rafmacalaba/datause-encoder-data huggingface dataset:
health care. Some of their population will relocate over time. But for reasons of both national efficiency and equity, it would make sense for the state to ensure that everyone has good basic education and health care, so that when people move they come with a solid foundation of human capital.

China’s highly decentralized fiscal system results in local government in many locations not having adequate resources to fund basic social services. As a consequence, households are left to fend for themselves to a remarkable extent. The average hospital visit in China is paid 60% out-of-pocket by the patient, compared to 25% in Mexico, 10% in Turkey, and lower amounts in most developed countries (Figure 6). Poor households either forego treatment or face devastating financial consequences. In the 2003 National Health Survey, 30% of poor households identified a large health care expenditure as the reason that they were in poverty.

The situation in education is similar. In a survey of 3037 villages in 2004, average primary school fees were 260 _yuan_ and average middle-school fees, 442 _yuan._ A family living right at the dollar-a-day poverty line would have about 888 _yuan_ total resources for a child for a year; sending a child to middle-school would take half of that. Not surprisingly, then, enrollment rates are relatively low in poor areas and for poor families.

# **_4. The trade balance and inequality_**

The theme of this panel is “disparity,” and I have interpreted that to be largely about China and largely about long-term structural policies that have led to unnecessarily large disparity in China. But I also want to comment briefly on an important contemporary issue, the trade imbalance between China and the U.S., because _ironically_ I think that the imbalance is exacerbating disparity _in both countries._ The integration of a populous country such as China into the global economy inevitably imposes adjustment on every other country, including the U.S. Economic theory suggests that the U.S. economy and the global economy gain overall from the interaction with China, but that there will be individual winners and losers. Since China began opening up, and especially in the past 15 years, the global economy and the U.S. economy have grown well, which supports the notion that there are overall benefits. The 12
processed food products and not by industrial exports.2 This underlines the general importance of the agricultural sector for the development of other sectors and as a potential source of growth in Ecuador.

**Table 1:**

|**Contribu**|**tion to GDP**|**in percen**|**tage share**|**of total G**|**DP**||
|---|---|---|---|---|---|---|
||**1965**|**1970**|**1973**|**1980**|**1990**|**1998**|
|**Agriculture**|**25.78**|**24.97**|**18.09**|**14.36**|**17.67**|**17.28**|
|**Industry**|**22.30**|**24.19**|**39.22**|**33.80**|**31.74**|**32.96**|
|**-**Oil|||19.38|10.21|11.81|13.52|
|- Manufacturing|15.23|17.17|<br>14.11|18.16|15.45|15.48|
|- Electricity|0.59|0.76|0.67|0.76|1.53|1.40|
|- Construction|6.49|6.26|5.07|4.68|2.94|2.56|
|**Services**|**47.64**|**50.33**|**40.14**|**50.02**|**48.96**|**48.67**|
|- Commerce|14.83|14.81|<br>12.39|14.66|13.35|13.52|
|- Transport|4.00|5.36|4.53|6.11|6.17|6.25|
|- Financial|1.72|2.46|2.22|3.88|2.36|3.55|
|- Other (non-governmental)|<br>18.96|<br>18.15|<br>14.27|16.08|18.25|18.53|
|- Government|8.12|9.55|6.72|9.29|8.82|6.81|

_Source:_ Banco del Ecuador

> **2** Table A6 in the appendix lends further evidence to this point and shows that exports are mainly primary or semi-industrialized.

