rafmacalaba/datause-encoder-data huggingface dataset:
# Table A2: Descriptive statistics: DHS Sample

|**D**<br>Variables|**HS**<br>Observation|Mean|Std.Dev|Min|Max|
|---|---|---|---|---|---|
|Submarine cables have arrived<br>|806,378|0.415852|0.492868|0|1|
|Proximity to road (KM)|805,199|-9.702026|17.28903|-221.4656|-.0000708|
|Submarine (2 km) * proximity to road (KM)|805,199|-.8802237|4.396371|-192.9587|0|
|Proximity to Hypothetical road (KM)|806,378|-34.45176|34.22753|-273.9815|-.0010025|
|Submarine (2 km) * proximity to hypothetical road (KM)|806,378|-6.145832|18.4237|-221.7079|0|
|lightening|803,469|.498743|.3286748|0|2.120674|
|Terrain elevation|784,523|679.2284|668.5519|-92|3979|
|slope|774,937|1.264677|1.726249|0|15.9138|
|Internet connected (0.5 m)|806,378|.0084253|.0914022|0|1|
|Internet connected (1 km)|806,378|.0244637|.1544839|0|1|
|Internet connected (1.5 km)|806,378|.0451314|.2075925|0|1|
|Internet connected (2 km)|806,378|.4184353|.4933026|0|1|
|Submarine Internet (0.5 km)|806,378|.0026824|.0517221|0|1|
|Submarine Internet (1 km)|297,612|.0143845|.1190699|0|1|
|Submarine Internet (1.5 km)|806,378|.02007|.1402398|0|1|
|Submarine Internet (2 km)|806,378|.1937032|.3951993|0|1|
|Currently working|717,077|.6770207|.4676152|0|1|
|Worked in the past year|781,920|.738738|.4393227|0|1|
|Low skilled worker|747,665|.4597527|.4983779|0|1|
|Services|740,750|.2290327|.4202106|0|1|
|Skilled worker|775,839|.1896076|.3919908|0|1|
|High skilled worker|793,272|.1090647|.3117206|0|1|
|Self-employed farmer|775,839|.208417|.4061767|0|1|
|Farmer (employee and self-employed)|775,839|.2830561|.4504838|0|1|
|More developed country|806,378|.388675|.4874495|0|1|
|age|806,378|32.65295|9.142657|20|64|
|Female individual|806,378|.6845698|.4646873|0|1|
|Rural residence|806,378|.6210524|.4851254|0|1|
|No Education|806,365|.3828874|.4860915|0|1|
|Primary education|806,365|.2653091|.4414979|0|1|
|No education up to primary education|806,365|.6481965|.4775333|0|1|
|Secondary education and more|806,365|.3518035|.4775333|0|1|
|20-34 age group|806,378|.6001949|.4898584|0|1|
|35 to 44 age group|806,378|.2650221|.4413453|0|1|
|45 to 64 age group|806,378|.1347829|.3414918|0|1|

53
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30
In this paper, we aim at laying out the conceptual foundation behind statistical capacity indexes, and construct a new index based on practical and theoretical considerations. We review existing measurement methodologies, posit desired attributes, and propose updated indicators, and an updated Statistical Capacity Index (hereafter referred to as the Statistical Performance Index, or SPI). On the empirical front, we expand the number of indicators in the old SCI by almost twice, and we extend the sample of covered countries by one-half to all countries in the world.

This paper consists of six sections. We provide a brief overview on the SCI in the next section before presenting the theory in Section 3. We offer in this section detailed discussion on the proposed index ranging from the conceptual framework (including its policy relevance, its concept of statistical capacity and desirable characteristics) to the technical framework (including its dimensions, aggregation methods, and axioms). We then describe the SPI and its new features before comparing it with the SCI in Section 4 and providing some empirical illustrations in Section 5. Section 6 concludes.

