rafmacalaba/datause-encoder-data huggingface dataset:
similarly detailed household data not only from the three districts analyzed here but also from all the districts in the province.<sup>20</sup> In their haste to adopt a type of program that has been successful elsewhere, policymakers incorporated some elements (the conditionality and relative size of the transfer) but changed others (the targeting based on gender). If the literature on program evaluation has taught us anything it is that generalizing from one setting to another is at best optimistic and at worst an exercise doomed to failure. This seems particularly true of evaluations of programs in developing countries where program design and implementation frequently are found to diverge. Consequently policymakers in these countries should be more careful to incorporate data collection at baseline and at follow-up to ensure that context-specific evaluations are possible.

The analysis presented here and existing evidence on the effects of the program suggest that it is successful in sending girls to school. A substantial body of evidence exists on the benefits to society of educating women.<sup>21</sup> The children of more educated mothers have better educational and health outcomes. Such evidence alone should justify the continuation of this gender-targeted stipend program – with obvious attention being paid to capacity constraints that are known to emerge in schools and education systems. However, an examination of data from the PSLM suggests another channel through which society could benefit from such programs. There is a stark correlation between regional levels of maternal literacy and the sex ratios of children within households (Figure 8). Specifically it appears that in regions with lower levels of literacy there is a substantially higher ratio of boys to girls. If we are to take this correlation seriously then it appears that in the long-run, the stipend program has the potential to impart far-

> 20 The analysis presented here was made possible by the fortuitous timing with which researchers fielded a survey and not the result of an attempt by policymakers to adequately monitor and evaluate the program.

> 21 See for instance the arguments in Schultz, 2001 on the need for governments the world over to invest more in girls’ education and the evidence in Andrabi et. al., 2007 on how educated girls transition from being students today to teachers tomorrow in the Pakistani districts studied in the present paper.

22
54 VI. References Barro, Robert J. (1986). 'Reputation in a Model of Monetary Policy with Incomplete information,' Journal of Monetary Economics, Vol 17, January, pp. 3-20.

Barro, Robert J. and David Gordon (1983.a). 'A Positive Theory of Monetary Policy in a Natural Rate Model," Journal of Political Economy, Vol 19, August, pp. 589-610.

(1983.b). 'Rules, Discretion and Reputation in a Model of Monetary Policy,' Journal of Monetary Economics, Vol 12, pp. 101-21.

Blejer, M. and LW,iatan, N. (1987),"Stabilization Strategies in Argentina and Israel, 1985-86', IMP Staff Papers, Vol. 34, no3, pp.403-38.

Blejer, Mario I. and Adrienne Cheasty. (1988) 'High Inflation, Heterodox Stabilization and Fiscal Policy.' World Development, Vol. 16, No.8, August. pp.867-881.

Bruno, M., G. Di Tella, R. Dornbusch and S. Fischer (1988). Inflation Stabilization, Cambridge: MIT Press.

Calvo, Guillermo (1983). 'Staggered Contracts and Exchange Rate Policy,' in Exchange Rates and International Macroeconomics, Jacob Frenkel editor, University of Chicago Press, pp. 235-55.

Calvo, Guillermo (1987). 'On the Costs of Temporary Policies,' Journal of Development Economics, Vol 27, pp. 245-62.

Cardoso, Eliana and Albert Fishlow (1988). 'The Macroeconomics of Brazilian External Debt,' mimeo.

Cardoso, Eliana and Rudiger Dornbusch. (1987) 'Brazil's Tropical Plan," American Economic Review Papers and Proceedings, May.

Cukierman, Alex and Nissan Liviatan (1989). 'Optimal Accommodation by Strong Policy Makers Under Incomplete Information," The Foerder Institute for Economic Research, Working Paper No. 13-89, May.

Cumby, Robert and Maurice Obstfeld (1983). 'Capital Mobility and the Scope for Sterilizations Mexico in the 19709,' in Financial Policies in the World Capital Markets: The Problem of Latin America, P. Aspe, R. Dornbusch and M. Obstfeld editors, Chicago: University of Chicago Press, pp. 245-76.

De Pablo, Juan Carlos (1972). Politica Anti-inflacionaria en la Argentina 1967-1970. Buenos Aires: Amorrortu Editores.

