rafmacalaba/datause-encoder-data huggingface dataset:
The systematic one in ten sample of patients from the admissions register covered patients admitted from December 1988 through October 1989. Data were analyzed according to four major service categories: pediatrics, internal medicine, gynecology and surgery. Obstetrical patients were excluded because of incomplete records.

Based on the hospital register, Table A.8 shows that the annual hospitalization rate was 10.5% for salaried workers and family members with employer-paid health coverage; 3.6% for members of the Bwamanda insurance plan; and 0.5% for Bwamanda residents without insurance or employer coverage.

Table A.8 1989 Acdmission Rates by Payment Category

||-----PaymentCategor|y-----||Over-|
|---|---|---|---|---|
|Service|Not<br>Insured<br>Insured E|mployed|TOTAL<br>ZONE|<br>all<br>Total|
|(Population)|47,362 81,142<br>Arnual Adm|6,176 1<br>issions:*|34,680|n.a.**|
|Surgery|166<br>547|71|788|840|
|Gynecology|29<br>132|26|276|<br>283|
|ALL (Exc.Obs.)|<br>231<br>2,640|594|**3,465**|3,330|
||Arv.uAl<br>Admi|ssion Rat|es X):||
|Surgery|0.35X<br>0.67X|1.15X|0.59X||
|Gynecology|0.06X<br>0.16X|0.43X|0.20C||
|ALL (Exc.Obs.)|<br>0.49X<br>3.25X|9.62X|2.57X||
||Risk Ratios for Admi|ssions:|||
|Surgery|1.0<br>1.9|3.3|1.7||
|Gynecology|1.0<br>2.7|7.0|3.4||
|ALL**(Exc.Obs.)**<br>.. ....<br>....<br>..<br>....|<br>1.0<br>6.7<br>......<br>............|19.7<br>.......|5.3<br>.....||

# NOTES:

* Mutbers of surgery and gynecology adtissions were extrapolated **from** complete **utilization** statistics from January through June, 1989 by doublIng the six-month total. Numbers of arusal ecmissions for all services (excluding obstetrics) were extrapolated from a 10X satpte of such admissions from December 1988 through October 1989 by multiplying the total by 12/11. The extrapotations were based on 657 wurgical a*dissions, 230 gynecology wdnissions, nd 329 total non-obstetricat admissions, respectivety.

* Overalt a*Issions lnctude admisslons of patients from outsid, of the Swawand hea'th zone.

The risk ratio for a payment category is the ratio of the admission rate for that category relative to the rate for uninsured persons. The risk ratios across these services show consistent patterns. Insured patients had from 1.9 to 6.7 times the admission rates of uninsured patients. Employed patients had dramatically higher admission rates: 3.3 to 19.7 times those of uninsured patients.

This pattern of higher rates for insured and employed persons, compared to uninsured, is probably the combined effect of moral hazard, some adverse selection, better access, (closer residents were more likely to be insured) lack of adequate controls (which may permit nonmembers to falsely present themselves at the hospital as members) and random variation due to small numbers. Table A9, based on the survey, provides further support of adverse selection and/or moral hazard.

24
A first conclusion is therefore that the results from studies using aggregate data lack robustness, Indeed, as shown above, different techniques (production function, growth regressions, growth accounting) produce very different results, even when looking at similar set of countries. Moreover, similar techniques, when applied to slightly different samples, also fail to produce consistent results. For example, we were unable to reproduce the results from Calderón and Servén (2004) in our sample of 93 developing countries.

Keeping these caveats in mind, what are the potential lessons for East Asian economies? Overall, our results give only limited support to the notion that infrastructure investment has driven growth in East Asia. Our results do not seem to be inconsistent with a story in which infrastructure can constrain growth, when that growth potential is generated exogenously, and that East Asian countries have been relatively successful in addressing infrastructure constraints as they arise. But the weakness of our data and results do not permit any definitive conclusions about the theoretical channels by which infrastructure may have influenced growth in East Asia.

