rafmacalaba/datause-encoder-data huggingface dataset:
Hanslow, K., Phamduc, T. and Verikios, G. 1999, ‘The structure of the FTAP model’, Research Memorandum, Productivity Commission, Canberra, December, available from http://www.pc.gov.au/research/rm/ftap/index.html.

- Hertel, T. 1997, _Global Trade Analysis: Modelling and Applications_ , Cambridge University Press, Cambridge.

- Hertel, T., Walmsley, T. and Itakura, K. 2001, ‘Dynamic effects of the “new age” free trade agreement between Japan and Singapore’, _Journal of Economic Integration_ , 16(4), pp. 446–84.

- ICAO (International Civil Aviation Organization) 2004, _Database of the World’s Air Services Agreements_ , Doc 9511, 2004 edition, CD-Rom.

- ITU (International Telecommunications Union) 2006, _ICT Statistics Database_ , available at http://www.itu.int/ITU-D/icteye/Indicators/Indicators.aspx.

- Kalirajan, K. 2000, _Restrictions on Trade in Distribution Services_ , Productivity Commission Staff Research Paper, Ausinfo, Canberra.

- Kalirajan, K., McGuire, G., Nguyen-Hong, D. and Schuele, M. 2000, ‘The price impact of restrictions on banking services’, in Findlay, C. and Warren, T. (eds) 2000, _Impediments to Trade in Services: Measurement and Policy Implications_ , Routledge, London and New York, pp. 215–30.

- Konan, D. and Maskus, K. 2006, ‘Quantifying the Impact of Services Liberalization in a Developing Economy’, _Journal of Development Economics_ , 81(1), pp. 142–62.

- Malcolm, G. 1998, ‘Adjusting Tax Rates in the GTAP Data Base’, GTAP Technical Paper No. 12, Purdue University, West Lafayatte IN.

- McGuire, G. and Schuele, M. 2000, ‘Restrictiveness of international trade in banking services’, in C. Findlay and T. Warren (eds), _Impediments to Trade in Services, Measurement and Policy Implications_ , Routledge, London and New York, pp. 201– 214.

- Nguyen-Hong, D. 2000, _Restrictions on Trade in Professional Services_ , Productivity Commission Staff Research Paper, Ausinfo, Canberra.

- OECD 2004, ‘The Economy-wide Effects of Services Trade Barriers in Selected Developing Countries’, TD/TC/WP(2004)42, OECD, Paris.

- OECD 2005a, Indicators of Regulatory Conditions in the Professional Services, 2 December 2005, accessible at

- http://www.oecd.org/document/24/0,2340,en_2649_37421_35858776_1_1_1_37421, 00.html.

- OECD 2005b, Indicators of Regulatory Conditions in Seven Non-manufacturing Sectors, 2 December 2005, accessible at

- http://www.oecd.org/document/32/0,2340,en_2649_37421_35791136_1_1_1_37421, 00.html.

- OECD 2005c, ‘Modal Estimates of Services Barriers’, TD/TC/WP(2005)36, OECD, Paris.

21
Table 3: Selection Traits - Correlations

||Education|Cognitive|Big Five|Non-Cognitive|Integrity|PSM|Altruism|
|---|---|---|---|---|---|---|---|
|Education|1|||||||
|Cognitive|0.0380|1||||||
|Big Five|0.0339|0.0435*|1|||||
|Non-Cognitive|0.0736***|0.0776***|0.912***|1||||
|Integrity|0.0451*|0.0512*|0.159***|0.218***|1|||
|PSM|0.0584**|0.0284|0.353***|0.385***|0.273***|1||
|Altruism|-0.0164|0.0615**|0.102***|0.102***|0.00573|0.0728**|*<br>1|

_Note:_ All figures in this table refer to a pooled sample of bureaucrats and politicians.