2~~~~~~~~~~~~~~ _5_
In some other transition economies the<sup>processof newfirmsformation has</sup> been much more dynamic than in Croatia.<sup>For examplein Poland the numberof firms</sup> increased by some 80 percent over the<sup>period1995-2000,implyingan averageannual</sup> rate of growth of almost 13 percent,<sup>whichismuch higherthaninCroatia.6</sup> The example of Poland shows that fast<sup>firmgrowthdoesnotprotectfromhigh</sup> unemployment. However, the unemployment problem<sup>wouldhavebeenfar more</sup> serious if the firm growth had been<sup>slower.</sup> A likely reason for the insufficient pace of<sup>newfirm formationis high costsof</sup> entry in Croatia, one of the highest<sup>amongtransition economiesof CEE.Forexample</sup> Djankov et al. (2000) report that<sup>the numberof procedures for entryis14in Croatia</sup> (7 in Latvia, 9 in Slovenia), time for<sup>entryis58days(26 days in Poland,35in</sup> Slovenia), and monetary cost is 34<sup>percent of GDP per capita (7% in Slovenia,17%in</sup> Bulgaria). These high costs are bound<sup>tosignificantlyslowdowntherateof new</sup> enterprise growth, with a detrimental<sup>effecton employment.Consequently,lowering</sup> the costs of firm entry is a condition for<sup>faster job creation.</sup> As expected, the structure of employment<sup>byfirmsizehaschanged.</sup> Employment in large firms fell by<sup>almost 5 percentduring1993-2000.It alsofellin</sup> medium-sized firms, although less<sup>precipitously(byabout4percent).Atthesame</sup> time, employment in small firms increased<sup>byover8percent.</sup> Looking from a different angle, employment is state<sup>ownedfirm decreasedby over12percentwhile</sup> in private firms increased by 11 percent.<sup>Thus theoldsector,consistingof largestate</sup> owned enterprises has been shrinking<sup>whereasthenewsector,consistingof small</sup> private firms is expanding. However,<sup>these changes,althoughsignificant,havebeen</sup> rather modest in comparison with<sup>leadingreformers(suchastheCzechRepublic,</sup> Hungary, or Lithuania).<sup>7</sup> Although small enterprises' share of employment<sup>has risen in Croatia,it is still</sup> relatively low. Small enterprises (employing fewer<sup>than50workers),whicharea</sup>

> 6 It should be borne in mind that cross-country comparability<sup>of data onthe numberof firmsis</sup> limited. For example in some countries (Poland is<sup>anexample)therehavebeen incentivesto switch</sup> from dependent employment to self-employment,<sup>whichhas inflated the number of firms.</sup> 7 Recent comparable data on employment<sup>by private/public sectoror firmsize arenot available.</sup> However, using the private sector share in<sup>GDP as a proxy,one sees that privatefirmsplay a lesserrole</sup> in Croatia than in more advanced transition economies.<sup>Forexample,in1999 the private sector share</sup> in GDP in Croatia was somewhat above 60<sup>percent,while it was around80percent in the Czech</sup> Republic or Hungary or Estonia or Lithuania<sup>(World Bank, 2002).</sup>