If indeed East Asia is more effective than other regions at responding to infrastructure constraints it would be useful to understand why. Various arguments could be mounted. For example, East Asia has high levels of savings, and the availability of financing may facilitate more rapid responses. East Asian countries have typically relied on powerful planning agencies, such as Japan’s MITI, etc. And to the extent that private investment in infrastructure has played a role in total investment, it is notable that the modalities employed in East Asia have differed from those employed elsewhere: for example, while East Asia focused on attracting investment at the wholesale level and greenfield sites (eg independent power producers), Latin America placed greater emphasis on the concessioning of existing retail systems. Testing such hypotheses is a subject for separate enquiry.

23
_Social Capital, Household Welfare and Poverty in Indonesia_ 70 Table A4: Index of Active Participation in Decision Making in Local Associations, by Region and Household Characteristics and by Type of Association

||Government/<br>National<br>Groups|Religious<br>Groups|Production<br>Groups|Social Groups|
|---|---|---|---|---|
|_Province_|||||
|Jambi|59.3|66.4|69.9|66.6|
|Jawa Tengah|55.7|56.6|58.9|70.8|
|<br>NTT|64.9|74.8|83.0|80.8|
|_Head of Household_|||||
|Male|60.3|68.3|68.5|74.4|
|Female|58.7|63.6|58.6|67.9|
|_Religion_|||||
|Muslim|57.1|62.8|63.7|68.2|
|Catholic|65.6|72.5|81.1|77.1|
|Protestant|63.9|76.8|83.3|86.1|
|_Education_|||||
|None|49.6|62.8|54.8|75.6|
|Primary School- Incomplete|55.3|65.9|64.1|72.2|
|Primary School-Complete|62.2|68.0|70.5|71.8|
|<br>Secondary School-Incomplete|69.4|65.5|71.6|77.1|
|<br>Secondary School-<br>Complete|68.8|81.5|77.4|78.6|
|Vocational|78.2|95.6|90.0|88.9|
|University/Other|61L5|80.0|100.0|83.3|
|_Quintile_|||||
|Poorest|49.9|61.9|59.6|73.8|
|2|60.1|66.5|69.4|71.2|
|3|61.5|71.6|64.9|74.0|
|4|64.3|67.7|74.1|71.9|
|Richest|66.5|73.5|70.1|78.4|
|All|60.2|68.0|67.7|73.8|
|Note: For definition of index and c|ategories, see text.||||
When controls on inflows are in effect, purchasing the underlying stock to sell the DR would require paying an inflow cost to re-enter the funds into the country. As a result, relatively low domestic prices will not be arbitraged away and the underlying stock will only be bought at a discount compared to the DR, as investors need to be compensated for the costs they incur by moving capital into the country. Thus, controls on inflows would introduce a negative cross-market premium. Under the presence of controls on capital outflows, an international investor seeking to buy the DR to sell the underlying stock would need to repatriate the proceeds from this sale and incur a cost. This makes it difficult for investors to profit from relative high domestic prices, introducing a positive cross-market premium. Given that arbitrage takes place mostly within a day (as documented in Levy Yeyati, Schmukler, and Van Horen 2008a), we expect that controls would have an effect right after they are imposed (or lifted), not before, even when anticipated.

# **_3.1. Brief Chronology of Capital Controls_**

Periods of capital controls are relatively easy to detect. Governments impose them through regulation. Moreover, a number of public institutions document them. Below we provide a brief summary of the capital controls in the countries under study, during the periods analyzed in this paper. Six countries in our sample experienced a period in which capital restrictions potentially affected the behavior of stock markets: Argentina, Chile, Indonesia, Korea, South Africa, and Venezuela.<sup>12</sup>

> 12 The sources for these measures are: Bloomberg, Clarin (newspaper, Argentina), IFC Emerging Markets Factbook, IMF Annual Report on Exchange Arrangements and Exchange Restrictions, Korea's Financial Supervisory Service's Regulation on Supervision of Securities Business, and Kaminsky and Schmukler (2008).

