rafmacalaba/datause-encoder-data huggingface dataset:
13. Shared revenue amounts SXTi and SDAi were drawn from actuals for 2001. As indicated by the formula, only seventy five percent of natural resource revenue shares received by each region enter into the fiscal capacity definition.

* (6). FCi = PADi + STXi + (0.75*SDAi)

14. The _expenditure needs_ term incorporates an index for regional factors such as population, poverty gap, area, and price levels relative to national averages.<sup>29</sup> The indexes are defined as the regional factors (e.g., POPi) divided by the national average (e.g., total population).

(7). IENi= (0.4 * IPOPi)+ (0.1 * IPOVGAPi) + (0.1 * IAREAi ) + (0.4 * ICOSTRELi) 15. The total expenditure needs index is a composite of a localities population, poverty gap, area, and cost level relative to the national average. An above average population would result in a higher expenditure needs indicator. In the 2002 formulation, population and cost were given the largest weight (together 80 percent). In contrast, the 2001 formula gave equal weights to all factors. A region with an expenditures needs index under one (i.e., higher than average population, area, poverty incidence, and cost index) will receive more DAU, relative to its fiscal capacity.

16. To arrive at an actual Rupiah amount of expenditure needs, the index was multiplied by as estimated of average expenditure needs (EN<sup>AV</sup> ). It is important to note that this measure does not reflect some actual cost measure, but rather to be reflective of the total resource pool available, in this care total local government spending (APBD) in the previous year. This contrasts with attempts to cost out individual sectoral needs (World Bank 2000).<sup>30</sup> 17. Subtracting actual fiscal capacity (FCi) from expenditure needs (ENi) for all local governments resulted in a total fiscal gap (FGi): (9). FGi = ENi - FCi 29 The poverty gap measure provides a reflection of the extent of poverty in that it measures the difference between expenditures (cf Deaton 1997:147). The DAU 2002 used an exponent parameter of 1 for the ratio of the difference between the poverty line and individual expenditure divided by the poverty line for the poor.

30 Lewis (2002) rightly points out that this average amount may be too much or too little. Since we can say little about the appropriateness of this overall resource envelope, we simply accept the EN<sup>AVfactor as given</sup> by political decision. The measure used in 2002 was total local government expenditures in 2000 or 169.62 billion Rupiahs per region -- available divided by the total number of regions. The choice of this measure is somewhat arbitrary, as arguably an inflation adjusted amount could have been used (e.g., projected PAD, STX, SDA, DAU and other transfers such as DAK). As the main use of this coefficient is scaled by the actual DAU available for the formula window (Eq. 9), differences are not that significant. However, one slight shift would be that a choice of higher average expenditure needs indicators would generated fewer zero or non-zero fiscal gap allocations.

31
Figure 3 documents how this measure of confidence in institutions from the GWP compares with the most closely related variables on confidence in institutions taken from the World Values Survey.<sup>6</sup> While the two measures are highly correlated in the common sample of countries for which both measures are available (a correlation of 0.81), it is worth noting the significantly smaller country coverage of the WVS. The circles in the graph represent countries that are present in our sample of the GWP but not in the most recent wave of the WVS. Using the GWP index therefore significantly increases the available sample to study effects of corruption on confidence in institutions.

Finally, Figure 4 documents the relationship between the corruption questions and the confidence in institutions index at the country level. The top panel plots corruption perceptions against confidence in institutions and the bottom panel plots corruption experiences against confidence. Both graphs display a negative relationship between corruption and confidence although this is much more pronounced for corruption perceptions. Here, all countries with very low average corruption perceptions score high on confidence in institutions. Scandinavian countries are the ones with the lowest perceived corruption and the highest confidence in institutions. Turning to corruption experiences, we see that in general countries with a higher share of people that have experienced corruption report lower confidence in institutions. However, there are a number of countries that have low levels of experienced corruption but still report low confidence. In this group we find particularly Latin American and Caribbean countries such as Panama, Argentina, Peru, and Trinidad and Tobago.