# **2. Background on the SCI**

The SCI is a tool for assessing country-level statistical capacity, developed in 2004 to assess the effectiveness of the World Bank’s lending projects related to improvements in countries’ statistical capacity. Table 1 compares the SCI with the statistical capacity measurement indexes used by other organizations including PARIS21, the Food and Agriculture Organization of the United Nations (FAO), the United Nations Economic Commission for Europe (UNECE), the United Nations Economic Commission for Africa (UNECA), and the U.S. Census Bureau. Table 1 shows that the SCI is the only tool that provides comparable data across different countries over time. Further, the SCI covers the largest number of countries—146 countries starting from 2004 and has by far the widest coverage of country-level indicators.

4
- 8 -

to respond to economic change. It could be argued that income composition variables are redundant given the presence of physical and human capital variables and given that earned income measures the returns to this capital. This argument would be valid if the income variables were included in absolute values. As shares, however, they add new information, since the same asset endowment can lead to different income composition due to unmeasured factors such as skill and entrepreneurship. A diversified income base helps reduce household vulnerability to shocks. The economic adjustment in Cote d'Ivoire during the 1980s has been characterized by major shifts in income sources--especially away from wage labor (due to declines in formal sector employment) towards self-employment income. People's ability to take advantage of this trend is a major determinant of their change in welfare, over and above what is captured by their asset endowment. We think that the income composition variables capture part of that ability, albeit imperfectly (but better than other variables in the CILSS data).

Finally, the first-difference variables include all human and physical capital variables (except for diplomas, where the changes were too few) and the income composition variables.

Tables 1A and 1B summarize the explanatory variables and show their means and standard deviations. In the case of "initial conditions" variables, the figures pertain to 1987, while the change variables were calculated by subtracting 1988 values from 1987 values. Mean per capita expenditure in urban areas is almost twice that in rural areas, but the relative change is less. On average, expenditure per capita fell about 15% in urban areas, against about 20% in rural areas. Human capital, at 19 years of education, is much higher in urban households than in rural areas, where it is a mere 6 years. Only 6% of rural heads of household have a diploma, against 41 % of urban heads. Physical assets show different patterns across urban and rural areas, much as one would expect. Land and home ownership are more frequent in rural areas, and the value of farm equipment is higher. However, the number of non-farm businesses and their revenue is much higher in urban areas. Urban households own almost three times as many durable goods than rural households, and the value of those durables is about one fourth of a year's worth of expenditure in urban areas, but barely 7% in rural areas.
# **5.2 Childhood Illness**

We also consider the impact of animal-source food intake on the incidence of childhood illness, as reported in the DHS surveys. Our exercise tests the impact of consumption of animal-source foods, particularly fish, on resistance to common childhood infections. We focus on the reported occurrence of diarrhea, fever and coughing immediately before the survey date.

As before, we consider the roles of fish consumption preference and fish supply during the birth period, after controlling for the socioeconomic, temporal and spatial determinants of fish intake during the survey period. We perform probit estimation on a stacked panel data set drawn from DHS 2011 and 2014. The dependent variable is the occurrence of illness, defined as diarrhea, fever or coughing. Our independent variables replicate those in the child mortality model (Table 9). We also introduce dummy variables to allow for variation in mean incidence rates across illness categories.

Our results (Table 11) indicate that child health status is strongly affected by socioeconomic status: Both mother’s education and household asset ownership have negative, highly-significant effects on illness incidence. Our results also suggest that female children have lower illness incidence, ceteris paribus. After controlling for the socioeconomic, temporal and spatial determinants of consumption of animal-source foods, we find that fish consumption preference has a negative, significant impact on illness. As before (Table 9), we find no significant impact for the intake of other animal-source foods. And again, we find a lower incidence rate for children born during the peak-catch period (September - December).

