted for 4 percent. Exports slowdown is pervasive across merchandise and services exports.

# **Figure 26: Exports of Goods and Services**

> 45 The figures present the decline from peak prices in June, March and July 2014 for oil, agriculture and metals, respectively, to the trough in January 2016, and are drawn from the World Bank’s Global Economic Monitor database.

> 46 We use trade data measured in current USD and focus on merchandise trade for data availability reasons. For a globally heterogeneous export basket, using national deflators (either from the US or India) is unlikely to yield credible estimates of constant export values. Hence, we rely on volume indices to decompose trade into volumes and prices.

43
the norm, and registration of IDPs is undertaken in most cases by national or local authorities,<sup>68</sup> civil society groups or by individual organizations, particularly UNHCR and IOM, although the World Food Programme (WFP) might keep records of individuals and communities provided with food assistance.

Registration data for IDPs present an incomplete or misleading picture. While registration of IDPs can be helpful in estimating the numbers of IDPs in camp and camp-like settings, such data presents an incomplete picture since in many contexts the majority of IDPs reside in dispersed settings where they are not registered or counted (UNSD 2014). IDPs may deliberately avoid registration activities to avoid drawing attention to themselves due to security concerns and reluctance to provide personal information (especially when the authorities are perceived as contributing to the causes of displacement) or because they are not motivated to register (e.g. registration does not confer a special legal status, no assistance is given or assistance is given in a discriminatory manner) (Brookings 2011). Additionally, IDPs might not be able to physically reach the registration location because of insecurity, medical reasons, limited mobility, distance and cost. When national or international actors provide assistance, there may be an incentive for people to register in camps even if they are staying elsewhere, or to register in multiple locations (Brookings 2011).<sup>69</sup> Furthermore, registration data provide only a snapshot of the stock of IDPs at a particular point in time and may be out of date if registers are not maintained regularly.

Registration methodologies can vary across displacement situations. For example, families may be registered rather than individuals and the population estimated based on an assumption of average family size, which can differ among organizations (UNSD 2014). IDPs may be required to present documentation, meet specific criteria or re-register periodically to maintain their benefits, which affects aggregate numbers (IDMC 2015). For example, in Ukraine, pre-requisites for IDP registration (including valid documentation,
EAs were replacement EAs. Replacements were done in three batches where each time new enumeration areas had to be drawn from the master sample frame. The replacement sequence was defined by assigning enumeration areas randomly to the original enumeration areas, maintaining the order of the original enumeration areas as in the original sample. The large number of replacements was concerning given fear of selection bias. Therefore, the team ran checks to ensure that the set of EAs surveyed do not systematically differ from a random sample as best as it could. It is important to keep in mind that assessing representativeness is a difficult task, generally due to the lack of a counterfactual or a point of reference to compare estimates. Despite these checks, it is plausible that selection bias in favor of less conflict‐affected areas leads to an under‐estimation of poverty. The resulting estimates are therefore interpreted as lower‐bound estimates.

The checks are based on comparisons of Wave 3 data from 2016 with the nearest available reference point, Wave 1 data from 2015. Specific outcomes were compared across the two waves as well as at lower levels of aggregation and within specific regions (Table 8 in Appendix E shows an example). This process was severely complicated by the magnitude of the South Sudanese crisis. The conflict, displacement crisis, and near‐hyperinflationary price increases are powerful shocks, which are expected to cause severe disruption even in a relatively short amount of time.<sup>16</sup> The checks therefore concentrated on outcomes that are less likely to be affected by the crises and are relatively time‐ invariant.

