rafmacalaba/datause-encoder-data huggingface dataset:
WPS3747 lines.<sup>6</sup> The macroeconomic and social impacts of the first set of scenarios are presented in Tables 3 and 4.

We first examine the impact of the pre simulation scenario (PRESIM). The aggregate real impact is small and negative. Private consumption decreases by 0.2 percent. Imports and exports also decrease by 1.4 and 1.0 percent respectively. These results stems from a slight deterioration of the terms of trade facing Indonesia as the demand for its exports fall in the wake of quota elimination and is driven by the fact that the estimated quota rents in the initial data base are much larger on China than on other textile exporting countries like Indonesia (François and Spinanger, 2004). At a more disaggregated level, the shock leads to some reallocation of factors across sectors. Not surprisingly, the textile and wearing apparel faces the worst terms of trade shock, with export prices falling by 3.8 percent and import prices increasing very slightly. As a result, value added from the textile and wearing apparel sectors decreases by more than 30 percent and factors of production are reallocated towards other manufacturing sectors.

At the household level, the poverty and inequality impacts are relatively small. However, these results likely underestimate the cost of reallocation suffered by workers in the textile and wearing apparel sector. While these costs are not captured by the model, some displaced workers could suffer, at least temporarily, from unemployment, particularly since they are likely to come from informal sectors that do not provide unemployment benefits.

Starting from the updated base year generated with the PRESIM scenario, the full trade liberalization impact is examined through a set of 3 simulations. In the first one, we examine the impact of unilateral liberalization of Indonesia (ULIB); in the second, we consider the impact of

> 6 The use of official poverty lines gives a much lower incidence of poverty than the $2 per day mark, and hence less scope for change.

10
municipalities’ needs may also affect spending ability. Our visits suggested that communities with more basic needs may find it easier to identify their needs during stakeholders’ consultations, and that the types of projects implemented in these municipalities may ease spending since they tend to be smaller, and of simpler design.

In spite of the strong attention that fiscal decentralization has received in the academic and policy literature, existing quantitative studies limit themselves at looking at specific constraints, and fail to assess the relative importance of the various factors highlighted above. On the other hand, more comprehensive studies reviewing factors affecting decentralization, which tend to be more of a policy nature (see, among many, Smoke, 2001; Tanzi, 2001; and Eaton _et al._ , 2010), remain for the most part qualitative. In this paper we bridge these two strands of literature by assessing quantitatively the relative importance of each factor in determining spending ability. In looking at spending ability, our research also complements existing literature looking at the extent to which various factors, such as gender, elite capture, or revenues from natural resources affect the type of spending (Chattopadhyay and Duflo, 2004; Araujo _et al._ , 2008; Caselli and Michaels, 2009) and spending efficiency (De Borger and Kerstens, 1996; García-Sánchez, 2006; and Balaguer-Coll _et al._ , 2007). While it would be of great interest to complement the analysis of spending ability with information on spending efficiency, to the best of our knowledge these data currently only exist for high income countries.

# **III. Data**

The analysis draws on a combination of particularly rich databases containing information at the municipal level. For each of the more than 1,830 municipalities in Peru we have information on public spending undertaken by the municipality, population, staffing levels at the municipality, poverty, reported social conflicts, and a number of features of the political process.

The public expenditure data at the municipal level is particularly detailed. For each municipality in Peru we have available the budgeted public spending as well as the actual public spending outlays for 2007 through 2009. While information is in principle available 8
Blau and Khan (2001) examined the role of cognitive skills in explaining higher wage inequality in the United States. Leuven _et al_ . (2004) used IALS data for 15 countries (including Chile) and explored the hypothesis that wage differentials between skill groups across countries are consistent with a demand and supply framework. They find that cognitive achievement is an important determinant of earnings in all countries examined except Poland and Finland. They also find that about one-third of the variation in relative wages between skill groups across countries is explained by differences in net supply of skill groups. Green and Riddell (2002) used the measure of literacy in the IALS dataset to examine the influence of cognitive and unobserved skills on earnings in Canada. They find that cognitive skills contribute significantly to earnings and that their inclusion in earnings equations reduces the measured impact of schooling. Their findings suggest that cognitive and unobserved skills are both productive but that having more of one skill does not enhance the other's productivity. Devroye and Freeman (2001) used the IALS survey and found that skill inequality among advanced countries explains only about 7 percent of the cross-country differences in earnings inequality. They also find that the bulk of cross country differences in earnings inequality occur within skill groups, not between them.

