rafmacalaba/datause-encoder-data huggingface dataset:
All fiscal instruments are equalizing except for indirect subsidies, which have an effect of approximately 0. The largest reduction in inequality comes from direct taxes, which cut the Gini coefficient by 1.4 points, almost 40 percent of the total reduction. In-kind transfers and indirect taxes also reduce inequality considerably, lowering the Gini coefficient by approximately one point, and 0.73 Gini points respectively. The effect of direct transfers is only 0.1 Gini point.

The redistributive effect of the fiscal system in Uganda for 2016/17 (3.2 Gini points), is slightly higher than in Ethiopia (2.3), approximately equal to Ghana (3.5), and lower than in Tanzania (5.3), Kenya (6.5), and South Africa (17.6).

_Figure 6. Inequality Before and After Fiscal Policy a. Gini Coefficient_ <!-- Start of picture text --> 0.434 0.434<br>0.420 0.419 0.419<br>0.411 0.411<br>0.402<br>Market Market Net Market Disposable Disposable Disposable Consumable Final Income<br>Income (PGT) Income plus Income Income Income + All Income - All Income<br>Pensions (PDI) indirect indirect Taxes<br>Subsidies<br>b. Redistributive effect, Sub-Saharan African countries<br>17.6<br>6.5<br>5.3<br>3.2 3.3 3.5<br>2.3<br>Ethiopia (2010) Uganda (2016) Uganda (2012) Ghana (2012) Tanzania (2011) Kenya (2015) South Africa<br>(2010)<br>Gini coefficient<br>Gini pooints<br><!-- End of picture text --> _Source: Authors’ calculation based on UNHS 2016/17; CEQ Institute database; and World Bank (2018a). Note for b): The year for which the analysis was conducted is indicated in parenthesis. The graph is ranked from the smallest to the largest redistributive effect, which is the change in the Gini coefficient from market income plus pensions to final income. Except for Kenya, where the effect is the change from market income to final income. When the redistributive effect is positive, inequality declines, when it is negative, it increases._ 21
a cointegrating relationship between import prices and the world import price. However, the homogeneity constraint does not hold ( σ� �σ� �0.79 ). In another formulation, adding in a timetrend the homogeneity restriction cannot be rejected (column 3 of Table 7), with the time-trend taking a coefficient of -.01 implying that import prices are growing 1 percentage point slower than can be explained by the other factors in the equation. Falling transport margins, changes in the composition of traded goods (notably manufacturing goods) or productivity differentials between exporting and importing countries may be among the factors that could explain that differential. With this specification, the error-correction parameter is also much higher, suggesting that it takes roughly three years to return to equilibrium holding all else constant.

**Table 7: Price equation for importers**

|Long-run|Standard specification|Specification with trend|
|---|---|---|
|�1 �τ�<br>�����p�,�<br>����e�,�|0.23***|0.23***|
|p�,�<br>�|0.56***|0.73***|
|θ�<sup>�</sup>|-0.19***|-0.31***|
|trend||-0.01|
|Short-run(average over country|list)||
|∆�1 �τ�<br>�����p�,�<br>����e�,�|0.35***|0.30***|
|∆p�,�<br>�|0.43***|0.35***|
|α��|-0.15***|-0.22***|
|n∗t|1639|1639|

Notes: *,**,*** signify statistical significance at 10%, 5% and 1%, respectively.

Figure 8 reports parameter estimates for the specification without a time trend. For most countries the ECM term falls within the negative unit interval, suggesting that the theoretical specification identifies the long-run. The impact of foreign prices on the import price deflator is very dispersed across countries, but primarily positive as expected. In MFMod, parameter estimates that fall outside the 80 and 20 percentiles are constrained to the closest boundary, and data poor countries are calibrated to the modal value of the associated parameter. Appendix B summarizes the shortrun and ECM parameters for the countries that hit the bounds in the sample.

**Figure 7: Import price elasticities**

<!-- Start of picture text --> Error Correction SR Foreign Price Domestic Price<br>12 8 20<br>7<br>10 16<br>6<br>8<br>5 12<br>6 4<br>3 8<br>4<br>2<br>2 4<br>1<br>0 0 0<br>-1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5<br>Frequency Frequency Frequency<br><!-- End of picture text --> 20
Table A6: Falsification tests — Impacts <u>of future refugee intensity on household welfare and poverty</u>

