rafmacalaba/datause-encoder-data huggingface dataset:
Rather surprisingly, in the immediate aftermath of the reform in Indonesia, we do find a positive and significant impact of democratic reform on the amount of revenue generated from own sources by the district governments. This result is contrary to the inverse relationship between own sources of revenue and electoral reforms suggested by the literature cited in Section 2. The statistically significant increase in the fiscal surplus also appears to contradict the prevailing notion in the decentralization literature that fiscal deficits at the district level are likely to increase based on the expectation that they will be absorbed by the central government.

# _Java and Bali vs. Other Regions_

The analysis so far has focused on the average impact of _Pilkada_ on the level of fiscal expenditures and revenues by districts. The wide diversity of Indonesia in geography, culture, ethnicity and religion warrants further investigation on the extent to which there is heterogeneity in the impacts of _Pilkada_ . For this purpose, equation (1) is estimated separately for the group of districts that had direct elections in the Java and Bali region and the group districts that had direct election in the other regions (Sumatra, Kalimantan, Sulawesi, Nusa Tenggara, Maluku and Papua). Of the 174 districts in total for which we have expenditure and revenue data in 2006, about 53 percent (or 92 districts) are not in the Java and Bali islands and about 61 percent of these (or 56 districts) have directly elected heads. Each of these two groups of districts is compared against the set of districts that did not have direct elections as of 2007 (irrespective of the region that comparison districts belong to).<sup>21</sup> A closer investigation of whether the impact of _Pilkada_ varies by region can shed light on an important question: In the geographic areas that benefitted the most, at least in terms of revenue, from the fiscal decentralization that started in 2001, is it the case that having _elected_ local leaders led to greater impacts on expenditure (compared to having district heads who were not elected)? For example, in 2002, approximately three-quarters of the total natural resource revenue was distributed to the district governments in the provinces of Aceh, Riau, East Kalimantan and Papua (Lewis, 2005). It is also the case that the fiscal decentralization was, in part, motivated by secessionist sentiments and centrifugal tendencies in a number of the outlying provinces like Aceh, and Papua.

Table 2a and 2b

> 21 We have also estimated an interaction model on the full sample of the treatment and control districts by allowing the treatment ( _Pilkada_ ) effect to differ between districts in the Java and Bali islands and the districts in the outer islands. These comparable estimates are available directly from the authors upon request.

19
<!-- Start of picture text --> in7u“—_———="<br><!-- End of picture text --> <!-- Start of picture text --> 90%<br>80% 2007 #2013<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%0% | I<br>< Ps Rd é&o & e &ij * eo &<br><!-- End of picture text -->
**Figure 10. Simulations of the impact of trade and firing costs Creation of formal jobs (ALL to F)**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>Actual Fitted No trade No Constitution<br>Destruction of formal jobs (F to U)<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Actual Fitted No trade No Constitution<br>Size of formal sector (F)<br>90<br>85<br>80<br>75<br>70<br>65<br>Actual Fitted No trade No Constitution<br>1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br><!-- End of picture text --> Note: Creation (ALL to F) and Destruction (F to U) “actual” rates are inferred from the continuous time transition matrix for each period using yearly pooled data from the Monthly Labor Survey (PME) from Jan 1983 to Dec 2002 following the procedure by Geweke et al. (1986) outlined in Annex 1. Computations are based on 10.000 Monte Carlo replications. Fitted rates are estimated from regressions reported in Table 7. Simulated rates estimated from regressions reported in Table 7 fixing Effective Tariff and Imports Penetration to its initial values (“No trade” scenario) or setting the Dummy to be 0 in all periods (“No Constitution” scenario). In all cases, the series correspond to the Industry Sector average (compounded by sub-sectoral inputs weighted by the participation of each Industry Sub sector) 54
<!-- Start of picture text --> A. Average duration (in years) B. Median amplitude (cumulative % variation)<br>, 12<br>-<oc=<br>6 10<br>--" -<br>5 ¢<br>~-orTM ----: 8 o* gt<br>4 --" ae<br>-- --"* 6 é<br>¢ 4<br>3 ?<br>o” ?¢ Fd7<br>, oa ar,<br>1 2<br>0 0<br>123 4 5 6 7 8 9 1011 1213 14 15 16 17 18 19 20 21 22 23 24 12 3 4 5 6 7 8 9 10111213 1415 1617 18 19 20 21 22 23 24<br>—e— Industrial Countries = @ eNon-SSA Developing Countries —e— Industrial Countries = @ eNon-SSA Developing Countries<br>———= Sub-Saharan Africa (SSA) ——— Sub-Saharan Africa (SSA)<br><!-- End of picture text -->
arrest charge does not have a match in the court data) and charges for which the court has not issued a decision.<sup>8</sup> We also drop misdemeanor C cases as these are inconsistently reported in our data. This leaves us with 2.6 million arrests. We geocode the addresses provided with the address information and match each arrest to the county in which the individual lived when they were arrested. We then collapse the data to the count of arrests at the county-day level. This leaves us with a balanced panel of 742,188 county-day observations from 2010 through 2017. We maintain separate counts of crimes by category of the arresting charge (for instance, violent crimes, assaults, etc).