# **The 1981 Capitation Grant (Vouchers) Scheme**

We use the school reform of 1981 to identify a binary instrument and estimate returns to schooling using instrumental variables, with and without controlling for cognitive skills. Chile, among developing countries, was a pioneer and has gone farther than any other country to redefine the roles of the state and private sector in education, by separating the financing from the provision of education. Consequently, the dropout rate declined sharply (from 8 percent in 1981 to 2.7 percent in 1982 for basic education, and from 8.3 percent to 6.2 percent for 3
### **The Role Of Factoring In the New EU Member States**

more important in producing information about payment performance than the factors, as found by Jappelli and Pagano (2001).

# **LEGAL ENVIRONMENTS IN THE EU 8**

This section examines EU 8 legal environments—and their effects on factoring—from various perspectives, assessing the strength of the rule of law, commercial law, contract enforcement, and bankruptcy proceedings.

## **Rule of law**

The strength of a country’s rule of law affects the level of mutual trust in transactions among state institutions, ordinary citizens, and firms (EBRD Transition Report 2003a). Arrow (1972, 1974) calls trust an “important lubricant” in commercial transactions, and it has been shown empirically that the level of trust influences the terms on which banks lend to SMEs (Harhoff and Korting 1998).

**Figure 7. Rule of law and progress in transition**

<!-- Start of picture text --> Rule of law governance indicator, 1996<br>0.8<br>0.7<br>Czech Rep.<br>Hungary<br>Slovenia Poland<br>0.6<br>Estonia<br>Latvia<br>Slovak Rep.<br>0.5<br>Bulgaria<br>Bosnia and Herz. Moldova Lithuania<br>Albania Romania<br>0.4<br>Armenia Croatia<br>FYR Macedonia<br>Kazakhstan<br>Ukraine Kyrgyz Rep.<br>0.3<br>Belarus Azerbaijan Russia Georgia<br>Uzbekistan<br>Turkmenistan Serbia and Mont.<br>0.2<br>Tajikistan<br>0.1<br>0<br>1.0 1.5 2.0 2.5 3.0 3.5 4.0<br>EBRD transition index, average 1997- 2003<br>Sources: EBRD and World Bank.<br>Note: The rule of law governance indicator measures perceptions of the effectiveness of the judiciary, the enforceability of<br>contracts, and the incidence of crime. In the chart, 0 represents the lowest and 1 the highest level of rule of law.<br><!-- End of picture text --> In Eastern Europe there is a positive association between the rule of law and structural and institutional reform. That is, the countries with the strongest rule of law have generally made the most progress in transition (Figure 7). Such countries also have the highest levels of trust (as measured by vendors’ demands for prepayments) and lowest levels of corruption (as measured by the World Bank’s “control of corruption” variable and Transparency International’s corruption perceptions index).

**28**

**THE WORLD BANK**
education maps democracy onto long-run changes in the distribution of human capital and then onto contemporary changes in income inequality.

The rest of the paper proceeds as follows. The next section briefly presents a review of four mechanisms that link democracy and inequality, their limitations, and presents an alternative approach. Section three describes the data. Section four describes the econometric approach and addresses the methodological challenges. Section five presents the main results and shows suggestive evidence that education may be one mechanism explaining the link between democracy and inequality. The last section concludes.

# **2 Do democracies breed more egalitarian societies?**

Meltzer and Richard (1981), Alesina and Rodrik (1994), and Persson and Tabellini (1994) propose that utility-maximizing individuals redistribute based on rational choices; if the median income lies below the mean income, the median voter chooses redistribution and higher taxation for rich people. This model predicts that democracies have lower levels of inequality than non-democracies (Acemoglu and Robinson, 1998).

