rafmacalaba/datause-encoder-data huggingface dataset:
results are comparable to the findings in Frankenberg and Thomas (2001).

# **3 Data**

This study uses the Indonesian Family Life Survey (IFLS) for the main empirical analysis. The IFLS is an ongoing longitudinal survey. So far, there are five waves which were conducted in 1993, 1997, 2000, 2007, and 2014. The fifth wave of the IFLS is primarily used, since the paper is interested in adult outcomes of children born between 1984 and 1994.

IFLS asks the village leaders and the head of the Village Women’s Group about the availability of any village midwife in the community, and the year a midwife started serving the community. This information is again verified with the volunteers at the Village Health Post, where women obtain prenatal and obstetric care for the consistency of reporting on the village midwife’s presence in the community (Cas, 2012). While the first wave of the IFLS has information on whether there is a village midwife in the community, it does not have the start year information. It is only from the second wave that the information on both the availability and start year is available. Since the empirical strategy requires both pieces of information, this paper uses the availability of midwife information from wave one and subsequent waves, and the placement year of midwives from the follow-up waves for the IFLS communities.

Birth year information is available on the roster, as well as book 3A of the fifth wave.<sup>12</sup> The sample is limited to individuals born between 1984 and 1993 and are listed in the first wave and individuals born in 1994 and listed in the second wave. This process eliminates any adults who may have joined the panel household later in their adulthood.

A notable feature of the IFLS is that it provides various measures on health, cognition, economic, and math skills. Since there are multiple outcomes under each broad category of outcome— including health, cognition, economic, and math skill— a summary index for each category is constructed such that the program impact on that category can be estimated using a single test

> 12In some cases, the roster birth year does not match the self-reported birth year information in book 3A of the fifth wave. For such cases, self-reported birth year is used.

7
- 16 -

for livestock prices. Unlike the crops, livestock commodities have not been supported with guarantee prices In Mexico; hence, historical behavioral responses would have been generated by market prices subject to stochastic varlation. Also, the Mexican livestock elasticities are of the same sign and about the same magnitude as the corresponding elasticities computed by Just for U.S. livestock price responses.

Also in accord wlth the findings of Just for the U.S., Mexican prices and production responded very weakly to a 10 percent reduction In required acreage diversion In 198182. Just ascribes the weak responses to a low participation rate (an average of 16%) In the foodgrain program at that time, resulting In only a small Increase In crop acreage due to a reduction in required diversion.

The elasticity estimates for Mexican agricultural trade In response to U.S. agricultural policy changes shown In table 2 suggest a strong response of feedgraln imports to changes In support and target prices In the U.S. Thus, In response to 10 percent Increases in either support prices or support and target prices, maize Imports decrease by 8 percent In the first year and 7 percent In the second, while sorghum Imports decrease by 1 percent In the first year and then increase by 5 percent in the second. In contrast, soybean Imports and beef exports show little response. A ten percent reduction In required diversion produces very weak responses by all traded commodities in the first year and quIte small responses In the second. The reasons advanced earlier for this effect of a diversion reduction clearly apply here also.
45-60 percent of lost tariff revenues while least developed countries recover less than 30 percent of lost tariff revenues (Baunsgaard and Keen 2005). Concern over this revenue issue is an important factor constraining low income countries in reforming external tariffs to reduce bias against exporting and a better structure of incentives for global integration.

**Figure 7: Duties and Taxes as share of GDP**

Source: Bank staff calculations But many countries collect far less in tariff revenue than the applied tax rates would suggest, due to the widespread (discretionary) granting of exemptions. Analysis for a number of ESA countries suggests that a large share of statutory customs revenues is not actually collected, such that the revenue impact of tariff reform will be considerably less than would be expected on the basis of statutory tariff rates (Brenton, Hoppe and von Uexkull (2007)). For example, a simulation of the impact of an EPA for Ethiopia suggests a revenue loss of 3.4 percent of total tax revenues when actually applied tariffs are used but a loss of 6.4 percent if the statutory tariff rates are used. If Ethiopia were to remove tariff exemptions as tariffs against the EU were removed then the simulated revenue loss would amount to 0.8 percent of total tax revenues.