|||All house<br>|holds<br> <br>||
|---|---|---|---|---|
||Consumption|Consumption poverty|<br>Wealth|Wealth poverty|
||(1)|(2)|(3)|(4)|
|Refugee Intensity|-0.184***|0.172***|0.030|-0.007|
||(0.069)|(0.051)|(0.047)|(0.046)|
|Refugee Intensity (Future)|-0.031|0.040|0.091|0.001|
||(0.084)|(0.055)|(0.062)|(0.053)|
|Constant|9.892***|-1.114**|-0.041|0.644|
||(0.662)|(0.474)|(0.445)|(0.447)|
|Round FEs|YES|YES|YES|YES|
|Fixed Effects (FEs)|HH|HH|HH|HH|
|Controls|YES|YES|YES|YES|
|R-squared|0.068|0.043|0.189|0.115|
|# of EAs|433|433|433|433|
|N|12937|12937|13522|13522|

_Notes:_ This table provides estimates of the effects of logarithm of future refugee intensity on logarithm of total household consumption expenditure per capita (column (1)), a binary measure of household consumption poverty status (column (2)), “started log” of household wealth score (column (3)), and a binary measure of household wealth poverty status (column (4)). Household sample weights from ESS2 applied. All columns control for the logarithm of (lagged) refugee intensity, household (HH) FEs, round FEs, and 12 time-varying characteristics. The coefficient estimates for the 12 characteristics are not reported here, but can be available on request. Robust standard errors clustered at EA level are given in parenthesis. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

56
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contributed to this increase (e.g., Bouwer, 2011; Neumayer and Barthel, 2010; IPCC, 2012). The main driver of risk is population pressure and economic growth in vulnerable locations, for example, in coastal areas susceptible to cyclones. The world’s low lying coastal elevation zone covers 2% of the world’s land area but contains 10% of the world’s population (McGranahan et al. 2007). In the last 30 years, global population living in flood plains increased by 114% and in cyclone prone coastlines by 192% (UN-ISDR, 2011). Due to the global urbanization process, cities are becoming increasingly predestined for risks. Estimates by the United Nations suggest that over 50% of the world’s population already lives in urban areas (UN 2008). Cities are predicted to absorb most of the future growth in the world population: the UN estimates that the urban population share will rise to 70% by 2050 (UN Population Division 2012). Cities in East Asia, for instance, absorb two million new urban residents every month (Gill and Kharas 2007) and are projected to triple their built up areas in the coming two decades (Angel et al. 2005).

While natural hazards and ongoing urbanization are inevitable, disaster losses can be minimized through adequate disaster risk management. Reducing risks _ex-ante_ through risk assessments, land use planning, building codes, early warning systems, adequate watershed management, and contingency planning leads to significantly reduced disaster impacts. The earthquake in Chile in March 2010 was one of the ten most powerful earthquakes recorded in the last century. It released 500 times more energy than the earthquake that struck Haiti in January 2010. Yet, only 521 people died in Chile, whereas Port-au-Prince was catastrophically affected with tens of thousands of deaths. The main reason for this difference is that buildings in Chile are built to codes and are regularly inspected whereas Haiti effectively has no building codes.

Because of the enormous loss potential that has developed and is expanding in the narrowest of urban space, disaster risk reduction efforts need to be intensified in cities. Human losses associated with natural disasters and economic damages relative to the size of the economy are larger the poorer a country is (Skidmore and Toya 2007). Almost 80% of deaths from disasters in the first decade of this century were in developing nations (Zakour and Gillespie 2013) and economic losses are 20 times greater as a 3
Policy Research Working Paper 10443

# **Abstract**

Routine immunization coverage estimated in surveys often substantially differs from figures reported in administrative records, presenting a dilemma for researchers and policy makers. Using high-frequency phone surveys and administrative records from government sources in 36 low- and middle-income countries, this paper shows that such misalignment has also been common in the case of COVID-19. Across the sample, survey estimates exceed administrative figures by 47 percent on average, at times suggesting markedly different policy conclusions depending on the data source consulted. This pattern is particularly stark and consistent in Sub-Saharan Africa. To investigate the sources of this discrepancy, the paper presents results from six methodological experiments that vary survey design choices and documents their effect on estimated COVID-19 vaccine coverage. The results show that design choices matter, in particular the selection of respondents to be interviewed. However, phone survey estimates prove remarkably robust to several commonly claimed biases. After accounting for observed errors of representation and measurement in the survey data, there remains a nonnegligible, unexplained residual gap with administrative records. The paper provides indicative evidence of flaws and weaknesses in administrative data recording and reporting that affect reported vaccination rates and could contribute to this gap. The findings matter for past research on COVID-19 vaccination, future immunization efforts, and the design of robust data production systems on health topics.

