rafmacalaba/data-use-mentions-extended huggingface dataset:
term debt is issued or renegotiated at higher interest rates. Long-term debt increases under floating rates.

# **V.** Summary of **Results and Conclusions**

There is growing literature on the effects of financial liberalization in previously closed economies. There is also a growing literature linking firms' characteristics to financial structure. Following these two literatures, this paper analyzed cross-country microeconomic data on financing choices during the process of integration with global financial markets. To our knowledge, this type of evidence has not been previously examined.

The paper investigated whether financial integration affects the financing choices of non-financial firms in emerging economies from East Asia and Latin America. Using a firm-level panel, _we_ studied the behavior of firms' financing choices when economies become liberalized and when firms access international bond and equity markets. We focused on leverage levels, debt maturity, and the choice between external and internal financing to study financial structure.

The results from this paper can be summarized as follows.

> * _Firm specific characteristics and financing choices:_ Although we

> extended the sample to include the 1990s, our results are consistent with the previous

> literature, which mostly covers the 1980s. In other words, larger firms and firms with more tangible assets extend their debt maturity. Higher profits are associated with more internal financing, less leverage, and shorter debt maturity. We also extended the existing literature **by** analyzing the effects of the production mix on financial structure. The evidence suggests

**27**
of 1991. We have data on the returns to schooling before transition (circa 1992) for 11 countries. We have data for 20 non-transition, European, and ECA countries (see Annex 2). Regarding schooling years, Barro and Lee (2013) are used.

Since Barro and Lee (2013) have schooling estimates for five-year intervals, we have computed the five-year average of the returns to education for the 48 transition and non-transition countries in our sample. This procedure leaves us with two observations per decade for each country, by taking as reference the decade of the 1980s, the first five-year observation contains the average of years 1980 to 1984, while the second observation has the average of the years 1985 to 1989. This gives us a total of 235 data points, of which 108 (46 percent) belong to the transition group and 127 (54 percent) are part of the non-transition group.

Since the fall of the Soviet Union and its bloc implied a deep transformation in terms of democracy and trade openness for most of the transition countries, which may have affected their labor demand, we include two sources related to these changes: the polity index and trade as a percentage of GDP. We chose these variables because they affect the labor market and their time series include pre-transition years (i.e., years before 1992). It has been shown that democracy is positively associated with an increase in the returns to education as a result of political and economic reforms adopted by democratic governments (Uwaifo 2008), namely the fraction of the economic output that workers received as wages for their work. International trade increases the demand for skills 4
as measured by the Gini coefficient.<sup>26</sup> For a given level of average income, a higher Gini will tend to be associated with higher poverty rates. Higher poverty in turn may be associated with a greater need for continued access to IBRD loans. However, the regression coefficient for the Gini measure, while positive, is insignificant in Table 6.<sup>27</sup> 23 The graduation policy does not define institutional development explicitly, but an unpublished 1982 memorandum to the Executive Directors describes as an example of institutional weakness “countries that have recently experienced rapid increases in real income because of improved terms of trade or new mineral discoveries” but with “social indicators such as health and education levels [that] are often much lower than in other countries at the same income level.” The same memorandum implies that a failure to address development disparities between groups or regions within a country reflects institutional weaknesses.

24 Data retrieved from the World Bank EdStats database. Barro and Lee provide estimates only for every fifth year between 1970 and 2010, so we linearly interpolate for the years in between. Results are very similar to those reported here if we treat the in-between years as missing data.

25 Data are from the World Bank’s World Development Indicators (WDI).

26 Data are from the World Bank’s World Development Indicators (WDI). Other measures of income equality (e.g. income share going to the middle quintile) or inequality (e.g. share going to the top decile or quintile) produced similar results.

27 Inequality changes slowly over time in most countries, so results in Table 4 are based on the country average values for Gini, from all available data over the 1982-2008 period. If we instead use the annual values and drop all years with missing data, the available sample is far smaller, and Gini is again not significant. Similarly, poverty headcount measures are available only for a very small number of annual observations,
# **References:**

Akin, John S., David K. Guilkey, and E. Hazel Denton, 1995. "Quality of Services and Demand for Health Care in Nigeria: A Mulitinomial Probit Estimation," Social Science and Medicine. 40(11): 1527-1537.

Al-Kabir, a. 1984. "Effects of Community Factors in Infant and Child Mortality in Rural Bangladesh," World Fertility Survey Scientific Report No. 56. Voorburg: Intemational Statistical Institute.

Alderman, Harold, and Paul Gertler, 1989. "The Substitutability of Public and Private Health Care for the Treatmnent of Children in Pakistan," LSMS Working Paper No. 57. The World Bank, Washington, DC.

