rafmacalaba/datause-encoder-data huggingface dataset:
the percentage of households receiving remittances who refuse to say how much they receive. In São Paulo, this is 19 percent for the first round, compared to 67 percent for the second round. This accords with the reports from our survey team that the main reason for refusal was concern about crime and reluctance to discuss financial matters. The extra survey effort was able to convince these households to provide a lot of other important information in the survey, including whether they receive remittances, even though most refuse to say how much they receive.

**Table 4: Comparing Households Surveyed in First Round to those who initially**

**refused or were not initially found**

||Sao Paulo <br>round 1|Sao Paulo<br>round 2|Parana<br>round 1|Parana<br>round 2|
|---|---|---|---|---|
|Household Size|3.36|3.09|3.31|3.33|
|_Percentage of households with member who:_|||||
|Reads Japanese/Nikkei newspapers|13|14|10|15|
|Listens to Japanese/Nikkei radio programs|7|10|8|5|
|Watches Japanese/Nikkei TV programs|19|29|27|19|
|Reads Japanese/Nikkei books/magazines|14|16|14|18|
|Reads newspapers from Nikkei associations|12|13|8|9|
|Checks Japanese/Nikkei websites on the internet|3|8|9|7|
|_% of households which:_|||||
|**Migration**|||||
|Have a Member currently in Japan|21|17|19|25|
|Have a Member who has returned|||||
|from work/study in Japan|38|45|31|51**|
|**Remittances**|||||
|Receive remittances from Japan|17|8|14|22|
|Refuse to say if they receive remittances|2|4|4|6|
|Refuse to say how much they receive if receiving|19|67**|47|79*|
|Mean amount conditional on receipt and report|5704|3223|3302|7164|
|**Sample Size**|109|95|138|61|

*, **, *** indicate significantly different from the 1st round at the 10%, 5% and 1% levels respectively.

# **4.2 The Intercept Point Surveys**

The Intercept survey was designed to carry out interviews at a range of locations frequented by the Nikkei population. It was originally designed to be done in São Paulo city only, but a second intercept point survey was later carried out in Curitiba, in Parana.<sup>8</sup>

> 8 The smaller than anticipated sample size from the snowball survey and stratified sample provided the budget resources to finance the additional intercept survey in Curitiba. In additional, an intercept survey of

20

-  -
The Polish data consists of the 1993-1996 panel component of the Household Budget Survey conducted by the Polish Central Statistical Office. The survey is fielded throughout the year. We used a balanced panel with 16,552 individuals in 4,919 households with complete demographic, income and expenditure information. More details of this dataset can be found in Okrasa (1999a,b).

Non-random attrition is a potentially serious problem. The University of North Carolina's website and Okrasa (1 999a) analyze attrition in each of the data sets. They find that households with better economic positions and households in urban areas are more likely to drop out of the sample. It is hard to infer whether and how this pattern of attrition affects the results.

The main measures of economic well-being used in this paper are the logarithm of monthly consumption expenditure and the logarithm of monthly income. Both measures are adjusted for household size using an equivalence scale.<sup>3</sup> In the Polish data, the recall period for all expenditure items is one month, while in the Russian data recall periods vary between one week for food expenditure, one month for services and utilities, and three months for clothes, shoes and durables. The expenditure data includes actual expenditure on durables rather than imputed rental values of these goods. Hence, one might worry that finding that most shocks are transitory largely reflects sporadic purchases of durab<sup>1</sup> es or other lumpy goods. To address this concern, the total analysis was repeated using only food expenditures, which accounts for 47% of total expenditure in Russia and 42% of total expenditure in Poland. The dynamics of food expenditures are broadly similar to those for total expenditure, and a discussed in more detail in the sections below.

The analysis below is performed on the whole sample as well as on subsamples conditioned on demographic characteristics. For the measurement of shocks, we take the second period of the 4 period panel as the base period. Hence, the base period is the fall of 1995 in Russia and 1994 in Poland. Unless otherwise noted, demographic characteristics are measured in the base period.

> <sup>75</sup> 3We use an equivalence parameter O of 0.75, i.e., equivalent size = (number of household members)<sup>0</sup> .

6
overlays flood maps from the GLOFRIS global model and poverty maps from the World Bank. For countries where this study does not provide data, we use an older, similar study by (Winsemius et al. 2015). This other World Bank study overlays the same GLOFRIS flood maps as above with geo‐localized household surveys (using the Demographic and Health Surveys<sup>8</sup> ) to assess the exposure of poor people to river floods relative to the exposure of non‐poor people.

