rafmacalaba/datause-encoder-data huggingface dataset:
output in the services sector is intrinsically difficult, as pointed out by Griliches (1992) and Triplett and Bosworth (2004). Given the heterogeneity in output produced by each sub-sector, the common practice is to use value measures of output. Crespi et al. (2006) argue that the value of output of a sub-sector is the value of the bundle of intermediation services provided, which is well captured by real value added when the basis of the service is the change in condition of a consuming unit, such as in retail trade, but may be less well captured by real value added in sub-sectors such as legal, technical, and advertising activities (e.g., management consulting). Nevertheless, we use real value added to measure services output, as it is the only measure with wide cross-country and time coverage for the ECA region.<sup>9</sup> Labor productivity is obtained as the ratio of real value added to total employment in each services sub-sector.<sup>10</sup> In order to perform cross-country comparisons of labor productivity within the ECA region, we need to convert a sub-sector’s nominal value added at domestic prices in domestic currency into real value added at common prices in a common currency. Average exchange rates are inappropriate for this purpose since they reflect capital movements, monetary policies, and speculation, and do not adjust for differences in relative prices across countries. The appropriate procedure is to use purchasing power parity (PPP) conversion rates in order to express real value added in services sub-sectors across countries at a common set of prices in a common currency. Given the different information that the WIIW and the KLEMS databases include, we use two different types of PPP conversion rates and thus compute two different measures of real value added and of labor productivity for each services sub-sector and year in each country.

The WIIW database does not include output price deflators, therefore we transform each services sub-sector’s nominal gross value added in domestic currency into real value added in PPP USD using country-level GDP deflators and 2001 country-level expenditure-based PPPs, applying the formula shown in Appendix B. Country-level expenditure-based PPPs convert domestic currency into PPP USD by equalizing the purchasing power of different currencies, thus eliminating the differences in price levels

> 9 The use of value added instead of gross output is preferable for our analysis given the large number of countries covered, which may differ substantially in their ratios of intermediate inputs to gross output. 10 Data on hours worked would be preferable to data on employment. However, that data is not available for ECA countries.

12
3 but consumption increases without bound. Wealth on this path is greater than under the standard Hartwick Rule, and maximum wealth is independent of the initial resource stock _S_ 0 .

_Proof:_ See Annex I.

Having established the properties of this specific sustainability rule for the Dasgupta-Heal economy, we now turn to the empirical application of the rule to historical data.

# **Hypothetical Estimates of Capital Stocks**

While the foregoing theory can be shown to apply to rules for _saving_ in an open economy, we will limit ourselves to _investment_ rules in the empirical application. All of the countries which are highlighted in the empirical results had significant net foreign debts in 2000. Rather than looking at the more complex question of whether resource rents could have been used to either pay down foreign debt or invest in domestic assets, we limit ourselves to comparing an estimate of the current stock of produced capital with a hypothetical estimate of how large this stock could be if resource rents had been invested in produced capital. We assume that all resource rents are invested in produced capital for simplicity, although the theory suggests more generally that resource rents could be invested in a range of assets, including human capital – if any of the countries highlighted below had in fact been investing their resource rents in human capital (quite unlikely given the observed levels of per capita income) then our methodology would produce a biased picture of their investment performance.

In order to examine a variety of counterfactuals, we derive four estimates of produced capital stock using empirical data covering 1970-2000: (i) a baseline capital stock derived from investment series and a Perpetual Inventory Model (PIM); (ii) a capital stock derived from strict application of the standard Hartwick Rule; (iii) a capital stock derived from the constant net or genuine investment rule; and (iv) a capital stock derived from the maximum of observed net investment and the investment required under the constant genuine investment rule. All investment and resource rent series are measured in constant 1995 US dollars at nominal exchange rates.

Details of the PIM are given in Hamilton (2002). For each country the estimate of baseline capital stock is given by, Here _I_ is gross investment, the average asset service life _T_ is assumed to be 20 years and the depreciation rate γis 5% – these are held constant across countries and over time. We use the year 2000 as our basis for comparison of capital stocks.

