rafmacalaba/datause-encoder-data huggingface dataset:
through 1994 with the result that in the final quarter of the year it was above the INRO stabilization range - see Figure 5 - and indeed even above the notional ceiling price of 270 MS c/kg. The compromise raised the symbolic floor price but left the effective stabilization range unchanged but subject to review within six months of the new agreement coming into force. The most important outstanding issue is whether the United States' administration will be able to persuade Congress to ratify a new INRA. If they do ratify, this will be the only ICA of which the USA is a member.

The INRA has survived essentially by being relatively innocuous. The determination of consuming country members of the agreement to ensure that the INRA does not distort prices has resulted in the stabilization range being revised down to a sufficiently large extent that the INRO has been left with little opportunity to stabilize prices. Indeed, at least since 1985 the rubber price history graphed in Figure 5 is almost a paradigm of the flat-bottomed sharp-peaked cycles generated by private sector storage in the Williams and Wight (1991) and Deaton and Laroque (1992) models and allows little residual role for the INRO.

# 3.6 ICA Performance

There has always been disagreement about the extent to which ICAs have either stabilized or raised prices. Evaluations have typically relied on counterfactual simulation of econometric models - see for example Palm and Vogelvang (1988) and Vogelvang (1988) on coffee. However, exercises of this sort are inevitably qualified by worries over the extent to which the models provide adequate descriptions of producer and consumer behaviour. Here I experiment with the simpler alternative of event study methodology, which is both a cruder and less formal method than econometric evaluation.

25
Figure 3: Simulation, using national distribution of books <!-- Start of picture text --> National<br>Santiago del Estero City of Buenos Aires<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>segregation segregation segregation<br>mean gap U - L meanmean gap U - Lgap U - L<br>14<br>15 14<br>12<br>12<br>10 10<br>8 10<br>Gains in test scores Gains in test score 5 Gains in test score<br>6 8<br>4<br>6<br>0<br><!-- End of picture text --> Note: The vertical lines indicate the degree of segregation found in each case.

the gap than for the average test scores. In other words, the equity aspect is significantly more sensitive to changes in the allocation of students on the basis of socioeconomic status than the mean is. Both these results are consistent with the pattern observed across localities in the introduction. Indeed, peer effects alone can account for, at least the sign of the relationship observed in the date. The variation across schools (with different human, social and physical resources and management) most likely works in the same direction, strengthening the results. Still, even disregarding the (certainly sizeable) differences across schools, there is a trade-off between gap and mean test scores that is entirely due to the effects that peers have on other pupils.

# **7 Conclusions and policy implications**

This paper has argued that the standard, linear-in-means approach to estimating peer-group effects does not fully account for the consequences of economic segregation. After relaxing two assumptions of the linear-in-means model, I find that wealthier pupils are more sensitive to peer effects. Second, there is evidence that peer effects are non-monotonic, such that the composition of classmates’ backgrounds – rather than simply their mean – matters for test scores. Estimates using a more flexible functional form reveal a pattern that is consistent with the hypothesis of social distance.

28
**Table 15 Gross monthly wages in manufacturing,**<sup>**2000**</sup>

||Gross<br>wages<br>|Labor<br>compensation <br>|GNIpercapita|Gross<br>wages<br> <br>|GIprcpt<br>GNIpercapita<br>|
|---|---|---|---|---|---|
|||$||Croat<br>|ia=100<br>|
|Bulgaria.|109<br>||1520<br>|22<br>|33<br>|
|Croatia|494|577.9<br>|4620<br>|100<br>|100<br>|
|CzechRepublic|341|503.5|5250<br>|69<br>|114<br>|
|Estonia|281<br>|373.3|3580<br>|57<br>|77<br>|
|Greece|1432<br>|..<br>|11960<br>|290<br>|259<br>|
|Hungary|312|449.7|4710<br>|63<br>|102<br>|
|Ireland|1804<br>|..|22660<br>|365<br>|490<br>|
|Latvia|225<br>|..|2920<br>|46<br>|63<br>|
|Lithuania|247<br>|..<br>|2930<br>|50<br>|63<br>|
|Poland|457|605.3|4190<br>|92<br>|91<br>|
|Portugal|679<br>|..|11120<br>|138<br>|241<br>|
|Romania|112<br>|..<br>|1670<br>|23<br>|36<br>|
|Slovakia|255|382.2<br>|3700<br>|52<br>|80<br>|
|Slovenia|793<br>|1093.9|10050<br>|160<br>|218<br>|
|Spain|1461|..|15080|296|326|

= Not available a) Gross wage plus payroll taxes.

