rafmacalaba/datause-encoder-data huggingface dataset:
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38
reduce the ability of countries to sustain undervaluations, loan-related flows tend to sustain it. Finally, the coefficient of liability dollarization is not robust.

With RER misalignments calculated using our PMG estimates of the long-run RER equation, the coefficient of trade openness is significantly positive while that of liability dollarization is negative and significant. These results implied that an undervaluation is more likely to be engineered by authorities in countries that are more open to trade and are not highly dollarized. The latter result may reflect the behavior of policymakers in preventing depreciations of the currency in highly dollarized economies due to their harmful effects on the balance-sheet of the economy. This is what the literature calls ― _fear of floating_ .‖ Finally, the evidence shows that the measure of exchange rate flexibility is robustly positive and significant, whereas intervention in the FOREX market has a significant effect on the incidence of undervaluation only in the presence of fiscal discipline.

The _Tobit_ analysis, on the other hand, shows evidence that the authorities may have a more limited ability to influence the magnitude of the RER undervaluation with both Johansen and PMG estimated RER misalignments. In contrast to our _Probit_ results with Johansen estimated RER misalignments, flexible exchange arrangements and FOREX market intervention have a less robust link with the size of RER undervaluations. The exchange arrangement is mostly not significant in all regressions, while FOREX intervention has a positive and significant effect only when controlling for the fiscal position. Fiscal policy is again effective only in small to medium undervaluations (below 20%). The central government budget balance has a negative and significant coefficient. This shows that the fiscal policy may play a role in determining the extent of undervaluation in the exchange rate market. It shows though that fiscal discipline may reduce the size of the undervaluation. With the PMG coefficient estimates of the long-run RER equation capital account openness variables (e.g. _TL_ and _TAL_ ) are positive and significant while the _Chinn-Ito index_ of financial openness is significant in regressions that do not control for fiscal discipline. Moreover, fiscal discipline and liability dollarization have a negative and significant coefficient while trade openness is positive and significant. Intervention is significant only when controlling for fiscal discipline while exchange rate regime has a robustly positive and significant coefficient estimate.

Consistent with the _Probit_ results, we find that both policy and outcome measures of financial openness fail to explain the magnitude of RER undervaluation. However, 33
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<!-- Start of picture text --> Box 2. The Importance of Regional Differences.<br><!-- End of picture text --> As we mentioned on Section B, there are important regional differences in terms of labor market outcomes, and, in particular, in terms of unemployment rates. These vary substantially across regions and differences have done nothing but increase over time. When we plot the regional unemployment rate against the ratio of unemployed workers to vacancies, we find that both variables are positively correlated. That is, regions with low unemployment rates, such as Bratislava, have few unemployed workers per vacancy, and regions with high unemployment rates, such as Kosice, have a large number of unemployed workers per vacancy.

<!-- Start of picture text --> Graph B2.I<br>Unemployment rates and number of unemployed workers<br>per vacancies in 2000, by region.<br>300<br>E 2 50F<br>20 5 12<br>NIr<br>| 250 - -- __ - __ __ _ --- -- __ - -__<br>150ph B IBAN<br>100<br>E _0<br>0<br>0 5 1 0 1 5 20 25 30<br>Unemploymnent rate<br><!-- End of picture text -->

<!-- Start of picture text --> Source: Unemployment Registry and Labor Force Survey<br><!-- End of picture text --> This implies that job creation is very poor in areas where unemployment is high, and, hence, that individuals living in these regions are less likely to find job than workers in regions with low unemployment. This is confirmed by the following graph: <!-- Start of picture text --> 50 _ _3 - - - Graph -- ---- - B2.11<br>E xit-to -ej m cploymen tprob abi lity, b y reg ion<br>0. B<br>0.7<br>&0.450 .6<br>0.42Tra<br>0 1<br>Duration of spell (in months)<br><!-- End of picture text -->

**Source:** Unemployment Registry and Labor Force Survey

The same regions that appear on the bottom-left area of the **first** graph (Bratislava, Trencin, Trnava), are the ones that have the highest probabilities of escape from unemployment (hazard rate), while exit probabilities are much lower in regions with high unemployment and low job creation (Kosice, Banska, Presov). Haulikova and Benc (World Bank, 2001) provide a detailed description of regional differences and a discussion of the factors that underlie the relationships described here.

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two aspects deepen the inefficiency across sectors as some incur very high abatement costs to reduce carbon emissions while others do not abate even when their abatement costs are significantly low. On the contrary, with a uniform carbon price endogenously determined by the model, all profitmaximizing firms will reduce emissions up to the point where marginal abatement costs are equal to the carbon tax rate ensuring that the required level of emissions reduction is achieved at least cost. Finally, as shown in Bohringer et al. 2008 and 2016, overlapping regulation can determine efficiency losses as the use of multiple policy instruments to curb greenhouse gas emissions as it is in the presence of a comprehensive emission pricing and energy taxes can cause what is defined as “excess cost”. Thus, the cut of energy taxes could reduce this additional cost due to the overlapping regulation.