For genuine investment _I_<sup>_G_</sup> , net investment _N,_ depreciation of produced capital _D_ and resource depletion _R_ we have the following basic identities at any point in time: _I_<sup>_G_</sup> ≡ _I_ − _D_ − _R_
# **Finding Missing Markets (and a Disturbing Epilogue): Evidence from an Export Crop Adoption and Marketing Intervention in Kenya**

Nava Ashraf Xavier Giné Dean Karlan Harvard Business School The World Bank Yale University, Jameel Poverty Action Lab Innovations for Poverty Action, and Jameel Poverty Action Lab

## JEL Codes: O12, Q17, F13. Keywords: Field Experiment, Export Crop, Food Safety Standards

We wish to thank Jonathan Campaigne, Vince Groh and Zack Lenawamuro for their work at DrumNet, and their patience and collaboration with this research. We also would like to thank IDS for the data collection efforts and IDRC, SAGA, and the World Bank for funding. Richard Akresh, Steve Boucher, Paul Dower Steve Jaffee, Doug Miller and Julius Okello provided valuable comments. Sara Nadel from Innovations for Poverty Action, Guillem Roig and Paola de Baldomero provided excellent research assistance. Karlan thanks the National Science Foundation for support. All errors are our own.
# **3 A theory of coercion and rent sharing**

To guide our empirical analysis, we outline a bargaining model of unionized international oligopoly, drawing on McDonald and Solow (1981) and Brander and Spencer (1988).<sup>11</sup> We use the model to examine the interplay between coercive institutions and collective bargaining in determining labor market outcomes in the natural resource industry. In addition, we examine if and how international price shocks, driven by exogenous shocks to global demand or foreign marginal costs, might affect domestic unionized wages in the context of this framework.

## **3.1 Model setup**

Consider a natural resource industry comprising _n_ domestic producers and _n_<sup>_∗_</sup> foreign firms, each supplying a homogeneous commodity to a globally integrated product market. The industry of interest is small relative to the economy as a whole. There are unspecified barriers facing new firms, and hence product market rents are not eroded by entry. The mode of product market competition is Cournot.

The inverse demand function in the industry is given by where _a, b >_ 0, _Y_ denotes total sales by home and foreign firms, _y_ is the output of the representative domestic firm and _y_<sup>_∗_</sup> is the output of the representative foreign firm.

Labor is the only variable factor of production. The marginal product of labor is constant, and is normalized to unity so that we can discuss output and employment interchangeably. Producers must also incur a fixed cost associated with natural resource extraction.<sup>12</sup> Domestic producers operate in a unionized labor market, while the foreign producers can recruit workers from a competitive labor market. We assume that the labor union cares both about wages and employment levels, and adopt a Stone-Geary utility function to represent union preferences: where _w_ is the negotiated wage; _<u>w</u>_ is the reservation wage of unionized workers, defined as what they would earn elsewhere in the labor market; and _ny_ is the level of employment

> 11There is a vast theoretical literature on unionized oligopoly, including contributions by Davidson (1988), Dowrick (1989), Mezzetti and Dinopoulos (1991), Naylor (1999), Lommerud et al. (2003), Lommerud et al. (2006a,b), Bastos and Kreickemeier (2009) and Bastos et al. (2009).

> 12This assumption approximates the short-run conditions of an industry operating with excess capacity of a fixed factor of production (Dowrick, 1989).

10
The second term, ∑ <u>,2007</u> U , can be interpreted as tradable job ~~�~~ ii )� llcc ii ii TT ii∈TT llcc,2007 × (∆logllcc −∆logll UU losses driven by county-specific issues. It is the difference between actual tradable job losses and those that are driven by aggregate shocks.

Using Bartik job losses as an exogenous source of variation in tradable job losses, we can rewrite the regression as follows: Note that all of the regressions are weighted by the number of households in each county (following Mian and Sufi, 2014). In addition, all of the standard errors in this paper are robust and clustered at the state level.

10
educational data and the resulting choice of measure for inequality in achievement. It also utilizes information on student background more comprehensively than all previous studies we are aware of, and is additively decomposable both across circumstances and population subgroups. The measure is also isomorphic to (inverse) measures of educational mobility.