Where neither study provides data, we use the average for countries which do have data weighted by their population (the average is 8%). This average is conservative, when compared with the few estimations found in published case studies ( **Table 3** ).

|Country|Hazard|Year|Poor|Nonpoor|Bias|Source|
|---|---|---|---|---|---|---|
|**Bangladesh**|Flood/cyclone|2009|25%|14%|56%|Akter & Mallick(2013)|
|**Mumbai**|Flood|2010|41%|24%|71%|Patankar and Patwardhan|
|**Kenya **|Flood|2012|99%|96%|1%|Opondo(2013)|
|**Guyana**|Flood|1997|40%|28%|18%|Pelling (1997)|
|**Vietnam**|Flood|2011|38%|29%|17%|Nguyen and James(2013)|

**Table 3: Exposure of poor and nonpoor people in selected case studies**

We assume there is no exposure bias for other hazards, due to the large scale of these events. This is a simplification considering for instance that local soil characteristics have an impact on earthquake vulnerability.

# 3.4. Macroeconomic characteristics

First, we translate the population exposure (separated into poor and non‐poor people) into asset exposures for the two categories. To do so, we assume that the capital used by an individual to generate his or her labor income is the ratio of this labor income by the average capital productivity. Since we have the labor income of the poor and non‐poor, we can translate those into the exposed non‐diversified capital of the poor and non‐poor. We calculate the average capital productivity as output‐side GDP divided by total reproducible capital within a country, both variables from Penn World Tables.

Figure 5 shows the average productivity of capital against income level. In spite of a large variance, high income countries tend to have more capital and as a result their capital is on average less productive. As shown in Hallegatte and Vogt‐Schilb (2016), countries with a high average productivity of capital have in general less to spend (in proportion of their income) in reconstruction, and this increases their resilience. This effect mitigates the effect of all other drivers, which tend to make richer countries more resilient than poorer ones.

> 8 <u>http://www.dhsprogram.com/.</u>

14
Table 2: Log Active Firms and Lagged Major Violent Events

||(1)|Log Acti<br>(2)|ve Firms<br>(3)|(4)|
|---|---|---|---|---|
|Major Violent Event (1 month lag)|0.665<br>(0.431)|-0.059**<br>(0.027)|-0.064**<br>(0.026)|-0.037<br>(0.023)|
|Mean Outcome|3.70|3.70|3.70|3.70|
|Observations|10642|10642|10642|10642|
|District FEs|No|Yes|Yes|Yes|
|Time FEs|No|No|Yes|Yes|
|District Linear Trends|No|No|No|Yes|
|District Quadratic Trends|No|No|No|Yes|
|Adjusted R2|0.003|0.958|0.960|0.976|

_Notes_ : Major Violent Event =1 if previous month in top 1% of killings distribution. Standard errors clustered at district level. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

33
Policy Research Working Paper 6297

# **Abstract**

Drawing on Public Expenditure and Financial Accountability assessment scores from 118 countries, this paper provides the first comparative analysis of public financial management performance in small Pacific Island Countries (PICs). It applies a Tobit regression model across the full cross-country sample of Public Expenditure and Financial Accountability scores and country variables to identify potential causes for the observed underperformance of Pacific Island countries relative to other countries of similar income. First, the analysis finds small population size to be negatively correlated with Public Expenditure and Financial Accountability scores, with the “population penalty” faced by small Pacific Island countries sufficient to explain observed underperformance. Second, through application of a new capacity index of Public Expenditure and Financial Accountability dimensions, it finds strong evidence in support of the hypothesis that small population size impacts scores through the imposition of capacity constraints: with a limited pool of human capital, small countries face severe and permanent challenges in accessing an adequate range and depth of technical skills to fulfill all functions assessed through the Public Expenditure and Financial Accountability framework. These findings suggest that approaches to strengthening public financial management in small Pacific Island countries should involve: i) careful prioritization of public financial management capacity toward areas that represent binding constraints to development; ii) adoption of public financial management systems that can function within inherent and binding capacity constraints, rather than wholesale adoption of “best practice” imported systems; and iii) consideration of options for accessing external capacity to support public financial management systems on a long-term basis, from regional agencies, the private sector, or donors.