> 6 The WVS asks about respondents‟ confidence in a variety of institutions. We aimed to match this confidence index as closely as possible to our GWP index and therefore aggregated the answers to the following four questions into an index ranging from 0 to 4: “I am going to name a number of organizations. For each one, could you tell me how much confidence you have in them [...]: a) the armed forces, b) the courts, c) the government (in your nation‟s capital), d) parliament.”

10
broader cross-section of firm sizes than is available in commercial data bases, such as Worldscope.

Panel A of Table 3 shows that the average firm age in the sample is 16 years. We include dummy variables to identify very young firms (1-5 years), middle-aged firms (520 years) and older firms (>20 years). The firms in the survey are broadly classified in terms of their current legal status into corporations, cooperatives and an other category. Corporations, both public and private, make up 40% of our sample where as 16% of our sample is composed of Cooperatives or Collectives. We also have detailed information on the ownership breakdown of these companies and the percentage owned by different entities in the private sector (eg: domestic firms, domestic institutional investors, foreign individuals, foreign firms, foreign institutional investors, etc) and different entities in the state sector (e.g.: national government, state/provincial government, local/municipal government or other government bodies like collectives, etc). We use a dummy variable **State Ownership** to characterize firms where the government owns more than 50% of the company. Nearly 22% of the sample (531 firms) is composed of firms with more than 50% state ownership and the remaining is made up of firms with more than 50% private ownership. We also identify the number of competitors of the firm in its main business line in the domestic market by using five **Competition** dummies for 1-3 competitors, 4-6 competitors, 7-15 competitors, 16-100 competitors and over 100 competitors respectively. Panel B of Table 3 presents the correlation between the access variables and firm performance. We find that the bank finance variables are all significantly correlated with each other and in fact Bank Dummy and Bank Financing dummy, where the latter variable takes into account bank financing of new investments and working capital are 22
# **COVID-19 Age-Mortality Curves Are Flatter in Developing Countries**

Gabriel Demombynes<sup>1</sup> World Bank JEL Classification: I18, O57, J10 Keywords: Coronavirus, Pandemic, Developing Countries, Mortality

> 1 The author expresses appreciation to Damien de Walque, Joshua R. Goldstein, Young Eun Kim, Ronald D. Lee, Norman Loayza, Andrew Noymer, Robert Oelrichs, Sharon Piza, and Piedad Urdinola for helpful suggestions, many World Bank colleagues for help in identifying data sources, and to Jill Adona for excellent research assistance.
# Table <u>6: Impact of hosting refugees on household wealth</u>

||||House|holds|||
|---|---|---|---|---|---|---|
|||All|||Rural|Urban|
||OLS|FE|FE|FE|FE|FE|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Refugee Intensity|0.123|0.024|0.028|0.047|0.055|-0.000|
||(0.077)|(0.050)|(0.050)|(0.049)|(0.058)|(0.042)|
|Constant|0.051|0.676**|0.508*|0.455|0.221|1.739***|
||(0.478)|(0.313)|(0.283)|(0.340)|(0.410)|(0.263)|
|Round FEs|YES|YES|YES|YES|YES|YES|
|Fixed Effects (FEs)|WOREDA|EA|HH|HH|HH|HH|
|Controls|NO|NO|NO|YES|YES|YES|
|R-squared|0.612|0.670|0.180|0.188|0.203|0.076|
|# of EAs|433|433|433|433|290|143|
|N|13925|13925|13687|13522|9734|3788|

_Notes:_ This table provides estimates of the impact of logarithm of refugee intensity on “started log” of household wealth score. Household sample weights from ESS2 applied. The first four columns report results for all households while columns (5) and (6) report results for rural and urban households, respectively. All columns control for round FEs. Further, column (1) controls for woreda FEs, column (2) controls for EA FEs, and all other columns control for household (HH) FEs. Columns (4) to (6) additionally control for 12 time-varying characteristics. The coefficient estimates for these 12 control variables are not reported here, but can be available on request. The overall and the within R-squared are reported in the first two and other columns, respectively. Robust standard errors clustered at EA level are given in parenthesis. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

