rafmacalaba/datause-encoder-data huggingface dataset:
of this paper comes from the coresidency restriction; most of the surveys suffer from sample selection due to coresidency used to define household membership. As noted before, this has been a strong discouraging factor for researchers worried about rejection by their peers, journal referees and the editors.

The recent economics research on intergenerational economic mobility in developing countries includes Behrman et. al. (2001), Hertz et al. (2007), Binder and Woodruff (2002), Thomas (1996), Lillard and Willis (1995), Lam and Schoeni (1993), Emran and Shilpi (2011, 2015), Bossuroy and Cogneau (2013), Maitra and Sharma (2010)). Most of the studies on economic mobility in developing countries rely on education and occupation as markers of economic status, because reliable data on income for long enough time periods to calculate permanent income are not available.<sup>13</sup> Most of them also use data selected nonrandomly due to the residency requirement for household membership. There is, however, no uniformity in the definitions of ‘household’ across different surveys, although all are concerned with ‘living together’, ‘eating together’, and sometimes with ‘pooling of funds’ (Deaton (1997)). Examples of household surveys that usually include coresidency as a defining criteria include Household Income and Expenditure Survey (HIES), Demographic and Health Survey (DHS), and Living Standard Measurement Survey (LSMS). There are some household surveys which include limited information on the parents of household head and spouse, but do not include the nonresident children of the household head. Hertz et al. (2007) use household surveys from 21 developing countries (10 Asian, 4 African, and 7 Latin American) and 8 formerly Communist countries where household surveys provide information on household head’s parents, but do not include the nonresident children.<sup>14</sup> When non-resident children are excluded from the survey, it results in truncation of the

> 13The data on the income of parental generation is especially difficult to find. Preponderance of home based economic activities including own-farming in parental generation makes it challenging to estimate income in many developing countries.

> 14Hertz et al (2007) are careful about sample selection bias, and they do not focus on the household head’s children as has been the case in many recent studies that rely on data without non-resident children. To the best of our knowledge, the only survey in Hertz et al. list of countries that cover all of the non-resident children in the survey is that for Bangladesh.

6
# **Table 7:  Agreement Ratio for Changes in Governance, 1998-2006**

||**Sample**|**A**<br>**Agree**|**LL CHANGE**<br>**No Change**|**S**<br>**Disagree**|**Agree / (Agree +**<br>**Disagree)**|
|---|---|---|---|---|---|
|**Voice and Accountability**|199|2.0|0.8|0.9|0.70|
|**Political Stability**|189|1.9|0.5|0.8|0.70|
|**Government Effectiveness**|194|1.9|0.9|0.9|0.68|
|**Regulatory Quality**|194|2.5|0.4|1.3|0.66|
|**Rule of Law**|194|2.5|1.7|1.4|0.64|
|**Control of Corruption**|194|2.0|1.3|1.1|0.65|
|**Average**|**194**|**2.1**|**0.9**|**1.1**|**0.67**|
|||**SIGNIFIC**|**ANT CHANG**|**ES (90%)**|**Agree / (Agree +**|
||**Sample**|**Agree**|**No Change**|**Disagree**|**Disagree)**|
|**Voice and Accountability**|15|4.3|0.5|0.3|0.94|
|**Political Stability**|23|3.9|0.3|0.3|0.93|
|**Government Effectiveness**|19|3.7|0.6|0.6|0.87|
|**Regulatory Quality**|15|5.1|0.1|0.8|0.86|
|**Rule of Law**|13|5.1|1.0|0.8|0.86|
|**Control of Corruption**|5|4.8|0.6|0.2|0.96|
|**Average**|**15**|**4.5**|**0.5**|**0.5**|**0.90**|

35
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22
22 shares, suggesting that the former is more important for the relative incomes of the poor.21 The method is straightforward to implement; however, time series data on income shares by deciles are not available in most countries making this method unlikely to be duplicated in ocher countries.

# 2.4 Quantitative Approaches: Partial Equilibrium

If a country is undertaking a sectoral adjustment program, then a partial analysis of that sector will provide more detail and will be useful for evaluating the program. Also, in countries where agriculture accounts for a large part of GDP, a partial examination of the agricultural sector will cover most of the activity. Finally, if a country has a specific issue that needs to be examined, a more partial approach is relevant.