Alderman, Harold, and Victor Lavy, 1996. 'Household Responses to Public Health Services: Cost and Quality Tradeoffs," World Bank Research Observer, 11(1):3-22.

Anand, Sudhir, and Martin Ravaillon, 1993. "Human Development in Poor Countries: On the Role of Private Incomes and Public Services," Journal of Economic Perspectives, 7(1):133-50.

Barrera, Albino. 1990. "The Role of Maternal Schooling and its Interactions with Public Health programs in Child Health Production." Journal of Development Economics. 32:69-91.

Barro, Robert J. and Jong-Wha Lee. 1996. "International Measures of Schooling Years and Schooling Quality." American Economic Review. Pagers and Proceedings 86 (2): 218-23.

Benefo, Kofi, and T. Paul Schultz. 1996. "Fertility and Child Mortality in Cote d'lvoire and Ghana." World Bank Economic Review 10(1):123-158.

Bidani, Benu, and Martin Ravaillon, 1997. 'Decomposing Social Indicators using Distributional Data," Journal of Econometrics, 77:125-139.

Bitran, Ricardo a., and D. Keith Mclnmes, 1993. "The Demand for Health Care in Latin America," EDI Seminar Paper No. 46. The World Bank, Washington, DC
|1996|2013|2016|1996|2013|2016|1996|2013|2016|
|**_Average across Kebeles_**||||||||||
|Avg. NTL|0.35|0.57|1.88|0.37|0.6|1.9|0.01|0.02|1.45|
|Avg. Urban Area (km<sup>2</sup>)|0.072|0.156|0.189|0.066|0.156|0.189|0.159|0.207|0.225|
|Avg. Cropland Area (km<sup>2</sup>)|40.365|40.854|40.557|40.86|41.334|41.031|30.918|31.647|31.512|
|**_N Kebeles with Positive _**|**_Value_**|||||||||
|NTL|628|1,278|6,472|626|1,267|6,214|2|11|258|
|Urban|542|910|1,062|520|884|1,035|22|26|27|
|Cropland|15,081|15,044|15,045|14,368|14,333|14,332|713|711|713|
|N|15,714|||14,933|||781|||

Nighttime lights data come from Li et al. (2020), who provides an intercalibrated nighttime lights dataset derived from DMSP-OLS and VIIRS from 1992 to 2018; DMSP-OLS data from 1992–2013 is used, and VIIRS is used to simulate DMSP-OLS-like data from 2014 to 2018. Urban and cropland categoriescome from the ESA Globcover dataset. The table shows values in 1996 (
|50|50|49|49|45|0.06|

_Source:_ Authors’ estimates based on data from NLSS 2019 _Note_ : The table shows different poverty rates: multidimensional poverty, monetary poverty, and non-monetary poverty at the national level and within Nigeria's six zones. The table also shows the level of overlap between monetary poverty and other dimensions poverty, materialized by the overlap coefficient and the life expectancy at birth. The variability of each of the above measures is compared using the coefficient of variation across stated presented in the last column.

# **Impact of assumptions on the fraction of poor**

We further study the sources of (multidimensional) poverty by using another method. We slightly change the assumptions of our design to study the impact of these assumptions.

First, we look at the respective impact of the three non-monetary dimensions of our baseline design (health, housing and security). More precisely, we contrast the fraction of other-dimensions poor and omitted poor when removing one of these three dimensions from our design. Table 3 provides the results. The results reveal that the importance of one dimension not only depends on the fraction of deprived individuals but also on the correlation between that dimension and monetary poverty. Indeed, for our baseline design, housing has the largest incidence of deprivation and removing it has the largest impact on the fraction of other-dimensions poor. However, housing is much more correlated with monetary poverty than health. As a result, health has the largest impact on the fraction of omitted poor. Table 3 23
# **E. Average primary balance in small states, by trade composition, 2011-23**

# **F. Average primary balance in small states, by population size, 2011-23**

<!-- Start of picture text --> Percent of GDP Percent of GDP<br>2<br>4<br>0 2<br>-2 0<br>-4 -2<br>-4<br>-6<br>Small states: Small states: Small states:<br>Commodity Commodity Tourism-reliant -6<br>exporters importers Micro states Other small states<br><!-- End of picture text --> _Sources_ : WEO (database); World Bank.

_Note_ : EAP = East Asia and Pacific; EMDEs = emerging market and developing economies; LAC = Latin America and the Caribbean.

A. Panel shows the average overall fiscal balance as a percent of GDP for a sample of 32 small states and 110 other EMDEs.

B. Panel shows the average primary fiscal balance as a percent of GDP for a sample of 32 small states and 110 other EMDEs.

C. Bars show the percent of countries with weaker primary balances as a percent of GDP in 2020-23 than the average for the period 2015-19. Sample includes 32 small states and 112 other EMDEs.

D.-F. Bars show the average primary balance over the period 2011-23.

