rafmacalaba/datause-encoder-data huggingface dataset:
# **Trade in ‘Virtual Carbon’: Empirical Results and Implications for Policy**

Giles Atkinson<sup>1</sup> , Kirk Hamilton<sup>2</sup> , Giovanni Ruta<sup>2,1</sup> and Dominique Van Der Mensbrugghe<sup>2</sup> 1 Department of Geography and Environment and Grantham Research Institute on Climate Change and Environment, London School of Economics and Political Science, Houghton Street, London, WC2A 2AE, United Kingdom.

2 The World Bank, 1818 H Street, NW, Washington, DC 20433 USA.

Background paper for _World Development Report 2010_

## **Acknowledgments**

The comments of Will Martin, Marianne Fay, Rachel Block, Ricardo Fuentes and Michael Friis Jensen are acknowledged with thanks. The usual caveats apply.
# **_What it all means, at a glance_**

92. We start by comparing the actual distribution of spells, with those that would have arose had everybody<sup>2 3</sup> /nobody received unemployment insurance. Not surprisingly, increases in the coverage rate (i.e. share of workers entitled to unemployment insurance benefits) cause average unemployment duration to increase.<sup>24</sup> Graphically, this translates into a rightward shift of the spell distribution. For instance, while 18 percent of all spells last less than three months given the actual allocation of benefits, this number increases to about 24 percent when no benefits are distributed (i.e. more workers leave the Registry early on), and decreases to almost 0 when everybody is entitled to twelve months of unemployment insurance (i.e. almost no workers leave the Registry during the first three months of unemployment). The same patterns arise for longer spells.

**Graph I**

<!-- Start of picture text --> Distribution of (pordlct.d) fnen,ployrnent.p.11a 41)<br>400 SMU<br><!-- End of picture text -->

**Source:** Unemployment Registry

93. Similarly, we explore the role of (maximum) benefit duration by comparing the distribution of predicted spells corresponding to systems with a maximum unemployment insurance duration of 3, 6, 9 or 12 months. Again more generous unemployment insurance benefits, this time in terms of duration, shift the distribution of spells to the right.

**Graph II**

<!-- Start of picture text --> Dl.t,Ib.tion of (pr-dIctd) u.mploy"ent speU1 (11)<br>Duration of b*neits<br>.2~~~~~~~~~~~~0,00 . .0J. 00 o<br>Source: Unemployment Registry<br><!-- End of picture text -->

> 23 We apply 1999 rules and provide the maximum Ul.

24 It is important to keep in mind that the role of this exercise is simply illustrative, and it should not be understood as a policy recommendation in itself 25
BFP is based on a combination of criteria including the income per capita of the household, which may not exceed R$ 120, and the number of children.<sup>77</sup><sup>_,_78</sup> The amount of transfer varies according to the income of the family, whether they are below the poverty line (R$ 120 per capita month) or the extreme poverty line (R$ 60 per capita month), and the number of children.<sup>79</sup> Figure 6 shows the relation between income per capita and participation in BFP. Participation falls with increasing income as expected and we can spot a considerable drop in participation around the second threshold of R$ 120 (corresponds to 4.8 in log income).<sup>80</sup> But the cut-off is not sharp. We notice that a considerable number of households who are above the threshold of R$ 120 receive the program. This indicates that the rule is either not strictly implemented, some households under-reported vis-`a-vis public authorities (but not to the household survey interviewer), we incur measurement error in the income measure or there is income volatility (see discussion in section 2). The latter is likely to explain part of the deviation from the eligibility rule due to the fact that household’s income is only re-assessed two years after successful entry into the program.<sup>81</sup> <!-- Start of picture text --> Relative frequency of treatment plotted versus hh income pc<br>3 4 5 6 7 8 9 10<br>HH income per capita (log)<br>1.0<br>0.8<br>0.6<br>Treatment<br>0.4<br>0.2<br>0.0<br><!-- End of picture text --> Figure 6: Participation in BFP plotted against income per capita

> 77The average exchange rate between 2007 and 2008 of US Dollar against Real was about US$ 1.00 = R$ 1.90.

> 78In the empirical analysis, we focus on the joint effect of the cash transfer and the conditionality on school attendance on investments in child human capital. Compliance with this conditionality is non-trival as households encounter an opportunity cost of time (see section 3). We use the number of children of school age (between 6 and 17 years old) as a forcing variable.

> 79These values correspond to the period in which POF 2008-2009 survey was conducted. The current values are R$ 140 and R$ 70 for the poverty line and extreme poverty line respectively. Updated information on current values is available on the website of the Brazilian Ministry of Social Development (www.mds.gov.br).

> 80The first threshold corresponds to the eligibility criterion for the basic transfer.