# a. Sectoral Models

Sectoral models typically provide a partial equilibrium analysis of a particular sector, usually agriculture. They are useful for examining agricultural price reform and policies targeting the rural poor, and are most useful for countries in which agriculture constitutes a large share of GDP. An example is the Quizon and Binswanger (1986) model for Indian agriculture which focuses on income distribution. The model has detailed output supply, factor demand and output demand equations for the agricultural sector, and prices and quantities clear the markets. This model has limited links to the rest of the economy and the nonagricultural sector is exogenous, and so they call it a 21A study by Blank and Blinder (1985) for the U.S. shows that unemployment is strongly regressive and inflation is weakly progressive. If the government cares about incomes of the poor they should concentrate on generating employment and not worry about the potential inflation tradeoff. For the Philippines, the tradeoff between underemployment and inflation is much weaker, and so the policy implications are less clear.
<!-- Start of picture text --> (a) No potential (b) No speed<br><!-- End of picture text -->

**Figure 4:** For some abatement measures, the data needed to calibrate our model cannot be derived from the emission-reduction scenarios provided in MACTool. In some cases (a), the long term potential is not binding, because it cannot be reached before 2030. In some other cases (b), the diffusion speed was not investigated (or maybe the measure can reach its full potential in less than one year).

in a short time lapse).<sup>4</sup> To avoid this situation in the future, we recommend that the terms of reference for the experts in charge of collecting data on emission reductions options should explicitly ask to report possible diffusion speeds (Appendix B).

Finally, some emission-reduction measures (reforestation, air conditioning and rapid bus transit) were included in the list while lacking either a marginal abatement cost or an emission scenario. These measures, as well as those for which the diffusion speed could not be estimated, are discarded for the rest of the analysis.

The remaining options allow to reduce Brazilian emissions by 223 MtCO2 in 2030 (compared with 812 MtCO2 in the original MAC curve).

# _2.3. Optimal strategy with a 2030 objective_

In a first simulation, we run our spreadsheet model to design the optimal strategy to achieve 223 MtCO2 of emission reductions by 2030 (Tab. 2).

The optimal emission-reduction strategy has the following characteristics. All negative-cost measures are introduced at full speed from year 2010, independently of the emission-reduction target. This simply reflects that these measures are desirable _per se_ , as they bring more benefits than costs even in the absence of any carbon pricing or climate change impacts. For the positive-cost

> 4 Livestock and forest management is a particular example. In the emission-reduction scenarios, this measures allows to save 229 MtCO2, that is almost one third of the total abatement potential by 2030, as soon as 2010. Since Brazil has already managed to reduce drastically its emissions from deforestation (-80% between 2004 and 2009), the study considered that this mitigation option is already enforced. Sustaining such effort over a long period will require disseminating new technologies in the livestock sector to ensure that productivity gains are enough to free-up pasture land at a rhythm that allows to accommodate the growth of the livestock-agriculture sector without deforesting, as recommended in the Brazil Low-carbon study (de Gouvello, 2010). Such dissemination will face a similar inertia issue; however, this is a slightly different discussion.

9
1990s, and the increased domestic interest rates to protect the rupiah against the contagion effect of the 1995 Mexican crisis (see World Bank 1995, 1996).

Table 4.2. A Profile of Poverty and Inequality in the 1990s

||1993|1996|1999|2002|
|---|---|---|---|---|
|Headcount|61.53|50.49|55.29|52.41|
|Poverty Gap|21.03|15.33|16.56|15.68|
|Squared Poverty Gap|9.16|6.02|6.49|6.09|
|Watts|28.12|19.73|21.33|20.12|
|Gini|32|36|31|34|

Source: Authors’ calculations.

This track record was briefly reversed by the 1997 Asian financial crisis that took the form of rapid currency depreciation. As a result, real GDP declined by 13 percent in 1998, and real per capita expenditure declined almost 9 percent per year between 1996 and 1999 (based on the data presented in table 4.1), while the incidence of poverty increased from 50.49 percent to about 55 percent in 1999. Since 1999, the country has made some progress in restoring macroeconomic stability and in reducing the economy’s vulnerability external shocks. Real per capita expenditure grew on average 7 percent per year between 1999 and 2002, and poverty incidence started heading back to the pre-crisis level, reaching about 52 percent in 2002.

