rafmacalaba/data-use-mentions-extended huggingface dataset:
households following their most recent survey. The comparison to non-displaced households is important in the current context because, as discussed above, violence was increasing over this entire time period. We estimate variants of the following baseline specification: (1) _V iolenceirt_ = _σi_ + _ρt_ + _βPostirt_ + + _δDisplacedirt_ + _γDisplacedirt ∗ Postirt_ + _εirt_ where _V iolenceirt_ denotes the number of fatalities or the number of violent incidents in district _r_ (origin district for displaced households and district of residence for nondisplaced) of household _i_ during month _t_ ; _Postirt_ denotes an indicator equaling one if the observation was following the displacement of household _i_ ; _Displacedirt_ denotes an indicator equaling one if the observation was for a displaced household and zero if the household was never displaced; _σi_ denotes household fixed effects to capture region- and household-specific heterogeneity; and _ρt_ denotes time (month-year) effects to distinguish the change in violence from changes that were occurring across the entire country over time. Standard errors are clustered at the district level.

The sample includes the 1,116 displaced households for which we can identify their district of origin and 9,992 households that were never displaced during the analysis period. For each displaced household, the sample is restricted to violence observations from the six months leading up to displacement, the month of displacement, and the 12 months following displacement; and for each non-displaced household, the sample is restricted to the same number of months leading up to and following their most recent survey.<sup>14</sup> The coefficient of interest is _γ_ , which represents how much more the monthly violence outcome increased on average for displaced households in the month of and 12 months following displacement relative to the increase in the month of and 12 months following the most recent survey of non-displaced households. Estimates of the baseline specification

> 14Many displaced and non-displaced households were constantly dropping out of the survey, and the latest survey of non-displaced households are spaced across the entire time period of the analysis (20152018).

12
- Tanzania National Bureau of Statistics (TNBS) (2015). National Panel Survey 2012-2013, wave 3. Public Use Dataset. Ref: TZA 2012 ~~N~~ PS-R3 ~~v~~ 01 ~~M~~ . Downloaded from `https://microdata. worldbank.org/index.php/catalog/2252` on 6 September 2019.

- Taraz, V. (2018). Can farmers adapt to higher temperatures? evidence from India. _World Development 112_ , 205–19.

- Tarnavsky, E., D. Grimes, R. Maidment, E. Black, R. P. Allan, M. Stringer, R. Chadwick, and F. Kayitakire (2014). Extension of the TAMSAT satellite-based rainfall monitoring over Africa and from 1983 to present. _Journal of Applied Meteorology and Climatology 53_ (12), 2805–22.

- Tennant, E. and E. A. Gilmore (2020). Government effectiveness and institutions as determinants of tropical cyclone mortality. _Proceedings of the National Academy of Sciences 117_ (46), 28692–9.

- Tesfaye, W., G. Blalock, and N. Tirivayi (2021). Climate-smart innovations and rural poverty in Ethiopia: Exploring impacts and pathways. _American Journal of Agricultural Economics 103_ (3), 878–99.

- Uganda Bureau of Statistics (UBOS) (2014a). Uganda National Panel Survey (UNPS) 2010-2011. Public Use Dataset. Ref: UGA ~~2~~ 010 ~~U~~ NPS ~~v~~ 01 ~~M~~ . Downloaded from `https://microdata. worldbank.org/index.php/catalog/2166` on 6 September 2019.

- Uganda Bureau of Statistics (UBOS) (2014b). Uganda National Panel Survey (UNPS) 2010-2011. Public Use Dataset. Ref: UGA ~~2~~ 011 ~~U~~ NPS ~~v~~ 01 ~~
7.7|
|Manufacturing|34,708.84|127,068.34|201,910.72|304,847.00|14.0|23.4|24.8|22.7|
|Construction|9,664.19|32,655.21|27,104.38|66,218.00|3.9|6.0|3.3|4.9|
|Trade|23,399.82|79,119.31|130,862.42|272,258.00|9.5|14.6|16.1|20.2|
|Transport|12,755.54|35,755.24|62,113.34|129,312.00|5.2|6.6|7.6|9.6|
|Business|11,846.30|45,653.54|83,219.30|152,796.00|4.8|8.4|10.2|11.4|
|Other|37,395.41|72,969.55|120,809.51|217,495.00|15.1|13.5|14.9|16.2|
|_Aggregate_|_247,551.99_|_542,005.85_|_813,043.69_|_1,345,531.00_|_100.0_|_100.0_|_100.0_|_100.0_|