We report our measures of inequality in educational achievement and opportunity for the 57 countries that took part in the PISA 2006 exercise. Each measure was computed separately for each of the three tests applied by PISA: mathematics, reading and science. But there was a good measure of agreement between their rankings, and we often refer only to the math results in the text.<sup>3</sup> We find considerable variation in the standard deviation of test scores, from lows of around 80 (for Indonesia, Estonia and Finland) to highs near 110 (in Belgium and Israel).<sup>4</sup> Similarly stark variation exists in our measure of inequality of opportunity, from 0.10 – 0.15 for Macau (China), Australia, and Hong Kong SAR, China, up to 0.33 – 0.35 in Bulgaria, France and Germany. Inequality of opportunity is uncorrelated with mean achievement and only weakly (negatively) correlated with GDP per capita. Broadly speaking, it is higher in continental Europe (except for Italy) and Latin America than in Asia and Scandinavia, with the US and the UK in intermediate positions. It is negatively correlated with the share of public educational spending that accrues to primary schools, and positively correlated with the proportion of technical and vocational enrollment at the secondary level (a measure of “educational tracking”).

The paper is organized as follows. Section 2 describes the data sets we use. Section 3 considers the implications of test score standardization and of the PISA sampling frame for the measurement of inequality in educational achievement, and reports the standard deviation in test scores for our sample of countries. Section 4 proposes our measure of inequality of educational opportunity (IOp), discusses some of its properties, and presents results. Section 5 applies the proposed measures by examining how they correlate with two educational policy indicators across countries. Section 6 concludes.

# **2. Data**

Two broad kinds of data are used for the analysis in this paper. The first is the complete set of PISA surveys, for all 57 countries that participated in the 2006 round. The second is a group of four household surveys, for Brazil, Indonesia, Mexico and Turkey, which are used as ancillary surveys in the two-sample non-parametric sample selection correction procedures described in Section 3. We briefly describe each of these in turn.

> 3 See Micklewright and Schnepf (2007) for a careful comparison of rankings from each of the PISA tests, as well as from TIMSS and PIRLS.

> 4 But the low variance for Indonesia is a good example of the sensitivity of these measures to assumptions made about the nature of selection into the test-taking sample. Under our scenario of “extreme” selection on unobservables, the variance of math scores for Indonesia triples. See below.

4
23 Table 17 summarizes the welfare effects of removing the HUS inferred from the “HUS as a demand shifter approach” and using the OLS estimates of the second column in table A.1 in the Appendix. The average loss of consumer surplus is 2,163 UAH (2014 UAH), or about 7.20% of net income, and is thus a bit larger than that computed in section 5.B.<sup>25</sup> The elasticity of gas demand with respect to the HUS implies that removing the HUS entirely would result in an 8% reduction in consumption. The CS loss (or the gains from the HUS) increases rapidly as we move from one consumption quartile to the next within a given income quartile.

Using the same approach, we compute that if the HUS were halved, the average CS loss would be 254.58 UAH per household per year, which is modest when compared with the CS loss from the full removal of the HUS and accounts for less than 1% of net income. Even among the poorest households, the CS loss is only around 1% of income (table 18). That the losses are modest follows from the fact that the elasticity of gas consumption with respect to the HUS is only about 0.08. The 50% change in the HUS would only shift demand by 4%, resulting in a small change in the CS. In absolute terms, the CS loss doubles as we move from the lightest to the heaviest consumers within each income quartile.

# _5.D. Alternate HUS Designs_

What would happen if the HUS were replaced by an assistance system that is more targeted towards the poorest families and is decoupled from consumption? Suppose for example households below the poverty line received a subsidy equal to the difference between the poverty

> 25 The unconditional mean loss of consumer surplus for the entire sample, inclusive of households who experience zero loss because they do not receive HUS assistance, is 1,384.51 UAH (2014 UAH). The average share of net income is 4.60%.
# **1. Introduction**

Childhood orphanhood is considered a major risk factor for poverty in adulthood, through, among other channels, shortfalls in human capital investments in children. This paper provides unique evidence on the long-term impact of orphanhood in a region of Tanzania near Lake Victoria, an area ravaged by HIV/AIDS. The underlying data set is a 13-year panel data set in which individuals interviewed at baseline were traced irrespective of their current residence. This allows us to focus on non-orphaned children experiencing the loss of one or both parents during the survey period, controlling for their characteristics before becoming an orphan. Furthermore, we can focus on the permanent impact in terms of height and educational attainment once these children reach adulthood and recovery is hardly possible anymore. Using within-sample estimates of the returns to height and education, we can also estimate the resulting life-time welfare loss.