Exemptions make the tax regime opaque and difficult to administer and can lead to a distorted incentive structure that discriminates against small firms with less influence. Further, there is little evidence that exemptions have a significant impact on investment, their primary justification. Many countries could substantially reduce applied tariffs while maintaining or even increasing revenue if exemptions were removed and collection improved. However, it is still necessary to address the development challenge highlighted in Figure 7 of moving from easy-to-collect trade taxes to harder-to-collect consumption and income taxes. Simply implementing a VAT is not sufficient; a high degree of collection efficiency (the ratio of actual to potential revenues) is needed.

To meet this development challenge and obtain the necessary funds to finance relevant projects requires an increase in the tax base for the “difficult to collect taxes” which will be achieved through economic growth, simplification, in terms of fewer rates and less exemptions, an increase size of formal relative to informal sector and through improved 18
# _Summary of Findings_

|**Comparison o**|**fADBandW **<br> <br>|**BProjects:S **<br>ADBProjects<br>|**ummaryof **<br>|**'rcoefficients,**<br>W<br>|**bySector:**<br>orldBankProjects<br>||
|---|---|---|---|---|---|---|
|Sector|Numberof<br>projects|Averager-<br>coefficient|Medianr-<br>coefficient|Numberof<br>projects|Averager-<br>coefficient|Medianr-<br>coefficient|
|Agriculture|7|0.023|0.010|22|0.047|0.019|
|Education|3|0.029|0.011|17|0.074|0.032|
|Energy|7|0.047|0.037|14|0.013|0.002|
|Environment|_5_|0.074|0.056|12|0.017|0.014|
|Health|_5_|0.073|0.020|15|0.030|0.029|
|Telecommunications|4|0.043|0.027|3|0.003|0.000|
|Transportation|6|0.019|0.010|15|0.025|0.009|
|UrbanDevelopment|2|0.016|0016|11|0.017|0.013|
|Water supply/sanitation|5|0.054|0.063|12|0.044|0.021|
|Averageallsectors(unweighted)|44|0.042|0.028|123|0.030|0.014|
|Averageallsectors(weighted)|44|0043|0028|123|0.035|0.017|

For most of the sectors in both the ADB and World Bank calculated R-coefficients, the median was found to be lower than the arithmetic average, indicating that the distributions of R-coefficients were positively skewed. This implies that for most of the sectors, a significant number of R-coefficients were lower than the sectoral arithmetic average. A cursory look at the tables confirmed this finding. Further, this implies that the values of the sectoral arithmetic averages were influenced by a few "outlying" high R- coefficients.

A statistical test of hypothesis was performed to determine whether or not there is a significant difference in the average R-coefficients of ADB- and World Bank-assisted projects. The test showed that there is no significant difference between the two averages based on the data given in the above table.

|t-Test: Two-Sample As|sumingUnequalVaria|nces|
|---|---|---|
||_ADB_|_WorldBank_|
|Mean|0.041984795|0.029955493|
|Variance|0.000477514|0.000479818|
|Observations|9|9|
|HypothesizedMeanDifference|0||
|df|16||
|tStat|1.166354174||
|P(T<=t)one-tail|0.130281398||
|tCriticalone-tail|1.745884219||
|P(T<=t)two-tail|0.260562796||
|tCriticaltwo-tail|2.119904821||
|Conclusion:Thereisno significant di|fferencebetweenthea|verage|
|"r"ratioforADB-andWorld Bank-fi|nancedprojects.||