A second mechanism alleges that democracy lowers the costs of political participation of organized labor, allowing labor unions to obtain a privileged position in the policy process (Schumpeter, 1942; Rodrik, 1999). As a result, democracy encourages unionization, centralized wage bargaining, and minimum wages, which reduce wage dispersion (Katz and Autor, 1999; Rodrik, 1999).

Democracies should also guarantee broad access to property rights. With well-defined property rights and broad access to them, the poor have the possibility to gain access over improved or produced assets by facilitating the development of efficient market-based economies and opening up markets and institutions, which also prevent the elite from erecting entry barriers and enjoying markets with monopoly power (Gerring et al., 2005; Acemoglu, 2008).

A fourth mechanism suggests that democracy increases competition among politicians for citizen support. This causes governments to invest more in public services, such as education (SaintPaul and Verdier, 1993). Education, in turn, acts as a redistribution channel reducing the dispersion of human capital and increasing a generation’s human capital relative to the previous generation.

I find these four mechanisms the most relevant.<sup>4</sup> However, recent empirical findings reveal caveats in most of these mechanisms. In a well regarded paper, Milanovic (2000), using microlevel data from the Luxembourg Income Studies, finds weak evidence for redistribution through

> 4See Thorbecke and Charumilind (2002) and Savoia et al. (2009) for further discussion.

4
Some further words of explanation are in order. The information on household consumption spending comes from the socio-economic surveys that are undertaken on a continuing basis by the National Statistics Office. About 3,800 different households are surveyed every month using stratified cluster sampling, for which the weights are known: the strata are the country’s provinces, and clusters of households are surveyed in randomly-chosen villages or urban wards. When aggregated to annual or even quarterly levels, the data are nationally representative, but are somewhat less so at the monthly level.

The socio-economic surveys do not collect price data, so in order to calculate real spending we had to match price data from the Bureau of Trade and Economic Indices (2012) with the survey data. More precisely, we matched the monthly price series for about 120 items of consumer spending, which are available for each of the five major regions of Thailand, with spending categories from the socio-economic surveys, region by region. This allowed us to construct real spending for each household. We did not deflate the value of goods produced by the household for its own consumption: a higher price for such goods would work on both the income and expenditure sides of the ledger, with no net effect on wellbeing (assuming no behavioral response to price changes).

Over the four years of interest to us (2007-2010), prices in Thailand were highly variable, rising sharply in early 2008, then falling just as quickly, before resuming a steady rise in early 2009. This pattern shows up clearly in the top panel of Figure 5, which tracks the official consumer price index.

The bottom panel of Figure 5 shows price indexes that we constructed, normalized to 1 in 2007, for households in the top and bottom deciles of expenditure per capita (over the period 20072010). Starting in early 2008 there is a clear divergence, with prices rising more rapidly for poor than for affluent households. This mainly reflects the relative rise in the price of food in early 2008, which carries more weight in the expenditure basket of poorer households.

# **Winners and Losers**

We are now in a position to address the central question of this paper, which is to measure the extent to which groups in Thai society saw changes in their wellbeing during the 2008-2009 recession. We start with some graphs, and then offer a more formal treatment of the subject.

Losers and Winners in Recession: Thailand 2008-09 10
**Table 9.** Count Data and Interval Regression Models of Reported Forest-Related Collective Action Behaviors with Household Controls.