# Sources:

Wages: Yearbook of Labor Statistics 2001, ILO Gross National Income: World<sup>Bank Atlas,2002</sup> Exchange rate: World Development Indicators,<sup>The WorldBank,2002.</sup> Do these wage differentials reflect productivity<sup>differentials? Theansweris</sup> no. Relatively high wages in Croatia are not<sup>justifiedbyproportionatelyhigher labor</sup> productivity. Using GNI per capita as a proxy<sup>forlabor productivity,it turnsout that</sup> wage differentials between Croatia and<sup>other transitioneconomiesarelarger than</sup> productivity differentials.<sup>2 4</sup> Croatia does not enjoy a productivity advantage<sup>which</sup> would warrant the existing level of wages. In other words, productivity adjusted wages are high in Croatia compared<sup>with othercountries.</sup> For example, manufacturing wages in Slovenia are about<sup>60 percenthigher thanCroatia,however</sup> -productivity is over twice as high, which<sup>impliesthat despite higher wages,unit labor</sup>

**24** Clearly, GNI per capita is a very rough proxy<sup>for manufacturingproductivity (outputper</sup> worker), and accordingly the results presented<sup>are grossapproximation.Nonetheless,given cross-</sup> country correlation between GNI per capita<sup>and productivity,the scope for large errors regarding the</sup> ranking of countries with respect unit labor<sup>costsseemslimited.</sup>

-47-
below the 5<sup>th</sup> decile are considered to be energy poor. Other authors, such as Pereira et al (2010), have focused on more aggregated effects, and analyze the effects of rural electrification at the local level on the decrease in energy poverty in Brazil.

Although Mexico is a middle income country, it still has high levels of poverty and inequality. In the past decade, moderate income poverty rates fell from 53.6% in 2000 to 42.7% in 2006, then increased again due to the economic crisis to 51.3% in 2010. Despite such figures, provisions for general access to some forms of energy have increased; for example, households with electricity constitute 98.2% of the total according to the 2010 Census. Even though some energy subsidies are provided to consumers (i.e. gasoline subsidy), some of the subsidies have regressive effects, which benefit the richer groups more than the poor (UNDP, 2011). Others have pointed at the regressivity of the subsidy for electricity (i.e. Yepez-Garcia et al, 2010). Therefore, there is room to analyze to what extent households along the income distribution demand some forms of energy, and to suggest better targeting policies.

The National Development Plans of Mexico, which are the general guiding planes for each government term, have not addressed energy issues from a social point of view. At most, the 2006–2012 Plan mentioned in a general manner a possible link between energy, hunger reduction, and food security, but did not investigate this further. In spite of this, in 2007 the anti-poverty program Oportunidades included in the cash transfer an extra amount of Mex$50 (about US$3.8) for poor households to spend on electricity, gas, and coal, among other things. For rural households receiving this extra amount from the anti-poverty program, energy represents about 13% of total monthly spending, second only to food (Gertler et al, 2009), a share that is common for all poor Latin American households (CEPAL, 2009).

However, there are no empirical studies that link energy use to income levels or to household characteristics for Mexico at the household level. Thus, this paper aims to fill this gap. The objective of the paper is to calculate the determinants of different sources of energy consumption at the household level for urban and rural Mexico, 3
**Figure 2. Sensitivity analysis on total own-price elasticity in the transportation sector for alternative values of aggregate energy elasticity**

<!-- Start of picture text --> LPG NG Electricity Diesel Gasoline 87<br>0<br>-0.1<br>-0.2<br>-0.3<br>-0.4<br>-0.5<br>-0.6<br>-0.7<br>75% of -0.85 (-0.6375) -0.85 125% of -0.85 (-1.0625)<br><!-- End of picture text --> How did these elasticities change over time? To answer this question, we split the sample into two subsamples while dropping the years 2005-2007; that is, we re-estimated the various parameters and recalculated the elasticities twice: The first estimates limited the data to the years 2000-2004, whereas the second focused on 2008-2012. We then compared total own-price elasticities for the five transportation fuels across the three estimates; that is, for the samples including data for 2000-2004, 2008-2012, and for the whole sample (Figure 3). Our results offer support that the elasticity of gasoline is becoming more inelastic with time. Similar trends are observed for electricity. However, the analysis suggests a different trend for natural gas and diesel, where the estimates suggest the elasticity increased with time. To this end, natural gas and diesel fuels are employed by the power sector and used in heavy duty vehicles. In addition, the consumption of natural gas and diesel has been expanding substantially during the investigated period.