This paper is a product of the Development Data Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at ymarkhof@worldbank.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
tax credit or accelerated depreciation stimulate investment, the effects of those increases depend critically on the timing of the announcement and its enactment.

Given these policy implications, it is indeed important to have a proper understanding of the determinants of capital expenditures, especially at the firm-level where these decisions are ultimately made. This is the primary objective of this study and forms the basis for the comparison of the alternative models of investment.

# **Data and** variables

The objective of this paper therefore is to compare the competing models of investment using panel data. In particular, one could question the premise of some recent research that argues that the Q-theoretic approach is the best way to think about investment decisions."6 This study goes back one step, and re-examines the competing theories of investment using firm-level data. In that sense, this paper is very similar in spirit to that of Kuh (1963), Jorgenson and Siebert (1968a, 1968b), and Elliott (1973), albeit with a bigger sample and for a later timeperiod. This study is based on the capital expenditure decisions of a panel of 331 U.S. manufacturing firms, taken from Standard and Poor's COMPUSTAT database for the 1972-1990 period. As noted before, this is the first comparison of alternative models of investment after 1973 using firm-level data.

In estimating the neoclassical and Bischoff models, two measures of the opportunity cost of capital--used in turn for computing the user cost of capital--have been used in this paper. In time-series regressions, the real rate of interest--the nominal rate less inflation--is used as the

**16** Chirinko (1993) provides a comprehensive survey of the current state of research on investment theory, with particular emphasis on Q-theoretic models.

14
economic status quo. Thus, relief and reconstruction spending in fact have to be considered as a kind of “defensive spending,” and consequently disaster losses may need to be adjusted for in national accounting statistics.

Given that disasters deplete capital stock and can be important in many countries, in this paper we examine and test whether disaster losses should also be appropriately considered in genuine savings and other savings measures.<sup>4</sup> Almost no work has been done on this issue. One paper, Barrito (2008), mentions this potential problem and suggests a way for revising wealth accounting, yet does not empirically test it.

Using a sample of large disaster losses over the 30 year period from 1971 to 2000, we examine whether factoring in such disaster shocks may help to better explain future variations in welfare as measured by private and public consumption expenditure. Overall we find some, albeit small and limited, evidence for adverse consequences of disasters on consumption. Focusing on alternative measures of welfare and assessing the contribution of disaster–related asset losses to changes in consumption, we conclude that accounting for disaster asset losses for disaster-exposed, low-income countries may help to better explain variations in post-disaster consumption and thus adjusting alternative welfare indicators (negatively) can lead to improved predictability of future post–disaster consumption changes.

The paper is organized as follows. We start in section 2 with introducing the concept of national savings and a proposal for deriving alternative savings measures. We further suggest considering disaster losses in savings measures and use a method to test the suitability of doing so. In section 3, we present results based on cross-country regressions for a sample of large-scale disasters. Section 4 ends with a discussion of these findings and implications of the analysis.

> 4 There may also be an anticipatory effect as individuals adjust their savings and consumption decisions before or without an event, and thus baseline savings already incorporate part of the response to an event. Yet, generally, the literature finds people to be myopic faced with rare events such as disasters, and thus this effect may mostly be important in areas with frequent events, which are not considered in this dataset examining the largest 200 plus events over the last three decades.

3
- approach, we address this issue with a systematic assessment of abatement benefits and costs.

# **6.1 Case 1: Beijing**

# **Abatement Benefits**

Beijing had a population of about 11,120,000 in 1993; the mortality rate was about 0.611%; total deaths were about 68,000; and total S02 emissions were about 366 thousand tons (of which 204 were from industry). From this base, a decrease of 1,000 tons in S02 emissions decreases total emissions by (1/366 * 100)%. Our econometric results imply an associated decrease of (.51 * 1/366 * 100)% in Beijing's ambient S02 concentration. Applying the Beijing dose-response result of Xu, et. al. (1994) to the new concentration, we obtain an estimated saving of 10.4 lives per year. Dividing both elements by 10 yields a useful round number for policy discussion: **1 life saved per 100 tons abated annually.** This is actually a very conservative estimate of potential abatement benefits, since it ignores the likelihood that abating S02 has significantly lower lifesaving impact per ton than direct abatement of fine particulates (including, of course, those in the sulfates associated with S02 emissions).