13
Policy Research Working Paper Series

|Title|Author|Date|Contact<br>for paper|
|---|---|---|---|
|WPS1411 Income Inequality, Welfare, and<br>Poverty: An Illustration Using<br>Ukrainian Data|Nanak Kakwani|January 1995|G. Evans<br>85783|
|WPS1412Foreign Technology Imports and<br>Econcmic Growth in Developing<br>Countries|Xiaoming Zhang<br>Heng-fu Zou|January 1995|C. Jones<br>37754|
|WPS1413 Endogenous Distortions in Product<br>and Labor Markets|Martin Rama<br>Guido Tabellini|January 1995|S. Fallon<br>38009|
|WPS1414The World Bank and Legal Technical<br>Assistance: Initial Lessons|The World Bank<br>Legal Department|January 1995|K. Mathernova<br>82782|
|WPS1415 China's GDP in U.S. Dollars Based<br>on Purchasing Power Parity|Ren Ruoen<br>Chen Kai|January 1995|E. O'RielUy-Campbell<br>33707|
|WPS1416 Informal Regulation of Industrial<br>Pollution in Developing Countries:<br>Evidence from Indonesia|Sheoli Pargal<br>David Wheeler|February 1995|E. Schaper<br>33457|
|WPS1417 Uncertainty and Global Warming: An<br>Option-Pricing Approach to Policy|Andrea Baranzini<br>Marc Chesney<br>Jacques Morisset|February 1995|C. Dell<br>85148|
|WPS1418 The Impact of Labor Market<br>Regulations|Lyn Squire<br>Sethaput Suthiwart-<br>Narueput|February 1995|G.Bayard<br>37460|
|WPS1419 Industry Structure and Regulation|Martin C. Stewart-Smith|February 1995|N. James<br>82758|
|WPS1420 Legislative Frameworks Used to<br>Foster Petroleum Development|William T. Onorato|February 1995|W. Onorato<br>81611|
|WPS1421 Distribution of Income and the Income<br>Tax Burden in Bulgaria|Zeliko Bogetic<br>Fareed M. A. Hassan|February 1995|F. Smith<br>36072|
|WPS1422 Efficiency in Bulgaria's Schools:<br>A Nonparametric Study|Zeljko Bogetic<br>Sajal Chattophadyay|February 1995|F. Smith<br>36072|
|WPS1423 The Role of Commercial Banks in<br>Enterprise Restructuring in Central<br>and Eastem Europe|Millard Long<br>Izabela Rutkowska|February 1995|R. Gamer<br>37670|
An Assessment Of Reform Options For The Public Service Pension Fund In Uganda

**Figure 39: Projected number of active employees (thous.  persons)**

<!-- Start of picture text --> 800.0<br>700.0<br>600.0<br>500.0<br>400.0<br>300.0<br>200.0<br>100.0<br>2003 2006 2009 2012 2015 2018 2021 2024 2027 2030 2033 2036 2039 2042 2045 2048 2051 2054 2057 2060 2063 2066 2069 2072 2075<br>Year<br>government teachers<br>People (000')<br><!-- End of picture text -->

**Source** : WB calculation based on PROST

Changes in age and gender composition of active employees were modeled by defining the age and gender distribution of new recruits. To derive the assumed age and gender profile of new employees, the actual data were smoothed with a five year moving average and recruitment after age 49 was eliminated, as shown in Figure 40 and Figure 41. The share of women among new recruits is lower for civil servants (31%) than for teachers (38%). New civil servants – both men and women – are also on average older than teachers: 33 and 31 years old for men and women, respectively among civil servants and 28 and 26 years old for men and women, respectively, among teachers. With no information on future policy regarding age and gender of newly hired employees, we assumed that in the future the age profile and the gender composition of new recruits remain the same.

A scenario with higher activity rates for women in the medium- to longer term may be considered in further simulations. The impact of the recruitment policy aimed at increasing the share of females is not obvious. On the one hand, women tend to live longer<sup>35</sup> and therefore collect their pensions for more years than men which may increase the overall pension liabilities in a defined benefit system. On the other hand, pension liabilities may increase to a lesser extent or even decrease if women’s wages remain lower than that of men.

> 35 For the population as a whole, female life expectancy in 2003 is 5% higher than men life expectancy.  In 2075, female life expectancy is projected to be 14% higher than men life expectancy see section 0 on population mortality assumptions.

- 85 -
12 health outcomes are caused by increased income and wealth, that is, international remittances raise the health status of rural Mexican households by providing these households with more money to invest in child health.

# 4.  International Remittances, Consumption, Investment and Development

In examining the impact of international remittances on development, the basic question is simple: How are remittance monies spent or used? Do households receiving remittances channel these earnings into human and physical capital investments, or do they merely use these monies to purchase new “status-oriented” consumer goods?

In the past, researchers have often taken a rather pessimistic view of how remittances are spent or used and the impact of these monies on development. For example, a recent review of the literature by Chami, Fullenkamp and Jahjah (2003:10-11) reported three stylized facts: first, that a “significant proportion, and often the majority,” of remittances are spent on consumption; second, that a smaller part of remittance funds goes into saving or investment; and third, the ways in which remittances are typically saved or invested – in housing, land and jewelry – are “not necessarily productive” to the economy as a whole.

