rafmacalaba/datause-encoder-data huggingface dataset:
13 until recently by data problems.

For example, as noted above, in studies to date the effects of prices (of food, health services, etc.) on health have appeared to be surprisingly small. But only recently have household surveys begun to combine information at the household level on income and expenAitures for other services as well as health, with the necessary information at the community level on quantity, quality and user prices of health care, related services, food, transportation and so on.<sup>16</sup> Information on other consumption and the prices of other services is necessary because even if households do not reduce use of health services in the face of higher prices for health care, the question arises whether they are reducing consumption of other goods or services that affect health, such as food or education; or reallocating their spending in other ways that imply large reductions in both family welfare and social welfare. Detailed information on the range of health services is necessary because consumers may switch among suppliers (and have high price elasticities across suppliers, though a low elasticity for health services overall.) For adequate study of the mechanisms by which mother's education affects child health, conventional quantitative household survey data probably needs to be combined with information on attitudes, day-to-day behavior, and on the intrafamilial bargaining process. Specific controlled experiments of effects of interventions across households with different educational profiles could also have high pay-off in generating new hypotheses. The potential for new work on the effects of education on health has been outlined recently by Caldwell (1989).

16 See footnote 10.
exporters, average exporter size, and concentration, we consider times-series evidence. We use country-sector-destination-year level data for years 2004 to 2008 for the sample of 34 developing countries and 3 developed countries. Assuming the productivity distribution is constant within country-sectors over time, focusing on within-country-sector variation allows us to rule out differences in the productivity distribution as the cause. The disadvantage of panel data is that the effect of independent variables that have little within-country-sector variation, like country size and stage of development, are harder to isolate with precision. For this reason, we focus on export growth, which varies across countries, sectors, and destinations.

One problem with using export growth is that its underlying cause is important. For example, exports could grow because of demand or supply shocks, which may affect exporter behavior but be unrelated to the resource allocation across firms in the country. To examine export growth that is likely to be a result of changes in resource allocation, we control for country-sectoryear fixed effects, which capture productivity shocks (as well as other country-sector time-varying factors in the exporting country-sector). We also include destination-sector-year fixed effects, which capture demand shocks (or other destination-sector-year time-varying factors) in the importing country-sector in our most stringent specification. This allows us to decompose export growth in a sector that is specific to the bilateral relationship, and hence very likely related to changes in trade costs, which we know affect resource allocation. For example, we are decomposing Peru’s export growth in apparel to the US that did not result from an increase in overall US demand for apparel or from Peruvian export growth in apparel.

The null hypothesis that we test is that export growth in a country-destination-sector is uncorrelated with average exporter size and exporter concentration, controlling for country-sectoryear shocks and destination-sector-year shocks. A positive correlation between export growth and 25
(vi) Blundell et al. (1992) have shown that while measured Q contains significant information for investment behavior, the implied adjustment process is unacceptably slow and therefore measured Q is not a sufficient statistic for investment as predicted by the underlying theory. Somewhat disappointingly, this conclusion was found to be applicable to panel data studies as well as aggregate time series studies.

(vii) Finally, it should be emphasized that neither stock prices nor the Q-ratio could be said to cause investment in any useful sense; it is more like stock prices and the Q ratio reacting to information relevant to the firm. Anticipated sales, profits or investments affect both investment and stock prices. Therefore, changes in stock prices could predict changes in capital expenditures without there being a causal link from stock prices to investment. In other words, managers may not look at the Q ratio in deciding their level of capital expenditures. However, the information set on the basis of which managers make their investment decisions is correlated with the information that investors use in evaluating securities, and both of these are correlated with the cash flows of the firm.

# **Modifications to the standard** Q **model**

These considerations suggest that the Q ratio is not a sufficient statistic to describe the behavior of capital expenditures at the firm-level.<sup>9</sup> One solution is to take an eclectic approach and formulate an investment equation that incorporates elements from the alternative theories of investment. This is the approach taken in Fazzari et al. (1988) and other studies. It should be noted though that the primary focus of these studies is to highlight the role of internal finance 9 When the alternative models of investment were compared for the sample firms, it was found that the adjusted r<sup>2</sup> for the different models were remarkably close, suggesting that output, cost of capital and cash flows were also important determinants of capital expenditures in addition to the Q ratio. These results are not shown here though.

8
perception (fundamentals) are cash flows, sales(level as well as growth rate), and dividends. The proxies for market valuation are Q, and share price.