Arresting charges are defined in the raw TPDS data. When we conduct analyses on total crimes, we pool all of these charges together. We also aggregate some of these charges into violent and non-violent crimes and analyze these separately. We consider the following crimes to be violent: assault, aggravated assault, homicide, manslaughter, kidnapping, domestic assault, and weapons crimes. We define non-violent crimes as: larceny, burglary, stolen property, traffic (excluding those resulting in manslaughter charges), marijuana possession, and marijuana dealing.

Our definition of violent and non-violent crimes is not exhaustive. There are some crimes that we do not consider violent or non-violent. Robbery, for example, is generally defined as “the action of taking property unlawfully from a person or place by force or threat of force.” Thus, while clearly a property crime, it might also be considered a violent crime due to the threat or use of force. Not all robberies, however, involve the actual use of force or violence. These crimes are included in our analysis of heat’s impact on total crime and we analyze heat’s impact on some of these uncategorized crimes individually, but we exclude them from our analyses of violent and non-violent crimes.

> 8These are indicated as cases where the result is “pending” or “no determination.” Dropping nonmatching court cases drops 11% of the arrests in our raw sample. Including these and conducting our main arrest analysis does not change our results.

11
-105-

- C.G. Langoni (1974), As Causas do Crescimento Economico do Brasil, APEC, Rio.

- A. Maddison (1952), "Productivity in Canada, the United Kingdom, and the United States", Oxford Economic Papsrs, October.

- A. Maddison (1967), "Comparative Productivity Levels in the Developed Countries", Banca Nazionale del Lavoro Quarterly Review, December.

- A. Maddison (1970), Economic Progress and Policy in Developing Countries, Allen and Unwin, London.

- A. Maddison (1980), "Monitoring the Labour Market", Review of IJrome and Wealth. June.

- A. Maddison (1982), Phases of Capitalist Development, Oxford.

- A. Maddi.*on (1983), "A Comparison of Levels of GDP per Capita in Deve1uped and 4eveloping Countries, 1700-1980", Journal of Economic History, March.

- A. Maddison (1987), "Growth and Slowdown in Advanced Capitalist Economies", Journal of Economic Literature, June.

- P. Marer (1985), Dollar GNPs of the USSR and Eastern Europe, World Bank, John Hopkins, Baltimore.

- R. Marris (1984), "Comparing the Incomes of Nations", Journal of Economic Literature, March.

- G.J.A. Mensink (1966), Comparisons of Labour Productivity in the United Kingdom and the Netherlands, 19:8, CBS, The Hague, January (Statistical Studies, no. 18).

- T.W. Merrick and D.H. Graham (1979), Population and Economic Development in Brazil, John Hopkins, Baltimore.

- F.C. Mills (1932), Economic Tendencies in the United States: Aspects of PreWar and Post-War Changes, NBER, New York.

- J.C. Musgrave (1986), "Fixed Reproducible Tangible Wealth in the United States: Revised Estimates", Survey of Current Business, January.

- J. Nyers (1981), "Comparison of Productivity Level of the Austrian and the Hungarian Industry: Methods and Ro-ults", IARIW, processed.

# OECD (1979), Measuring Employment and Unemployment, Paris.

- H. Van Ooststroom and A. Maddison (1985), "An International Comparison of Levels of Real Output and Productivity in Agriculture in 1975", revised version, Groningen, processed.

- D. Paige and G. Bombach (1959), A Comparison of National Output and Productivity, OECC, Paris.
Since the vegetable oils incidence ( i.e., in 2010-2012) nine initiations against China versus 24 against other countries.<sup>23</sup> Baracat et al. (2013, pp. 104-104) documents retaliation by Brazil against Argentine restrictions on automobile imports, another instance of countries resorting to retaliation – a basic tool of the mercantilist system that the GATT/WTO legal system is intended to replace.