_-15-_
SAV_gov=IDMACSAM(5,4) SAV_hh=IDMACSAM(5,3) mps_hh=SAV_hh/((1-ytx_hh)*Y_hh) CONS_hh=(1 - mps_hh)*(1 -ytx_hh)*Y_hh/PQ SAVTOT=SAV_hh + SAV_gov + (EXR*FSAV) MODEL CALIBER omega_x.fill 0.2, 0.5, 0.75, 2, 5 'Different values of export transformation elasticity for sensitivity analyis sigma_q.fill 0.2, 0.5, 1.26, 2, 5 'Different values of trade substitution elasticity; CALIBER.APPEND rho_q=(1/sigma_q) - 1 CALIBER.APPEND phi_x=(1/omega_x) +1 CALIBER.APPEND alpha_x = 1/((PDT/PE)*(XE/XD)^(1/omega_x) + 1) 'Share for the CET function CALIBER.APPEND a_x = XS/(alpha_x*XE^phi_x + (1-alpha_x)*XD^phi_x )^(1/phi_x) 'Scale factor for the CET function CALIBER.APPEND beta_q=( (PM/PDT)*(QM/XD)^(1+rho_q) )/(1+ (PM/PDT)*(QM/XD)^(1/sigma_q) ) 'Share factor for the CES function CALIBER.APPEND b_q = QQ/(beta_q*QM^(-rho_q) + (1-beta_q)*XD^(-rho_q) )^(-1/rho_q) 'Scale factor for the CES function CALIBER.SCENARIO ACTUALS CALIBER.SOLVE(s=d, d=s,o=n) CALIBER.MAKEGROUP CALGRP @ENDOG FREEZE(CALTAB) CALGRP '================Simulations================================================ IDGSS.SOLVEOPT(s=d, d=s, c=1e-15, o=n) '*****Baseline Solution***** IDGSS.SCENARIO(c) BASELINE SOLVE IDGSS IDGSS.MAKEGROUP(a) BASEGRP @ENDOG FREEZE(BASELINE) BASEGRP '*****An Increase in the World Price of Export***** SERIES PWE_tot=1.20*PWE IDGSS.SCENARIO(n, a=tot) BOOM IDGSS.OVERRIDE PWE SOLVE IDGSS IDGSS.MAKEGROUP(c) BOOMGRP @ENDOG FREEZE(BOOMTAB) BOOMGRP '*****An Increase in the Tariff Rate***** SERIES tm_tar=1.50*tm IDGSS.SCENARIO(n, a=tar) TARIFFUP IDGSS.OVERRIDE tm SOLVE IDGSS IDGSS.MAKEGROUP(c) TARGRP @ENDOG FREEZE(TARITAB) TARGRP 'END OF PROGRAM 32
# **1. Introduction**

As has been widely documented, the world-wide economic crisis of 2008 and 2009 was accompanied by a severe fall in international trade. Baldwin (2009) characterizes the collapse as ―sudden, severe, and synchronized... the sharpest in recorded history and deepest since WWII.‖ A number of hypotheses exist to describe what caused the collapse and why it became so widespread and deep (for summaries, see Baldwin 2009 and CEA 2010). In addition to the fall in aggregate demand, a number of supply-side factors may have played a role. Anecdotal evidence abounds that trade finance was restricted, which could have resulted in a decline in trade from the supply side. Others have noted that with the globalization of supply-chains, a fall in manufactures could lead to an outsized fall in total trade, particularly if supply chains are disrupted. Finally, protectionism rose, although not to the extent that was initially feared.

The speed and durability of the recovery could depend on which margin, extensive or intensive,<sup>2</sup> has been the most affected by the crisis, and by how fast it responds to fiscal stimulus. For VOXEU, Peter Schott wrote in November 2009 that ―If the current ̳shock‘ to US trade is similar to those that have occurred before, most of the decline in exports and imports we‘ve seen is due to less intense trade rather than firm and firm-product exit. To the extent that this is true, trade will bounce back relatively quickly once conditions improve. It is of course possible that the severe shortage of credit available to firms over the past year has led to a higher-than usual share of harder-to-reverse firm exit, potentially dampening the speed of recovery‖.

As indicated by Schott, if there are significant fixed costs associated with exporting or importing new products, then identifying whether most of the changes in trade were on the intensive margin is useful in predicting the speed of the recovery. There has also been

> 2The intensive margin refers to changes in the value of exports due to changes in the quantities and/or prices of already exported goods. The extensive margin refers to changes in the value of exports due to changes in the number of goods exported and/or changes in the number of destinations to which a country exports old or new goods.

2
India and Pakistan are the only countries that have succeeded in exporting a noteworthy share of manufacturing products to the region. India in particular displays a higher regional orientation for manufactured products spanning a broad array of product groups. India’s leading regional manufactured exports during this period included motor vehicles, cotton yarn, medicines, textiles, and apparel.

