rafmacalaba/datause-encoder-data huggingface dataset:
shifts in returns to schooling rather than changes in the distribution of skills. Positive shifts in the market returns to schooling are associated with increases in the proportion of inequality accounted for by skill endowments, hence indicating that skills are an unevenly distributed asset.

Applying the same semi-parametric model to decompose within-sector income inequality, we show that skill endowments are also unevenly distributed within sectors, hence increases in the market results to schooling, have an adverse within-sector distributional impact. Difference in educational endowments and their market returns account for as much as 1/4 of total income inequality in the _earnings_ sectors but as little as 5 percent in the _non-earnings_ sectors. The currency crisis of December 1994 together with the enactment of NAFTA caused a sectoral redistribution favoring the tradable sectors. This redistribution was associated with lower returns to schooling in the nontradable sectors and a higher premium for well-educated workers in the tradable sector. Given the positive relationship between returns to schooling and within-sector income inequality, after 1994, skill endowments had a lower contribution to income inequality in the non-tradable sectors and and a higher in the tradable sectors. These results corroborate the results found by the trade versus relative wages literature, supporting the view of a skill-biased shift in labor demand brought about by the Mexican liberalizing reforms of the 1990s.

# **References**

- [1] Bourguignon, F., Fournier, M. and Gurgand, M. (2001) ‘Fast development with stable income distribution: Taiwan, 1979-1994’, _Review of Income and Wealth_ , vol. 47 (June), pp. 1-25.

- [2] Cowel, F.A. (2000) _‘Measuring inequality’_ , Oxford Unversity Press.

- [3] Cowel, F.A. and Jenkins, S.P. (1995) ‘How much inequality can we explain? A methodology and an application to the United States’, _The Economic Journal_ , vol. 105. pp. 421-430.

- [4] De Hoyos, R.E. (2005a) ‘Constructing inequality and poverty indexes using Mexican ENIGH’, available from: http://www.econ.cam.ac.uk/phd/red29/

- [5] De Hoyos, R.E. (2005b) ‘The microeconomics of inequality, poverty and market liberalizing reforms’, research paper 2005/63 _UNU-WIDER_ , Helsinki.

25
# Forest Carbon Supply in Nepal: Evidence from a Choice Experiment

Sahan Dissanayake<sup>1</sup> Randall A. Bluffstone E. Somanathan Harisharan Luintel N. S. Paudel Michael Toman

## **JEL codes: Q23 Q54 Q56**

## **Keywords: Nepal; Carbon Sequestration; Carbon Supply; Choice Experiment**

## **Acknowledgements**

The authors acknowledge the contributions of ForestAction Nepal to the collection of field data and Alec Kretchun for the preparation of the research site map. They also gratefully acknowledge the expert research assistance of Russell Drummond, without which this paper would be on a much shakier foundation. Bluffstone thanks the Fulbright Scholar Program and USEF-Nepal, which provided support for his input into the paper. Financial support to carry out the research was provided by the World Bank’s research budget.

> 1 Corresponding author sahan@pdx.edu. Affiliations: Dissanayake, Portland State University; Bluffstone, Portland State University; Somanathan, Indian Statistical Institute; Luintel, Portland State University and ForestAction Nepal; Paudel, ForestAction Nepal; Toman, World Bank. Development Research Group.
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higher levels of welfare than otherwise identical households with no education. If such information could be obtained accurately at a low cost, targeting of transfers would be enhanced. Column 3 of Table 2 (Model 3) focuses on the ownership of durable goods. Ownership of a car or refrigerator is strongly associated with higher levels of welfare, but bicycle ownership indicates lower levels. Thus data on the possession of these goods may further enhance targeting.

It was seen above that a dwelling's floor area had little explanatory power. It turns out that a better indicator is floor area per capita, as confirmed in column 4 of Table 2 (Model 4). Yet this requires knowledge of household size, and it is possible that households could misrepresent this. In any case, the value of accurate information on household size is evident in this regression. The last column of Table 2 includes all variables discussed so far. If all this information can be obtained fairly accurate predictions on per capita expenditure levels can be obtained, as indicated by the R2 coefficient of 0.654.

# B. Welfare Levels and Rural Households' Characteristics

The 1985 CILSS data include community characteristics from the rural areas sampled, as well as data on farming activities. One can use these data for regressions in rural C8te d'Ivoire. The new explanatory variables are defined in Table 3 and the regressions are given in Table 4.

The first column in Table 4 (Model 1) gives the basic regression.