# **REVERSION TO THE OLD POLICY CULTURE**

Argentina has reverted to inward-looking policies, most of them administered by obscure institutional arrangements characterized more by discretion and top down decisions than by participatory, transparent and accountable processes. In addition, the severity of the exchange control restrictions has intensified. The domestic currency is increasingly overvalued, the gap between the official and black market rates is widening. Again, the exchange rate (controlled by the Central Bank) is used primarily as an instrument to control inflation. Trade and exchange control measures have been used to defend the (nominal) exchange rate and to accommodate import-competing interests. These are in essence the institutional arrangements and the policies that characterized the protectionist era of Argentina’s economic history. These institutions and policies are a central part of the explanation for why, during eight decades starting around 1930, the economy has underperformed comparator economies.

> 23 Tabulated from WTO data at http://tmdb.wto.org

22
exception: while Uruguayan banks may have ultimately felt the adverse effect of bank failures in Argentina, they did benefit from the deposit run across the border at earlier stages of the crisis, as indicated by the positive and significant impact of the decline in deposits in the neighboring country (see the negative and significant coefficient in column 9 of Tables 4 and 5).<sup>15</sup>

# **3.2 Dynamic Estimates**

While the systemic variables used in the previous section explained reasonably well the evolution of deposit and interest rates at a monthly frequency, it is well known that during a crisis financial variables tend to display extreme high frequency volatility that is the result of sudden changes in risk perception, either directly reflected in the systemic risk variables used in the previous tests or associated with specific events or news that are taken as indicators of new developments in the unraveling of the crisis. At any rate, unlike bank-specific fundamentals, the incidence of systemic factors is likely to be apparent also (and perhaps even more so) at high frequencies.

In this section, we use daily data to examine the incidence of systemic factors from this different perspective. Specifically, we look at depositor reactions to daily events by estimating the following vector autoregressive regression (VAR) model, where _yt_ is a vector including daily system-wide deposits or interest rates (the latter, due to data availability, only in the case of Argentina) along with measures of country risk, exchange rate risk (again, due to data availability, only in the case of Argentina), and a dummy capturing

> 15 Similar results were obtained using Argentina’s sovereign risk. However, given the strong trade links between the countries, Argentina’s sovereign risk may have affected Uruguayan banks both (positively) through its incidence on the neighbor’s financial sector and (negatively) through its adverse real impact on the local economy. In this sense, the decline of deposits lends itself to a clearer interpretation as a substitution effect between different locations.

17
countries.<sup>5</sup> Moreover, in the setting I analyze, the sibling composition and size of the household of origin is not always random. As mentioned, a sub-group of Muslim couples has an incentive to produce a son so as to exclude other relatives from inheritance. Therefore, adult Muslim men who have a surviving brother and adult Muslim men who have only sisters or no siblings may come from nuclear and extended households that have different structures.<sup>6</sup> They may have different endowments or a preference for having sons that is independent of the incentive created by the inheritance rule. These differences by sibling composition are systematic and cannot be eliminated by a comparison relative to non-Muslim men as they do not have incentives to engage in strategic fertility behaviors.

Nevertheless, after subtracting the post-period difference between adult Muslim and nonMuslim men with different sibling composition from the pre-period difference, only the effect of the classical Islamic inheritance rule is expected to explain the differences in son-preference and fertility outcomes between Muslim and non-Muslim couples. Any alternative influences that remained after the second difference between adult Muslim and non-Muslim men are expected to stay fixed in the post-compilation period. Potential systematic gaps in the evolution of Muslim and non-Muslim adult and child mortality rates, contraceptive use and the like are eliminated by the third difference.<sup>7</sup>

# **3.2. Data**

I use data on men and women’s gender and fertility preferences, women’s birth histories, and men and women’s sibling histories from the 2007 Indonesian Demographic and

> 5 For instance see Steelman et al. (2002), Butcher and Case (1994), Morduch (2000), Parish and Willis (1993).

> 6 If the inheritance rule is binding, adult men who only have surviving sisters are more likely to be born in larger households and from parents who were practicing DSB and, and are more likely to have a male agnate uncle (brother of their father).

> 7 Between 1987 and 2007, the total fertility rate declined from 3.3 to 2.6 children per woman; the infant mortality rate declined from 70 to 34 deaths per 1000 live births; the male (female) life expectancy at birth increased from 58 to 68 (62 to 72) years old; and the use of any (modern) method of contraception for women of reproductive age increased from 48 to 61 (44 to 57) percent (DHS 1987, 2007).