We find significant permanent education and health effects. Adults who had been maternally orphaned between the ages of 7 and 15 experience a loss of, on average, nearly 2 cm of final attained height and one year of schooling. In contrast, paternal orphans have significantly lower height and years of schooling but our analysis shows that a causal link does not seem to exist. Our projections suggest that maternal orphanhood creates a life long deficit in consumption expenditure of roughly 8.5 percent.

In Sub-Saharan Africa, the prevalence of orphanhood among children has been greatly exacerbated by the HIV/AIDS pandemic. While there are other, more prevalent diseases in Africa, the characteristics of HIV/AIDS suggest that its economic and demographic impact will be profound. Because HIV in Africa is transmitted primarily through heterosexual contact, the epidemic is having a major effect on the mortality of men and women in their prime childbearing and earning years. With rising mortality rates and decreasing adult life expectancies, orphanhood rates in Africa continue to increase, placing increasing numbers of children at risk.

Orphanhood is expected to influence health outcomes and schooling although there are multiple potential pathways of this effect. Obviously, income effects are a strong candidate, especially when parental deaths are associated with costs and income losses due to chronic illnesses. If households are credit constrained, then reduced incomes can result in lower investments in education. Aside from these direct wealth effects, orphanhood can be associated with an increased value of the child’s time in home production (as a substitute for adult labor) which results in less schooling. There may be discrimination against orphans and favoritism towards biological children for double orphans or among single orphans who do not reside with their surviving parent (i.e. are fostered out). Beyond the financial consequences of adult deaths and the implications of a loss of parental involvement, children who become orphans may 1
**Table 9. Marginal contribution of belonging to the experimental group,** 1994.

||**Group**|**Mean of dependent**<br>**variable**|<br>**Unit**|**Estimated coefficient**<br>**Exnerimental**|**Marginal contriibtion:**<br>**Exneximental**|
|---|---|---|---|---|---|
|Table 6|Rural|10.778|Gain in scores|4.043|0.375|
||Native|11.356|Gain in scores|9.998|0.880|
|Table 7|Rural|10.778|Gain in scores|3.644|0.338|
||Native|11.356|Gaininscores|10.259|0.903|
|Table 8.|Rural|0.494|Probability|0.495|0.190|
||Native|0.493|Probability|1.272|0.450|
|**Table 8a**|Rural|0.670|Probability|0.271|0.070|
||Native|0.640|Probability|0.115|0.030|

Source: Own calculations.

Note: For Tables 6 and 7, the percentage gained to the mean. For Table 8, the percentage gained to the initial probability of success, estimated at the means of the independent variables.

_Aside from increasing the student's cognitive achievements while at school the PARE program also increases the probability that the student will continue in school._ The two outcomes are probably linked: children who perform better are more motivated to continue and their parents may be more inclined to allow them to continue in school. This is clearly the case for rural students, as shown in Table 10. The probability of school desertion is 20% lower amongst students supported by the program, and the effect is just as large for the broader group of students who benefited from only a partial application of the program. Surprisingly, however, the result does not seem to hold for the native population. One-third of the native students who received the full program from 4th grade onward abandoned the _school before completing_ the 6h grade. Their probability of desertion was 12% _greater_ than that of the comparable control group.

**Table 10. Desertion.**

**Percentage of students who quit school by the end of the 6th grade,** 1994.

||**Complete Pro**|**aram***|**Partial oro**|**cram**|
|---|---|---|---|---|
||**Native**|**Rural**|**Native**|**Rural**|
|Experimental|32.9%|28.4%|36.2%|31.1%|
|Control|29.4%|35.7%|36.0%|38.5%|
|Difference|11.7%|-20.5%|0.7%|-19.2%|
|N|698|809|841|1,006|

Source: Own calculations based on PARE's database.

* Students in school that received all PARE components simultaneously.