44
Policy Research Working Paper 7058

# **Abstract**

Understanding the trade-offs in improving the precision of agricultural measures through survey design is crucial. Yet, standard indicators used to determine program effectiveness may be flawed and at a differential rate for men and women. The authors use a household survey from Mozambique to estimate the measurement error from male and female self-reports of their adoption and knowledge of three practices: intercropping, mulching, and strip tillage. Despite clear differences in human and physical capital, there are no obvious differences in the knowledge, adoption, and error in self-reporting between men and women.

Having received training unanimously lowers knowledge misreports and increases adoption misreports. Other determinants of reporting error differ by gender. Misreporting is positively associated with a greater number of plots for men. Recall decay on measures of knowledge appears prominent among men but not women. Findings from regression and cost-effectiveness analyses always favor the collection of objective measures of knowledge. Given the lowest rate of accuracy for adoption was around 80 percent, costlier objective adoption measures are recommended for a subsample in regions with heterogeneous farm sizes.

This paper is a product of the Impact Evaluation Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at v.mueller@cgiar.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
that conflict intensity could increase substantially the paper invites a recognition of the fragility risks Afghanistan faces going forward.

# **2. Previous research**

This paper is part of a growing literature on empirical studies of conflict. Departing from a longer tradition with roots in political science and strategic analysis, this literature builds on disaggregated data and statistical tools. Until not long ago, the lack of reliable data tended to dissuade this kind of approach. Conducting censuses and surveys is challenging in conflict affected areas, while administrative records tend to be incomplete in highly informal environments. But these obstacles are gradually being overcome thanks to the availability of non-traditional sources of data, from military statistics to satellite imagery to mobile phone traffic.

Several studies in this emerging literature explore the dynamics of conflict. Biddle, Friedman, and Shapiro (2012) and Hultman, Kathman, and Shannon (2013) use panel data to assess the relationship between troop deployment and violence-related casualties at the subnational or regional level. Data are from Iraq and across countries respectively. Both studies find that large deployments are effective at reducing violence, even in the most challenging environments. The impact of large deployments, however, depends on the number of troops and their equipment, as well as on having a clear mandate to use force to prevent conflict.

The key role played by international troops is confirmed by other studies. Also using data across countries, Hegre, Hultman, and Nygard (2010) conclude that the size of peacekeeping operations—as measured by troop strength or budget—is the main determinant of the ability to effectively curb a conflict. In the cases of Iraq and the Philippines, Berman et al. (2011), Crost et al. (2014a), Berman et al. (2013), and Sexton (2015) find that violence, as perceived by households, declines with the presence of international troops.

The impact of international troops on conflict intensity typically varies spatially. The abovementioned study by Hegre, Hultman, and Nygard (2010) suggests that it is important to take time and location into account, in addition to troop numbers. Using data from the Kunduz and Badakhshan Provinces in Afghanistan’s Northeast region, Derksen and Ruttig (2013) provide further evidence that the ability of troop deployments to contain conflict is not even.

Dynamics can be complex as well. Condra et al. (2010) document the persistence and pervasiveness of the conflict in Afghanistan, claiming that local exposure to civilian casualties caused by international forces has led to increased insurgent violence over the long run. Also using data from Afghanistan, Trebbi and Weese (2015) show that the insurgency is capable of acting strategically across districts and provinces.