De Pablo, Juan Carlos (1974). 'Relative Prices, Income Distribution
_Table 3: Average household-level vehicle-related consumption use by income quintile in the US, 1980-2014_

||Quintile 1<br>_(lowest 20%)_|Quintile 2|Quintile 3|Quintile 4|Quintile 5<br>_(highest 20%)_|
|---|---|---|---|---|---|
|Income, before tax|13,607|27,212|44,478|69,444|132,768|
|Vehicles|0.9|1.3|1.7|2.0|2.4|
|Vehicle age|10.2|9.4|8.4|7.7|6.8|
|Price per vehicle|8981|9927|11,750|14,115|18,930|
|VMT|7274|11,042|15,180|19,218|24,344|
|Fuel expenditure|1008|1545|2125|2673|3290|
|Vehicle price / income|0.66|0.37|0.26|0.20|0.14|
|Fuel expenditure / income|0.07|0.06|0.05|0.04|0.03|

_Adapted from Greene & Welch (2018)_ Regressivity is a common argument levied against fuel taxation – but reality is not that simple. Poorer households spend a larger share of their income on fuel. Hence, the direct distributional impact of raising fuel taxes is regressive. However, poorer households are generally also more price responsive. They are more willing to reduce vehicle-kilometers traveled and switch to alternative modes. Their larger price elasticity mitigates the regressivity of fuel taxes if adequate alternatives are provided, e.g., public transportation, non-motorized transport infrastructure, appropriate land use planning, etc. (West, 2004). Additionally, the money paid in fuel taxes do not disappear. They enter fiscal budgets, and economists increasingly recognize that how they are recycled determines the distributional impact of fuel taxes (Davis and Knittel, 2019). Due to the larger absolute expenditure of high-income households, a flat recycling scheme has been found to be progressive and welfare-enhancing for the bottom 40 percent (Bento et al., 2009). Finally, a switch to distance-based taxes allows more precise targeting while still taxing vehicle use. For example, a relatively simple urban-rural differentiated VMT tax is both more efficient and more progressive in the US context (Langer et al., 2017).

Although less apparently so, instruments on purchase are broadly regressive in the US context. The CAFE standards in the US have contributed to the improvement of fuel economy of vehicles across all market segments. In turn, this has lowered the per mile fuel consumption and total fuel expenditures. However, to satisfy the standards, manufacturers have had to develop more efficient vehicles. Concretely, a one mile-per-gallon tightening of the CAFE standards is estimated to have cost manufacturers $9-$27 per vehicle (Anderson & Sallee, 2011). The costs of 17
demographic trend alone could account for nearly 30% of the decline in occupant fatalities per VKT during this period.

The positive relationship between YOUTH and fatalities per VKT finding agrees with results in the literature, including Peltzman (1975), Noland (2003a) and Page (2001).<sup>20</sup> However, the importance of demographics is more pronounced here than in most within-country studies that are limited to shorter periods of analysis. Using countylevel data for 1970 and 1980, Keeler (1994), for example, finds increases in the young population to increase traffic fatalities but the result is not significant.

An interesting implication of Table 7 is that, because the coefficient on the YOUTH-income interaction term is negative in all specifications, the importance of young drivers diminishes as incomes increase. The elasticity with respect to YOUTH exceeds 2.0 _(0.50)_ for per capita incomes less than $5,900 (1996 international dollars)<sup>21</sup> but falls to 0.08 _(0.26)_ once per capita income reaches $25,000.<sup>22</sup> This result could reflect changes in young people’s attitudes toward risk or an increase in education of young drivers with increases in economic prosperity.

In contrast to the effects of YOUTH, the percentage of driving age population aged 65 and over (ELDERLY) has a small, positive but insignificant effect on occupant

> 20 Peltzman (1975) found the elasticity with respect to the vehicle occupant death rate to be 0.594 (t-stat = 7.002) in the U.S. over 1947-65, although he included motorcyclist deaths with non-occupants.  Page (2001) estimates the elasticity with respect to total fatalities to be 0.83 _(0.11)_ for OECD countries over 1980-94.  Noland (2003a) finds the elasticity ranges from 0.49 (t-stat = 2.17) to 1.11 (t-stat = 6.58) over 1970-96, depending on what other controls are included in the model.