Examining first dwelling characteristics, it is surprising that floor area is negatively associated with per capita expenditures. Yet recall that floor
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# Table 31: BENEFIT AND COST STREAMS FOR VTE GRADUATES, BY LOCATION (CFAF)

||Secondary<br>Level|Post Secondary<br>Teacher Training|J<br>Apprent|iceships|
|---|---|---|---|---|
|Benefits||Abidjan|||
|Starting<br>Salary Increment<br>Final Salary Increment<br>Duration<br>of Benefit<br>Stream (Years)|119,929<br>363,721<br> <br>35|599,784<br>1,680,574<br>34.5|22,799<br>69,144<br>35||
|Costs 1/|||||
|Private Costs<br>Social Costs<br>Duration<br>of Cost Stream (Years)|546,561 (100)<br>2,207,063 (404)<br>2.79|<br>654,858 (100)<br>3,985,340 (608)<br>3.15|<br>384,39<br> <br>384,39<br>3|6 (100)<br>6 (100)<br>.05|
|||Other Cities|||
|Benefits|||||
|Starting<br>Salary Increment<br>Final Salary Increment<br>Duration<br>of Benefit Stream (Years|926,030<br>3,852,870<br>)<br>35|3,483,595<br>14,804,976<br>34.5|0<br>0<br>35||
|Costs1_|||||
|Private Costs<br>Social Costs<br>Duration<br>of Cost Stream (Years)|583,368 (100)<br>2,260,957 (388)<br>2.79|1,519,990 (100)<br>4,079,978 (268)<br>3.15|<br>301,32<br> <br>301,32<br>3|0 (100)<br>0 (100)<br>.05|

# 1/ Index in parentheses.
**Figure 4:** _Simulating the impact on Switzerland's real GDP per capita of weaker growth spillovers associated with its hypothetical relocation to the centre of sub-Saharan Africa_

<!-- Start of picture text --> 29<br>Actual<br>28 Counterfactual<br>27<br>26<br>25<br>24<br>23<br>22<br>21<br>20<br>1970 1975 1980 1985 1990 1995 2000<br>Year<br>GDP per capita (thous; cnst 2000 international dollars)<br><!-- End of picture text --> Although the welfare gains for the Central African Republic from greater spillovers would obviously be lower in absolute terms than Switzerland’s simulated losses, it is clear that the welfare losses associated with weak cross-country spillovers stemming from a lack of integration are very large for landlocked countries. Indeed, if anything, our simple exercise considerably underestimates the losses. This is because of the highly conservative assumptions which underpin the exercise.

# **8. Conclusion**

In this paper, we have examined the strength of cross-country spillovers of long-term growth both globally and in various geographically and income defined sub-samples. Our objective was to find evidence for the possible spatial heterogeneity of such effects which can be linked to differences in the integration of countries, both with their immediate neighborhoods and globally. We have further investigated the relationship 37
<!-- Start of picture text -->
-90PO<br>80 4.<br>SCcai\<br>B 70 ty<br>a<br>& \<br>zw 60 tH<br>‘o= AAAi<br>= 50 IN<br>B40 PRAT A<br>2 \Q<br>oO<br>aM ee<br>5 \s<br>10 SSS<br>0 | SSS ——— :<br>0 20,000 40,000 60,000 80,000<br>GDP per capita (PPP, constant 2011 international dollars)<br><!-- End of picture text -->
# **Predicted Values 2011 DV: EA % Using Gov. Schools**

<!-- Start of picture text --> 2 4 6 8 10 0.00 0.05 0.10 0.15<br>Median Contact with Mean % Disputes with<br> Chiefs by Council Chiefs by Council<br>0.6 0.6<br>0.5 0.5<br>0.4 0.4<br>0.3 0.3<br>0.2 0.2<br>EA % Using Goverment Schools EA % Using Government Schools<br>0.1 0.1<br>0.0 0.0<br><!-- End of picture text -->

**Figure 4:** Predicted values of median contact with chiefs (left) and mean percentage of disputes with chiefs (right) on EA-level mean percentage use of government schools. Mean predicted intercept in red and council specific intercepts in black.

Taken together, our findings are striking. For each of our three measures of local public service provision, we have found that two out of our three measures of councilor/chief relations achieve statistical significance – and all six of these findings move in the same theoretical direction. Further, no other council-level variables are consistently significant across models. On average, we find that closer ties between councilors and chiefs, as measure by more frequent self-reported contact and a higher percentage of councilors from chiefly families, are associated with worse local development outcomes. In contrast, higher frequencies of councilors’ selfreported disputes with chiefs in 2008 are associated with improved levels of service provision in 2011. Our measure of councilor/chief contact is the most consistent result, achieving a negative and statistically significant value across all three measures of local public goods provision. We now turn to a series of robustness considerations to test the stability of our results before moving to a discussion of their implications.