10
Table 2. Harmonized welfare indicators

|**Topic**|**Indicator**|**Countries**|**N**|
|---|---|---|---|
||Respondent was currently working at time of survey|77|471,967|
||Respondent stopped/started working at time of survey rel-<br>ative to prepandemic|75|443,902|
||Respondent changed job, sector or employment type relative<br>to prepandemic, if working before the pandemic and at the|Job: 69<br>Sector: 54|184,324<br>115,828|
|**Employment**|time of survey|Type: 43|96,152|
||Respondent gained employment during the pandemic, if not<br>working in a previous HFPS wave (panel respondents)|52|136,478|
||Respondent recovered employment during the pandemic,<br>if stopped working relative to prepandemic in a previous<br>HFPS wave (panel respondents)|52|78,019|
||Household income decreased relative to the prepandemic<br>reference period|54|186,073|
||Household received no or partial payment for wage work in<br>the past week, if the respondent was a wage worker|47|36,421|
|**Income**|Household gained income during the pandemic since the<br>date of the previous HFPS wave|29|102,091|
||Household recovered income during the pandemic – income<br>increased since the date of the previous HFPS wave – if the<br>household reported income loss relative to prepandemic in<br>a previous HFPS wave (panel households)|23|56,360|
|**Food security**|Household adults went a full day without eating in the last<br>30 days|61|309,457|
||Household received any form of public assistance since the<br>start of the pandemic|70|359,736|
|**Assistance**<br>**di**|Household<br>reduced<br>consumption<br>of<br>goods<br>during<br>the<br>pandemic|49|229,090|
|**an copng**<br>**strategies**|Household used money saved for emergencies to cover basic<br>living expenses|52|285,059|
||Household sold assets such as property to pay for basic living<br>expenses|51|283,790|

_Notes_ : The number of observations includes all survey rounds; questionnaires varied across survey rounds and countries.

and individuals across panel survey rounds where possible. This both minimizes the recall period and reduces potential variation in recall bias across survey rounds. We observe that prepandemic employment estimates are stable across surveys, suggesting the constant recall bias assumption is valid in our data (Figure A3.2).

A further cross-check on employment statistics from HFPS can be provided by comparisons with labor force survey (LFS) data, primarily in UMICs and high-income countries (HICs) that continued to collect LFS after initial interruptions due to the pandemic. It 11
to dominate, taking up 81 percent of the discussions. We also ...nd that at least a decision is reached in 56 percent of the meetings, at least a for decision in 51 percent of the meetings, and at least an against decision in 17 percent of the meetings. The time dedicated to decisions is very brief as it only takes a couple of lines to say the decision. Given this briefness, in the following results we will focus only on the occurrence of decisions and not the time dedicated to them.

In Table 3 we present the summaries of gram sabha topic<sup>12</sup> measures overall, by speaker’s position in the hierarchy, by speaker’s gender, and by whether the paragraph contains a decision. From this table we take away that there are no systematic di¤erences between the topics discussed by villagers and o¢ cials, or men and women. The rank-ordering of both the occurrence and intensity measures are nearly identical across the speaker type partitions. We also note that the ordering is nearly identical for the topics where decisions for and against were reached, the only striking di¤erence being the decisions about roads.

Table 4 presents the summary statistics for the individual level variables, including preferences. We ...rst look at whether individuals with di¤erent characteristics have signi...cantly di¤erent preferences. Table 5 presents these ...ndings. We observe that the amount of land owned leads to a large and signi...cant di¤erence in preferences. Large landowners are more likely to have a preference for roads and education, and less likely to have a preference for housing, in contrast with the landless villagers. Preferences also vary signi...cantly across caste groups, but not across gender and age groups. The forward castes are more likely to have a preference for roads, as compared to Scheduled Castes and Scheduled Tribes(SCST). The backward castes (BC/OBC) are more likely to have a preference for water, as compared to the two other groups. Muslims are more likely to have a preference for water and less likely to have a prefer-

> 12 There are topics discussed in the gram sabha that are not expressed as priorities by the households. The priority topics of the households, taken together, take up 53 percent of the meetings.

14
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Policy Research Working Paper 5113

# **Abstract**

Two of the most salient trends surrounding the issue of migration and development over the past two decades are the large rise in remittances, and an increased flow of skilled migration. However, recent literature based on cross-country regressions has claimed that more educated migrants remit less, leading to concerns that further increases in skilled migration will hamper remittance growth. This paper revisits the relationship between education and remitting behavior using microdata from surveys of immigrants in 11 major destination countries.