Focusing on the 1993-2002 period, we address the following question: _is the observed poverty reduction more or less than what the country would have experienced_ , _had economic growth been distributionally neutral_ ? To answer this question, we consider the following sub-periods: 1993-1996, 1996-1999, and 1999-2002. We also consider a counterfactual for the crisis period 1996-1999. In this case we estimate what might have happened to poverty had per capita expenditure grown by about 9 percent a year instead of declining by the amount implied by the data as presented in table 4.1. We use this aggregate data to recover the underlying size distributions of expenditure from the means and parameterized Lorenz curves. Our parameterization is based on the General Quadratic model described in Datt (1998). We then use numerical integration to compute and interpret measures of pro-poorness according to the analytical framework discussed above for all sub-periods, except 1999-2002. The measures reported for this 19
<!-- Start of picture text --> Figure 4: Average Score - Public Sector Management and Institutions<br>2000-2008<br>(Top, Middle, and Bottom Countries in terms of Per Capita Income)<br>4.5<br>4.34.1 —_ ——————————<br>3.9<br>3.7<br>3.5<br>ee<br>ee<br>2.9<br>2.7<br>2.5<br>S ty AY % y & © A 2<br>S S iS S S S iS S S<br>v - ° - ° ? ° ? °<br>—Top -——Middle -Bottom<br><!-- End of picture text --> <!-- Start of picture text --> Figure 5: Change in Average Score (2000-2004 and 2004-2008) - Public<br>Sector Management and Institutions<br>(Top, Middle, and Bottom Countries in terms of Per Capita Income)<br>0.60<br>0.50<br>0.50<br>0.40<br>0.34<br>0.30 @ 00-04 Change<br>0.19 @ 04-08 Change<br>0.20<br>0.10<br>0.00<br>Top Middle Bottom<br><!-- End of picture text -->
appropriate, if paradoxical, to adopt some sort of survey approach here as well. However, this might prove difficult, since the lack of transparency is likely to reflect a combination of a genuine lack of information, departmental turf wars, and the preference of some of the wellinformed to avoid close scrutiny.

Third, the valuation of private activities is often fairly straightforward, and relies on the assumption of consumer sovereignty coupled with revealed preference. If a household purchases a quantity of a commodity, it is assumed that the household values the marginal unit at the purchase price. There may be various wedges (such as taxes or market imperfections) driving a wedge between producer and consumer prices, but these prices can be directly observed, and weighted using estimates of supply and demand elasticities. Valuing public expenditures is much more difficult. They may be pure public goods, or involve very substantial externalities. Even when they do not, typically the quantities consumed are rationed, so even if a price is charged, it offers no direct evidence on value. In consequence, a variety of strategies has been adopted to try and tease out how valuable the public expenditure actually is.

# _1.3 The structure of the paper_

The agenda stated in 1.1 poses (at least) five different challenges.

First, these matters may be considered from at least three perspectives, the normative, the positive, and a plausible hybrid of the two. The normative approach would focus on some sort of optimal or best practice expenditure responses, whereas the positive would focus on what is likely to happen in practice. The hybrid approach would consider how instruments might be designed to shift actual practice closer to best practice. These matters are considered further in section 2.

Second, the focus of the current research program is on what improvements could be supported by an appropriate choice of Sovereign Disaster Risk Financing and Insurance (SDRFI), or possibly a set of alternative choices. Hence, the issue is one of differential performance under different choices. This is discussed in section 3.

Sections 2 and 3 are concerned with how to establish how expenditure outcomes may differ. The third challenge is to attach values to these different outcomes. Assessing the benefits accruing from public expenditure can be exceedingly difficult even in what might be called the routine case, where no disasters or other shocks intrude. What is involved here is still more difficult, since it involves disruptions to the routine. The matter is considered further in Section 4, which shows that asymmetric damage to the public capital stock may mean that there is a very high cost to delayed reconstruction of public infrastructure, not only directly, but also indirectly, by compromising private incentives to rebuild damaged private capital as well.

Section 5 examines the fourth challenge, relating to macroeconomic impact. Papers by Fattal Jaef (2014) and Hallegatte (2014) consider macroeconomic modeling directly, so the treatment 3
Again, we get all coefficients significant as with the intensive margin and, for simplicity, we present a summary table of the coefficients by groups of countries in Table 5. In particular, this is the set of results more relevant for the purpose of the focus of our study as it deals specifically with the extensive margin of trade (the probability that zero values switch to be positive values).