Source: Authors’ calculations based on data from DOSM It is evident from Table 3 as well as Figure 2 that in the three decades from 1978 to 2018 the structure of Malaysia’s economy changed dramatically. During this time period, the country was transformed from an economy still largely based on agriculture to a services-driven economy. From 1987 to 2018, the share of agriculture in aggregate output fell from 22
Figure C3: Poverty rates across urban versus rural areas <!-- Start of picture text --> (A) $2.15 poverty line<br>Official definition DOU DB<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>(B) $3.65 poverty line<br>Official definition DOU DB<br>100 100 100<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>Poverty rate (%)<br>Poverty rate (%)<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: Each boxplot shows the distributions of poverty rates over different geographic areas in 20 countries. WorldPop 250m is used for the DOU and DB methods. The dashed lines represent the average national poverty rate in the sample.

Figure C4: Comparison of urban poverty rates between official and DOU/DB urban definitions <!-- Start of picture text --> (A) DOU vs. official definition (B) DB vs. official definition (C) DB vs. DOU<br>60 60 MWI 60 MWI<br>MWI<br>50 50 50<br>NER NER<br>40 40 40<br>UGA UGA UGA<br>TZA TZA TZA<br>30 NER 30 30<br>TCD TCD<br>ETH ETH<br>20 ETH 20 LSO 20 LSO<br>BFATCD LSOAGO BFA AGO BFAAGO<br>10 VNMGABEGYGINSENCOLMRTCIVGNBBGDGHA 10 VNMGABEGYGINSENCOLMCIVRTGNBBGDGHA 10 VNMGABEGYCOLMRTGINSENGHACIVGNBBGD<br>10 20 30 40 50 60 10 20 30 40 50 60 10 20 30 40 50 60<br>Urban
periods, and involves political as well as technical challenges, with resistance from certain public officials who benefit from maintaining less-transparent systems and procedures (OECD, 2009b; Allen, 2009; IEG, 2008).

Concentrating a typical donor‟s aid in fewer countries and sectors would reduce the enormous transactions costs imposed on recipient governments. It would also tend to increase donors‟ use of country systems, as the leading donors in a recipient would internalize a greater share of the benefits. Even if donors‟ aid shares were all unchanged in a country, but divisions of labor by sector were sharpened, donors would have less incentive to micro-manage aid. The lead donor for education, for example, would have a strengthened reputational stake in education outcomes, and similarly for the lead donor in health. Incentives for them would shift away from delivering donor-managed, successful-looking projects, and toward working with governments to deliver improved sector-wide development outcomes. Concentrating aid in fewer countries or sectors, however, potentially reduces a donor agency‟s visibility, with possibly adverse consequences for its budget (see Andersen, 2000: 193).

Of course, the urgency of pursuing such measures for the purposes of increasing use of country systems depends on learning more about the latter‟s effects on development outcomes. When donors choose to rely on country systems or bypass them, how important are the associated “learning-by-doing” externalities? While the Paris Declaration‟s premise that the quality of aid matters as much as its quantity is surely correct, research on the implications of donor‟s aid management practices for aid quality is still in its infancy. Further research using the PDMS data can test for the effects of different categories of aid – based on whether it is delivered in ways consistent with PD principles - on governance and other outcomes.

32
Enumerators recorded basic information about the firm that could be observed without talking to the firm owner – the full street address, the business sector, the “fantasy name” of the firm (the name on a sign outside the firm if they had one), whether or not the business had a sign, the approximate area in square meters of the premises, and the approximate number of employees in the business. A photo was also taken of the firm to aid in subsequent identification.