> 81During this period, households income may increase above the threshold without having the benefit suspended.

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Policy ReseaRch WoRking PaPeR 4755

# **Abstract**

The Rothbarth model of intrahousehold resource allocation has consistently failed to detect child gender bias in many applications over the past two decades. This paper challenges the current consensus that the Rothbarth method is not effective in revealing child gender bias from consumption behavior of adults. It proposes an approach to the Rothbarth model that restricts its application to samples of nuclear households, and employs an index of child gender based on the number of children in the household and related to a specific selective mechanism of discrimination. It demonstrates the effectiveness of this approach with an application to a 2005-06 Ethiopian consumption survey of 21,299 households conducted by Ethiopia’s Statistical Authority, covering both urban and rural areas. The paper presents the first clear and extensive evidence of discrimination against girls by all four adult goods employed, and the outcome persists, in various degrees, when reexamined with a lower definition of child age, and with female-headed households. The findings provide support for gender-based policies in child-health and education in Ethiopia.

This paper—a product of the Poverty Reduction and Economic Management(AFTP2) Department, Africa Region—is part of a larger effort in the department to engage in evidece based policy dialogue. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at fkoohi@levy.org, or through cruggeriladerchi@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
To classify security issuances as domestic or international, we consider the main exchange where the issues are listed and compare it to the issuing firm’s nationality.<sup>5</sup> For offerings that take place in more than one market, we consider issues in each market as separate issues. In the case of subsidiaries, one could consider the nationality of the firm’s parent company instead of its own nationality for classifying issues as foreign or domestic. That is, for instance, an equity issue by a British subsidiary of a U.S. firm in the London Stock Exchange would be classified as international, instead of domestic as in our classification. Which approach provides a better criterion for classifying security issues depends on the degree of integration of financing decisions between firms and their subsidiaries, among other factors. If financial decisions are highly integrated, considering firms’ parent nationality may provide a more accurate classification of security issuances. On the other hand, if financing decisions are relatively decentralized, considering subsidiaries’ own nationality may be a better criterion. All the results reported in the paper are obtained classifying issues as foreign or domestic based on issuers’ nationality. In unreported robustness tests, we classified issues by subsidiaries based on their parents’ nationality and obtained results similar to those reported throughout the paper.

To analyze the characteristics and performance of firms that raise capital through security issues in international capital markets, and compare them to firms that raise capital in domestic markets and to firms that do not raise capital, we match the data on security issuances from SDC with firm-level accounting and income statement data from Worldscope.<sup>6</sup> After eliminating firms

> 5 SDC classifies Eurobonds as being listed on the Luxembourg exchange, although these securities are issued all over Europe and trade mostly over the counter. This implies that Eurobond issues by firms from Luxembourg are classified as domestic issues, even though they may trade in other European countries. However, the number of firms from Luxembourg carrying out bond issuances at home according to SDC is relatively low. We re-did all our analyses excluding these firms and obtained results similar to those reported below.

> 6 We exclude U.S. firms from this analysis, given that Worldscope’s coverage of U.S. firms is very limited. Also, note that the U.S. is one of the main markets where foreign firms raise capital. Moreover, as reported below, U.S. firms tend to raise significantly less capital in international markets than firms from other countries. Therefore, excluding U.S. firms is not likely to affect our conclusions on the characteristics and performance of firms that raise

10
Besides an expansion in the supply of education, rising marriage market returns to women’s education could be driving women to pursue higher education. Marriage is of great importance in India, especially to women and their parents (Anderson 2003), as women typically leave their parental household and migrate to live with their husband’s family (see Rosenzweig and Stark 1989; Banerjee et al _._ 2013). Despite a growing literature on women’s earnings capacity and their bargaining power within marriage in developing countries (e.g. Luke and Munshi 2011), there is little evidence on the marriage market returns to women’s education. According to Anderson (2003), the most important quality of women on the marriage market in India is a good appearance, while for men their ability to earn a living is most important. Data for India in Banerjee et al _._ (2013) show both men and women have a preference for marrying a highly educated spouse, though their sample includes only educated persons from the urban middle-class in West Bengal. Rising labor market returns to education would be an obvious reason why men prefer more educated women. However, even in absence of labor market returns, women’s education can contribute to husbands’ social status directly and through higher productivity in status production (Eswaran et al _._ 2013). Another important channel could be increased productivity of maternal time in the production of child human capital (Lam and Duryea 1999), which raises the demand for educated wives if labor market returns to men’s education increase (Behrman et al. 1999).