The parameter estimates for both the managerial and market perception terms are significant in all instances except where dividends and sales growth have been used as proxies for fundamentals. In most of these cases, the market perception term is significantly different from the managerial perception term as well. This suggests that dividends and sales growth are not good proxies for managerial perception elements and have other roles. Of all the models, the Q-sales model performs the best with Q as the proxy for market perception and sales as the proxy for managerial perception. As noted before, the negative sales coefficient is consistent with the presence of imperfect competition, which introduces an additional wedge between marginal and average Q that depends on the present value of current and future output. Further, the managerial perception term is higher than the market perception term in almost all of these regressions. This implies that managers pay much more attention to their own perception of fundamentals than those conveyed by the market. Overall, the evidence suggests that both market valuation and fundamentals have strong effects on investment, even though the elasticity of investment with regard to fundamentals is larger. Based on the best-fitting stock price-sales model, the results suggest that an increase of I per cent in market valuation not matched by an increase in fundamentals leads to an increase in investment of 0.0011 per cent, whereas an increase in market valuation matched by an increase in fundamentals leads to an increase in investment of 0.018 per cent. These results are broadly consistent with the evidence in Blanchard et al. (1993) based on aggregate U.S. data for the 1922-1990 period, though the elasticity estimates are somewhat lower.

18
# **1. Introduction**

Malawi is one of the poorest countries in Africa. Three-fifths of the population is poor and unable to meet their daily needs. Life expectancy is very low and declining as Malawians struggle with HIV/AIDs as well as other traditional diseases (Mukherjee and Benson 2003). While poverty is highest in the southern part of the country, rich and poor districts are found all over (IFPRI 2000). With a mean per capita income of $170 in 2004, poverty reduction is of central concern to policy makers (WDI 2005).

Poverty and forest degradation appear to go hand in hand in Malawi. Forest degradation by all accounts is rampant. A third of Malawi is considered forested, but these patchy forests have been declining at a rapid rate. Between 1990 and 2000, the area under forest cover in Malawi decreased at a rate ranging from 1% to 2.6% per annum (FAO 2005, WRI 2003).<sup>2</sup> In a country where 90% of the poor live in rural areas and share space with and use forests and shrub lands, it is not surprising that many express concern about the effect of poverty on the environment.<sup>3</sup> Loss of forest cover in Malawi is attributed to agricultural expansion, biomass use for fuelwood, charcoal production and a number of other factors including tobacco curing, brick burning and so on (Malawi SDNP 1998, Minde _et al._ 2001). While land under agriculture in Malawi increased by some 31% between 1972 and 1990 (Eschweiler 1993), this type of expansion has apparently slowed in recent years because of the limited supply of cultivable land. Over 90 percent of total energy demand in Malawi is met by biomass. With population growth in Malawi being one of the highest in Southern Africa, this demand is considered a serious threat to forests (Malawi SDNP 1998).

There is currently considerable policy interest in Malawi on the nexus between poverty and biomass scarcity. To cite a recent newspaper article, “ _Environmental degradation and poverty alleviation are urgent national issues that have a lot in common, but are often treated separately”_ (Davie Mkwambisi, 20 January 2006, Opinion, Malawi Nation). This paper focuses on a subset of questions that seem to be driving this kind of concern: a) What is the extent of biomass available for meeting the energy needs of the poor in Malawi and how is this distributed? b) To what extent are poverty and forest degradation inter-linked and what factors affect this

> 2 Estimates of deforestation in Malawi done by different global organizations differ.

> 3 Note a recent New York Times articles (Nov, 1, 2005) by Michael Wines entitled “Malawi is Burning and Deforestation Erodes Economy.”

2
Table 2: Impacts on learning— by teacher gender and identity

||Te|acher gen|der|Prefe|rred profe|ssion|
|---|---|---|---|---|---|---|
||(1)<br>Either|(2)<br>Female|(3)<br>Male|(4)<br>Either|(5)<br>Teacher|(6)<br>Other|
|In-Kind|0.230<sup>_∗∗_</sup>|0.194<sup>_∗_</sup>|0.189|0.255<sup>_∗∗∗_</sup>|0.284<sup>_∗∗_</sup>|0.220|
||(0.100)|(0.111)|(0.157)|(0.098)|(0.115)|(0.159)|
|Recognition|0.159|0.227<sup>_∗_</sup>|0.004|0.206<sup>_∗∗_</sup>|0.222<sup>_∗_</sup>|0.007|
||(0.105)|(0.125)|(0.156)|(0.104)|(0.116)|(0.189)|
|Observations|58949|47741|11208|46872|36048|10824|
|R-squared|0.099|0.115|0.183|0.141|0.157|0.225|
|Test:In-Kind=Recognition|0.501|0.780|0.250|0.633|0.592|0.252|