17
2

# **I. Introduction and Summary**

During 1997-1999, Latin America was subject to external and internal shocks of sizable magnitudes. Consequently, the region's growth rate in 1999 was negative for the first time since 1983. Among the external shocks, the most notable were associated with sudden stops in capital flows and terms of trade deterioration. On the internal side, real interest rates and **i** 4al exchange rates adjusted both endogenously in response to external shocks and as a result of shifts in domestic factors such as policy variables. How do these variables interact with output fluctuations in Latin America, and in particular, what is their relative importance in terms of explaining the 1998-1999 slowdown?

To answer these questions, we used two complementary approaches. In the first one, we pooled quarterly data for the eight largest economies of the region and estimated panel regressions for regional GDP growth, considering as exogenous external factors the terms of trade, US real interest rates, non-Latin Brady bonds sovereign spreads, the current account of industrialized countries (as a proxy for exogenous capital flows) and an index of El Nino effect. The role of internal factors and other non-specified external variables was captured by means of the lagged value of GDP growth, which also subsumes dynamic effects in the panel. This simple approach has several advantages: first, it permits an assessment of the relative importance of external vs. domestic factors at the regional level; second, by allowing the disturbances of the equations to be related, it captures interdependence among countries' output fluctuations; finally, by pooling the data we minimize small sample problems. The simplicity of this method, however, comes with drawbacks such as the lack of flexibility for country-specific analysis and the absence of explicit treatment of domestic factors. To overcome these limitations we estimated country-specific VARs incorporating domestic real interest rates and real exchange rates as additional explanatory variables.

The paper is divided in three parts. The first section presents the quarterly GDP panel regression exercise used to determine the importance of external factors explaining the regional slowdown . Using quarterly data from 1992 to 1998 for the eight largest countries of the region, we estimated panel regressions, and using the 1997 exogenous external conditions as a benchmark, we found that at least 52% of the regional slowdown was due to the adverse behavior of external factors.

The second section summarizes the country-specific "stylized facts" about the relationship between events in the external capital markets<sup>2</sup> , terms of trade, domestic real interest rates, real exchange rates and industrial production growth, using monthly data

> 2 To capture the external capital markets shocks we use the Brady Bond sovereign spreads provided by JP Morgan. In the last section of the paper we discuss the inclusion of this "price" variable instead of a "quantity" indicator.
_Performance?_ Georgia Water Planning and Policy Center, Water Policy Working Paper No. 2005-014. Albany, GA.

Foulon, J., P. Lanoie and B. Laplante. 2002. Incentives for pollution control: regulation or information? _Journal of Environmental Economics and Management_ 44: 169-187.

- Garcia, J., Sterner, T., and S. Afsah. 2007. Public disclosure of industrial pollution: The PROPER approach for Indonesia? _Environment and Development Economics_ , 12, 739-756.

- Greenstone, M., 2003, Estimating Regulation-Induced Substitution: The Effect of the Clean Air Act on Water and Ground Pollution, _The American Economic Review_ , 93(2): pp. 442-448.

- Gupta, S. and B.N. Goldar. 2004. Do stock markets penalize environment-unfriendly behavior? Evidence from India. _Ecological Economics_ . 55, 2.

- Hamilton, J.T. 1995. Pollution as news: Media and stock market reactions to the toxics release data. _Journal of Environmental Economics and Management_ , 28, 1, 98-113.

- Healy, P. and K. Pelapu.  2001.  Information asymmetry, corporate disclosure, and the capital markets: a review of the empirical disclosure literature. _Journal of Accounting and Economics_ , 31: 405-440.

- Jin, Yanhong, Hua Wang and David Wheeler, 2010a, The Impact of Environmental Performance Rating and Disclosure: An Empirical Analysis of Perceptions by Polluting Firms’ Managers in China, World Bank Policy Research Working Paper #5419.

- Jin, Yanhong, Hua Wang and David Wheeler, 2010b, Environmental Performance Rating and Disclosure: An Empirical Investigation of China’s Green Watch Program World Bank Policy Research Working Paper #5420.