To assess the quantitative significance of these results, we replicate our simulation strategy for child mortality. We use the probit results reported in Table 11 to predict incidence rates for children with birth year 2012 whose mothers reside in the grid square identified in Figure 8; have no formal education; belong to a household with zero tabulated assets; and have zero consumption preference for eggs and 24
One advantage of the approach chosen in this paper is that it allows us to estimate the effect of improved sanitation and improved water access on child morbidity as well as on child mortality across a large number of countries using the same methodology. As water and sanitation are often competing for the same resources (WHO/UNICEF, 2000; Clark and Gundry, 2004), having a broad comparison of their marginal impact seems desirable both from a scientific and from a policy perspective.

One obvious limitation of the analysis presented in this paper is that we are not able to directly measure the quality of water. Because of inadequate water transportation or water storage, water that is clean at the source can be contaminated at the point of use. For an overview of an extensive literature on this issue see Wright et al. (2004) or Kremer et al. (2007). The water technology measures used in this paper are also likely to represent both water quantity and water quality. A similar concern applies when it comes to interpreting our coefficients on sanitation. Policies targeted at sanitation improvements in developing countries are often implemented in conjunction with hygiene education programs, so that the observed sanitation effects may to some degree reflect underlying behavioral change rather than the true infrastructure effect itself.

The estimates presented in this paper should thus be viewed as total impact estimates associated with access to basic or advanced water and sanitation technologies. While some may view the inability to disentangle the various channels through which water and sanitation infrastructures affect health outcomes as a short-coming, the total impact is likely the key variable of interest when it comes to evaluating the costs and benefits for such interventions from a policy perspective (Whittington et al., 2008).

Table 2 summarize the prevalence of water and sanitation infrastructure (following the technological definition discussed before in Table 1, column 2) for children in the DHS surveys for the 1990s and the 2000s for all those countries where we have at least one survey in each period. Since we constrain the countries listed in Table 2 to countries with at least one survey in the 1990s and 2000s, not all surveys and countries used in our estimation are represented here.<sup>6</sup> It is furthermore worth noting that these statistics deviate from official WHO statistics not only due

> 6 See Appendix, Table A1 for a complete list of countries and surveys.

13/36
Table 2: Randomization checks: Household Variables

||(1)<br>(2)<br>Control<br>Treatment|(3)<br>Difference|
|---|---|---|
|11- Household size|4.793<br>4.978|0.184|
||[2.275]<br>[2.299]|(0.156)|
|12- Household members under 5|0.824<br>0.845|0.021|
||[0.941]<br>[0.904]|(0.057)|
|13- Household members 6-18 y.o.|1.575<br>1.654|0.078|
||[1.530]<br>[1.559]|(0.098)|
|14- Access to public tap|0.432<br>0.422|-0.010|
||[0.496]<br>[0.494]|(0.077)|
|15- Access to unprotected well|0.228<br>0.248|0.020|
||[0.420]<br>[0.432]|(0.054)|
|16- Access to unprotected spring|0.140<br>0.125|-0.015|
||[0.347]<br>[0.331]|(0.038)|
|17- Has any toilet|0.066<br>0.054|-0.011|
||[0.248]<br>[0.227]|(0.023)|
|18- Has radio|0.244<br>0.252|0.008|
||[0.430]<br>[0.435]|(0.032)|
|19- Firewood is main fuel|0.956<br>0.935|-0.021|
||[0.204]<br>[0.247]|(0.018)|
|20- Has no window|0.319<br>0.324|0.005|
||[0.466]<br>[0.468]|(0.066)|
|21- Number of separate rooms|1.833<br>1.855|0.022|
||[1.199]<br>[1.183]|(0.105)|
|22- Number of sleeping rooms|1.380<br>1.382|0.002|
||[0.819]<br>[0.714]|(0.051)|
|23- Number of sleeping spaces|4.608<br>4.444|-0.164|
||[2.453]<br>[2.347]|(0.190)|
||P-value [variables 11-23]|0.7575|
||P-value[variables 4-23]|0.5555|

Note: Note: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Column (1) and Column (2) report sample means restricted to control and treatment group, standard deviations in brackets. Column (3) reports the difference between (2) and (1) estimated using OLS regression of the correspondent outcome on the treatment indicator. Standard errors clustered at village level are reported in parentheses. To control for joint significance, we run a probit regression of the treatment indicator on the selected groups of variables and we report p-values of an F-test for the joint significance of the coefficients on the selected variables. Variable 6a is not used in the joint test since it is conditional on having attended school.