> 21 This is the approximate per capita income (1996 international dollars) of Portugal and Hungary in the late 1960s to early 1970s and Turkey in the early 1990s.

> 22 This is approximately the income of the U.S. around 1990 and Denmark and Norway in the late 1990s.

18
_15_ sample period<sup>16</sup> . In most countries, the "own" shocks to output explain between 8% and 40% of output's error variance, implying that 60% or more of output variability is explained by shocks to the terms of trade, to external spreads, to the real interest rate and to the real exchange rate. Only in Venezuela and Peru is a substantial percentage (between 55% and 60%) of output variability left unexplained by these factors. These countries are the most volatile in the Hausman-Gavin (1995) paper on volatility in Latin America, where the authors find that in these two countries a good part of GDP volatility is explained by policy variability, financial deepening and political volatility'<sup>7</sup> .

Output's "own shocks" capture effects from unidentified variables outside the model. They are, partly, the result of the a-priori marginalization process in which the researcher chooses the specific set of variables whose interaction will be studied. These, however, might be external or internal. These results (Table 3)show that the variance explained by external variables is less than the variance accounted for by the internal factors, except in Argentina If the own shocks to output are classified as reflecting mainly the effect of omitted internal factors, the relative importance shifts even more towards the domestic **Table 3**

||Generalized Variance Decompos|ition of**Industrial**|**Output- Argent**|**ina**|
|---|---|---|---|---|
||Percentage of the<br> <br>|Variance Associat<br>|ed with Shock to<br>|:<br>|
|Horizon<br>(in<br>months)|Terms of<br>Spreads<br>Trade|Domestic<br>Real Interest<br>Rate|Real<br>Exchange<br>Rate|Output|
|1|_5_<br>10|4|11|90|
|4|11<br>37|18|13|69|
|8|15<br>54|27|_15_|49|
|12|15<br>59|21|18|46|
|16|14<br>53|22|22|41|
|20|14<br>_51_|26|22|39|
|24|14<br>54|27|21|38|
||Generalized Variance Decomp|osition of**Industri**|**al Output- Brazi**|**l**|
||Percentage of the|Variance Associat|ed with Shock to|:|
|Horizon<br>(in<br>months)|Terms of<br>Spreads<br>Trade|Domestic<br>Real Interest<br>Rate|Real<br>Exchange<br>Rate|Output|
|1|4<br>6|12|3|77|
|4|4<br>32|10|59|30|
|8|4<br>31|14|77|11|
|12|4<br>41|10|74|9|
|16|4<br>47|10|68|8|
|20|_5_<br>48|11|62|8|
|24|_5_<br>48|12|61|8|

**16** We will use the terms forecast error variance of output and output variability interchangeably. The same with the other variables of the model. In Appendix 3 you can see the main statistics of the VAR estimates of each equation. You can see that the R<sup>2</sup> for the output equations are quite good for most countries. 17 See Table 14 of the second part Annual Report, 1995, IADB.
In order to test these predictions we constructed a data set of 56 countries between 1977 and 1997. In addition to financial liberalization and transparency variables, which we describe below, the data set includes macroeconomic and financial variables that are identified by Demirguc Kunt and Deteragiache (1998b) as increasing the probability of a financial crisis. The macroeconomic variables are: GDP growth rate, inflation rate, change in terms of trade, real interest rate, and exchange rate depreciation rate. The financial variables are: M2 to gross international reserve ratio, claims on private sector as percent of GDP, bank liquid reserves to bank assets ratio and real credit growth two years ago. The source of the data is the International Financial Statistics and the World Bank GDF & WDI.