Through this process more than 10,000 firms were listed during January and February of 2011 (appendix 3 provides a timeline). They were then matched by Gauss against two databases of formal firms – a database from PBH of 140,628 firms with municipal registration, and a database from JUCEMG (the Chamber of Commerce) of 117,350 firms with state registration. This was used to eliminate the “definitely formal” firms, i.e. firms who appeared on both of these lists, giving a sample of 7,852 listed 11
. In column (2) of Table (7), we turn to the estimation of the full model specified in equation (1) that also includes student, teacher and school characteristics. While the coefficients on the teacher variables are reduced somewhat, they are still large and significant. A one standard deviation reduction in absence from class is associated with an increase in student test scores of 0.05 standard deviations. Moving a student from a teacher with no content or pedagogical knowledge and no pedagogical skills to a teacher with full scores on these three dimensions would raise student test scores by almost one standard deviation. Looking at a standard deviation change in knowledge and skills, obviously, yields smaller changes (a one standard deviation change in teacher content knowledge, pedagogical knowledge and skills is associated with roughly a quarter of a standard deviation increase in student test scores), but also implies relatively modest changes in teacher quality.

In column (3), we estimate specification (2). As we exploit within-student variation, we can no longer calculate the effect of classroom absence and pedagogical skills as they are

> 21 Metzler and Woessmann (2012) derive a reliability ratio for first-differenced test score measures by assuming that the measurement errors are unrelated across the two subjects. We follow their approach here.

> 22 Student test scores are standardized in each country. For the teacher pedagogy score, we focus on the lesson preparation score and the evaluating students score, as the comparing writing score is more specific to language teachers only.

21
# **1. Introduction**

This paper explores the long-term fiscal effects of an aging population in New Zealand and considers several options available to government for containing and financing its future liabilities. Using the New Zealand Treasury long-term fiscal model, the paper analyzes the baseline fiscal outlook and its sensitivity to several economic, demographic, and policy assumptions. The results are presented from two different perspectives: In a balanced budget-tax rate (or zero net debt) approach, which shows the dynamics in the annual tax revenues that would be needed to balance future budgets; and in a constant tax rate (or tax smoothing) approach, which shows the dynamics in the government financial position when tax rates are held constant.

Based on the survey of international literature and the sensitivity tests, the paper derives several policy options for alleviating both the demographic pressures on the future fiscal balances and the problem of government financing in the long term. Attention is given to a calculation of the government gross implicit pension debt, to the tradeoff between a pre-funding of future spending and future tax increases, and to the fiscal and behavioral implications relating to the level of New Zealand Superannuation (NZS) benefits.

# **2. An Aging Population**

An aging population is an international phenomenon of growing concern to governments and multilateral institutions. Studies have examined issues relating to the public provision for retirement, in particular the implications of pension promises for future government liabilities and for economic efficiency and national saving.

# **2.1. Implicit Public Pension Liabilities**

Because of the increasing difficulty of maintaining fiscal balance in an aging society, recent studies by the OECD, IMF, and World Bank have advised governments to consider and periodically measure implicit public pension liabilities for current and future generations (OECD 1993 and 1995; IMF 1996a; and Kane and Palacios 1996.) The studies have concentrated on countries with large social security systems and direct 3
(Kaufmann et al. 2010). The indicator ranges from -2.5 (i.e., the lowest level of voice and accountability) to 2.5 (the highest level of voice and accountability). Data on population density and the median age of the population come from the DELVE COVID-19 database (Bhoopchand et al., 2020). We also control whether a country belonged to the axis of powers during WW2,<sup>5</sup> and if the country was a member of the communist bloc.<sup>6</sup> The descriptive statistics for our main variables and a summary of data sources are shown in Table 1.

Figure 1 provides a scatterplot of the cumulative deaths from COVID-19 per million people on the total losses in WW2 as a proportion of the pre-war population. Consistent with our hypothesis, we see that countries with the highest death rates during WW2 tended to have lower COVID-19 mortality rates. Next, we see if this is robust to adding controls relevant to the probability of experiencing a big shock, and whether the finding is robust to various changes in the specifications for our test.