This result deserves more analysis. An intriguing possibility is that high-achieving students in native communities move to rural schools where they are immersed in a Spanish-speaking environment. On the other hand, a multivariate analysis (controlling for "<sup>4</sup> supply" and "demand" variables) of the probability that the student was in school in the 6t grade (given that she had been at school in the 4t grade) indicates that the program had a positive impact on both rural _and_ native schools, see Table 8a. The percentage change 13
Figure 2: Estimates of Poverty Evolution in Indonesia during<sup>the Crisis</sup> <!-- Start of picture text --> 300<br>264<br>250 24S 248<br>234 S<br>_M ",218<br>5"<br>200 199<br>188<br>1MYV<br>172<br>148 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~153m<br>} _ lso tr / ~~~~~~~~~~~~~~~~~~~ 149M M<br>115/<br>100C X<br>Data Sources:<br>so = Susenas Consumption Modulb<br>C =Sussas Core<br>M = Mini Susenas<br>V 100 Village SurVey<br>I Indonesian Family Ufe Survey<br>0<br>Feb-96 May-96 Aug-96 Nov-96 Feb-97 May-97 Aug-97 Nov-97 Feb-98 May-98 Aug-98 Nov-98 Feb-99 May-99 Aug-99 NOV-99<br><!-- End of picture text -->

> Fourth, after the peak point, the poverty rate has started to decline<sup>again.</sup>

Nevertheless, by August 1999, two years after the crisis started,<sup>the poverty rate</sup> was still substantially higher, i.e. around 50 percent, than<sup>its pre-crisis level.</sup> If we allow a line to _not_ pass through the anomalous point in December<sup>1998,</sup> this series of poverty estimates paints a very reasonable picture,<sup>around which the</sup> data show a striking consensus, as it neatly tracks known<sup>events (e.g. inflation</sup> stabilization, rice prices). This still leaves a puzzle as to the<sup>timing of the decline</sup> from the post crisis peak.

28
|**Model**|**Time Dimension**<br>**Role of Water in the Model**|**International Trade**|**Contribution to literature**|**Main Findings**|
|---|---|---|---|---|
|**Roe****_et. al._, 2005**|CGE is static, Farm level is<br>intertemporal<br>Production factor|Small open economy|Takes macro-micro linkages into<br>account. Combines a farm model<br>with a CGE model|trade reform (top-bottmo or macro to<br>micro linkage) has a higher effect<br>compared to water reform(bottom-up<br>or micro to macro linkage). They<br>also conclude that the sequencing of<br>reforms<br>is<br>also<br>important<br>in<br>determiningthe final effect.|
|**Diao****_et. al.,_2005**|Static<br>Production factor|Small open economy|Frist to analyze groundwater in a<br>CGE framework|GW has a critical role in mitigating<br>the<br>negative effects of these types of<br>shocks|
|**Beritella****_et. al.,_2005a**|Static<br>Water resources are non-market<br>goods, water is a production factor.<br>water is combined with the value-<br>added-energy<br>nest<br>and<br>the<br>intermediate inputs|Global trade model|Uses GTAP to analyze water policy,<br>introduces water module for GTAP|Welfare<br>losses<br>are<br>substantially<br>larger in the non-market situation.<br>Water-constrained<br>agricultural<br>producers lose, but unconstrained<br>agricultural producers gain; industry<br>gains as well. There are regional<br>winners and losers from water supply<br>constraints.|
|**Beritella****_et. al.,_2005b**|||||
|**Beritella****_et. al.,_2006**|||||
|**Smajgl****_et. al.,_2005**|Static<br>water cycle is approximated on a<br>catchments level. Crucial indicators<br>for an integrated assessment are<br>remaining water volumes in streams<br>and in aquifers.||Integrates a CGE model with an<br>agent based which allows dynamics<br>spatially explicit by introducing<br>hydrological<br>system<br>and<br>the<br>ecological system.|Integration of agent based model<br>with CGEgives more reliable results.|
|**Letsoalo ****_et. al._. 2005**|Static<br>Production factor|Small open economy|Analyzes the relationship between<br>poverty issues and water policies<br>combines poverty reduction with<br>environmental management|show that there can be a triple<br>dividend<br>of<br>water<br>policy,<br>simultaneously<br>reducing<br>water<br>scarcity,<br>improving<br>economic<br>growth,and reducing poverty.|

48