This paper is a product of the Poverty Reduction and Economic Management Unit, East Asia and the Pacific Region. 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 author may be contacted at thaque2@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
|||**Energy/En**<br>**Lb**|**vironmental**<br>**Eil**|**E**|**Long-ter**<br>**If**|**m Impacts**<br>**Econom**<br>**R&D**|**ic/Social**|||
|---|---|---|---|---|---|---|---|---|---|
|||**-**||||||||
|||<br>**technologies**<br>**/ strategies**|<br>**Impact**|<br>**security**|<br>**&**<br>**Productivity**|<br>**and**<br>**innovation**|**Market**<br>**Failures**|**Economic /**<br>**Climate**<br>**Resilience**|**Decarbonization /**<br>**Effect on NDC**|
|**M10**|Scrappage scheme for old cars to be<br>replaced with battery electric vehicles|||||||||
|**M11**|Replacement of streetlights in<br>municipalities and villages with energy<br>efficient lighting|||||||||
|**M12**|Tree planting along urban and intercity<br>roads|||||||||
|**M13**|Fiscally neutral carbon taxation for<br>economic sectors out of the EU Emissions<br>Trading System|||||||||

56
Table 8 Effects of Selected School Inputs by Cohort (in years of schooling)

|Variable|1925-39|1940-44|1945-49|1950-54|1955-59|1960-66|
|---|---|---|---|---|---|---|
|Books|||||||
|Hales<br>Females|*54**<br>.64**|<br>.25<br> <br>.69**|.31<br> <br>*89**|.S8**<br>-.06|<br>.92**<br>1.14**|<br>.32<br> <br>.72**|
|Number of grades<br>Males<br>Females|.56**<br>.47**|<br>.85**<br> <br>.28|<br>.96**<br>.52**|1.20**<br> <br>*49**|1.21**<br> <br>*8'**|1.34**<br>1.14**|
|Teachers 1-3<br>Males<br>Females|2l<br>29**|.04<br> <br>.06|.27<br>.16|.17<br>.51**|.48**<br> <br>.22|<br>41**<br>-.04|
|Note:<br>**** (*)** <br>a two-t|means stati<br>ailed test.|stical si<br>|gnifican|ce<br>at 5 (1|0) percen|t level ii|

43
average OECD rice producers receiving eighty-plus percent of their revenue as a result of some policy intervention. Both sugar and milk producers in the OECD generate over forty percent of their revenue from some combination of market intervention and direct government support, while other grains and oilseeds lie below that level.

Across countries, the producer support for OECD member countries varies widely ranging from a low value of 1 percent of producer revenue in New Zealand to a high value of 69 percent of producer revenue in Switzerland (Annex Table A.6.2). For the OECD in aggregate, transfers to producers account for 31 percent of revenues. Producer support in the EU is near the OECD-wide average. In Western Europe and East Asia producer support is considerably above the OECD average, while that in North America and Central and Eastern Europe is somewhat below. Australia and New Zealand provide minimal support to producers through agricultural policies.

The OECD producer support estimate is a combined measure of all support to producers capturing the transfer of treasury monies paid to farmers as well as the transfers from commodity transactions to producers resulting from prices supported above world price levels. Thus this subsidy measure can be broadly decomposed into market price support (i.e. policy measures applied at a country’s border) and farm policy transfers including output and input subsidies, area and headage based payments, and the various payments tied to land use, farm income, and historical payments. The relative importance of these differs across countries but in most instances the division between market price and other support is roughly equal. The primary exception is in East Asia (Japan and Korea) where producer support is nearly all provided as market price support.

22
14 14.4 per cent in 1982. Fourth, _the monetary authorities pegged the value of the Irish punt within the_<sup>_EMS_</sup> _and, thus, relative to the German mark._ It is especially significant, as Dornbusch points<sup>out, that in both</sup> 1982 EMS realignments Ireland maintained the central rate. Not taking advantage<sup>of an EMS</sup> realignment, he says, signified a shift from an accommodating exchange-rate policy to a determined effort to squeeze inflation by the use of the exchange rate.

# _2.2.3. France._

The case of France is somewhat different. The program of March 1983 constituted quite a drastic turnaround in the Socialist government's economic policies. Indeed, President Mitterrand enjoyed from the beginning more budgetary freedom of manoeuvre than was actually available to most other European countries because the bright side of the Giscard and Barre legacy resided in public finances. France's public debt (relative to GDP) was in 1980 one of the smallest among OECD countries. It is not surprising, as Sachs and Wyplosz (1986) conclude, that the Socialists took advantage of the fiscal prudence legacy. The first two years of the government of President Mitterrand were thus characterized by fiscal expansion with monetary accomnmodation, minimum wage policies, a reduction of the work week, and new laws concerning labor relations.