_Table 3: Macroeconomic results in 2030 (% change wrt Baseline)_

|**Scenarios**|**consumption**|**gov.**<br>**Expend.**|**investment**|**public**<br>**investment**|**export**|**import**|**gdp**|**Gini index**<br>_(Average_<br>_2022-2030)_|
|---|---|---|---|---|---|---|---|---|
|f55|-0.9|-0.5|0.4|-0.5|-1.0|-1.3|-0.5|0.009|
|f55 & wage tax|-0.6|-0.4|0.1|-0.4|-0.9|-1.2|-0.4|0.004|
|f55 & CIT|-0.5|-0.3|0.0|-0.3|-0.9|-1.1|-0.3|0.001|
|f55 & VAT|-0.5|-0.4|-0.6|-0.4|-1.0|-1.3|-0.4|-0.006|
|f55 & EXCISES|-0.3|-0.3|-0.5|-0.3|-0.8|-1.0|-0.3|-0.004|
|f55 & Income tax|-0.7|-0.5|-0.1|-0.5|-1.0|-1.3|-0.5|-0.006|

In terms of income distribution, we can look at the Gini index (average 2022-2030). From Table 3, we can observe that there are regressive effects on income distribution in the “f55” scenario to comply to the stringent climate policy constraints. The negative impacts on income distribution are reduced in all recycling schemes. The Gini index slightly declines in some scenarios with the largest reductions in the “f55 & Income tax” and “f55 & VAT” recycling schemes where carbon revenues are used to reduce the personal income tax and VAT tax.

In Figure 4, welfare impacts are differentiated across household deciles. We observe that welfare effects differ significantly across households, with poorer households bearing a disproportional 13
particular econometric results to not by themselves undermine the significant relationships between financial availability and firm performance found in other papers, which are more pro-active in countering endogeneity problems.

# _Corruption and Crime_

Carlin et al.’s summary of subjective indicators from a large number of enterprise surveys identifies crime and corruption as problems reported primarily in less developed countries. Of the two, corruption is more commonly perceived as problematic: ‘Crime and/or corruption show up as important constraints in all groups of countries except the OECD: crime in only one-quarter of countries and corruption in 70%.’ The analysis of subjective firm level data by Gelb et al. (2007) shows that concern about corruption and crime tends to peak in the middle of developing countries’ income range. The authors interpret this as showing that once economies overcome utmost poverty and the most basic limitations related to infrastructure, finance and macroeconomic stability, problems of low administrative and bureaucratic capacity come to the forefront of firms’ concerns.

Recent studies that examine the relationship between firm performance and business climate indicators generally find significant effects for corruption and crime indicators. Fisman and Svensson (2005) use their Ugandan firm-level dataset for a study focused on corruption and its effect on growth. Their OLS and IV regressions of sales growth on a corruption indicator and a variety of controls show a ‘strong, robust, and negative relationship between bribery rates and the short-run growth rates of Ugandan firms, and [...] the effect is much larger than the retarding effect of taxation.’ Escribano and Guasch’s (2005) study of productivity in Guatemala, Honduras and Nicaragua includes the explanatory variables ‘payments to deal with bureaucracy faster as % of sales’ and ‘number of criminal attempts suffered’. The coefficient for the number of crimes suffered shows the expected negative sign and is significant. However, the size of bribe payments has a robust positive relation with productivity. This may mean that firms that can afford paying (more) bribes will tend to be more productive in the first place and/or reap productivity advantages from their payments. However, in terms of policy implications it certainly does not imply that the incidence of corruption in the three countries should be seen as positive for productivity in general. Rather, the authors argue, ‘it is clear that there is room for improvement in the administrative procedures followed in the three countries...so that no more arbitrary administrative gains in productivity [arise] from bribes of firms.’<sup>85</sup> Still, the difference in the direction of the sign of the corruption variable in the two studies is somewhat puzzling, and further research would be required to reveal the source of the difference (which could be genuine cross-country

> 85 Escribano, A. and J. Guasch. 2005: 54.

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cohorts (2005, 2009, and 2012) and for male and female 15 to 18 year olds. Also, we changed the period of analysis from a year to one quarter to show a different perspective of the data.

Figure 2 shows that at any given point in time between 45 percent and 55 percent of male Ninis (bottom part of the bars) come from being Ninis during the previous quarter, between 35 percent and 45 percent come from working only and the rest, between 10 percent and 15 percent, come from either studying only, or working and studying. In contrast, nearly 80 percent of female Ninis observed in the second interview come from being Ninis in the first interview, around 5 percent come from studying only, and around 15 percent come from working only. These results once more capture the persistence of Nininess, in particular for young women.

Figure 3. Which states do Ninis transition into? Status at time of second interview for those who were Ninis in the first interview (15 to 18 year olds.as % of Ninis in first interview) <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>Work only<br>50%<br>Study only<br>40% Work + Study<br>Nini<br>30%<br>20%<br>10%<br>0%<br>2005 2009 2012 2005 2009 2012<br>Males Females<br>Year in which cohort Year in which cohort<br>had the 1st interview had the 1st interview<br><!-- End of picture text --> _Notes_ : Weights are used in the estimation. A person is considered a _Nini_ if s/he is not working nor studying. _Source_ : Authors' calculations based on the _Encuesta Nacional de Ocupación y Empleo_ (ENOE).