# **Table 5: Tobit estimation – Average Marginal Effect on the change in the probability to export more goods (by groupings)**

Dependent variable: log (exports)

||log (GDPd)|log (dist)|North<br>Pa|rts & Comp|FTA|
|---|---|---|---|---|---|
|LAC|0.039|-0.103|-0.004|0.071|0.005|
|Developed|0.110|-0.092|0.022|0.113|0.126|
|SSA|0.008|-0.070|0.076|0.018|0.028|
|ASIA|0.051|-0.072|0.111|0.048|0.077|
|ECA - MENA|0.012|-0.041|0.036|0.026|0.031|

Authors calculations using COMTRADE and WDI data. The estimation also includes sector and time dummies. Groupings: LAC: Argentina, Brazil, Chile, Peru and Mexico. Developed: Australia, Canada, European Union, Japan and the United States. SSA: Ghana, Kenya, Mauritius, South Africa and Uganda. ASIA: Bangladesh, China, Indonesia, India, Malaysia and Thailand. ECA-MENA: Morocco, Tunisia and Turkey.

As we can see, we confirm the evidence found before with respect to the importance of the gravity pattern in the determination of a change in the probability to export more goods. Distance and size of the destination market determine significantly the change in the probability to diversify. Again, we observe a higher impact of destination market size for developed countries and of distance for developing countries.

Trading with a partner in the North also helps to the process of diversification. The impact is slightly larger for developing countries, especially for Asian, African and ECA-MENA countries.

Signing FTAs and therefore reducing trade costs helps to boost diversification. Developed countries benefit more from this. Of the developing countries, those who seem to benefit the most from this are the Asian countries (trade more with the North) and the countries surrounding Europe (ECA-MENA). This finding also emphasizes the importance of the gravity variables in our estimation, as it would show that reducing trade costs impacts more on trade when the agreement is either with a larger or a closer economy. Finally, trade in parts and components also helps the diversification process, particularly in the case of Asian and Latin American countries.

Even though these last results do not provide estimation on the exact impact on the extensive margin of trade, they provide an indication of how the likelihood to diversify more is affected and confirm the main finding about the impact of our gravity and trade costs variables in creating trade in new varieties.

20
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39
# Table Al:

# Elasticity Specification (Central Case)

|Sector|(1)|Colu<br>(2)|mn No<br>(3)|tes<br> (4)|<br> <br>(5)|Elasticity<br>of<br>Substitution<br>Intermediates(.)<br>(1)|Elasticity<br>of<br>Substitutio<br>Capital/Lab<br>(2)|Elasticity<br>of<br> <br>Transformatio<br>n Domestic/Expo<br>or<br>Sales<br>(3)|n Price<br>Ela<br>rt of Fina<br>Domestic<br>(4)|sticiti<br>l<br>Demand<br> Inport<br>(5)|es<br>Premia<br>s Rates<br>(6)|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Agriculture|a|c|*|k|f|1.4|0.6|4.0|0.76|0.8||
|Food|a|c|*|f|f|0.3|0.6|3.0|0.80|1.1||
|Mining|b|_b_|a|j|f|0.5|0.8|3.0|0.50|1.0||
|Crude Oiland Natural||||||||||||
|Gas|f|c|*|j|***,f,e**|2.4|0.8|8.0|.6|.9||
|Iron and Steel|a|d|*|i|t|8.0|1.0|8.0|1.0|1.4||
|Motor Vehicles|a|c|*|h|h|2.0|0.8<br>S.0||1.2|1.1|22.8X|
|Textiles and Apparel|a|c|*|I|t|2.6|1.0|8.0|0.4|8.9|40.56|
|Other Manufactures|a|c|*|f|f|836|0.8|8.0|1.5|1.8||
|Other Consumr|a|c|a||f|3.2|0.8|8.0|1.9|2.4||
|Petroleum Products|f|c|*|J,e|*f|2.4|0.8|a.0|.5|.9||
|Traded Services|b|c|*|g|9|2.0|0.8|0.7|0.6|0.6||
|Ron-TradedServices||b||g|||0.8||0.5|||

C.) CES and CET functions Imply that the corresponding elasticities of substitution (transformation) correspond to compensated import demand (export supply) elasticities.

All price elasticities of demand defined **as** positive numers. For premis estimates **see** de M-lo and Tarr (1988). Column notes correspond to **the** sources from which stimates **are** Interpolated. For Interpolation details **se** Tarr (1988).

(a) Shiolls, Deardorff and Stern (1986); (b) Dixon *t al. (1982); (c) Caddy (1976); (d) Hlkman; (e) own estimate s (f) Stern, Francis and Schumacher (1976); (9) Houthakkor and Talyor (1970); (h) Levinsohn; (i) Crandall (1981); (j) Bo%i and Russell (1978); (k) USDA (1984); (l) Hufbauer t al. (1986).