D. EAP small states include 10 economies, LAC small states include 11 economies, and other small states include 11 economies.

E. Small states commodity exporters include 13 economies, small states commodity importers include 19 economies, and tourism-reliant small states include 20 economies.

F. Micro states have a population of less than 200,000. Micro states include 12 economies, and other small states include 20 economies.

35
The sign restrictions are sufficiently flexible to capture a wide spectrum of effects. For example, some studies argue that improvements in energy efficiency may not translate to a one-to-one reduction in energy use or may even increase energy consumption as the real price of oil falls—a phenomenon known as the “rebound effect” (see Gillingham et al., 2013; Bruns et al., 2021). Because the maintained sign restrictions do not restrict the magnitude of responses to structural shocks and are only imposed on the impact period, the framework is agnostic about these general equilibrium effects that may result in a sign reversal in later periods.

My main empirical findings can be summarized as follows. First, the results from the dynamic factor model estimation indicate the presence of a world business cycle that has increased in importance in the last two decades. The estimated common factor (henceforth, the global GDP factor) captures key global economic and financial events of the past four decades. The global GDP factor also tracks the changes in world industrial production but it is relatively moderated in amplitude. Moreover, I find that it accounts for an average of 21.6 percent of the variation in GDP growth rates across countries for the period 1980–2019, while its average contribution to output variability has increased to 39.1 percent since early 2000s.

Second, impulse responses from the FAVAR indicate that U.S. energy efficiency shocks have a larger impact on global output and real price of oil than U.S. oil supply shocks. Moreover, I reaffirm the key insight from Kilian (2009) that it is important to disentangle the underlying drivers of oil price fluctuations. In particular, the impulse responses show that both positive U.S. energy efficiency and U.S. oil supply shocks decrease the real price of oil and increase global GDP, while their effects on total oil production and consumption have opposite implications for global oil use.

4
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE The state of sanitation is particularly dire in the rural areas, where only 31 percent of individuals have access to improved sanitation (figure 9b). The difference is largely due to the ubiquity of flush toilets in urban areas (figure 9b). In rural areas, on the other hand, there has been a shift away from improved latrines and back toward inferior traditional latrines—and even open defecation in some cases. Indeed, up to 5 percent of rural Zimbabweans lost access to improved latrines between 1999 and 2008. This is due to the fact that the construction of the improved latrines was becoming obsolete, at a time when cement had become too scarce and expensive to allow for the necessary repairs and reconstruction of the latrine structures to take place. Despite the progress in traditional latrines and, to a much lower extent, flush toilets, the combined expansion of improved sanitation facilities fell short of rural population growth, which stood at 1.4 percent per year.

**Figure 9. Access to water sources is highly inequitable between urban and rural communities**

<!-- Start of picture text --> a. Water supply b. Sanitation<br>100 100<br>90 90<br>80 80<br>70<br>70<br>60<br>60<br>50<br>50<br>40<br>40<br>30<br>30<br>20<br>20<br>10<br>10<br>0<br>0<br>Urban<br>Rural Urban Rural<br>% population % population<br><!-- End of picture text --> _Source:_ AICD water supply and sanitation database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ Access figures calculated by the AICD using data from the 2005 Demographic and Health Survey.

Water utility performance
# **2.2 KHDS**

The Kagera Health and Development Survey (KHDS) is a study into the long-run wealth dynamics of households and individuals within Northwest Tanzania. This study entails the resurvey of a panel of households, originally interviewed for 4 rounds from 1991 to 1994. Resurveys were then organized in 2004 and 2010. A multi-topic household questionnaire is administered to all split-off households originating from the baseline households, including those that have moved out of the baseline location.

This constitutes one of the longest-running African panel data sets of this nature and offers an unprecedented set of research opportunities for examining long-run (nearly 20 years) trends in and mechanisms of poverty persistence and economic growth in rural households. As the children of the original respondents have now formed their own households, intergenerational and migration issues can be addressed by the survey data. The data is of particularly high quality and the 2010 round of the survey was conducted using electronic survey questionnaires administered on handheld computers, with automated skips and validation checks run during the interview when errors could be corrected at source. The resulting improvements in data quality have been formally assessed by Caeyers et al. (2012).

KHDS has maintained a highly successful tracking rate. Table 1 shows that in 2010 88% of the original 6,353 respondents had either been located and interviewed, or, if deceased, sufficient information regarding the circumstances of their death collected.