# **5. Controls and tests for robustness**

Adding controls for other variables likely to influence COVID-19 mortality, we still find that it is negatively correlated with total losses during WW2.<sup>7</sup> Our most parsimonious specification is in Table 2, which includes five controls. The specification in column (1) uses total WW2 deaths per capita. At mean points, the elasticity of COVID-19 deaths to WW2 deaths is about -0.4. We also see that countries with higher GDP per capita have lower death rates from COVID-19. On the other hand, countries with older and larger populations, and countries with a higher share of educated people have significantly higher deaths per million from COVID-19.

Our variable for WW2 deaths includes both civilian and military losses. Some countries in our sample that suffered military losses (e.
Development Board (BWDB) in 2006 has been used to simulate the trans-boundary flows with estimates of runoffs generated from basins of three major rivers: Ganges, Brahmaputra, Meghna.<sup>16</sup> _Second,_ the National Flood Model of Bangladesh developed by the FFWC of BWDB has been used to generate flood levels, inundation extent and duration for the extreme flood events in Bangladesh during the monsoon period for each 300m x 300m grid.

The National Flood Model operates a real time numerical model based on one dimensional fully hydrodynamic model (MIKE 11 HD), and is linked to a rainfall-runoff model (MIKE 11 RR). The rainfall runoff model comprises 157 sub-catchments having a total area of 122,437 sq. km. Rainfall data from more than 200 rainfall stations and potential evapotranspiration data of around 30 evaporation stations have been used in the model. The hydrodynamic model, on the other hand, includes all important rivers and _khal_ s of Bangladesh (10,235 km), floodplain routing channels (1,147 km) and link channels. It is based on the existing national DEM for Bangladesh and model simulations included all existing flood protection measures like flood embankment and flood control and drainage infrastructure in Bangladesh.<sup>17</sup> _Third,_ the daily inundation depth results are synthesized to determine the highest inundation depth for each grid point during the monsoon period, and areas vulnerable to floods of a minimum duration of 24 hours are identified.

_Finally_ , for the subsequent economic analysis, risk exposure zones are formed by grouping grid points into the five flood risk zones.

> 16 For this research, the model was updated by the Institute of Water Modeling (IWM) with river alignments of GBM basins using available physical maps of India, Nepal, Tibet; and sub catchments of the GBM basins were redelineated using the Digital Elevation Model (DEM) based on SRTM-version 3. The model has been first calibrated using round the year hydrological feature of 2005 and validated for two subsequent years
.

- (ii) It incorporates unique exogenously defined drought and flood risks to measure vulnerability and exposure to climate risk using detailed three-wave householdlevel panel data.

- (iii) It assesses how exposure to climate risk affects vulnerability and resilience.

- (iv) Finally, the paper assesses whether adopting common CSA practices mediates the effects of climate shocks on vulnerability and resilience in Zambia, and if so, by how much.

The multidimensionality of poverty, resilience and vulnerability and their causes make measuring any of these indicators a daunting task. For this paper and given our focus on the last two metrics, we follow Barrett and Constas (2014) and define resilient households over three waves of data as those that were never poor or those that escaped poverty by the third wave. We classify vulnerable households as those that are poor in all the three waves, or poor in the first and third waves but not in the second wave, or those poor in the second and third waves but not in the first wave, or indeed those that were not poor in the first and second waves but fell into poverty in the third wave.

Vulnerable households are more likely to be adversely affected due to either exposure or lack of capacity to cope and adapt. Exposure refers to the presence of rural households or 3
countries in the world economy is a priority to contribute to economic policy both in developing and developed economies. The best way to do this is to use international input-output tables. I use three input-output tables databases to cover an extended period, starting in 1965, and with as many countries as possible.

I use the Long-Run World Input–Output Database or long-run WIOD (Woltjer et al., 2021) to go back in time as much as possible. The long-run WIOD provides annual time series of world input-output tables covering the period 1965-2000 (Woltjer et al., 9
in the informal sector. Even though labor regulation is the same in all Brazilian states, its enforcement is highly decentralized. This generates considerable variation in the degree of government monitoring faced by fi rms in different areas (which can be explored econometrically). Finally, there are three good sources of data which we combine in our study. First, we use a fi rm level dataset collected by the World Bank with standard information on investment, employment, sales and value added, but with unique information on the employment of informal workers (and other types of illegal behavior of fi rms). Second, from administrative data from the Ministry of Labor we construct measures of the enforcement of labor regulation at the local level. Third, we use information on the economic and demographic characteristics of all the cities where the fi rms in our sample are located collected by two Brazilian statistical and research institutes.