Table 4: Descriptive Statistics - District Level

||Mean|SD|p50|N|
|---|---|---|---|---|
|_Panel A - Health outcomes_|||||
|DPT3 Coverage|100.77|20.50|98.20|75|
|Deliveries in govt and PNFP facilities|47.78|17.56|46.80|75|
|OPD Per Capita|1.22|0.48|1.10|75|
|HIV testing in children born to HIV positive women|93.26|83.69|71.60|75|
|Latrine coverage in households|75.21|16.18|78.20|75|
|IPT2|53.89|11.59|54.00|75|
|ANC4|34.69|10.92|33.80|75|
|TB success rate|79.91|12.98|81.90|75|
|Approved posts that are flled|70.15|20.08|70.20|75|
|Monthly reports sent in on time (3)|88.87|11.64|92.40|75|
|Completeness monthly reports (2)|84.39|28.88|100.00|75|
|Completeness facility reporting (3)|95.99|7.03|99.50|75|
|Completeness of the annual report (2)|70.85|34.19|84.80|75|
|Medicine orders submitted timely|94.00|10.22|96.40|75|
|Total Score Health DLT|73.30|7.25|75.00|75|
|_Panel B - District socio-economic characteristics_|||||
|District Population|324001.89|251864.32|251512.00|75|
|Urban population share|0.13|0.09|0.11|75|
|Poverty rate 2013|0.23|0.16|0.23|75|
|Number of radios (HQ)|1.89|3.06|1.00|75|
|Voter Attachment to National Party - 2011|0.68|0.15|0.70|75|
|Average Score UCE - 2011|53.37|3.31|54.04|75|

_Note:_ This table presents descriptive statistics of health outcomes and characteristics of the 75 Districts included in the survey. Health outcomes are from the District League Tables produced by the Ministry of Health. The remaining district characteristics are from the 2010 Census and the Uganda Electoral Comission - details on the variables are provided in the Annex.

45
**<u>Table 7: O&M Cost Recovery Index</u>**

|**Indicators**|**Jalani**<br>**Sche**|**dhi**<br>**mes**|**Other Co**<br>**Managed**|**mmunity**<br>**Schemes**|
|---|---|---|---|---|
||**Mean**|**SD**|**Mean**|**SD**|
|Revenue as Proportion to O&M Costs|1.21|0.77|1.74|2.28|
|ProportionofSchemes with FullO&MCostRecovery|0.47|0.5|0.5|0.51|

Performance of Jalanidhi schemes in most of the dimensions of institutional sustainability is better compared to other community driven schemes (Table 8). Almost one-third of the BG executive committee members of Jalanidhi schemes are women with 9 percent of the BGs having a woman president and 14 percent having a woman secretary. Almost half of the Jalanidhi BGs have women treasures compared to one-fourth for the other community managed schemes. Jalanidhi BGs also have more regular elections for the executive committees and 74 percent of these BGs reported preparing and presenting Annual Reports in general body meetings compared to 64 percent of the BGs for the other community managed schemes. Similarly, a larger majority of the Jalanidhi BGs reported having a savings bank account and the average balance in these accounts is almost three times compared to other community managed schemes. However, the average balance even for the Jalanidhi BGs is only ₹ 17,161 which severely limits the ability of the majority of the BGs to undertake big investments when necessary.

**<u>Table 8: Institutional Sustainability Index</u>**

|**Indicators**|**Jalanidhi**<br>**Mean**|**Schemes**<br>**SD**|**Other Co**<br>**Managed**<br>**Mean**|**mmunity**<br>**Schemes**<br>**SD**|
|---|---|---|---|---|
|Proportion of Female in BG Executive|||||
|Committee|0.32|0.19|0.27|0.20|
|Female President in BG|0.09|0.28|0.11|0.32|
|Female Secretary in BG|0.14|0.34|0.16|0.37|
|Female Treasurer in BG|0.49|0.5|0.25|0.44|
|Female Pump Operator in BG|0.13|0.33|0.08|0.27|
|Regular Election (Yes/No)|0.66|0.48|0.41|0.5|
|Annual Report (Yes/No)|0.74|0.44|0.64|0.48|
|Maintenance of Records (Yes/No)|0.16|0.37|0.00|0.00|
|Investments post-Project Completion (Yes/No)|0.13|0.33|0.11|0.31|
|Proportion of BGs with Savings Bank Account|0.70|0.46|0.54|0.50|
|Current Balance in the Savings Account (₹ )|17,161|26,217|6,181|7,472|