Several interrelated factors seem to be responsible for this dim view of the impact of remittances on economic development. On a most basic level, since decisions on how to spend remittances are made by thousands (if not millions) of individual households, it is difficult to establish exactly how these monies are used. Much of the literature in this area thus tends to be anecdotal, rather than empirical. At the same time, household budget surveys, which represent the best possible source of information about how remittances are spent, are often poorly designed. Oftentimes, these household surveys ask “naïve” questions about remittance earnings were spent or used. Since remittances are fungible like any other source of income, simply asking respondents about how remittances were spent is not enough. Remittances that are not being spent directly on investment may well have freed other resources for expenditures on investment. Third, the small handful of empirically-based studies that do exist on remittances and economic development are often based on small, unrepresentative household samples. For instance, the Adams (1998) study of how international remittances are used in rural Pakistan is
<!-- Start of picture text --> development programming, including FEWS NET and the Integrated Food Security Phase Classification<br>risks. These major analyses, however, require significant resources and time to conduct and are typically<br>into price signals and economic deterioration specifically were provided by Seaman and Holt (1980),<br>using data on food prices, agricultural productivity shocks, and conflict. Using the same data, Wang, et<br>a specific focus on parsimony, simplicity, and transparency. The proposed approach is a response to<br>detected before they occur, using only a few critical indicators to capture inflation, conflict, and<br><!-- End of picture text -->
Rather than focus on how parent banks transmit shocks to their affiliates through the lending channel, this paper explores the association between parent banks’ and subsidiaries’ default risks during the recent crisis. More specifically, we examine whether an increase in the default risk of a parent bank is positively correlated with a similar rise in its foreign subsidiaries’ default risk. By focusing on the correlation between the default risk of parents and subsidiaries, we believe that we are answering a more fundamental question regarding the role of foreign banks in transmitting shocks, since default risk is a broader, more forward looking concept that is more intrinsically related to financial stability.

We also examine the factors that dampen or amplify the correlation between parent banks’ and foreign subsidiaries’ default risks. In particular, we examine the role of subsidiary financial characteristics (such as capital and funding structure) and the impact of host country bank regulations (e.g., pertaining to bank capital, reserve requirements, bank activities, etc.). The question of how host regulators can limit the transmission of shocks to the affiliates of foreign banks that operate in their country is related to the recent discussion on ring-fencing (see Song, 2004; Cerutti et al., 2010; Cerutti and Schmieder, 2014; D’Hulster, 2014). In cross-border banking, ring-fencing refers to restrictions (whether regulatory or supervisory) on internal transfers of banks’ capital, liquidity, and profitability across jurisdictions within the same international banking group. To our knowledge, this is the first paper to examine the impact of subsidiary characteristics and host country regulations in limiting the association between the default risk of a parent and its subsidiaries.

We use data for 93 publicly listed foreign bank subsidiaries, operating in 36 host developing countries and owned by 41 parent bank groups, headquartered in 24 home countries, during the period from September 2008 to December 2009. We estimate the weekly correlation 3
- 30 -

indicated by their greater use of water rotation and of specially designated water management personnel.

Table 4 Mean areas irrigated before and after the NIA's construction assistance (in hectares)

|Time period|Participatory<br>systems<br>(n=24)|Non-participatory<br>systems<br>(n=22)|
|---|---|---|
|Wet season|||
|Before the project|88 ha|127 ha|
|Crop year 1984-85|104 ha|149 ha|
|Expansion|16 ha|22 ha|
|Expansion as a percent of|18%|17%|
|area irrigated before the|||
|project|(z=0.|08;p<0.21)|
|Dry season|||
|Before the project|56 ha|105 ha|
|Crop year 1984-85|76 ha|123 ha|
|Expansion|20 ha|19 ha|
|Expansion as a percent of|35%|18%|
|area irrigated before the|||
|project|(z-1|.38;p<0.08)|

Source: de los Reyes and Jopillo in Korten and Siy, Jr (1988:97)

# _The Grameen Bank_

The second case used to illustrate ID potential, the Grameen Bank, demonstrates both the financial and the social benefits that can accompany institutional development investment. Investing in institutional arrangements in credit can produce dramatic financial results when they lead to reductions in arrears. In their article, "Credit for the Rural Poor: A Comparison of the Policy Experiments in Nepal and Bangladesh," Mosley and Dahal (1987) analyze
Figure I 1