||Negative Binomial Regre|ssion|Interval<br>|
|---|---|---|---|
||||Regression|
||NUMTREES_FARM|MONITORING|NUMTREES_<br>CF|
|CONTRIBUTION1|0.009|0.008|0.009|
||(2.50)**|(1.51)|(1.37)|
|CF_MEM|-0.749|-1.942|-0.255|
||(1.20)|(2.41)**|(0.35)|
|CF__OFFICER|-0.191|1.007|0.608|
||(0.66)|(2.31)**|(0.95)|
|MONITOR_PAY|0.180|0.887|-0.562|
||(0.23)|(1.30)|(1.71)*|
|HILL|1.071|0.207|-0.133|
||(2.87)***|(0.31)|(0.23)|
|AGE|0.015|0.012|-0.014|
||(1.24)|(1.21)|(0.95)|
|FEMALE|-0.301|-0.159|0.068|
||(1.02)|(0.50)|(0.30)|
|DALIT|-1.643|-0.192|-0.831|
||(3.23)***|(0.34)|(1.99)**|
|FAMILY|0.118|0.153|0.047|
||(1.61)|(3.03)***|(0.83)|
|MAXEDUC|-0.001|0.033|0.078|
||(0.02)|(0.50)|(1.60)|
|EDUC_RESP|0.080|0.061|0.025|
||(2.41)**|(1.20)|(0.66)|
|LAND_HA|-0.015|-0.482|-0.143|
||(0.17)|(1.62)|(0.85)|
|FOREST_ROP|0.085|-0.001|0.088|
||(1.28)|(0.02)|(1.09)|
|OXEN|0.070|0.165|-0.243|
||(0.70)|(1.19)|(1.19)|
|BUFFALO|-0.131|-0.066|-0.138|
||(1.50)|(0.86)|(1.89)*|
|CONSTANT|0.544|-0.315|0.295|
||(0.47)|(0.22)|(0.32)|
|lnalpha|1.392|1.866||
||(6.14)***|(6.78)***||
|lnsigma|||0.761|
||||(3.80)***|
|Wald Χ<sup>2</sup><br>|136.92|48.74|18.13|
|Prob> Χ<sup>2</sup>|0.000|0.000|0.26|
|Log pseudolikelihood|-939.75|-489.19|-692.82|

N=327 in all models; * _p_ <0.1; ** _p_ <0.05; *** _p_ <0.01; robust t-statistics and errors clustered at the forest user group level 36
15

# VI. Trade Flows

The real values of exports and imports, shown as indices in Tables 4A, B and C, indicate the extent and mode of capital flows and real adjustment.* For every aggregation in Table 4A (except India), real exports increased relative to the volume of imports in the 1980s; in most cases the imports fall while exports rose. For HICs, real exports rose by almost one fifth, and<sup>4</sup> 'ports fell by one fifth. Major countries that raised real export by well over one-half from 1980 to 1986 -- Turkey, Korea, Pakistan, Thailand, Malaysia -- have not had severe debt-servicing difficulties, and have maintained voluntary access to commercial credit. Mexico, the Philippines, and Brazil became HICs, however, despite export growth in the 15 and 40 percent range. Some commodity and oil exporters nad slow growing exports, like Argentina and Cote d'Ivoire, or even saw exports decline, like Indonesia, Jamaica, Nigeria, and Zambia.

The countries that maintained strong export growth and avoided debt servicing difficulties have sustained real growth of imports, shown in Table 4C. All the HICs, on the other hand, have reduced real imports. Argentina, Nigeria and Zambia have cut them by over one-half.

A study comparing the HICs and 10 major non-HICs found that, while the two groups had nearly identical statistical profiles in 1980-82, the nonHICs had distinguished themselves with much more rapid export growth since the

* The real values are calculated as the nominal flows divided by the MUV, which is the World Bank's index of Manufactures Unit Value -- the dollar unit value of G-5 exports of manufactures to developing countries.
the current UDB. We then assess errors resulting from the ranking process and investigate a simple improvement to the approach used to rank households actually registered in the UDB.

# **Misenumeration Errors**

Improving the enumeration process by increasing the number of households registered in the UDB is one of the possible options to improve the targeting performance of the single registry. Here, we assess the performance of the UDB that would be observed if _all_ households had been registered and scored in the UDB, rather than only surveying households expected to be poor (based on the prelistings from poverty mapping and consultation with community members). By simulating outcomes under this census-based scenario, we remove potential errors due to poor households not being enumerated and instead isolate the role of the PMT-based ranking process in contributing to targeting errors.