_Notes._ Each column represents a separate regression on the midline data, estimated using equation 1. Data in columns (1) to (3) are restricted to observations where all teachers in a given grade and school report the same gender. This allows us to unique identify the gender of the teacher. Columns (2) and (3) split this sample to female and male teachers, respectively. Similarly, columns (4) to (6) restrict the sample to schools and grades where all teachers either selected “teacher” or all teachers selected another profession to the question, asked at baseline: “if you could choose again, what profession would you choose?”. Columns (4) and (5) split this sample to teachers who selected “teacher” or not. Standard errors are in parentheses, and clustered at the school level. _∗p <_ 0 _._ 1, _∗∗ p <_ 0 _._ 05, _∗∗∗p <_ 0 _._ 01
WPS 3282

# **Fiscal Decentralization in Developing and Transition Economies**

## **Progress, Problems, and the Promise**

## **By**

**Anwar Shah**<sup>**1**</sup>

**World Bank**

**Washington, DC USA**

## World Bank Policy Research Working Paper 3282, April 2004

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 Lead Economist, World Bank Institute.  An earlier abridged version of this paper was presented at the International Conference on Federalism 2002 in St. Gallen, Switzerland, August 27-30, 2002. The author is grateful to Dr. Raoul Blindenbacher, Professors Richard Bird, Bernard Dafflon and Claude Jeanrenaud for comments.  Please address comments to Anwar Shah, World Bank, Mail Stop J4-403, 1818 H St., N.W., Washington, D.C., 20433, U.S.A. Email: ashah@worldbank.org.
megaprojects like the Padma Bridge are supporting economic growth and will raise economic activity permanently, but they are not captured in the model.

Fourth, improvements in standard macro stabilization aspects did not seem to boost growth but may have been conducive. The growth contribution of this block of variables is rather small, in line with the findings of earlier growth regression studies (Araujo et al. 2016; Moller and Wacker 2017). The negative contribution of this group of variables in some periods is mostly explained by the unconventional positive (but insignificant) coefficient estimate for government consumption in our otherwise preferred specification in column (2) in table 1. We elaborate in subsection 5.2 why the strong government focus on macroeconomic stability after deep macroeconomic imbalances in the 1980s may have enabled Bangladesh to continue growing in the context of receding structural improvements. In particular, inflation and fiscal deficits have remained contained over recent decades, the debt level has been low, and external borrowing has been mostly concessional. In addition, the central bank has successfully smoothed exchange rate volatility.

Fifth, increasing political stability gave an additional boost to growth in the 1990s. Bangladesh returned to democratic governance through the 1991 parliamentary election and stability increased further following prolonged strikes and a siege in the first half of 1996. The Chittagong Hill Tracts conflict also reached a resolution with the peace negotiations that started in the early 1990s, leading to the signing of a peace accord in 1997.

Sixth, improvements in human capital and lower inequality helped raise income levels starting from the 2000s. However, the combined magnitude of both remains relatively small, with contributions to overall growth hovering between 0.11 and 0.19 percentage points over the past two decades.

The same key results are confirmed in our robustness checks when applying GMM estimation and including the employment rate as an additional variable in the demography block. These results are reported in figure A.1, in the appendix. In both scenarios, the three periods of highest contemporaneous growth impulses from structural improvements were still between 1990 and 2004. This confirms our main message that key improvements took place during this period, and growth was mostly driven by persistence effects afterwards. Moreover, both scenarios underpredict growth in the last three periods as well.

-19-
Global Investment Costs for Coastal Defense through the 21st Century Report to the World Bank 11 Figure 4: Proposed river distributaries and surge barrier positions as illustrated for the southern Netherlands.

# **3.6 Initial protection levels**

There are no global data sets on current levels of protection and initial protection levels (taken as 2010) have to be estimated in an expert manner. Hence, current protection levels are taken from Sadoff et al. (2015) who applied current protection levels for the biggest 136 coastal cities from Hallegatte et al. (2013) and complemented these through expert judgement for coastal areas not associated to one of these cities (Table 4). Protection level zero (i.e. no protection) is assumed if the population density in the 1-in-100-years floodplain is lower than 30 people per km2 and in rural areas in countries with low and low middle incomes. As the socio-economic scenarios assume substantial economic growth, this moves people in rural areas from no protection to protection over the 21st century in these poorer countries, and the global length of protection increases with time.

Table 4: Protection standards adopted in this analysis (following Sadoff et al., 2015).

|Wealth Class (annual income per capita)<br>(2014 US$ GDP per capita (PPP))|Urban<br>(>1000<br>people/km<sup>2</sup>)|Rural<br>(30 to 1000<br>people/km<sup>2</sup>)|Uninhabited<br>(<30<br>people/km<sup>2</sup>)|
|---|---|---|---|
|Low income (<$1,035)|1:10|no protection|no protection|
|Lower middle income ($1,035 - $4,085)|1:25|no protection|no protection|
|Upper middle income ($4,086 - $12,615)|1:100|1:20|no protection|
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