- Kathuria, V.  2006. _A Concept Note on Public Disclosures – Using Information to Reduce Pollution_ . Madras School of Economics, Chennai, India.

Kennedy, P. W., Laplante, B., and J. Maxwell. 1994. Pollution policy: The role for publicly provided information. _Journal of Environmental Economics and Managament_ , 26, 1, 31-43.

Killmer, A.B. 2005. Designing mandatory disclosure to promote synergies between public and private enforcement. _In:_ Zealke, D., D. Kaniaru and E. Kruzikova (eds). 2005. _Making the Law Work: Environmental Compliance and Sustainable Development, Vol. 2_ , pp 49-66. London, Cameron May Ltd.

31
Table 8 shows the results of the regression using a sample from which South Africa was excluded and instead of including colonial dummies for the ACP countries a dummy was included for Pacific countries and a dummy for Caribbean countries. Even though there are small differences, the results of the previous estimations remain valid. The coefficient of the distance variable slightly increases and remains significant. The coefficient of the variable measuring the hypothetical value of preferences is significant and has a somewhat higher coefficient as with the previous specifications. Similarly to previous results the variable of dutydifference is not significant. Furthermore, all sectors have a higher coefficient indicating that sectoral differences are more important for the sub-sample excluding South Africa from the ACP countries. The dummies for the two country groups are significant only in the selection equation. The coefficients for both Pacific and Caribbean countries are negative in the selection equation indicating that these two country groups are less likely to request preferences than African countries.

**Table 9 Results of Heckman estimation without using the results of threshold estimation**

||(1|)|(2|)|(3|)|(|4)|
|---|---|---|---|---|---|---|---|---|
|Ldistance<br>LGDP|Utilization<br>rate<br>-0.023<br>(0.011)*<br>0.003<br>(0.003)|request<br>0.035<br>(0.003)**|Utilization<br>rate<br>-0.026<br>(0.012)*|request|Utilization<br>rate<br>-0.030<br>(0.014)*<br>0.006<br>(0.003)*|request<br>0.035<br>(0.003)**|Utilization<br>rate<br>0.052<br>(0.051)|request|
|LGDP/POP|||0.008<br>(0.002)**|0.014<br>(0.002)**|||||
|LPOP|||||-0.011<br>(0.003)**|0.008<br>(0.003)**|||
|LPOPeu|||||0.057<br>(0.015)**|0.235<br>(0.016)**|||
|LGDP/POPeu|||-0.070<br>(0.014)**|-0.199<br>(0.016)**|||||
|LGDPeu|-0.033<br>(0.003)**|0.032<br>(0.004)**|||-0.088<br>(0.014)**|-0.194<br>(0.016)**|||
|Freedom index|0.003<br>(0.002)|-0.004<br>(0.002)|0.005<br>(0.002)**|0.012<br>(0.002)**|0.006<br>(0.002)**|-0.007<br>(0.003)*|||
|French colony|0.025|0.048|0.009|0.083|0.030|0.055|||
||(0.012)*|(0.017)**|(0.012)|(0.018)**|(0.013)*|(0.018)**|||
|Non French<br>colony|0.005|-0.047|-0.008|0.004|0.012|-0.022|||
||(0.007)|(0.008)**|(0.007)|(0.009)|(0.008)|(0.009)*|||
|SouthAfrica<br>preferences|-0.243<br>(0.014)**|-0.326<br>(0.013)**<br>0.061<br>(0.001)**|-0.233<br>(0.008)**|-0.209<br>(0.008)**<br>0.061<br>(0.001)**|-0.219<br>(0.016)**|-0.347<br>(0.014)**<br>0.060<br>(0.001)**||0.061<br>(0.001)**|
|dutydifference|0.123<br>(0.027)**||0.135<br>(0.027)**||0.124<br>(0.027)**||0.126<br>(0.027)**||
|DUMagriculture||0.227<br>(0.010)**||0.223<br>(0.010)**||0.228<br>(0.010)**||0.274<br>(0.011)**|
|DUMtextiles||<br>0.248<br>(0.018)**||<br>0.251<br>(0.018)**||<br>0.244<br>(0.018)**||<br>0.217<br>(0.018)**|
|DUMfootwear||0.189||0.180||0.181||0.186|
|||(0.027)**||(0.027)**||(0.027)**||(0.028)**|
|DUMmachinery||-0.181||-0.176||-0.179||-0.135|

27
3 and fail to explain differences in population ratios in nations of similar culture. It follows that discrepancies in the share of women in the population must be primarily a r;atter of differences in survival probabilities.