How should lifesaving be valued for comparison with abatement costs? A useful benchmark is provided by the average wage of a worker in Beijing, which is approximately $800 (6526 yuan) per year. A baseline estimate of the loss to society when one worker dies from respiratory disease is the presented discounted value of the annual wage over a working lifetime. For Beijing, this is approximately $8,000 in present value terms at a 10% discount rate.<sup>20</sup> We should stress that it is a very partial index of loss, for two main reasons. First, it takes no account of pain and suffering. Secondly, it focuses exclusively on mortality, although very large losses are also associated with working days lost to nonfatal respiratory disease. It is a tiny figure when compared with statistical life values commonly employed in the OECD countries. 21 20 This estimate implicitly assumes that the actual discount rate in China is higher, and that the increment is equal to the expected growth rate in real wages for a currently-employed worker. 21 For comparison, itis not uncommon to see public policy decisions in the U.S. reflect 'statistica **l** ife' values of several million dollars.

32
month on international routes, but on national routes the number of firms does not reach 10 at less than 500 trips per month.

Market segmentation also appears to be associated with low truck utilization, which raises average costs. Average annual truck utilization in kilometers travelled ranges between 10,500 and 200,000, and averaged only 59,000 among surveyed firms (Table 9).<sup>17</sup> Although meaningful comparators are difficult to obtain, this is much lower than for South Africa, Pakistan, and the United States, which have rates of approximately double this level.<sup>18</sup> At the same time, high empty backhaul rates translate into even fewer kilometers served with a payload. As shown, this averaged a low of 14,000 in Guatemala and range up to 39,000 in Costa Rica and 44,000 in El Salvador for the survey year.

**FIGURE 7:  NUMBER OF FIRMS RELATIVE TO DEMAND (TOTAL TRIPS)**

<!-- Start of picture text --> International Routes National Routes<br>0 50 100 150 200 0 500 1000 1500<br>Number of trips per month, sample Number of trips per month, sample<br>40 40<br>30 30<br>20 20<br>10 10<br>Number of sample firms serving route (spline curve) Number of sampled firms serving route (spline curve)<br>0 0<br><!-- End of picture text -->

**TABLE 9: AVERAGE ANNUAL KILOMETERS SERVED WITH LOAD (PER TRUCK)**

|**Country**|**“Mileage”**<br>**Per Truck in**<br>**Kilometers**|**Kilometers served per**<br>**Truck (Estimated)**|
|---|---|---|
|**Costa Rica**|68,652|38,915|
|**El Salvador**|64,419|44,242|
|**Guatemala**|59,328|14,205|
|**Honduras**|56,398|36,611|
|**Nicaragua**|56,398|37,107|
|**Panama**|44,309|18,579|

Note: Average kilometers served per truck is estimated using total mileage and empty backhaul rates reported on firms’ main routes, weighted by the percentage of total business on the same route type.

A variety of factors may contribute to low truck utilization rates and kilometers served, potentially including high wait times, low travel speeds, high seasonality in demand. Long lags between trips due to

> 17Only four of the sampled firms had utilization over 105,000 kilometers per year.

> 18See Londoño-Kent (2008) and Teravaninthorn and Raballand (2009).

13
**Table 2: Descriptive Statistics on Household access to credit and social assistance** **<u>programs</u>**

||_Benefited from a_<br>_Housing program_<br>_%_|_Benefited from an_<br>_Agriculture_<br>_program_<br>_%_|_Received a loan_<br>_from an formal_<br>_creditor_<br>_%_|_Received a loan_<br>_from a informal_<br>_creditor_<br>_%_|
|---|---|---|---|---|
|**Total**|1.07|2.63|10.42|15.04|
|**Area**|||||
|Rural|1.52|6.03|7.82|15.5|
|Urban|0.74|0.19|12.28|14.71|
|**Region**|||||
|Managua|0.20|1.37|12.79|13.31|
|Pacific|0.63|1.34|10.42|15|
|Central|2.46|5.03|11.44|17.38|
|Atlantic|0.40|2.20|2.95|12.8|
|**Quintile**|||||
|Quintile 1|2.04|4.80|4.11|16.25|
|Quintile 3|0.80|3.04|7.09|16.25|
|Quintile 5|0.44|1.06|15.89|12.43|
|**Poverty**|||||
|Non poor|0.79|2.03|12.96|14.59|
|Poor|1.55|3.63|6.13|15.8|
|**Number of**|||||
|**Observations**|6861|6861|6861|6861|

Source: Authors using the 2005 Nicaragua EMNV. Formal creditors: private banks, government/MAGFOR, micro-finance institutions, credit cards, cooperatives, and Non conventional banks; Informal Creditors: Merchants, informal lenders or “ _usureros_ ”, family, and fiends. Social Programs: utility programs, titling programs, agriculture programs, and health and education programs