# INDUSTRLAL DEMAND OF FUELS Metropolitan Region

<!-- Start of picture text --> .6%<br>REFINED<br>PRODUCTS<br>Year 1998 NATURAL GAS<br>Total: 8.768 (teracal) 43%<br>a 5 % COAL<br>COAL 3%<br>Total: 14 152 (tomacal)<br>NATURAL GAS<br><!-- End of picture text --> SOURCE: NATIONAL ENERGY COMMISION 24
data set product 3B42RT 3 hour product gives the best results in a basket of 8 near real‐time rainfall products. The 3B42RT daily derived product is what is used in this paper.

To construct a flood proxy from the TRMM data, one has to define when a flood event is happening. In Wu et al. (2012) they propose four runoff based methods to define a flood threshold, and in addition Wu, Adler and Tian, et al. (2014) propose a slightly modified flood threshold definition with a point being flooded when: R�P�� �σ and Q�10m<sup>�</sup> /s where R is the routed runoff in millimeters, P�� is the 95th percentile value and σ is the standard deviation of the routed runoff. Q is the discharge in cubic meters.

As the TRMM data do not provide runoff estimates, only rainfall amount, that will be used as a proxy for flooding in a cell. The modified equation is: Ra�P�� �σ where Ra is the rainfall in millimeters, P�� is the 95th percentile value and σ is the standard deviation of the rainfall.

Following the definition, one has a data set with daily flood data. To use this with the nightlight data it has to be aggregated up to monthly values. In this case, it was chosen to construct two variables as flood proxies, one with values for number of days and the other with values for total rainfall above the flood threshold for the month.

Once the flood data set is finished, a data set with the flood and nightlights data are made, ref Figure 4. With this combined data set, we do a case study of the 2017 floods in Southern Thailand and run the fixed effect regression: L�,� � β� � Σ����� β��� FD�,�,��� � θ� � e�, � , which is the same as in section II.c. As before, it gives the coefficients for the effect that excessive rainfall (floods) have on nightlights for the flood month and the 12 consecutive months.

_Results.—_ The case study is the 2017 Southern Thailand Floods, which happened in the southern parts of Thailand during December 2016 and January 2017. These were the largest floods in over 30 years in these parts of Thailand. Malaysia was also affected, although to a lesser extent. There were almost 100 fatalities and the total costs was USD4 billion, making the floods fairly significant. The reason why these floods were chosen is threefold. Firstly, they lasted only 2 months, unlike the ones in 2013 that lasted roughly 4 months. Secondly, they 26
4 of "contemporaneous" associations because we examina average growth rates and average levels of financial development over the same time period. Second, we explore the "channels" through which financial development and growth are linked. specifi,-ally, we find that financial development is positively associated with both the rate of physical capital accumulation and a measure of improvements in economic efficiency.

# A. Data: The financial indicators

We conduct both a purely cross-country analysis using data averaged over the 1960-1989 period and a pooled cross-country, time-series study using data averaged over the 1960s, 70s, and 80s, so that each country has three observations, data permitting. Our data base includes the 119 developed and developing countries studied in Levine and Renelt (19923, but lack of financial data and elimination of major oil exporters typically restricts the analysis to about 80 countries.

We construct four indicators of the level of financial sector development.2 The traditional practice (e.g., Goldsmith [19691 and McKinnon (19731) has been to use the size of the formal financial intermediary sector relative to economic activity to measure financial sector development or "financial depth." Users of financial depth hypothesize that the size of financial intermediaries is positively related to the provision of financial services. one measure of "financial depth" equals the ratio of liquid liabilities of the financial system to GDP, which we term LLY. Liquid liabilities consist of currency held outside the banking system plus demand and interest bearing liabilities of banks and nonbank financial intermediaries.<sup>3</sup> The pure size of the financial system, however, may not be

# 2 King and Levine (1992a] study a broader array of financial indicators.

3 This measure equals "M3" or line 551 from the International Financial Statistics, or when 551 is not available we use line 34 plus line 35, which equals "M2.n The problem of deflating financial stocks (measured at the end of the period) by GDP flow (measured ovor the period) is mitigated by using the arithmetic average of this year's end-of-period and last year's end-ofperiod financial stock values. Thus, LLY in 1965 is the average of liquid