Another relevant strand of this literature focuses on the relationship between conflict and household wellbeing. Brück et al. (2016) review the World Bank’s Living Standard Measurement Surveys to suggest concrete ways to capture in these surveys the causes, functioning, and 5
18 _LS// Dependent Variable is_ **_MEXDIS_** _Sample: 7/29/94 - 7/12/96 Included observations: 103_

|Variable|Coefficient|Std. Error<br>T-St|atistic|Prob.|
|---|---|---|---|---|
|C|-207.4990|49.24584<br>-4.21|3533|0.0001|
|D2|554.0909|28.96765<br>19.1|2792|0.0000|
|POLDIS|1.080204|0.067715<br>15.9|5221|0.0000|
|R-squared|0.834172|Mean dependent var|809.|9767|
|Adjusted R-squared|0.830855|S.D. dependent var|297.|5443|
|S.E. of regression|122.3717|Akaike info criterion|9.64|2820|
|Sum squared resid|1497483.|Schwartz criterion|9.71|9560|
|Log likelihood|-639.7559|F-statistic|251.|5167|
|Durbin-Watson stat|0.451944|Prob(F-statistic)|0.00|0000|

All variables are very significant and the estimated coefficient of _d2,_ 550, shows the dramatic change in the mean of the relation after 1/20/95. Thus, we generated the variables MEXDISI=MEXDIS-550*d2 and DIFFI=MEXDISIPOLDIS to perform tests of cointegration. The idea is that, except for a change in the mean of the relation, the two variables are cointegrated, i.e. have the same random walk component. As we will discuss in section 5, the change in the mean of the relation can be interpreted as the change in the relation between Mexico's and Poland's fundamentals induced by the Mexican crisis.

To test properly for cointegration, we consider the following Error Correction Model (ECM)1<sup>9</sup> :

> _AMEXDISI, I=aA(c+MFXDIS1,-l_ + _POLDIS,l)+3MAMEXDISI,+y M-APOLDIS,+m,_ (4) _APOLDIS, =a p(c+ MEXDIS1,_1 +13 POLDIS,_ 1 )+8 pAMEXDS1,_ 1 + y pAPOLDIS,_1_ **_+S p,_**

> _19_ In order to test for cointegration and to have a proper estimate of the cointegration vector we have to specify an error-correction model (ECM). This is because cointegration tests are usually performed on the error-correction representation. Note that, if two or more variables are cointegrated, it is guaranteed that an error-correction model representation exists. This is the so called _Granger representation theorem:_ for any set of _1(1)_ variables, cointegration and error-correction are equivalent representations. Moreover, the most common procedure to test for the lag length is to estimate a Vector Auto Regression (VAR) using data in _levels._ We consider VARs with 2 or more lags: the VAR with one lag presents evidence of autocorrelation in the. The Akaike Information Criterion suggests a specification with 2 lags and the Chi test suggested by Sims (1980) does not reject the null of a lag length of 2 versus 3 or more lags .
# **Table 10. Sample Split by initial capital levels**

The dependent variable is the quarterly stock return. See Table 4 for a detailed description of the estimated model. The sample period for the stock return is Q1-2006 to Q1-2009. All control variables are lagged one year. Crisis is a dummy that takes value one from Q3-2007 to Q1-2009. Tier1 (Tier2) is Tier1 (Tier2) capital scaled either by risk-weighted assets (RWR) or total un-weighted assets (LR). Tier1 capital includes shareholder funds plus perpetual, non-cumulative preference shares, plus retained earnings. Tier2 capital includes subordinated debt, hybrid capital, loan loss reserves, and valuation reserves. Common equity is shareholders fund and other capital total regulatory capital minus common equity. RWR stands for riskweighted capital ratio and LR for leverage ratio. In this table we report separate estimates for the sub-samples of initially well and poorly capitalized banks. The high capital sub-sample includes banks with capital above the sample median in 2006, while the low capital sample includes banks with capital below the sample median in 2006. In order to keep a reasonable number of countries in each sub-sample (at least 7 countries) we lower our threshold number of banks by country to 3 banks that is we obtain samples with at least 48 observations per country (12 observations per year). Standard errors clustered by bank are reported in brackets and the p-value - for the test of significant difference between the pre <u>crisis and crisis coefficients in parentheses.</u>