Table 9 and Table 10 compare the performance indexes of Jalanidhi schemes with traditional supply driven KWA schemes and the other community managed schemes respectively. The indexes have been computed separately for two groups – Jalanidhi-KWA schemes and Jalanidhi-other community driven schemes. As a result, the mean values of the indexes for Jalanidhi schemes differ across tables. Since the performance indexes are z-scores, the mean of each of these indices is 0 by construction. So construction of indices separately for the two groups makes the comparisons straightforward. Positive 15
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19. Kwon S. Billing of medical services and the financial burden of patients in Korea, 2005.
# **1. Introduction**

Climate is often referred to as an important determinant of economic performance. More recently, largely motivated by the ongoing debate on global warming, the influence of climate factors on economically relevant outcomes has attracted even more attention. With temperatures expected to continue rising and scientists projecting an increase in the frequency and severity of extreme weather events, understanding the consequences of weather-related shocks on economic development, particularly on human welfare, is increasingly important. The aggregated first-order effects of natural disasters such as human deaths and injuries, destruction of critical infrastructure, and disruption of economic activities are evident. Yet, quantifying the direct and indirect (short- and long-term) effects of large shocks on the wellbeing of households and assessing how they cope with these risk factors is more challenging while at the same time it is central to more fully estimating their economic impacts and designing effective risk management strategies (World Bank, 2013).

The last few years have seen a large body of empirical research that examines the effects of anomalous deviations in weather outcomes on a wide range of variables associated with household welfare as well as households’ capacity to protect their welfare when confronted by such shocks. The outcome variables analyzed range from consumption to income to asset ownerships to mortality to investments in education, health and nutrition to risk-coping actions, among others (See Dell et al. 2013 and Baez et al. 2010 for surveys of this literature). At least three clear patterns emerge from the existing literature. First, households possess numerous strategies for dealing with extreme weather but overall their mitigation capacity is insufficient for the task of maintaining –let alone improving– their welfare. For instance, excess rainfall and droughts have been found to reduce by half the crop income among affected households in Burkina Faso and more than half of this loss is directly reflected in consumption (Kazianga and Udry 2006). Similarly, rainfall shocks were found to force households in rural Ethiopia to deplete their productive assets between 8% and 62% (Dercon 2004). The second observation has to do with the persistence of the effects. There is a host of empirical results indicating that the immediate negative consequences of weather shocks often carry over the longer term. Alderman et al. (2006) show, for instance, that children who became stunted due to a drought in Zimbabwe never fully recovered later in life and exhibited lower school attainment and earnings in adulthood. Finally, there is remarkable impact heterogeneity. The 2
on assumptions about the signal-to-noise ratios of the GDP data. Without invoking assumptions about any signal-to-noise ratios, Hu and Yao (2021) use statistical capacity and geographic locations as additional information, allowing for a general relationship between measurement errors of GDP and nighttime light growth, statistical capacity, and geographic location in a non-classical and non-linear measurement error model. Compared with those two approaches, using the number of nightly observations avoids additional assumptions and greatly simplifies the estimation.

To estimate equation (7), we first divide the sample into country-period groups to obtain var( _zi,t_ ) and cov( _yi,t, zi,t_ ) and then estimate _β_ through a regression based on equation (8). Specifically, for each country _i_ , we divide the observations into _K_ time periods. Within each time period _k_ , we calculate the variance and covariance in equation (8) with quarterly observations as _σz_<sup>2</sup> ; _i,k_<sup>and</sup><sup>_σ_</sup> _yz_<sup>2</sup> ; _i,k_<sup>,aswellastheaverage</sup> 1 _/Ni,k_ . We then estimate the following regression to obtain _β_ : where _β_ and _α_ are coefficients and _ζi,k_ is the residual. In equation (8), 1 _/Ni,k_ is proportional to the variance of measurement errors in nighttime light growth. Other factors, such as the area of a country or its average cloud cover, affect measurement errors in nighttime light growth in a similar way. As a robustness check, we hence also estimate equation (8) by replacing 1 _/Ni,k_ with the area of a country or its average cloud cover.