27
Table 6 GDP Growth Regressions: OLS and Instrumental Variable" (Dependent Variable: Per Capita GDP Growth in 1985 International Prices)

|Independent Variables|OLS|OLS|IVb|IV|
|---|---|---|---|---|
|Constant<br>Dummy for 1974-83|0.171***<br>(0.022)<br>_-0.015***_<br>(0.003)|0.161***<br>(0.024)<br>_-0.015***_<br>(0.003)|0.167***<br>(0.027)<br>_-0.015***_<br>(0.004)|0.136***<br>(0.038)<br>-0.014***<br>(0.004)|
|Dummy for 1984-93|-0.017***<br>(0.004)|-0.016**<br>(0.004)|*-0.017***<br>(0.004)|-0.016***<br>(0.004)|
|Initial Per Capita GDP|30.019***<br>(0.003)|-0.019***<br>(0.003)|-0.021***<br>(0.003)|-0.021***<br>(0.003)|
|Initial Schooling|0.003<br>(0.003)|0.003<br>(0.003)|0.003<br>(0.003)|0.003<br>(0.003)|
|Population Growth|-0.184<br>(0.192)|-0.209<br>(0.192)|-0.260<br>(0.204)|-0.304<br>(0.203)|
|Investment/GDP|0.009***<br>(0.003)|0.009***<br>(0.003)|0.008**<br>(0.003)|0.007**<br>(0.003)|
|Government Size|-0.016***<br>(0.004)|-0.022***<br>(0.008)|-0.023***<br>(0.008)|-0.038***<br>(0.015)|
|Quality of Bureaucracy|0.017*|0.041|0.027***|0.085*|
|(O worst, I best)|(0.009)|(0.027)|(0.010)|(0.044)|
|Government Size*Quality of Bureaucracy||0.014<br>(0.014)||0.033<br>(0.024)|
|Policy Distortion|-0.006***<br>(0.002)|-0.006***<br>(0.002)|_-0.005***_<br>(0.002)|_-0.005***_<br>(0.002)|
|Terms of Trade Changes|0.034<br>(0.040)|0.034<br>(0.040)|0.042<br>(0.042)|0.044<br>(0.042)|
|Latin America Dummy|-0.017***<br>(0.004)|-0.017***<br>(0.004)|-0.015***<br>(0.004)|-0.015***<br>(0.004)|
|Sub-Saharan Africa Dummy|-0.030***<br>(0.006)|.0.030***<br>(0.006)|-0.028***<br>(0.006)|-0.028***<br>(0.006)|
|Socialist Dummy|-0.008<br>(0.006)|-0.008<br>(0.006)|-0.013**<br>(0.005)|-0.013**<br>(0.005)|
|Number of Observations|271|271|258|258|
|R-squared'|0.5196|0.5213|0.487|0.4921|

*** significant at 1% level

** significant at 5% level * significant at 10% level

' Standard errors, corrected for heterscedasticity, are in parentheses.

**b** For the IV regressions in Table 6, the instruments for the following variables are the previous five year lag of itself and regional and decade dummy variables: policy distortion and investment/GDP. The instrument for the govemment size variable is the prediction from the government size instrumental variable regression reported in Table 2. All other variables in the regression were treated as exogenous.

'The R-squared is not an appropriate measure of goodness of fit with instrumental variable regressions.