Data on financial liberalization episodes are based mainly on Williamson and Mahar (1998), Lindgren, Garcia, and Saal (1996), and Drees and Pazarbasioglu (1998). Williamson and Mahar survey 51 financial liberalization episodes in 28 countries between 1977 and 1994, Lindgren, Garcia, and Saal survey 6 financial liberalization episodes in 3 countries, and Drees and Pazarbasioglu survey 4 financial liberalization episodes in 2 countries between 1986 and 1992. Data on banking crises are constructed based on the data and description in Lindgren, Garcia, and Saal (1996) and Demirguc-Kunt and Detragiache (1998)3. Since the crises may affect the macroeconomic variables, we delete the five years following the crises. If the crisis lasts longer than five years, we drop the observations while the crisis lasts.<sup>4</sup> Table 1 presents the countries in our sample, the periods and types of financial liberalization, and the periods of banking crises. The financial liberalization varies in their scope and magnitude. Some liberalization episodes were a move from very control financial

> 3We drop three countries, Guyana, Papua New Guinea, and Zaire in our regression estimations because of lack of macro/financial data. Therefore, there are 89 financial liberalization and 57 banking crises from 56 countries in the final sample.

> 4 The results are robust to these specifications.

15
Similar findings are extracted from the joint densities of CIW for rural and urban Malawi plotted in Figure 3. The correlation between income and wealth is higher in urban areas, 0.36, than in rural areas, 0.17.<sup>46</sup> In rural areas income and wealth have a correlation of 0.10 and 0.12, respectively, for the bottom 80% and top 20% of the income distribution. In urban areas, we find a slightly opened L-shaped joint density, which implies that for the bottom 80% of the income distribution, the correlation between income and wealth is 0.06, while this correlation is 0.30 for the top 20% of income earners. Thus, only the income-rich households in urban areas are able to accumulate wealth. Overall, there is a weaker link between income and wealth in SSA than in the United States, where the correlation of these two variables is 0.57 (D ́ıaz-Gim ́enez et al. (2011)). Finally, the correlation of consumption and income is also higher in urban areas, 0.53, than in rural areas, 0.30.

To summarize, only the households at the top of the income distribution in urban areas are able to transform their rural-urban income differential gain (i.e., their higher income compared with the top income counterparts in rural areas) into a somewhat sizable rural-urban differential in wealth. The joint densities of income and wealth reinforce this result, implying that the incomerich households in urban areas are able to accumulate more wealth than their rural counterparts. This suggests that the high saving rates of the top-income earners in rural areas might be less persistent than those of the top income earners in urban areas. Finally, we find less growth for the rural-urban consumption gap than for income, which suggests an ability to partially insure. The joint density of consumption and income suggests that this insurability of consumption is larger in rural areas than in urban areas, an argument further developed in De Magalh ̃aes et al. (2015).

# **3.3 Saving Rates**

New insights emerge by looking directly at the saving rates — that is, one minus the consumptionto-income ratio. As is the case in the United States, a large fraction of the population has a negative saving rate in Malawi. Using the Consumer Expenditures Survey (CEX), Sabelhaus and Groen (2000) find that approximately half of the households in the United States have a negative saving rate. In rural Malawi, we find that the saving rates are, on average, positive for only the top 10% of the income distribution, while in urban Malawi the saving rates are, on average, positive only for the top 5% of the income distribution (see Table 5). This implies that while in the United States the average saving rate is 0.12 for the 1992 CEX, the saving rates in rural and urban Malawi are, on average, negative and, respectively, -0.23 and -0.15.

> (32%).

> 46In Tanzania, this correlation is 0.45 for urban households and 0.40 for rural households. For Uganda, the numbers are 0.42 and 0.32.

21
**Table 7. Effect of Minimum Wage Increase on Labor Market Outcomes in Scenario 2: Convergence to Living Wage Estimate**

(16.41% of Full-Time Employees Affected by the Raise)