Adults’ educational outcomes is one such indicator which is expected to remain relatively stable from one year to the next assuming only small demographic changes. In South Sudan, the adult literacy rate (18+), the proportion of adults with no education, and the proportion of adults with only primary education were comparable between 2015 and 2016 (Table 8). Similarly, cultural norms should be expected to remain stable, such as the
**Figure 7: Government financing per participant across health security schemes introduced during 1950‐ 2007**

<!-- Start of picture text --> 3000<br>Per capita<br>government Employees for Public Administrative<br>Units and Organs (10 mil) 2629<br>2500 funding (RMB)<br>2000<br>1500 Urban Medical<br>Financial<br>Assistance Family Members of<br>Employees of Public<br>1000 Sector Services Units and (4.44 mil) 797 Revolutionary Martyrs<br>and Ex‐Servicemen<br>Organs (39 mil) 533<br>(4.68 mil) 510<br>500 Rural Medical<br>Financial Assistance<br>RCMS<br>(7.6 mil) 396<br>(815 mil)<br>0 78 Urban Residents'<br>Basic Medical<br>Public Health Security<br>Insurance (199<br>for Civil Servants<br>‐500 mil) 40<br>(Gongfeiyiliiao)<br>year of<br>1950s 1990s 2002 2003 2007<br>launch<br><!-- End of picture text --> Note: Bubble size is equivalent to the number of participants. Number of participants is shown in parentheses. Government spending per participant is shown in red. Government funding figures are annual per person, except for the rural and urban medical financial assistance, reported per case.

Source: National Health Account Report 2009 and China Health Statistical Digest 2010 19
slightly more than previously thought. Moreover, 44 percent of the extreme poor are children under 15, and households with three or more children comprise nearly 60 percent of the extreme poor. The prevalence of child poverty raises the prospect of long-term consequences on the physical and intellectual development of poor children, which could in turn impede their future earning capacity. The gender gap in poverty is not apparent in traditional poverty measures, because poverty is measured based on household per capita welfare, whereas gender disparities are most apparent in individual-level indicators such as education, decision making power and labor market outcomes. Male headed households, however, are disproportionately likely to be poor, at least in the lower middle-income countries that contain the majority of the extreme poor. In general, these findings emphasize the potential benefits of programs that directly or indirectly support children, large households, and rural households, as well as indirect measures to reduce the future incidence of child poverty.

Shifting out of low productivity agricultural work, while important, is not sufficient to escape poverty. Consistent with the clustering of the poor in rural areas, poor workers are much more likely than non-poor workers to make their living from agricultural work. Yet a substantial proportion of the extreme and moderate poor who live in rural areas – 24 and 40 percent, respectively – work outside the agricultural sector. Within the scope of this study, it is unclear why

> 34 Multiple imputation methods, which can address problems created by non-random patterns of missing survey data, can be explored in the future (Rubin, 2004).

30
|+|-|- -|+/-|+/-|+ +|(6)|(7)|
|Croatia|+/-|.||+|+|-|-|+|(6)|.|
|Poland|-|-|+|-|- -|-|+/-|+ +|(8)|(4)|
|Czech Republic|-|+/-|+|- -|- -|-|+/-|+ +|(8)|(2)|
|Romania|+|-|+|+|+ +|+/-|- -|- -|(10)|(10)|
|Bulgaria|-|-|+|- -|- - -|+/-|- -|- -|(11)|(11)|

Note: Overall tax level: measured by tax revenue as a percent of GDP. Labor tax wedge: measured various indicators such as .... Other tax policies: family taxation, in-work benefits, simplified tax regimes etc..Tax administration: measured by the time spent by a typical firm for tax compliance. General efficiency of institutions: measured by the Control of Corruption index of the World Bank. Size of informal work: measured by European Employment Observatory EU Commission (+ +) means that the size of the informal sector is significantly below, and (+) means just below the average of the country group, (0) means that it is around average. (-) means that it is above and (- -) that it is much average the country average.

50
in the LFS Bulletins from 1977 to 2007, we use a somewhat unconventional definition of the FLFP rate, which we are able to reproduce throughout the period. The numerator of the rate is the economically active female population aged 12 to 64 and the denominator is the manpower basis, which is the population age 6 to 64, excluding the permanently disabled, plus the employed population 65 and older. The indicator is calculated in the same way from 2008 to 2021 using the LFS microdata and for the special round of the LFS in October 1988 and the various waves of the ELMPS.

3