We explore the within country variation in the enforcement of regulation to analyze how it affects the fi rm’s employment of informal workers and the fi rm’s performance. We argue that the main channel by which enforcement of regulation affects fi rm performance is by reducing the fi rm’s access to unregulated and fl exible labor. However, enforcement can vary across locations for several reasons, and not all of them can be considered exogenous in our empirical work. Therefore we control for an extensive set of fi rm and local level variables which are likely to be important determinants of informal behavior, fi rm performance and enforcement. In addition, we use a set of instrumental variables for enforcement which are measures of access of labor inspectors to the fi rms in their region, and a proxy for general law enforcement in the region.

We fi nd that increased enforcement in the area where fi rms are located leads to a substantial reduction in the amount of the informal workers they employ. We also fi nd that it leads to a large decreases in value added per worker, sales per worker, net wages and capital per
al., 2019), while others employ an empirical best predictor model (Battese Harter and Fuller, 1988 Masaki et al., 2020).

We undertake this exercise in the context of Mexico, which has publicly available data that makes our approach feasible. We focus on three separate levels of aggregation: the state, the municipality, and the AGEB (Área Geoestadistica Básica, i.e. the Basic Geo-Statistical Area). Importantly, the census data has AGEB-level identifiers for all urban AGEBs in the country. This allows us to match geospatial data to AGEBs. In addition, we simulate a random sample from the census before implementing our preferred small area estimation approach. The use of census data allows us to compare the resulting estimates to the “truth,” derived from the census data itself. This type of data is rarely available in developing countries and, as such, Mexico is the perfect context in which to apply our approach.

It is important to note that our results on unemployment and labor force participation differ from official rates in two key ways. First, we include only urban AGEBs due to data limitations. Second, we are using census data, not a labor force survey, so the instruments differ. For example, the age range for which employment questions are asked differs across the instruments. However, despite these differences in the instruments, the underlying concepts of labor force participation and unemployment in the census and labor force survey are similar. There is therefore no reason to believe that the findings on the benefits of incorporating geospatial data would not apply to the official definitions as well.

This paper implements a sub-area model, which is essentially a unit-level model in which the unit is taken to be the AGEB. In particular, we specify a weighted empirical best predictor model, with conditional random effects specified at the municipality level in our main results. Municipal 3
through the following steps: (a) estimate the models using OLS and extract the residuals; (b) estimate the unconditional variance parameters _σv_<sup>2and</sup><sup>_σ_</sup> _ε_<sup>2,usingHenderson’s</sup> method III (see Henderson, 1953; and Searle et al., 1992); (c) estimate the conditional variance _σε,ah_<sup>2;andfinally(d)constructthecovariancematrixΩ=</sup><sup>_E_</sup> � _vv_<sup>_T_</sup> + _εε_<sup>_T_</sup> _|x_ � = _σv_<sup>2</sup><sup>_In_+ diag(</sup><sup>_σ_</sup> _ε,ah_<sup>2),andcomputethefeasibleGLSestimatorfor</sup><sup>_β_.</sup>

While the regression coefficients purely capture correlations and not causal effects, it is instructive to verify what predictors stand out and inspect the signs of their effects. As one would expect, higher (lower) quality materials used for roof, floor, and walls as well as a larger (smaller) number of rooms are positively (negatively) correlated with (log) household expenditure. Household assets, particularly, owning a car, fridge, cooker, and tv, all report positive regression coefficients. Furthermore, we observe that per capita household expenditures tend to be higher (lower) for smaller (larger) households, and for households with a more (less) educated household head.

# **4.1.2 SEM approach: Village level data with village poverty rate as dependent variable**

A similar procedure is adopted to build the model for village level poverty using variables derived from public remote sensing data as predictors which are grouped into: (i) population density and land size, (ii) night time lights, (iii) road connectivity