11
Economic Parameters of Deforestation 24 Consider two exogenous shocks to the timber price path: k' is an increase in the level of the price pat pc' does noc change the slope of the price path). _le'_ is some other increase in the timber price path, which could alsorchange its slope, for example, a value tax on timber. With these assumptions, the following properties result: 7e, > _0, 74 >_ 0, 7eO < _, 74t_ C 0, ARC = _0,_ r 0 _lede_ i,>Ot > O, i > ak _<_ nd 4< ando7e C 0 ofits r f from agriculure are assumed to be independent of p, c', cJ, and r. Furthermore, profits from agriculure are assumed to be non-declining, te > 0. For simplicity it is assumed that managed forest produces a constant timber crop with residual instantaneous returns to land of i7et). 'nis model abstracts from the question of optimal effort and optimal rotation periods in the managed forest and focusses squarely on the question of land conversion. land that is left idle after logging yields zero returns.

# The Optimal Logging Time

After unmanaged forest is logged, land use will be determined by maximizing residual returns to land. The optimal time of logging the unmanaged forest is determined by maximizing the present value of returns fom loggng and subsequent profit-maximizing cultivation where s is the integration variable, running from the time of logging, _te,_ to infinity subject to the condition max LPV > 0. If max LPV •0, logging .would not take place within finite time. The first order condition is: where subscripts to a and LPV denote partial derivatives. The intuition of this first order condition is that at the optimal time of logging, the rate of appreciation of the timber
the contrary, the weather during the 2009/10 growing season was relatively good while the one in 2003/04 was comparable to the average. The later growing season was preceded by the severe 2002 drought and followed by the dramatic 2005 drought and flooding.

The analysis conducted in this study to examine the impact of droughts and excess rainfall on the welfare of Malawian households shows that exposure to these shocks decreases households’ consumption and elevates their vulnerability to poverty. Weather shocks have severe impacts on welfare of households and push non-poor households into poverty. Drought during crop growing season decreases consumption per capita without aid by significant margin, by 5–12 percent. Similarly, flooding during the first two months of growing season has an adverse effect on consumption per capita. As a result, households’ vulnerability to poverty is generally higher than static poverty. In other words, even if poverty rate is already very high in Malawi, the non-poor households are likely to fall into poverty when exposed to these shocks. Design of safety net programs, such as social cash transfer, need to take into account vulnerability to poverty and such programs can be scaled up during disaster periods to benefit households that become poor as a result.

The rest of this paper is organized as follows: Section 2 describes the data and methodology. Sections 3 and 4 present the results and robustness checks. Finally, we conclude in Section 5 by highlighting policy implications of the main results and acknowledging the study’s limitations.

# **2 Data and Methods**

## **2.1 Data and Descriptive Statistics**

To analyze vulnerability of households to poverty, we used the three rounds of the IHS, which is nationally representative, comparable over time, and used to produce the official poverty statistics. The second round (IHS 2) was conducted from March 2004 to March 2005, and a total of 11,281 households were interviewed. The third round (IHS 3) was implemented from March 2010 to March 2011 and covered 12,271 households. The fourth round (IHS 4) was carried out from April 2016 to May 2017 and included 12,447 households. As mentioned in the introduction, 2010/11 is considered to be a good weather year while 2016/17 is a dire one.

The key dependent variable for the vulnerability analysis is consumption per capita. To ensure comparability and capture changes in real consumption per capita, all consumption expenditures are expressed in 2016 prices, thousands of Malawian Kwachas [MWK]. In addition, to account for the fact that public and private support to households increases during periods of extreme shocks, the analysis excludes in-kind and cash support from the total consumption basket. The amount of assistance is estimated from self-reported cash and 5
# Central Bank Losses Origins, Conceptual Issues, and Measurement Problems

by Mario 0. Teijeiro

# Table of Contents

|I.|Most Important Sources of Central Bank's Quasi<br>Fiscal Deficits|1|
|---|---|---|
|II.|Deviations of Accounting from Economic Losses|4|
|III.|Summary of Issues|9|
|IV.|Measuring Central Bank'a Financing Needs|15|
|V.|Conclusion|17|
|Appen|dix|18|
# BASIC ?f , w hIC %ITA~ Isble 7a romn 1911-1983