26
**Figure 4: National Household Access to Electricity for Census and Statutory Towns**

<!-- Start of picture text --> 100%<br>80%<br>CT ST<br>60%<br>40%<br>20%<br>0%<br>10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Level of Household Electrification<br>Source: Census 2011<br>Share of settlements<br><!-- End of picture text --> However, in the four states being studied, the situation is not as clear. As seen in Figure 5, there is considerable variation between states regarding electricity supply in STs and CTs.

**Figure 5: State Wise Household Access to Electricity for Census and Statutory Towns**

<!-- Start of picture text --> Bihar Jharkhand<br>100% 100%<br>90% 90%<br>80% CT ST 80%<br>70% 70% CT ST<br>60% 60%<br>50% 50%<br>40% 40%<br>30%<br>30%<br>20%<br>20%<br>10%<br>10%<br>0%<br>0%<br><!-- End of picture text --> <!-- Start of picture text --> 100% West Bengal 100% Odisha<br>90% 90%<br>80% 80%<br>70% CT ST 70% CT ST<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br><!-- End of picture text --> Source: Census of India 2011 27
and interact all policy variables in Equation 2 with an indicator for whether the state was proworker or pro-employer in 1985. The inclusion of state-industry fixed effects controls for timeinvariant, state-level characteristics. In addition to this _de jure_ measure of labor regulations, we have developed a _de facto_ measure of how easy it is for firms to adjust their size or shut down based on court outcomes. We gathered data from various publications of the Ministry of Labor that provided the number of times a firm requested permission to close down or to lay off workers between 1988 and 1992, as well as the number of cases in which permission was granted or denied. We calculated the fraction of cases in which permission was granted, and constructed a dummy variable equal to one for states in which the fraction granted was above the median.

Tables 10 and 11 present results for the two measures of labor regulations.<sup>20</sup> The effects of all policy reforms, except FDI liberalization, are largely similar across states, regardless of labor regulations. Table 11 indicates that FDI reform is associated with a 7% increase in average productivity in states where it is difficult to lay off workers, but only a 2.5% (7%-4.5%) increase in average productivity in states where it is easy to lay off workers. This difference suggests that in states where it is difficult for firms to achieve an optimal input mix by laying off workers, they may instead be able to increase their productivity through other means, such as attracting FDI. In other words, FDI reform matters more when existing rigidities make it difficult for firms to optimize their production.

Another factor that may have influenced the impact of liberalization on firm productivity is exposure to trade. We use three measures as proxies for trade exposure. First, firms located close to ports may have been more exposed to trade competition prior to the reforms, or may have been more affected by trade liberalization. We create a dummy variable that is equal to one if a stateindustry group is located in a state with a port, zero otherwise. We also develop measures of the extent to which particular industries might have been more or less exposed to competition from imports, or to foreign markets through exports. To so do, we use data from the COMTRADE database to estimate total exports and total imports for each industry in 1990. We then calculate

> 20The samples we use are restricted to states for which we have data on labor regulations, and are therefore smaller than the baseline sample.

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time as they are running their business, putting a strain on the time and resources they can devote to the business. This interpretation is in line with the finding from Section 4.b that female-owned firms are more likely to perceive child care responsibilities as an obstacle to firm operation and growth than male-owned firms. However, the results in this section are correlations and are thus not necessarily causal. More research is needed to determine whether child care responsibilities lead women to own smaller and less profitable firms. Finally, note that the differences in size and performance across female- and male-owned firms are still present in households where there are no children under the age of 12, although the differences are smaller in magnitude.

**Table 7: Child Care Responsibilities and Differences in Firm Characteristics**

||Female<br>dummy|Coefficients on<br>Children<br>under 12 in<br>household<br>dummy|<br>Female *<br>Children<br>under 12|
|---|---|---|---|
|Ln employees|-0.053***|0.005|-0.034*|
||(0.014)|(0.012)|(0.018)|
|Ln machinery value|-0.884***|0.018|-0.342***|
||(0.075)|(0.055)|(0.099)|
|Ln sales (last month)|-0.164***|-0.011|-0.056|
||(0.048)|(0.036)|(0.064)|
|Profits (last month, in thousands)|-0.055***|0.009|-0.002|
||(0.014)|(0.012)|(0.019)|
|Profits (last month, in thousands)|-0.044***|0.006|-0.025***|
|- median regression|(0.010)|(0.008)|(0.014)|

_Note_ : Robust standard errors in parentheses. Significance levels: *** 1 percent, ** 5 percent, * 10 percent. _Source_ : Encuesta Nacional de Micronegocios (ENAMIN), 2002, and Encuesta Nacional de Empleo (ENE), 2002-I.