In Klasen and Pieters (2013) we show that marriage market returns to education became significantly more convex between 1987 and 2009. In 2009 women needed at least secondary education to have a reasonable chance of marrying a highly educated and high-earning spouse. In contrast, primary school and certainly middle school generated reasonable odds to attract a high-

> the causal effect of education on women’s labor force participation in India or on the impact of rapidly increasing schooling supply on women’s educational attainment, other than evaluations of recent policies and programs for primary education and a growing literature on higher education quota for low-caste men and women (e.g., Bertrand et al. 2010; Kingdon 2007).

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# 3. Results

## 3.1 Baseline estimates

Table 2 presents estimation results using OLS and instrumental-variable estimation.

### Table 2

Baseline results: OLS and IV Regression (a) gives OLS estimates (with importer, exporter and time effects) for comparison with instrumental-variable regressions. Regressions (b), (c) and (d) are two-stage treatmenteffect regressions where PSI is instrumented in three different ways. In (b), the first-stage regression (a probit) is on corruption and its square. As expected, the probability of observing a PSI program is a concave function of corruption control, but the turning point is around an index value of 10, so beyond the relevant range it rises in corruption control. Both instruments combined pass Sargan’s overidentification test. In (c) and (d), the first-stage regression is on a variety of instruments including other governance indicators, the amount of OECD aid and several dummy signaling the presence of World Bank Structural Adjustment Programs (identified in Appendix 2), which combined pass Sargan’s test in (c). Results are very similar both qualitatively and quantitatively.

The coefficient on private inspection is stable across specifications and there is no significant bias in OLS estimation. Given the semi-log specification, the coefficient on private inspection is the percent increase (decrease) in trade associated with a change in the private inspection variable from zero to one. The estimates in Table 2 suggest an increase between 8% and 11.5%. While in a plausible range, these estimates are quite high, suggesting, if true, that with an average tariff rate of, say 10% private inspection would roughly just pay for itself _even if it did nothing to tariff avoidance_ .

## 3.2 Robustness

Table 3, Table 4 and Table 5 report several robustness exercises. First, while the baseline results are realized using exports data RHS, we check if they are robust to using mirror data instead. Table 3 presents the results with imports data RHS which are very similar with the results in Table 2.

### **Error! Reference source not found.** Table 3

Page | 12
adequately reflect the rise in food prices; most of these showed higher inflation than the CPI from the World Bank’s Development Data Platform (DDP).

In the remaining 75 countries in our data base, the CPI is from the DDP. As a check, we compared the implied rates of inflation with the food price index produced by the International <u>Labor Organization</u> (ILO). For 65 of these countries the rate of inflation between 2005 and 2008 was over 90% of the rate implied by the ILO’s food price index. (In 15 countries, the inflation rate was actually higher than the ILO food price index, and for 39 it was over 95%.) In the remaining 10 countries, the CPI increased by less than 90% of the ILO index. We cannot rule out the possibility that the price indices we have used for these 10 countries are understating price increases for the poor over the period 2005-08 though the countries concerned only represent 3% of total population in the developing world and so the problem is minor.

_Relative poverty:_ Following Atkinson and Bourguignon (2001), Chen and Ravallion (2001) and Ravallion and Chen (2011) we use the relationship between national poverty lines and mean consumption for identifying a schedule of relative poverty lines.

The relativist gradient in national lines emerges from quite low consumption levels. Eyeballing Figure 1, the gradient starts to be more pronounced at consumption levels over $3 a day (a log value of about unity). The overall elasticity of the poverty line to mean consumption is about 0.7.<sup>12</sup> The elasticity is positive but significantly less than unity. If one was to set the constant of proportionality in a strongly relative poverty line based on these data using the regression coefficient it would be 0.382 (s.e.=0.021).<sup>13</sup> However, it is clear from looking at Figure 1 that neither a constant elasticity nor a (homogeneous) constant slope functional form fits these data well, as already noted. The slope (and hence elasticity) is essentially zero amongst the poorest 20 or so countries, where absolute poverty clearly dominates. The data on national poverty lines are more suggestive of model in which the elasticity starts from roughly zero but rises to something close to unity.

Quite generally one can think of the relative poverty line as a non-decreasing function of the country and date specific mean, _M it_ for country _i_ at date _t_ .<sup>14</sup> Write this function as _Z_ ( _M it_ ) .

> 12 The regression coefficient of the log poverty line on log consumption per capita is 0.653 (s.e.=0.048).

> 13 The intercept was suppressed to assure that the calculation was consistent with a strongly relative line.

> 14 We assume that the mean is the relevant parameter rather than the median. However, counterarguments can be made and there has been some debate on this choice (Saunders and Smeeding, 2002; Easton, 2002; de Mesnard, 2007). The median is more robust to measurement errors at the extremes, although poverty lines set as a constant proportion of the median can have perverse properties when the Lorenz curve shifts (de Mesnard, 2007).