The data show a mixed pattern between education and the likelihood of remitting, and a strong positive relationship between education and the amount remitted conditional on remitting. Combining these intensive and extensive margins gives an overall positive effect of education on the amount remitted. The microdata then allow investigation as to why the more educated remit more. The analysis finds that the higher income earned by migrants, rather than characteristics of their family situations, explains much of the higher remittances.

This paper—a product of the Finance and Private Sector Team, Development Research Group—is part of a larger effort in the group to study the determinants and consequences of migration and remittances. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dmckenzie@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. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Box** 1: Manpower Forecasting Problems In Tanzania

In the early stages of its independence, ranzania relied on manpower requiraments forecasting to guide **its** investment in post-primary education and training. The World Bank with these forecasts recommended Tanzania curtail expansion of secondary education. The 1980s began with scarcities of skilled personnel in a large number of fields, while government investment in secondary education was reduced to one of the lowest levels in Africa.

A manpower and employment planning project was launched in 1980 to implement a new approach to manpower planning. Its objectives were to develop a labor market information system; engage in coordinated and centralized planning and programming for training; and conduct studies of organizational structure, pay, and staffing policies. Recommendations were made to improve the timeliness and reliability of labor market statistics.

A tracer study of secondary leavers and a study of newspaper job advertisements was set in motion to analyze labor market Nsignals.' The emphasis on signals was an important component of the shift away from the use of manpower requirements forecasting. The studies revealed that the demand for secondary graduates has been grossly underestimated. The new approach to manpower planning led to a reversal of the decision to cut investments in secondary education.

Sources: UNESCO (1981). Manpower Requirements as a Tool of Educational Planning in Tanzania. Paris: UNESCO, and UNESCO (1984). United Republic of Tanzania: Development of TechnicalNocational and Agricultural Education and Training. Paris: UNESCO.

The technique, nevertheless, remains popular in many developing countries. In a recent review of the Asian experience in manpower planning, seven out of ten market economies used manpowet requirements forecasting (Amjad, 1987). The popularity of the technology is even acknowiedged by its critics. Having demonstrated that "making long-term projections of manpower supply and demand on the basis of general economy-wide methods is not likely to be a very fruitful exercise for a developing country, Hollister (S 983:69) concludes that, "unfortunately, in many developing countries this is precisely the activity which has taken up most of the time and resources of manpower p,anning groups." Dougherty (1983:48) concedes that "the iiew that planning consists of the preparation of detailed forecasts of supply and demand may he deeply entrenched even where the approach is 6
Table 7: Union and Non-union Hourly Earnings Functions Northem States

||Sector|Marginal<br>Contribution|
|---|---|---|
|Variable|Union<br>Nonunion|:@"-r)|
|Education levela|||
|Primary|0.224<br>0.214t<br>(0.53)<br>(2.19)|0.004|
|Secondary Incomplete|0.465<br>0.584t<br>(1.09)<br>_(5.54)_|-0.039|
|Secondary Complete|0.703<br>0.901*<br>(1.64)<br>(7.88)|-0.027|
|Tertiary|1.080t<br>1.289t<br>(2.52)<br>(11.34)|0.032|
|Experience|0.053t<br>0.063t<br>(6.62)<br>(12.46)|-0.170|
|Experience squared|-0.0008<br>-0.001t<br>_(5.08)_<br>(10.58)|0.095|
|Male|0.088<br>0.151|-0.042|
||_(1.57)_<br>(3.86)||
|_Municipio_Indigenous Percent|0.017<br>-0.006|0.024|
||(1.23)<br>(0.96)||
|Industrial Sectorab|||
|Industrial Manufacturing|-0.0006<br>-0.196<br>(0.005)<br>(1.11)|0.054|
|Utilities|0.167<br>-0.527<br>(0.873)<br>(1.80)|0.007|
|Construction|0.191<br>-0.343|_0.051_|
||(1.04)<br>(1.89)||
|Commerce|-0.072<br>-0.217|0.024|
||(0.48)<br>(1.22)||
|Transportadon|0.484<br>-0.152|0.023|
||(3.17)<br>(0.75)||
|Fmancial Services|0.245<br>0.117|0.003|
||(1.33)<br>(0.56)||
|Services|0.312 t<br>-0.293|0.225|
||(2.58)<br>(1.67)||
|Constant|6.742<br>6.964|-0.222|
|Adjusted R<sup>2</sup>|0.292<br>0.214||
|N|541<br>1,652||
|Adjusted Wage Gap||-0.021|

# Source: INEGI (1989)

Notes: t significant at the 95% level. * signifcant at the 99% level. 'Residual is no education. "Residual sector is extraction. Numbers is parenthesis are t values. 0 is the estimated coefficient. u and n subscripts represent union and nonunion subsamnples, respectively. I is the fill sample weighted variable mean.