In addition to child care responsibilities, other household obligations could also prevent female business owners from devoting more attention and resources to their business, limiting the firm’s growth. A recent World Bank report finds that female-owned firms in Bolivia are twice as likely to operate inside the owner’s home as male-owned firms (World Bank, 2008). This finding is based on the same Bolivian data as is used in this paper. Table 8 shows the corresponding percentages of female- and male-owned firms that operate inside the home. The number is only 10 percent for male-owned firms, but it is 23.2 percent for female-owned firms.

**Table 8: Percentage of Firms Operating Inside the Owner’s House**

||Female-Owned Firms|Male-Owned Firms|Difference (Female- Male)|
|---|---|---|---|
|Bolivia|23.2|10.1|13.1***|
|Mexico|30.3|11.1|18.8***|

_Note_ : *** 1 percent, ** 5 percent, * 10 percent.

_Source_ : Bolivian World Bank Micro Enterprises Survey, 2007, Encuesta Nacional de Micronegocios (ENAMIN), 2002.

19
**Figure 4: Data Coverage and Representativeness**

# **1. Number of EMDEs’ companies per region 2. Number of EMDEs’ companies per**

<!-- Start of picture text --> industry<br><!-- End of picture text --> <!-- Start of picture text --> No. Firms No. Firms<br>18000 18000<br>16000 16000<br>14000 14000<br>12000 12000<br>10000<br>10000<br>8000<br>8000<br>6000<br>6000<br>4000<br>4000<br>2000<br>2000<br>0<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 0<br>Basic Mat Cons Goods Cons Serv Energy 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>Health Care Industrials Technology Telecom EAP ECA LAC MENA SA SSA<br>Source: Bloomberg Source: Bloomberg<br><!-- End of picture text -->

# **3. Reported total corporate debt**

<!-- Start of picture text --> (% GDP)<br>Total<br>Debt/GDP<br>(%)<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>EAP ECA LAC MENA SA SSA<br>Median per region, 75th and 25th percentiles.<br>GDP available as of 2015.<br>Source: Bloomberg, WB-World Development Indicators<br><!-- End of picture text -->

# **4. Common shares of firms covered**

(% Stock market capitalization) <!-- Start of picture text --> Shares/Total<br>St. Mkt<br>Capital (%)<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>EAP ECA LAC MENA SA SSA<br>Median per region, 75th and 25th percentiles.<br>Stock market capitalization available as of 2015.<br>Source: Bloomberg, WB-Finstats<br><!-- End of picture text --> Our sample is not homogenous across regions (see Figure 4.3). The regional median of total corporate debt (% of GDP) ranges from 2.9 percent of GDP for SSA to 10.8 percent of GDP for EAP. The companies covered in our data set are nevertheless an important part of EMDEs’ capital markets as the median value of their common shares (i.e. % of stock market capitalization) ranges from 17.6 percent in SSA to 61 percent in ECA (see Figure 4.4).

11
land availability by introducing _<u>ai</u>_ into _κ_ as follows: for water transfer type _j_ = _L, P, C_ . Note that _β_ is assumed to be the same across transaction types, which is consistent with its interpretation as the effect of availability of adjacent land to irrigate. As for the constant term _<u>κj</u>_ , we allow for a separate _<u>κL</u>_ , but restrict _<u>κC</u>_ = _<u>κP</u> ≡_ _<u>κT</u>_ . Whereas we expect that _<u>κL</u> >_ _<u>κT</u>_ , since a land lease is likely to be more costly to arrange than a water sale, our presumption is that the cost of arranging a water sale is independent of its specific contractual terms.