The comparatively high intra-regional trade ratios for manufactured goods displayed by India suggest that it has developed “appropriate technology” (i.e. technology that meets basic functional features but are comparatively low cost) to meet demand in the region as substitutes for products from industrial countries. The ascendance of certain manufactured exports differentiated between what used to be the industrial countries’ domain such as automobiles, pharmaceuticals, and agricultural implements. These highly segmented industrial products accounted for 36 percent of India’s regional exports, compared with 7 percent of its exports to the rest of the world.<sup>13</sup> This development illustrates the segmentation of India’s evolving “comparative advantage,”<sup>14</sup> i.e. its ability to fulfill, to a certain extent, the region’s demand for manufactured goods, a characteristic that the other countries have not sufficiently demonstrated.

In summary, the region has established a mutual dependency in basic foods and agricultural products, although they are not fully liberalized. A narrow group of products on which most countries display comparative advantage has made inroads in regional trade and are mostly made up of agriculture and raw material for manufacture. The exception is India. The extent to which Bangladesh, Nepal, and Sri Lanka initiated trade policy reforms resulted in sizable increases in imports from India both in agriculture and manufacture. They helped India expand as well as diversify its exports to the region during the period 1990– 98.

# **Box 1: The Impact of Trade Reforms**

Trade reforms in fact helped South Asia to increase regional and extra-regional trade between 1990 and 1998.<sup>B1</sup> The reforms reduced the levels of protection, but stimulated only a moderate shift in the product

> 13 Comparison based on SITC 4-digit exports.

> 14 A further interesting observation is that India seems to be acquiring market share particularly in other developing countries beyond the South Asia region for this group of manufacturing products, conceivably emerging from typical demand attributes to suit those in the region.

22
# 1 Introduction

Designing voluntary programs that induce behavioral change can be fraught with problems of selfselection: households who would have sent their children to school without incentives are compensated for enrollment (De Janvry & Sadoulet 2006), investors who were already building lowincome housing receive tax benefits intended to induce them to do so (Sinai & Waldfogel 2005, Gyourko 2009), energy efficiency programs enroll individuals who would already have adopted energy-efficient technologies (Joskow & Marron 1993, Boomhower & Davis 2014), or producers who did not intend to deforest are paid for forest conservation efforts (Alix-Garcia et al. 2008, Ferraro 2008). While conditional cash transfer programs (CCTs) usually benefit the voluntary recipients, they often fall short on the behavior-change goals, and thus can result in inefficient allocation of scarce resources. One of the important lessons from this literature is that effective targeting requires being able to predict which potential beneficiaries are most likely to engage in the targeted behavior (truancy, pollution, deforestation, etc.). Yet obtaining the information necessary to identify where transfers are most effective and determining the appropriate levels of compensation can be immensely difficult and expensive in practice. Practical strategies that successfully allow policy-makers to do this are scarce (Johnson & Lipscomb 2017, Allcott & Kessler 2018).

In this paper, we study CCT design in the context of a major environmental cash transfer program in Mexico. We propose that adding a simple stated preference referendum-style willingness to accept (WTA) question to occasional surveys of potential recipients could be used to improve program additionality and cost-effectiveness as well as to generate estimates of program social cost. Specifically, we illustrate two strategies to refine program design for conditional cash transfers: re-targeting based on the WTA estimates and a constructed risk index using observable geographic covariates, and changing the program’s schedule of payments based on geographic zones and WTA estimates.

The conditional cash transfer we study is focused on reducing deforestation. Tropical deforestation threatens biodiversity, carbon storage, watershed function and rural livelihoods, and policymakers continue to struggle to find effective strategies to combat it. More than eight countries have adopted large payments for ecosystem services (PES) policies, which compensate landowners for conservation activities on their own land. Payments for forest conserva2
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Hirsch, F., 1976, _The Social Limits to Growth_ , Routledge & Kegan Paul, London.

Hopkins, E., 2008, "Inequality, happiness and relative concerns: What actually is their relationship?" _Journal of Economic Inequality_ , 6(4): 351-372

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- 4 -

1987 output price structure, and what It would be If producers had recelved world trading prices but used unchanged technology and did not adjust to the dlfferent relathve prlf 9.