We reconstruct PMT scores for all households in the SUSETI sample by applying the original districtspecific PMT algorithms used by UDB planners to the same variables collected for each household in SUSETI. We then calculate targeting errors by comparing household expenditure rankings from SUSETI against program eligibility status (based on the reconstructed PMT scores and UDB-based coverage levels as in column 2 of Table 2).

Table 4 shows the improvement in targeting errors expected under this full census scenario relative to the UDB targeting errors presented earlier. Leakage and undercoverage rates in the UDB are projected to improve under this scenario for both Jamkesmas and BLT, by about 18% and 11-14%, respectively. The improvements are even more striking for severe leakage and particularly for severe undercoverage, with gains in the latter ranging from 25-31% across programs. In other words, expanding the number of households enumerated in the national targeting survey holds significant potential to improve targeting outcomes, particularly by reducing exclusion of the poorest.

Figure 6 provides a nonparametric look at benefit incidence based on this full enumeration scenario compared to the baseline and UDB-targeted receipt as presented in Figure 4. In line with results from Table 4, households from the poorest two to three consumption deciles have a significantly higher 20
and work off the family farm. In the peer effects literature, the reflection problem is often addressed using friends-of-friends to identify peer effects [Lee, 2007, Bramoull ́e et al., 2009, de Giorgi et al., 2010]. However, since social ties are endogenous, we use the likelihood of contact, specifically the number of women of the same caste in the village, as an instrument for networks. Since program participation is also likely endogenous, we use exposure to the program as an instrument. This analysis thus relaxes the restrictive assumption of separability of group formation and information flows within the group that is implicit in studies that randomize treatment within networks. Understanding network formation is important for policy design because several development interventions rely on altering network formation to enhance impact. Allowing individuals to select into participation not only yields a larger direct effect but also provides a truer estimate of the second-order spillovers. Finally, we relax the equally restrictive assumption of directed networks by estimating the impact of a participant friend’s empowerment on her own nonparticipant friends. The directionality assumption is key to identification in the presence of the reflection problem but can lead to significant overestimates. Since we can isolate the marginal effect of friends’ participation on nonparticipants, our identifying assumption is that participants affect their nonparticipant friends’ autonomy and say in intra-household bargaining.

Our sample consists of 404 women and includes data on their ties to 942 friends and friends-of-friends from 69 randomly chosen villages, stratified into four program districts and two non-program districts.<sup>1</sup> _Mahila Samakhya_ aims to increase female

> 1We collected data on 487 women and 1,132 of their friends, but 66 of these have some data missing and the networks of 17 of the women violated our exclusion restriction (discussed further

6
**Table 4.** Effects of the lottery incentive intervention on HIV incidence and prevalence

|||HIV in|cidence||HIVpre|valence|
|---|---|---|---|---|---|---|
||OLS|Probit|OLS|Probit|OLS|OLS|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Any lottery|-0.025<sup>**</sup>|-0.026<sup>**</sup>|||-0.034<sup>*</sup>||
||(0.010)|(0.010)|||(0.018)||
|High lottery|||-0.033<sup>**</sup>|-0.035<sup>***</sup>||-0.041<sup>**</sup>|
||||(0.013)|(0.014)||(0.019)|
|Low lottery|||-0.016|-0.016||-0.027|
||||(0.014)|(0.013)||(0.020)|
|Mean control group|0.117|0.117|0.117|0.117|0.269|0.269|
|P-value (TH=TL)|||0.297|0.253||0.390|
|Observations|2422|2422|2422|2422|2888|2888|

Note: HIV incidence: Sample of HIV negative individuals aged 18-32 at baseline with the dependent variable equal to one if the individual tested HIV positive at least twice after 24 months and zero otherwise. HIV prevalence: Sample of individuals aged 18-32 at baseline with the dependent variable equal to one if the individual tested HIV positive at baseline or least twice after 24 months, and zero otherwise. Any lottery is high and low lottery combined. Probit estimates are marginal effects calculated at the mean. P-value (TH=TL) is the p-value for the test that the treatment effects are equal in the high and low lottery arm. All regressions include village fixed effects. Robust standard errors in parentheses, clustered at the village level. *** 1% , ** 5% , * 10% significance.