# **5.2 Baseline Estimates**

First, we remove country and time fixed effects by regressing nighttime light growth and GDP growth on country and time dummies, respectively. Working with the residuals, we use two years of data for each country to calculate the variances and covariances. The weak correlation between nighttime light growth and GDP growth in advanced economies would reduce the efficiency of our estimation, so that we only include EMDEs.<sup>17</sup> In addition to the number of valid observations each quarter, we consider the area of each country and the annual average cloud cover as additional control variables. Both are likely to affect the variance of nighttime light growth, since larger and cloudier countries tend to have larger noise in nighttime light growth (see figure D.1 in the appendix).

> 17Nighttime lights are also a more important source of information in EMDEs, since they typically have less accurate national accounts and fewer subnational measures of GDP.

14
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|Switzerland-Italian<sup>~~2~~</sup>|6.8|5.3|5.4|
|---|---|---|---|
|Italy<sup>2</sup>|4.7|3.4|4.0|
|Chile<sup>1</sup>|9.3|6.9|10.0|
|Nuevo Leon<sup>1</sup>|13.4|11.4|14.8|
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|_Mean_|_6.7_|_5.5_|_5.3_|

1: Using IALS 2. Using ALL The two sets of estimates (using estimated coefficients β1 and δ1) suggest that the cross-country average of the component of the return to schooling associated with skills acquired outside school is about 70-80 percent of the gross return; that is, about 70-80 percent of what the labor market rewards in schooling is its non-literacy component (with the set of estimates associated with estimated coefficient δ1, on average slightly lower); however, there is significant heterogeneity and a dichotomy between the two subgroups of countries – the educationally advanced and the less educationally advanced.

In Table 2, we divide the 14 countries in two groups of 7 after ranking the countries by average achievement score. We see that there is a strong negative association between country achievement score and dispersion of score. Chile, Slovenia and Italy rank at the bottom in average score and at the top in score dispersion; the opposite is the case for countries such as Norway, Finland, Denmark and Switzerland (German and French); that is in the best performing group of countries, not only quality of skill is higher on average, but skill is more evenly distributed.

14
incomes. Given that households with nonagricultural incomes have lower poverty rates (Figure 6b), the stagnation in rural income diversification is probably holding back further welfare improvements in rural Sudan. Poor households earn a significantly smaller proportion of their income from nonagricultural sources.

Figure 13: Nonagricultural Income for the Poor and Non-poor, 2009 and 2014/15 (a) Nonagricultural Income as a Proportion of Total Income for Rural **Poor** Households <!-- Start of picture text --> 2009 2014/15<br>80%<br>60%<br>40%<br>20%<br>0%<br><!-- End of picture text -->

- (b) Nonagricultural Income as a Proportion of Total Income for Rural **Non-poor** Households

<!-- Start of picture text --> 2009 2014/15<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br><!-- End of picture text --> _Source:_ Authors’ calculations using NHBPS 2014/15.

Incomes are fairly specialized in rural Sudan, for both the poor and the non-poor (Figure 14). Almost one-fourth of non-poor households identify earning all their income from agricultural sources, while almost one-third of poor households identify earning all their income from agricultural sources. Around 40 percent of non-poor households earn all their income only from nonagricultural sources, while about 25 percent of poor households can claim the same. The non-poor are therefore observed to be less reliant on agriculture than the poor. It is hard to establish whether poverty leads to a greater reliance on incomes earned solely from agriculture or whether finding a way to diversify incomes away from agriculture leads to reduced poverty. If it is the latter, then policies that wean households away from complete reliance of agricultural incomes may reduce poverty.

15
# Domestic Government Spending on Human Capital: A Cross-Country Analysis of Recent Trends

UKathryn Andrews, Ciro Avitabile and Roberta Gatti<sup>1</sup> _Updated October 2020_ JEL Classification: I00, O15

**Keywords** : human capital, domestic government financing, social sector spending

1 Kathryn Andrews, Human Development Chief Economist Office at the World Bank (kandrews@worldbank.org); Ciro Avitabile, Human Development Chief Economist Office at the World Bank (cavitabile@worldbank.org); Roberta Gatti, Human Development Chief Economist’s Office at the World Bank (rgatti@worldbank.org). This paper has benefited from discussions with Samer Al-Samarrai, David Coady, Zelalem Yilma Debebe, Maura Francese, Amer Hasan, Aart Kraay, Christoph Kurowski, and Ajay Tandon. We are particularly grateful to Celine Ferré, whose inputs have been particularly important at an early stage of this work.