Direct analysis of mortality rates supports the proposition that discrepancies in female population ratios are caused by gender differences **m** survival probabilities (Table 2). These differences are most pronounced for girls and boys between the ages of **1** and 4. Moreover, these current mortality rates and examination of cohort data<sup>4</sup> demonstrate that the present low female population ratios do not reflect excess female mortality that took place in the past but indicate an ongoing problem. The precise mechanisms which cause these discrepancies in mortality rates between boys and girls are not fully understood. But studies have found evidence of differential feeding, additional work burden inside and outside the home, and less attention during illness for girls tian for boys. Women also suffer excess mortality due to the risks of childbearing. One third of all deaths among Pakistani women aged 15 to 49 are due to complications o' pregnancy. 5 Given that women get less than their share of the goods necessary for survival, it is hardly surprising that their treatment falls short in other respects (Table 1). There is a considerable tendency for various indicators of the relative treatment of women to be correlated across countries. I am afraid that Pakistan clearly illustrates this point. It lags badly on almost every indicatcr. As I have said, Pakistan has the lowest ratio of women to men, and among low income countries, the fifth lowest ratio of female to male primary enrollment, and the seventh lowest female/male ratio in secondary enrollment.

# The Vicious Cycle of Deprivation?

Why are girls deprived in so much of the world and what explains the large variations across countries? These questions defy any simple answer. But even a cursory examination of the available informnation suffices to reject some explanations and to support others. It is a misconception that low female population ratios are an inevitable consequence of poverty. Africa has a far higher fraction of women than South Asia despite the fact that it fares equally poorly on measures of per-capita income and much worse on most other social indicators.

Comparisons between populations also allow us to rule out the notion that low female population ratios are unavoidable due to cultural tradition. It would be foolish to deny that culture has a role in explaining differences in sex ratios; any explanation of differing survival ' Examination of the female population ratios for cohorts in 1951, 1961, and 1972 show near equal increases as the cohort ages from 0-10 years to 10-20 years, indicating consistently higher female mortality.

I Senior Minister Begum Nusrat Bhutto. Inaugural Address. Safe Motherhood South Asia Conference. Lahore, Pakistan. March 1990.
9. Although many labor-intensive nonfarm activities provide work opportunities for the very poor, the aggregate data suggest a mixed Impact on Income distrlbution. In rural and urban areas alike, nonfarm Income constitutes the largest share of Income among both the very poor and the very rich (Table 3). For the wealthy, salarles, business profits and rents are most Important, while the poor depend most heavily on wage Income, both farm and nonfarm.<sup>2</sup> 10. By landholdlng, no such ambigulty arises. The smallest landholders routinely depend more heavily on nonfarm earnings than do families with larger holdings (Tables A.2 and A.3). Moreover, the recent NCAER panel study of rural households Indlcates that over time the smallest rural landholders, like all rural households, have become Increasingly dependent on nonfarm earnings (Table A.3).

B. Location 11. Not surprisingly, the density of nonfarm activity Increases dramatically In urban areas and with town size. In rural settlements, about 50 people per thousand work In nonfarm occupations (Table 4). Yet In even the smallest rural towns, of 10,000 to 20,000, that flgure quadruples. A second Jump In nonfarm activity occurs as town size Increases to 50,000 and then 100,000.

1 2. Currently, about 20 percent of Indla's nonfarm employment Is based In rural towns, defined In this paper as localitles between 5,000 and 100,000 In populatlon.<sup>3</sup> A further 35 percent reside In large citles over 100,000, while rural areas house the remaining 45 percent (Table A.4).

> 2 Unfortunately, **as** In **Tables** 2 **and** 3, most **studies fail** to **disoggregate between farm and** nonfarm **wages when** presenting income distribution prof I **I-_.** But **aggregate** income **figuroe** indicate that **nonfarm wagso** account for about 40 percent of rural **wages and 95 percent** of urban **wages.** Applying **these percentages** to **the wage** data In **Table** 3 **indicatoe** that under **any** conceivable **form-nonfarm** distribution, **nonftrm wages** account for **a far** higher **share of** incom for **the** poor then for **the** rich. **Pal and** Quison (1983, Table 13) corroborate this with NCAER **data** from 1970/71.

> J Tho remaining urban **settlements, those** over _100,000_ **are** roforred to **as** largo cities.