Where do Ninis transition to in the following quarter? Figure 3 shows, not surprisingly given the results above, that nearly 50 percent of male youths and around 80 percent of female youths continue to be Ninis (irrespective of which cohort we look at). Nearly 45 percent of male Ninis 14
# **2. The AGE Program**

AGE is part of a broader school reform designed to improve the supply and quality of education in schools in highly disadvantaged communities. The Compensatory Program consists of: (i) infrastructure improvement, (ii) provision of school equipment , (iii) provision of materials for students (e.g. notebooks, pens, etc), (iv) pedagogical training for teachers, (v) performance based monetary incentives for teachers, and (vi) AGE. Not all of the sub-interventions were introduced at the same time and not all of the schools received all of the sub-interventions.

The Compensatory Program progressively expanded from more to less disadvantaged areas. Between 1992 and 1995, the program was introduced in the poorest municipalities of the poorest 23 states.<sup>6</sup> Coverage was extended to disadvantaged schools in the eight remaining Mexican states in 1998. We use data from schools incorporated starting in 1998 in our analysis. They have lower poverty rates and better educational outcomes than the States incorporated earlier. The worst performing schools were targeted using an index based on (i) a community marginality index (ii) teacher-student ratios (iii) the number of students per school and (iv) repetition and grade failure rates.<sup>7</sup> Each State then decided which sub-interventions would be allocated to which school based on their budget and logistic capacity.

AGE as a sub-intervention was first introduced in the 1996-97 school year. AGE finances and support the schools’ parent associations. The monetary support varies from $500 to $700 per year depending on school size. The use of funds is restricted and subject to annual financial audits for a random sample of schools. Amongst other things,

> 6 Poverty levels are defined according to the marginality index developed by the Population National Council or CONAPO ( _Consejo Nacional de Población_ ).

> 7 CONAFE (2000) provides specific details on the weighting of variables to construct the index.

6
<!-- Start of picture text --> ~~<br>ice)<br>ha ~<br>5 —_— NN<br>S TM~<br>cD) a<br>aT oo+o + .<br>Ss x x ~ ~ /<br>o —+<br>= ND x x x + — “<br>Oo x<br>x<br>5 CN .<br>_I<br>_ __ __ Plot without conflict<br>Plot with conflict<br>©<br>—{ 0 1 2 3 4<br>Log of plot size<br><!-- End of picture text -->
- 40 -

25. Nicholas Prescott pointed this problem out to us in his comments on an earlier version. We considered an alternative deflation method, using the Department of Manpower's 'Minimum Physical Requirements Index" (KFM) which is intended to be a cost-of-living index for working households, and is derived independently of the CPI. We studied this index closely, including a discussion with the Department of Manpower officers responsible for the index. For a number of reasons we decided that the index is quite unsuitable for our purposes. Details are available from the authors.

26. For the lower poverty line, the headcount index obtained using the spatial CPI drops from 34.70 percent in 1984 to 23.47 percent in 1987, while for the higher poverty line, the corresponding figures are 58.96 and 47.62. As for the ordinary CPI, first-order dominance holds over the entire range.

27. The study referred to is CBS (1984). For further discussion see Ravallion and van de Walle (1989).

28. Though it may be argued that real consumption "standards" are higher in urban areas, and so a higher real poverty line is called for. This is, in our view, difficult to defend on ethical grounds; we take it as axiomatic here that poverty comparisons should be symmetric, in the sense that the poverty line in terms of real living standards (leaving aside the problems in measuring the latter) should be constant over time and space.

29. See Ravallion and van de Walle (1989). An urban and rural price differential of 1lO is also in close accord with Rao's (1984) estimated poverty line differential.
**Figure 29: Chinese Trade Balance with East Asia**

<!-- Start of picture text --> 20<br>0<br>-20<br>Taiwan, China<br>-40<br>-60 Korea<br>-80<br>Japan<br>-100<br>-120<br>-140<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Source: United Nations Comtrade, World Integrated Trade Solution; authors' calculations.<br>US$, billions<br><!-- End of picture text --> 46. The above evidence shows that the exchange rate does not seem to be a significant determining factor for the evolution of the U.S.-China current account deficit. Over the last decade, the rise in U.S. imports from China has been continuous and stable. Those slow trends are not affected by the depreciation of the U.S. dollar-renminbi real effective exchange rate in 2001-2004, or by its appreciation in 2005-2008. Key reasons are structural changes in the economy of China and, related to that, of other Asian economies as well as US monetary and fiscal policies (as discussed in section IV).

47. The above three hypotheses all imply that the East Asian economies are driving the global imbalances. If any of the three or the combination of all three represents the causes of the global imbalance, the share of the US‘s trade deficits with the East Asian economies in its total deficits should have increased. However, while the United States‘ trade deficits with China increased substantially, the share of the U.S. trade deficit with East Asian economies as a region actually declined significantly (Figure 30). The above evidence indicates that the three commonly accepted causes may have contributed to but cannot be the main cause of the global imbalances.

36