|Group|Percent<br>Increase in<br>mean wage*,<br>short-run<br>(1 )*|Percent<br>Increase in<br>mean wage*,<br>long-run<br>(2 )*|Percent<br>Increase in<br>mean net<br>labor<br>income<br>gains**,<br>short-run<br>(3)|Percent<br>Increase in<br>net<br>labor<br>income<br>gains **,<br>long-run<br>(4)|Formal<br>Employment<br>Reduction***:<br>Percentage of<br>Employment<br>Experiencing<br>Job Loss in<br>the Short Run<br>(5)|Formal<br>Employment<br>Reduction***:<br>Percentage of<br>Employment<br>Experiencing<br>Job Loss in<br>the Long Run<br>(6)|
|---|---|---|---|---|---|---|
|All|1.58|1.91|0.29|0.03|1.27|1.84|
||(1.57;1.58)|(1.90;1.91)|(0.15;0.36)|(-<br>0.07;0.14)|(1.26;1.27)|(1.84;1.84)|
|Age: up to|1.84|2.18|0.37|0.04|1.44|2.09|
|<br>29|(1.82;1.85)|(2.16;2.19)|(0.19;0.56)|(-<br>0.14;0.22)|(1.43;1.45)|(2.08;2.10)|
|Ae: 30 –|1.58|1.94|0.27|0.03|1.29|1.87|
|g<br>39|(1.57;1.59)|(1.93;1.96)|(0.08;0.46)|(-<br>0.16;0.23)|(1.28;1.29)|(1.86;1.88)|
|Ae: 40 –|1.58|1.92|0.28|0.03|1.28|1.86|
|g<br>49|(1.57;1.59)|(1.91;1.93)|(0.09;0.47)|(-<br>0.16;0.22)|(1.27;1.28)|(1.85;1.87)|
|Age: 50 –|1.45|1.73|0.28|0.03|1.15|1.67|
|<br>59|(1.44;1.46)|(1.72;1.74)|(0.06;0.49)|(-<br>0.19;0.24)|(1.15;1.16)|(1.66:1.68)|
||1.55|1.87|0.29|0.03|1.24|1.80|
|Age: 60+|(1.53;1.57)|(1.85;.1.90)|(-<br>0.33;0.91)|(-<br>0.59;0.65)|(1.23;1.25)|(1.79;1.92)|
|Only<br>|13.19|13.19|4.45|0.49|7.72|11.22|
|affected|||||||
|(wage<new<br>MW)|(13.19;13.19)|(13.19;13.19)|(4.44;4.45)|(0.49;0.50)|(7.72;7.73)|(11.21;11.22)|

_* Sample restricted to wage earners, assuming all dismissed workers move to unemployment or inactivity._ _** Net labor income gains encompass both the losses incurred from job displacements and the earnings increases resulting from higher wages for those who retained their employment. Since it is assumed that all dismissed workers move to unemployment or inactivity, this represents a lower bound of the net gains (or an upper bound of the earning losses). If workers move to formal or informal employment and have a positive labor income, net labor gains could be higher._ _*** This column presents information on the share of full-time employees who become unemployed or inactive after the minimum wage increase. 95% confidence intervals are provided in parentheses._ _Source: Own simulations based on administrative tax data._ 39
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In answering this question it is important to recognize that bank nationalizations are very common in dealing with systemic financial crises. Indeed, governments have always taken ownership positions in banking either deliberately or indirectly as a result of banking crises. In almost every major banking crisis in recent history – in East Asia, Latin America and others – governments have become temporary caretakers of financial institutions.<sup>9</sup> Hence, the recent crisis is not the exception in this regard. The way in which this is done has important implications for maintaining and restoring a functioning financial system and minimizing the short term and long term costs of such interventions.<sup>10</sup> But, the fact that governments find themselves involved in resolution of insolvencies does not imply this role should be permanent. To the contrary, all evidence suggests that they would be well-advised to do otherwise.

# _Bureaucrats as Bankers?_

Government ownership of banking has been popular throughout history.<sup>11</sup> Early proponents of state control argued that the government can better allocate capital to highly productive investments. Gerschenkron (1962) was among the first to argue that private banks would not be able to overcome deficiencies in information and contracting in weak institutional environments. State ownership also makes appropriation of the surplus from finance and directing credit much easier, making it attractive for policymakers. Moreover, there is the concern that, private ownership and concentration in banking may lead to limited access to credit by different parts of the society,

> 9 Indonesia, Philippines, South Korea, Mexico, Chile are examples.  See Caprio and Klingebiel (updated 2005) for a complete list.

> 10 Whether or not the control of the bank completely passes into public hands during the resolution stage is secondary to the fact that after the resolution, the bank should be well-capitalized. Problems often emerge because governments are unable to do the financial restructuring properly; and banks that continue to operate with insufficient capital have every incentive to resume reckless risk-taking.  We discuss different ways of restoring solvency below.

> 11 See World Bank (2001) for a discussion.

12