The data used in this paper can be found at <u>https://datacatalog.worldbank.org/.</u> _The views expressed here are the authors’, and do not reflect those of the World Bank, its Executive Directors, or the countries they represent._
App. Table 3c: Complete regression results for Panel C of Table 6

||Overall<br>growth<br>1989→2010|Overall<br>growth<br>2000→2010|1-Person firm<br>growth<br>1989→2010|1-Person firm<br>growth<br>2000→2010|Average 1-<br>person firm<br>employment<br>share 1989-2010|Tradables 1-<br>person firm<br>growth<br>1989→2010|Tradables 1-<br>person firm<br>growth<br>2000→2010|Tradables 1-<br>person firm<br>employment share<br>1989-2010|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Log population|-0.076|-0.110|-0.124|-0.079|-0.248|-0.131|-0.062|-0.301|
||[0.031]**|[0.080]|[0.060]**|[0.112]|[0.084]***|[0.101]|[0.088]|[0.096]***|
|Log population density|0.008|0.009|-0.018|0.001|-0.074|-0.013|-0.098|-0.037|
||[0.021]|[0.039]|[0.025]|[0.047]|[0.052]|[0.067]|[0.077]|[0.065]|
|Share of population that is urban|0.070|0.084|0.078|0.120|-0.153|0.399|0.075|-0.012|
||[0.028]**|[0.047]*|[0.044]*|[0.153]|[0.074]**|[0.164]**|[0.136]|[0.090]|
|Share of population that is middle aged (15-49)|-0.073|0.008|-0.078|0.127|-0.061|0.035|0.138|-0.101|
||[0.057]|[0.053]|[0.070]|[0.100]|[0.098]|[0.114]|[0.096]|[0.110]|
|Share of population in scheduled caste/tribe|-0.026|0.053|-0.029|0.048|0.161|-0.149|0.028|0.149|
||[0.034]|[0.048]|[0.050]|[0.051]|[0.053]***|[0.105]|[0.054]|[0.062]**|
|Female labor force participation rate|0.045|0.081|0.067|-0.045|0.015|-0.067|-0.168|-0.020|
||[0.062]|[0.050]|[0.089]|[0.141]|[0.078]|[0.084]|[0.162]|[0.088]|
|Educated worker share (middle and up)|0.002|-0.042|0.030|0.087|-0.374|-0.081|0.060|-0.269|
||[0.045]|[0.077]|[0.062]|[0.160]|[0.132]***|[0.198]|[0.120]|[0.151]*|
|Literacy rate|-0.024|0.078|-0.090|-0.064|0.329|-0.052|-0.042|0.183|
||[0.068]|[0.079]|[0.093]|[0.148]|[0.152]**|[0.266]|[0.132]|[0.178]|
|Infrastructure: share of villages with electricity|-0.005|-0.120|-0.044|0.066|-0.037|0.037|0.068|0.166|
||[0.052]|[0.049]**|[0.078]|[0.100]|[0.102]|[0.121]|[0.129]|[0.102]|
|Infrastructure: share of villages with paved roads|0.058|0.089|0.121|-0.229|-0.109|-0.075|-0.267|0.207|
||[0.072]|[0.073]|[0.086]|[0.154]|[0.108]|[0.158]|[0.154]*|[0.113]*|
|Labor laws strength|0.015|0.033|0.038|0.009|-0.067|0.109|0.029|-0.108|
||[0.019]|[0.029]|[0.024]|[0.043]|[0.060]|[0.086]|[0.052]|[0.052]**|
|Travel time to nearest of India's ten largest cities|0.030|0.049|0.035|-0.104|0.152|-0.004|-0.069|0.241|
||[0.036]|[0.044]|[0.047]|[0.121]|[0.074]**|[0.089]|[0.098]|[0.076]***|
|Observations|203|203|203|203|203|203|203|203|
|Adjusted R-squared|0.042|0.088|0.054|0.047|0.300|0.085|0.073|0.234|

Notes: See Table 6.
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