The particular parameter estimates In this work do not generalize to the Sovlet Union as a whole. Producer prices for grains In 1986 and 1987 were higher In Lithuania than In the graln belt of the USSR, and prices for livestock products, particularly milk, were lower than In more easterly regions. Because the USSR Is so large and Its separate regions have not been connected through rationally Interlinked prices, It Is Inappropriate to genera!tir about the effects of reform on the USSR as a whole from analysis of a partIcular region. Conclusions based on analysls of average prices throughout the USSR do not capture the potential for better utilization of regional comparative advantage and Interregional trade within the c,)untry, and this woud be a major payoff of the reform. The methodological approach of this paper; Le.; estimation of marginal value products of land under alternative output price structures, could be used throughout the USSR as the methodology of valuation If land markets continue to be repressed.

The following discussion Is based on data from the 1986 and 1987 annual accounts (godovye otchety) of 1032 state and collective farms In Lithuanla. The LlthuanIan data are additionally valuable because they Include measures of land quality rarely available with production data. Lithuanian researchers have made detailed analysis of different soil types on each of the state and collective farms. Each farm Is assigned a soil quallty Indicator (bonitet) based on the yield of a standard crop (standardized according to feed units) on comparable soil under conditlons of average management. The variable measuring land quality (bonitet) ranges from 27 to 66 with a mean value of 41.
Second, CoRD estimated using the household survey data, but which is restricted to select the same food items that the CPI data select, is _higher_ than the CoRD estimated using the CPI data (compare the third and fourth bars to the fifth bar). That is, the food items selected by the CPI price data are more expensive when estimated using household survey unit values. Calculating CoRD using average unit values (with the same CPI-selected items) is higher than the CPI-based CoRD by 19 percent in Sri Lanka and by 10 percent in Pakistan. Third, using median unit values always results in a lower CoRD than using average unit values, as using the median diminishes the influence of outliers (compare the first bar to the second bar, and the third bar to the fourth bar).

One of the key advantages of using household survey data is our ability to get closer to the concept of affordability. Specifically, we can calculate the proportion of individuals whose current food expenses fall below CoRD. We interpret this as the proportion of individuals who potentially cannot afford to pay for the kind of food that comprise a nutritious diet, based on how much they currently spend on food.

Ultimately, we choose to calculate CoRD calculated using median unit values from household survey data. This allows us to use a precise measure (eliminating outliers in unit values) and to be consistent in data sources (comparing food expenses to CoRD both taken from the same dataset). Note that CoRD calculated using median unit values is also our lowest estimate of CoRD across methods and datasets. As such, we will be presenting our lowest-bound estimate of individuals who cannot afford CoRD. In Sri Lanka, we use the per capita spatially adjusted food expenses (at the district level). In Pakistan, we use the per adult equivalent spatially adjusted food expenses (at the PSU level).<sup>28</sup>

**Figure 10: Percent of Population with Food Expenses Falling Below National CoRD** a) Sri Lanka 2016 b) Pakistan 2015–16

<!-- Start of picture text --> 80 100<br>64<br>60 59 82<br>60 54 52 50 49 80<br>45<br>41 40<br>40 60 59 58 58<br>47<br>20<br>40<br>0<br>20<br>0<br>Balochistan Sindh Punjab KPK Pakistan<br>Source : Authors’ calculations using Sri Lanka HIES 2016 and Pakistan HIICS 2015-16.<br>Sabaragamuwa Northern Uva Central EasternSouthern WesternNorthCentralNorthWesternSri Lanka<br>Percent<br>Percent<br><!-- End of picture text --> Figure 10 shows the proportion of the population who spend less on food than the cost of a nutritious diet, as calculated using CoRD in Sri Lanka and Pakistan, and across their provinces. In Sri Lanka, 49 percent of the population spends less on food than CoRD. It is highest in Sabaragamuwa province (64 percent) and lowest in Northwestern province (40 percent). In

> 28 Population weights are used to calculate means.

19