30
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49 Table 26. Estimated pollutant enissions by passenger cars and buses in selected cities (in metric tons per day)

|||1|980|||||2000|||
|---|---|---|---|---|---|---|---|---|---|---|
|Cities|CO|HC|NOx|S02|PM|CO|HC|NOx|SO2|PM|
|Bangkok|||||||||||
|Auto|265|21|8|1.7|1.9|678|53|21|4.2|5.0|
|Bus|184|24|16|4.1|1.4|471|60|41|11.0|4.0|
|Bombay|||||||||||
|Auto|159|13|5|1.0|1.1|535|42|17|3.3|4.0|
|Bus|278|35|22|3.3|1.9|936|117|73|11.0|6.5|
|Jakarta|||||||||||
|Auto|153|12|5|1.0|1.1|658|48|20|4.0|4.7|
|Bus|1|1|2|2.6|0.1|6|4|9|11.0|0.6|
|Karachi|||||||||||
|Auto|120|9|4|0.1|1.0|219|17|7|1.4|1.6|
|Bus|522|66|41|6.7|3.6|955|119|75|15.0|6.6|
|Seoul|||||||||||
|Auto|470|37|15|3.0|3.0|609|47|19|3.9|4.3|
|Bus|6|4|10|12.0|0.6|8|6|13|16.0|0.8|
|Bogota|||||||||||
|Auto|85|7|3|_0.5_|0.6|168|13|_5_|1.0|1.2|
|Bus|2|2|3|4.1|0.2|4|2.9|7|8.0|0.4|
|Mexico Ci|ty||||||||||
|Auto|635|_50_|20|4.0|4.5|931|73|29|6.0|6.6|
|Bus|1054|133|86|17.5|7.5|1543|195|126|26.0|11.0|
|Rio de Jan|eiro||||||||||
|Auto|427|33|13|2.7|3.1|634|50|20|3.9|4.5|
|Bus|84|13|13|9.4|1.0|124|19|19|14.0|1.4|
|Cairo|||||||||||
|Auto|195|15|6|1.0|1.4|606|47|19|3.8|4.3|
|Bus|2|2|3|4.0|0.2|6|_5_|10|13.0|0.6|
|Nairobi|||||||||||
|Auto|43|3|1|0.3|0.3|211|17|7|1.3|1.5|
|Bus|34|4|3|0.3|0.2|170|21|13|1.7|1.2|

Note: 1. 'Auto' includes taxis; 'bus' includes paratransit vehicles.

2. Emnission estimates for 1980 and 2000, based on assumptions and procedures given in Box 3, are intended to show the massive increase in pollutant emissions from motor vehicles over the 20-year period without emission controls. The actual emission levels may be significantly different from these estimates.
# Fiscal Multipliers Over the Growth Cycle: Evidence From Malaysia

Sohrab Ra...q & Albert Zeufack<sup>�</sup> Khazanah Research and Investment Strategy Abstract This paper explores the stabilisation properties of ...scal policy in Malaysia using a model incorporating nonlinearities into the dynamic relationship between ...scal policy and real economic activity over the growth cycle. The paper also investigates how output multipliers for government purchases may alter for di¤erent components of government spending. We ...nd that ...scal policy in Malaysia has become increasingly pro-cyclical over the last 25 years and establish that the size of ...scal multipliers tend to change over the growth cycle. A 1 Malaysian Ringgit rise in government (investment) spending leads to a maximum output multiplier of around 2.7 during growth recessions, and around 2 in normal times. The returns to government spending in Malaysia are greater when the focus is on public investment, as opposed to consumption. Changes in tax policy are less e¤ective in stimulating economic activity than direct government spending. These results provide empirical backing to conjectures in the recent literature implying that procyclicality in ...scal policy reduces the e¤ectiveness of ...scal actions in emer <mark>g</mark> in <mark>g</mark> markets. _<mark>JEL codes:</mark>_ <mark>E32, E62, E65</mark> Keywords: ...scal multiplier, investment, consumption, nonlinearity

## 1 Introduction

After a couple of decades when the primary focus was on monetary policy, the 2008 ...nancial crisis refocused the attention of economists onto discretionary ...scal policy as a potentially potent economic stabilisation tool. Despite the emergence �The views in this paper solely re‡ect those of the authors, and do not necessarily correspond to the views of Khazanah National Berhad.

1