The evolution of the French economy during 1981-83 failed to conform to the Government's expectations. GDP growth was lower than forecasted, inflation was higher, unemployment did not fall and the Franc suffered several crises. Unlike other OECD countries, French GDP never fell during<sup>those</sup> years. On the other hand, inflation, that increased after the oil shock, increased again in 1982, while most other countries were achieving; significant reductions. The government was also confronted with several speculative attacks on the franc, having to devalue in October 1981 and June 1982.<sup>This second</sup> time, the devaluation was accompanied by a price and wage freeze, a rise in social security contributions and a reduction in the targeted budget deficit to a level not higher than 3 per cent of GDP.<sup>A third</sup>
Similar mechanisms are likely at play in determining firm exposure as well. Larger, more productive firms may be more able and more likely to choose to locate in areas at lower risk from climate hazards. Although the presence of many large firms in hot states like Gujarat underline that there are other important determinants of firm location choice than climate risk. Like in the case of households, the consequences of this sorting are likely to be magnified by the direct impact of climate hazards on firm (and worker) productivity.

Our results have several important limitations. Crucially we do not directly model how exposure to climate hazards will change in the future with additional climate change. To the extent that locations that have been highly exposed will remain relatively more exposed, the overall message of our results would remain the same. The magnitudes of the difference in exposure between wealthy and non-wealthy areas is likely to change even in this case, however. If climate change changes relative risk profiles on the other hand – increasing risk in currently low-risk areas – our results would not be a good guide to the future exposure to climate driven risks.

Our results also rely on estimated wealth levels from the RWI. These estimates are based on observable features of a location and the modelled relationship between those features and measured wealth in locations for which wealth measurements exist. But these measurements do not exist for most of the locations in our sample and so our results rely on these estimated measures of wealth. These could be over- or under-estimates of true wealth. If there is systematic bias in the estimation of wealth in the RWI, it could bias our results. The direction of this bias is not clear and depends on the nature of any mis-estimation in the RWI.

Our results have several important implications for policy-makers. The first is the critical relationship between poverty alleviation and building resilience to climate change. We have shown that the poor are disproportionately exposed to climate hazards. This implies that polices to increase resilience to these climate hazards that are otherwise distributionally neutral will naturally have a pro-poor bias. Pursuing these policies will yield both climate resilience and poverty alleviation benefits.

A second implication is that many policies that can help alleviate the climate risk faced by the poorest are ‘no-regret’ policies. Policies like those targeted at addressing factors that prevent poor households from moving to more desirable locations would help reduce their disproportionate exposure to heat and other natural hazards. Such policies also offer general development benefits 14
adverse economic and social impact of natural disasters can be simply devastating. Hurricane Mitch in Honduras in 1998, for instance, destroyed 34% of national GDP and was equivalent to 158% of total government annual revenue. Even for large national economies, the fiscal implications of natural disasters can be quite significant. The Gujarat earthquake, for instance, had a rather small impact on the overall Indian economy, around 1% of GDP, but the fiscal impact was considerably larger, amounting to 7% of government revenues.

**Table 2. Uninsured economic loss from some recent natural disasters**

||||Loss<br>|
|---|---|---|---|
|Event (year)|Country|Percent of<br>GDP|Percent of government<br>revenues|
|Earthquake (1992)|Northridge, USA|0.3%|2%|
|Earthquake (2001)|Gujarat/Bhuj, India|1%|7%|
|Floods (1997)|Poland|3%|11%|
|Earthquake (1999)|Armenia, Colombia|3%|23%|
|Earthquake (1985)|Mexico City, Mexico|4%|26%|
|Earthquake (1999)|Izmir, Turkey|5%|21%|
|Floods (1998)|Bangladesh|17%|152%|
|Earthquake (1986)|El Salvador, San Salvador|27%|180%|
|Hurricane (1998)|Honduras|34%|158%|

Source: Gurenko (2004).

Beyond the immediate adverse macroeconomic impacts of disasters, the indirect longterm consequences of natural disasters may be even more severe. The fiscal impact of Gujarat/Bhuj quake forced the Indian government to increase its fiscal deficit by a few percentage for several subsequent years (Gurenko 2004). Hurricane Mitch in Honduras caused a stagnation of national GDP for several years despite the original pre-event projections of highly positive economic growth. Natural disasters are also likely to worsen the economic fundamentals of key industries in the affected developing countries. The increasing post event government borrowings to absorb the financial consequences of a disaster are likely to coincide with a decline in government tax revenues due to the fall in both GDP and export earning capacity of key industries which in turn may trigger downgrades of sovereign debt ratings due to the worsened public sector debt service ratios.

Following major natural catastrophes, the ability of an affected country to have immediate access to ample liquidity has critical implications for the ultimate size of indirect economic losses generated by the destruction of physical assets. Prompt funding of post-loss projects is likely to speed the recovery of the economy and consequently, 6