In 2010, households were found in three cities: Dar es Salaam, Mwanza and Kampala. This is defining cities as agglomerations with more than 500,000 inhabitants. The 2012 census puts the population of Dar es Salaam at 4.36m and Mwanza at 0.7m.<sup>4</sup> There are a further 21 respondents who moved to areas that, while administratively recognized within Tanzania as cities, have a population below 500,000 (Arusha, Tanga and Mbeya). Results do not change

> 4For Mwanza this is the sum of Nyamagana Municipal Council (363,452)
fell even more on inputs. The textile group (except garments) is a case in point, where tariffs fell from 36% to 30%, between 1999 and 2009, but raw materials decline significantly, some to 5% by 2009. ERP on textiles rose from 84% to 229% Consumer benefited, certainly, but returns to domestic value added in the industry increases by 145 percentage points.

Turning to the 23 aggregated sectors making up the Egyptian economy, we find that the average tariff across lines is approximately 8 percent and the dispersion of nominal protection on tradables is not very high, the levels of tariffs ranging between zero on fertilizers to 25.8 percent on food. The sectors with the relatively highest tariffs are food and tobacco (not production agriculture), textiles, aluminum products, other industries and non-metal industries, in that order. But there is an unexpectedly high dispersion of ERPs relative to what one would expect looking at only applied tariffs. Despite the relatively low dispersion of nominal tariffs, the dispersion of effective rates of protection is wide, from high positive to high negatives. Even excluding NTBs, the standard deviation of total ERPs is 373 percent in comparison to an average level of 190 percent. The bulk of the average ERPs and their dispersion derives not from energy subsidies but from the tariffs and output subsidies. The average ERP due to energy subsidies over these 20 sectors is 53 percent with a standard deviation of 82 percent. The estimates of the tariff equivalents of NTBs are also highly dispersed. And so the dispersion of ERPs is even greater if one adjusts for NTBs, as shown by comparing in Tables 5a and 5b.

A notable result of the ERP estimates by the sectors available from the 2006/2007 I-O matrix is that energy subsidies are very important. For example, in the absence of subsidies on natural gas electricity prices would have to rise. Transport and communications (non-tradables), while taxed indirectly through imported input use, enjoys a positive total ERP due to fuel subsidies.
Policy Research Working Paper 8637

# **Abstract**

Inadequate infrastructure impedes the productivity of manufacturing firms, with negative consequences for the wider economy. This study examines how water infrastructure copes with severe weather fluctuations and analyzes the effect of unreliable water supplies on the productivity of manufacturing firms, focusing predominately on firms in developing economies. This is achieved using firm-level data from World Bank Enterprise Surveys covering more than 16,000 manufacturing firms in a cross-section of 103 countries between 2009 and 2015. The study finds that periods of significantly low rainfall lead to higher water outages, and that the overall impact is driven by the effects of drought on low-income and lower-middle-income economies, with upper-middle-income and high-income economies benefitting from more resilient water infrastructure. Furthermore, the study finds that incidents of water outages lead to lower firm productivity for firms in less developed economies. For the average firm located in a low-income or lower-middle-income economy, one additional water outage incident per day in a typical month can lead to losses of approximately 8.2 percent of annual sales. This finding calls for increased policy focus on water infrastructure services, particularly in poorer countries where water infrastructure and firms seem to be particularly vulnerable to the vagaries of rainfall.

This paper is a product of the Global Indicators 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/research. The authors may be contacted at aislam@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
lifted above this level of utility, they are no longer in poverty. The framework uses the AIDADS system to represent consumer preferences, and is calibrated using Tanzania’s Household Budget Survey 2000/2001<sup>10</sup> . Households are stratified into seven groups based on earnings sources. The poverty line in Tanzania is taken to match the observed national poverty headcount ratio reported by the World Bank (2006). This in turn dictates the poverty level of utility in the initial equilibrium.

# **3.2 EXPERIMENTAL DESIGN**

The patterns of historical production deviations from trend for each of the five case study years highlighted in the previous section – Scenarios 1995 and 1971 (“Tanzania +”), Scenario 1980 (“Tanzania 0”), and Scenarios 1982 and 1983 (“Tanzania –”) are reproduced in the model via an appropriate combination of international productivity shocks<sup>11</sup> . We then consider the impacts on trade and poverty in the context of a range of different trade scenarios to explore the interplay between trade regimes and the poverty impacts of climate shocks<sup>12</sup> .

In order to mimic the short run/transient nature of these productivity shocks, these simulations are conducted under the assumption that agricultural land and capital are immobile across sectors. That is, farmers are limited in their ability to adapt to different climate outcomes. For example, they are able to mobilize labor in response to a climate shock and adjust harvesting dates, but are not able to bring new irrigation infrastructure online in response to a single climate outcome. The allocation of cropland to a given crop is also pre-determined in this short run model closure.

The production deviations for the five case study years are simulated under two different trade regimes, as perturbations from the benchmark world economy. Conducting all simulations as perturbations from trend allows for comparability across trade regimes. The two trade regimes are:

- Regime 1 (Baseline) – The trade regime prevailing in