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of 0.5. The Central Highlands have the highest R-squared of 0.74. Other regions have an R- squared above 0.6.<sup>3</sup>

# **_4.2 Spatial patterns of poverty_**

Table 1 presents the regional estimates of the poverty rate and per capita expenditure which are computed directly using per capita expenditure data of the 2010 VHLSS and those estimated from the poverty mapping method. The 2012 VHLSS is representative at the regional level, and the regional poverty rate directly estimated from expenditure data can be thus regarded as the benchmark against which to compare the poverty map estimates. Table 1 reveals that estimates of the poverty rate are quite similar across the two approaches.

Table 1: Per capita expenditure and poverty indexes

||Estimates from<br> <br>|the 2010<br>|VHLSS<br>||Predictions fr<br>|om SAE<br>|||
|---|---|---|---|---|---|---|---|---|
||Per<br>capita<br>expenditure<br>(thousand<br>VND)|P0|P1|P2|Per capita<br>expenditure<br>(thousand<br>VND)|P0|P1|P2|
|Northern Mountain|10927.1|44.87|0.1558|0.0701|10826.4|43.85|0.1483|0.0679|
||(250.2)|(1.54)|(0.0069)|(0.0042)|(340.9)|(1.76)|(0.0082)|(0.0046)|
|Red River Delta|21546.0|11.95|0.0265|0.0088|20515.2|10.65|0.0203|0.0060|
||(605.6)|(0.85)|(0.0025)|(0.0010)|(592.2)|(1.02)|(0.0025)|(0.0009)|
|Central Coast|14222.6|23.73|0.0635|0.0251|14002.1|22.48|0.0520|0.0180|
||(267.3)|(1.33)|(0.0051)|(0.0028)|(268.7)|(1.05)|(0.0031)|(0.0013)|
|Central Highlands|13069.0|32.74|0.1149|0.0542|12931.0|33.29|0.1146|0.0536|
||(490.9)|(2.75)|(0.0128)|(0.0077)|(351.8)|(1.25)|(0.0056)|(0.0032)|
|South East|24297.4|7.02|0.0172|0.0064|23350.9|7.07|0.0139|0.0043|
||(935.9)|(0.96)|(0.0036)|(0.0018)|(844.9)|(0.84)|(0.0020)|(0.0007)|
|Mekong River Delta|14858.2|18.71|0.0425|0.0143|14497.9|17.45|0.0359|0.0112|
||(265.8)|(1.10)|(0.0033)|(0.0015)|(280.7)|(1.08)|(0.0029)|(0.0011)|

Standard errors are in parentheses. <u>Source: Estimation from the 2009 VPHC and the 2010 VHLSS.</u> 3 We conduct two exercises to shed light on the question how well the models do in predicting per capita expenditure. Firstly, within the 2010 VHLSS, we randomly split the six regions into two samples, and estimating the regional models in the first sample, we predicted expenditure in the second sample and compared the results to the actual per capita expenditure. We find that the observed poverty rates are in all regions’ subsamples similar to the predicted ones. Secondly, we also examine the sensitivity of the expenditure poverty rate of districts and provinces to different expenditure models. We estimate two expenditure models: one with a large number of explanatory variables and another with a smaller number of explanatory variables. Both models give very similar estimates of poverty indexes at the district and province level. For interpretation in this paper, we will use the estimates from the large model, which give lower standard errors of welfare estimates.

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production practices as the expected difference between the floor price and the price in the unregulated market increases.

The estimation results for the cane quality models are presented in table 6. Factoryspecific data from annual NFCSF and STAI publications are used to estimate these models (NFCSF various years; STAI various years). The coefficient on the private factory variable is positive and statistically significant, consistent with the hypothesis that these processors benefit from receiving increased cane quality under ex post permits.

The preceding results suggest that the AP permit system decreases the overall efficiency of sugar production and processing. The sugar processor’s average cost of cane per metric ton of sucrose content is Rs. 7,367.19. The estimated increment in the sucrose content in the cane (pol%) due to a factory being private is 0.514 percent. This increment reduces the private factory’s cane cost to 7,260.46 Rs./metric ton of sucrose content, a reduction of 106.73 Rs./metric ton. This reduction in processor cost is lower than the increase in the farmer’s production cost of the cane per metric ton of sucrose content, which is Rs. 221.41 to Rs. 293.44. The ex post permit system thus decreases economic efficiency relative to alternative procurement methods within the framework presented here.<sup>17</sup> Robustness and Placebo Tests A number of alternative specifications of the models reported in tables 4 and 5 were also estimated, including ones that added variables for farmer demographics (age, education, caste),

> 17 A referee noted that a possible cost of the permit system that is not accounted for in this analysis is a loss in utility

> to risk-averse farmers from the price uncertainty introduced by the permit system.

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