|Indicator|1971|1972|1973|1974|1975|1976|1977|1978|1979|1980|1981|1982|1983|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Per capita GNP<br>g<br>rate|7.1|-2.9|-7.1|-0.7|-14.4|1.8|8.0|6.4|6.5|6.0|3.9|-15.7|-2.4|
|<br>kaalqgoymmnt<br>rate|5.7|3.7|4.6|9.2|16.8|19.4|18.6|17.9|17.7|17.4|15.6|28.2|24.5|
|Inflatimn rate b/<br>|26.7|108.3|441.0|497.8|379.2|232.8|113.8|50.0|33.4|35.1|19.7|9.9|27.3|
|Nominal devaluation|||455.0|649.5|490.3|165.8|64.9|47.0|17.7|4.7|0.0|30.5|54.9|
|Mh/GNP|17.0|.7.5|12.9|6.4|7.1|7.6|10.5|12.5|14.6|16.2|19.6|31.9|27.3|
|Pllicoectoz-|-10.7|-13.0|-24.7|-10.5|-2.6|-2.3|-1.8|-0.8|1.7|3.1|1.7|-2.3|-3.8|
|Currentacommt-|-17.8|-48.1|-19.7|12.9|-27.1|5.4|-21.8|-37.8|-26.1|-33.9|-88.4|-48.6|-26.6|
|alww<br>loblex|119.3|108.2|80.0a/|64.8|62.1|63.0|71.1|75.7|82.0|89.4|97.5|97.1|86.8|
|Fal effective<br>e1InEe<br>rate c/|91.9|96.4|107.2|93.5|100.2|87.1|84.4|97.7|87.4|75.1|67.4|78.9|83.5|
|Thrss of trade|80.6|75.5|84.7|88.3|55.4|59.3|54.1|48.6|54.7|51.6|44.6|39.8|40.7|

B.sad on Rbmos (1986).

> a/ Jamnry-h43st.

b/ Vard&iatis In the CPI.

c/ Ecdhazp rate vwrlAtlims deflated by the differen betwemn eoternal and iternul W?I.
**Table A12. Heterogeneity for Employment and Work by Age, Excluding Controls**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Wo<br>|men<br>|Me<br>|n<br>|
||Employed|Working|Employed|Working|
|LOA|0.045|0.059|0.009|0.014|
||(0.039)|(0.038)|(0.035)|(0.029)|
||[0.216]|[0.103]|[0.812]|[0.619]|
|ESR|-0.006|-0.028|-0.053|-0.048|
||(0.039)|(0.039)|(0.037)|(0.031)|
||[0.897]|[0.453]|[0.175]|[0.131]|
|LOA x Youth|0.026|0.010|0.012|-0.032|
||(0.071)|(0.073)|(0.077)|(0.072)|
||[0.719]|[0.881]|[0.881]|[0.636]|
|ESR x Youth|0.035|0.044|0.155*|0.135*|
||(0.068)|(0.070)|(0.083)|(0.074)|
||[0.579]|[0.542]|[0.081]|[0.095]|
|LOA + LOA x Youth|0.071|0.070|0.021|-0.018|
||(0.058)|(0.061)|(0.067)|(0.064)|
||[0.204]|[0.252]|[0.739]|[0.779]|
|ESR + ESR x Youth|0.029|0.016|0.102|0.088|
||(0.057)|(0.061)|(0.073)|(0.066)|
||[0.586]|[0.786]|[0.178]|[0.215]|
|Observations|1,361|1,361|1,119|1,119|
|Outcome(Control Group)Mean|0.436|0.537|0.735|0.832|

_Notes:_ This table presents the treatment heterogeneity of including the LOA module (LOA) or enforcing self-reporting (ESR) by age for the reporting of employment or work status. We present the ITT estimates of 1 - 4 based on the following equation: 0 1 2 3 4 + . We also include estimations of the total effect of each survey method (iihss hss hss iihss1 3 for the LOA iihss module, and iihssββ ββss iihss2 4 for proxy-reporting) at the end of each table. The indicator YY = αα + ββ TT1 + ββ TT2 + ββ TT1 × DD takes the value of 1 if + ββ TT2 × DD ƟDD + bb + εε ββ + ββ the respondent is a youth between the ages of 15 to 24 years (Columns [1] to [4]). All estimations include iihss ββ + ββ DD strata fixed effects. Each column is a separate dependent variable as defined in Section 3. The control mean refers to the mean of the control group for each outcome. Standard errors clustered at the household level (randomization level) are presented in parentheses, and the randomization inference _p_ -values are shown in brackets. *** _p_ <0.01, ** _p_ <0.05, * _p_ <0.1 49