28
such that it is zero for the median income country, which implies that the estimated time effects trace out the level of relative ICT abundance for the median income country.

# 2.3. The Relative Price of ICT & Physical ICT Abundance

While we have so far focused on the market value of ICT relative to NICT, it is also instructive to compare the _physical_ abundance of ICT, that abstracts from cross country differences in the relative price of ICT. This exercise is especially important as a number of studies have shown that the relative price of investment goods varies dramatically across countries and is significantly higher in poor countries (e.g., Hsieh and Klenow, 2007). Since ICT is likely imported to a large degree in many poor countries, it is natural to expect that ICT is more expensive in these countries relative to other, domestically produced capital goods.

Intuitively, we would like to capture the number of “computers” (units ICT capital goods) relative to “buildings and other equipment” (units of non-ICT capital goods) to measure true abundance of ICT. To accomplish this, we use two sources of information on ICT prices: (1) US price data from the BEA as in Eden and Gaggl (2014) to estimate the path of relative prices over time, and (2) item-level price data from the International Comparison Program (ICP) to measure the cross-country distribution of the relative price of ICT.

A wide literature has documented a precipitous decline in the relative price of ICT which is reflective of technological progress that reduces the costs of computations and communication (e.g., Autor and Dorn, 2013, and references therein). Figure 2 illustrates these patterns for the US based on estimates by Eden and Gaggl (2014) using the BEA’s detailed fixed asset accounts. The Figure clearly illustrates the dramatic fall in ICT prices relative to NICT prices over the past 30 years. Our construction of the capital stock series assumes that this trend is shared by all countries in our sample. This assumption is commonly applied in this context, and follows the procedure in the PWT, among others. While plausible, data limitations prevent direct assessment of this hypothesis. In this section, we present some evidence suggesting that there is no strong relationship between the relative price of ICT and income per-capita.

We utilize two waves of item-level price data from the International Comparison Program 12
altruistic component, a repayment-of-loans component, an insurance component, an inheritance component, and exchanges of a variety of services. In the discussions below we select three of these motives - altruism, exchange, and investment - both for their general empirical relevance and for the fact that they are the ones through which education is most likely to affect remittances.

# _2.1 Altruism_

Building on Rapoport and Docquier (2006) and Niimi et al. (2008), we write the migrant’s utility function as: _<mark>U</mark> M_ <mark> 1( </mark> _N_ <mark></mark> _S_ <mark>)</mark> _<mark>V</mark>_ <mark>(</mark> _<mark>C</mark> M_ <mark>) </mark> _N_ _<mark>V</mark>_ <mark>(</mark> _<mark>C</mark> N_ <mark>) </mark> _S_ _<mark>V</mark>_ <mark>(</mark> _<mark>C</mark> S_ <mark>) ,</mark> _V’>0_ and _V’’<0_ , where: _C M_ is the migrant’s consumption level, _C N_ is the consumption of the family members in the host country (North), _C S_ is the consumption level of the family members in the home country (South) <mark></mark> _N_ <mark> </mark> _N_ _<mark>f</mark> N_ and  _S_   _S_ <mark></mark> 1  _f N_ <mark></mark> , with <mark>1  </mark> _N_ <mark> </mark> _S_ to denote that the migrant prefers to have his relatives close to him,<sup>3</sup> and

> <sup>_f_</sup> _N_<sup>is the fraction of the family (of total size normalized to unity) who lives in the North.</sup>

the migrant’s remittance decision may be written as: From the first order conditions we get the optimal levels of transfers to the accompanying family and of remittances: and

* _TS_   _S y M_  _y S_ 1(  _N_  _S_ ) .

> 3 Another interpretation is that the people who live with the migrant are closer relatives -- spouse, children -- than those left behind and therefore receive a higher altruistic weight in the migrant’s utility function.

5