# **4.7 Likelihood function**

The mixed continuous/discrete choice likelihood function involves the probabilities of the different water arrangments (choices) _j_ = _U, A, L, C, P_ and the densities of irrigated areas, _lji_ , conditional on these choices. Choice probabilities are determined by the set of thresholds, ̃ _εjj′i_ , that solve the following crossing conditions for arrangement-specific value functions: The solution to the first of these equations yields _ε_ ̃ _AUi_ , the upper limit of integration for the autarky probability, which has a simple closed form. The second set of equalities in (12) yield the cultivation costs at which the well-owner is just indifferent between autarky and transferring water under arrangement _L, C_ and _P_ , respectively. Given nonzero fixed costs, these thresholds do not have closed-form solutions and, hence, must be solved for numerically given data and parameters. The third set of equalities give the cost thresholds _between_ alternative transfer arrangements and also do not have closed form solutions in general.

Letting Pr( _j|_ Θ _, Zi_ ) denote the probability of choice _j_ conditional on parameters Θ = ( _α, η, γ,_ _<u>κL, κT</u> , β, λ,_ _<u>c, σε</u>_ ) and data _Zi_ = ( _πki, Ri_<sup>2</sup><sup>_, ai,_</sup> _<u>ai</u>_ ) _,_ we have

25
feedstock supply in developing countries, thus affecting the profitability of the biofuel sector in those countries (Msangi and Evans, forthcoming).

While the focus of this paper has been on tax exemption/tax credit and mandates in large countries, the interactions between biofuel policies in small and large countries in biodiesel production are certainly very important as well. Equally important is the question about the relative welfare efficiency of prospective biofuel policies in small developing countries, as well as the question of what impact biofuel production will have in developing countries on their food security. These and similar questions present opportunities for future research.

# **References**

Cui, J., H. Lapan, G. Moschini, and J. Cooper. (2011). “Welfare Impacts of Alternative Biofuel and Energy Policies.” _American Journal of Agricultural Economics_ 93(5): 1235–1256.

de Gorter, Harry and David R. Just. (2009). “The Welfare Economics of a Biofuel Tax Credit and the Interaction Effects with Price Contingent Farm Subsidies.” _American Journal of Agricultural Economics_ 91(2): 477–488.

de Gorter, Harry, Dusan Drabik and David R. Just. (2011). “The Economics of a Blender’s Tax Credit _versus_ a Tax Exemption: The Case of U.S. ‘Splash & Dash’ Biodiesel Exports to the European Union.” _Applied Economic Perspectives and Policy_ . 33 (4): 510-527 Drabik, D. (2011). “The Theory of Biofuel Policy and Food Grain Prices.” Working Paper 201120. Charles H. Dyson School of Applied Economics and Management, Cornell University. December.

Environmental Protection Agency (EPA). (2007). “Guidance for Biodiesel Producers and Biodiesel Blenders/Users”. EPA420-B-07-019. http://www.epa.gov/otaq/renewablefuels/420b07019.pdf FAPRI (2012). “Biofuel Conversion Factors”. Food and Agricultural Policy Research Institute, Missouri. http://www.fapri.missouri.edu/outreach/publications/2006/biofuelconversions.pdf Lapan H, Moschini G. (2012). Second-best biofuel policies and the welfare effects of quantity mandates and subsidies. _Journal of Environmental Economics and Management_ 63(2): 224–241.

Msangi, S., and Evans, M. (forthcoming) “Biofuels and Developing Economies: Is the Time Right?” _Agricultural Economics_ .

14
-4-

exports has changed over time; the extent that differences exist in imports of labor intensive products by OECD countries; and the extent that individual developing countries have been able to capitalize on labor intensive manufactures in their export base. While the initial analysis is largely e- post in that it relates to the 1965-1986 period, the comparative advantage indices are updated to the mid-1980s and their implications for trade patterns in the 1990s and beyond assessed.

# II. The Measures of Comparative Advantage

In this study, the measures of comparative advantage are based on labor intensity indices derived by Lary (1968) for the NBER. Lary's analysis employed the criteria of value added per employee, both in the U.S. and other countries, for identification of products which were capital or labor intensive. The geneial rule followed was to classify labor intensive products as those which met twc conditions: (1) value added per employee did not exceed the national average for all United States manufacturing by more than 10 per cent, and (2) imports by developed from developing countries totaled at least $100,000 at the three-digit level of the Standard International Trade Classification (SITC) system in 1965. 6/ According to the NBER reasoning, this approach excluded clearly capital intensive products while applying the test

# 6/ The factor intensity indek for industry j (L) is defined as,

# (1) Lj = (Vj -Nj)/(Vt + Nt) x 100

where V. and V represent value added in industry j and all United States manufacuring respectively, while N- and Nt represent the number of workers in the industry and in all madufacturing activity. With regard to the NBER's condition that imports totalled $100,000 it should be noted that the absolute value of trade is not an appropriate indicator of labor intensity.