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||High Capit|al in 2006|Low capit|al in 2006|
||RWR|LR|RWR|LR|
|Tier1*PreCrisis|-0.006|0.111|0.294*|0.18|
||[0.079]|[0.125]|[0.162]|[0.126]|
|Tier2*PreCrisis|0.12|-0.044|0.177|0.061|
||[0.105]|[0.123]|[0.137]|[0.134]|
|Tier2*Crisis|-0.023|-0.455*|0.390*|0.579*|
||[0.187]|[0.268]|[0.204]|[0.316]|
||(0.432)|(0.129)|(0.210)|(0.160)|
|Tier1*Crisis|0.018|-0.048|0.496***|0.498**|
||[0.108]|[0.163]|[0.182]|[0.198]|
||(0.792)|(0.126)|(0.092)|(0.054)|
|Controls*Crisis|x|x|x|x|
|Controls*PreCrisis|x|x|x|x|
|Country*Year FE|x|x|x|x|
|Nber. Obser.|1857|1795|2192|1931|
|R squared|0.26|0.26|0.22|0.23|

Note: (*), (**) and (***) stand for statistically significant at the 10%, 5 % and 1 % level, respectively. Robust standard errors clustered by bank reported in brackets. In parentheses we report the pvalue for the test of equality of effects during crisis and pre crisis.

27
# **Table 10 - Chairman Appointment and Sector Activities**

This table investigates whether the selection of chairman is endogenously determined by the economic development of his state of origin. Panel A compares sector-level productivity (both in level and growth rate) in five sectors (Agriculture, Commerce, Transport, Energy and Telecommunications) for states with and without a senator serving as chairman in the corresponding sector in the year when committees are formed (2003, 2006 and 2009). We use indicators (by row order) to proxy for productivity of the sectors reported for a given state and year. The indicators are obtained from the _Banco de Informacion_ of the National Institute of Statistics and Geography (INEGI). Panel B displays a similar comparison by focusing on two important macroeconomic indicators at the state level: growth rates of GDP and of employment. Columns 1 and 2 report the mean of each sector's productivity indicator for states with and without at least one senator presiding the corresponding committee while column 3 shows the mean difference between the two groups with the t-statistics reported in the underlying parentheses.

||(1)<br>States with Chairman|(2)<br>States without|(3)<br>Difference<br>(t-statistics)|
|---|---|---|---|
|**Panel A Productivity of Sectors**||||
||Level|||
|Total agricultural production<br>(relative to state's GDP)|0.064|0.046|0.018<br>(0.83)|
|Gross production of commerce sector|0.124|0.125|-0.001|
|(relative to state's GDP)|||(0.06)|
|Length of highway network|0.114|0.069|-0.045|
|(relative to state's GDP)|||(1.17)|
|Registered vehicles in circulation|0.227|0.155|0.072|
|(relative to state's population size)|||(1.14)|
|Users of electric energy<br>(relative to state'spopulation size)|0.296|0.294|0.002<br>(0.05)|
||Growth Rate|||
|Total agricultural production|-0.040|0.069|-0.109|
|(relative to state's GDP)|||(-1.10)|
|Gross production of commerce sector|0.076|0.078|-0.002|
|(relative to state's GDP)|||(-0.05)|
|Length of highway network|-0.028|0.026|-0.054|
|(relative to state's GDP)|||(-0.69)|
|Registered vehicles in circulation|-0.104|0.079|-0.183|
|(relative to state's population size)|||(-2.41)|
|Users of electric energy|0.017|-0.004|0.020|
|(relative to state'spopulation size)|||(0.20)|
|**Panel B Economic Indicator**||||
|GDP Growth|-0.013|0.006|-0.018<br>(-1.16)|
|Employment growth|0.019|0.020|-0.001<br>(-0.24)|

44