rafmacalaba/datause-encoder-data huggingface dataset:
### **_Keypoints_**

There are just two key points from this section. First, both the 1997 DHS data and the 1998 VLSS data indicate that inequalities in child survival between poor and less poor children now exist in Vietnam. Second, these inequalities appear to be a recent phenomenon-they were not evident in the 1993 VLSS data. Under the recent years of Doi Moi, reductions in child mortality have not been spread evenly, being heavily concentrated among the better-off. Poorer Vietnamese children do not appear to have seen any appreciable improvement in their survival prospects in recent years.

# **4. Modeling child survival in Vietnam**

The estimates discussed in the previous sections suggest, then, that rates of infant and under-five mortality continued to fall during the last decade in Vietnam at the population level, but that these improvements were heavily concentrated among betteroff children. In this section we outline and estimate a model intended to help explain these differential changes in survival prospects.

## **_The basic model_**

We could model child survival using either a production function (an equation linking survival to the proximate determinants of survival, such as usage of health care, dietary and sanitary practices, etc.) or a reduced-form or "demand" equation for child survival (an equation linking survival to the variables that influence households in the choices they make over the proximate determinants of survival) (Schultz 1984). We opt for the demand equation, and thus link survival to household resources and variables affecting the "shadow price" of child survival (Grossman 1972). Variables that lower the shadow price of child survival-such as parents' education, the availability and quality of medical services locally, health insurance coverage, etc.-Ought to increase the household's "demand" for child survival. By contrast, variables that _raise_ the shadow price of survival-such as poor local sanitary conditions and bad environment-would be expected to be _negatively_ associated with survival in the survival demand function. Increases in mean survival time could be due either to movements along the demand equation (e.g., rising incomes allowing households to feed their children better) or shifts in the demand equation (e.g., households being able to buy more or better quality food from a given amount of income).

We use the complete fertility history data over the first ten years of life to estimate the determinants of child survival. It makes sense in such a context to use a duration model. Like Lavy and others (1996), we use a Weibull model with covariates (Greene 1997). Let _S(t)_ be the survival function at time t, and A(t) be the hazard rate at 11
live in an officially designated poor or remote commune. With the exception of educational level of the spouse, land allocated and livestock owned in rural areas, Table 7 shows that household level targeting variables are generally much better at identifying poor individuals than whether or not they live in a poor and remote commune. The four categories of provincial poverty headcounts identified in our national poverty map also do quite well according to this criterion. Nonetheless, as shown by this and the ranking of poor communes according to their mean expenditures, there is considerable potential for improving the targeting of Vietnam's poor and remote communes programs.

Table 7 also shows that the most effective poverty targeting variables are ones related to housing quality and ownership of durable assets. Floor type is generally a better predictor of both food poverty and overall poverty than roof or toilet type.<sup>18</sup> The level of education completed by household heads and their spouses performs considerably better as a targeting indicator in urban than in rural areas. Demographics, as proxied by the number of children under 15 years of age (the age by which Vietnam children should have completed lower secondary school) are a better indicator of food poverty than overall poverty in both rural and urban areas. Ethnicity of the household head is a reasonable predictor of both food and overall poverty in rural areas, but performs poorly in urban areas where few ethnic minority households live.

An unexpected result is that a simple index of radio and television ownership is a better targeting indicator than all other asset, demographic or educational variables. Indeed, inspection of Table 7 will confirm that the radio and television ownership index dominates all other targeting variables with the exception of communes ranked by the level of their median per capita expenditures. Using a cut-off point corresponding to ownership of neither a radio nor a television, the index is able to correctly classify some 76 percent of poor people in the VLSS sample.<sup>19</sup>

**18** Ownership of the dwelling in which a household lives was considered for inclusion in the list of asset based targeting variables, but found to perform poorly because the vast majority of households in the VLSS98 sample (5703 out of 5999) own their own dwellings.

19 It may seem surprising that in a country with Vietnam's level of per capita income, radio and television ownership has such potential for targeting the poor. Radio and television ownership is however, quite widespread throughout Vietnam with 53 percent of households owning a television and 45 percent of households owning a radio according to the 1999 Population and Housing Census. 1<sup>9</sup> Many of the televisions owned, especially in rural areas, are relatively inexpensive 14 inch, battery operated televisions produced in China. Of course, the use of an index of television and radio ownership for targeting would be problematic, as it would be relatively easy for households to 33
widened the distance of most Indian industries from the frontier. Progress in catch up was mixed. Relative to China, in the Textiles and Food industries, India’s distance to the frontier has barely shortened. In Electrical machinery, Iron and steel and Transport equipment, India is neck and neck with China, and in Non-electrical machinery and Industrial chemicals, Indian industries are closer to the Korean frontier than China.

<!-- Start of picture text --> Figure 15: India – Trade Liberalization<br>India - Trade liberalization (weighted tariffs %)<br>100<br>Wearing apparel<br>75<br>Food product s<br>Non-elect rical machinery<br>50<br>Transport equipment Iron and st eel<br>25<br>Pet roleum ref ineries<br>0<br>1990 1992 1994 1996 1998 2000 2002 2004 2006<br>Food products Textiles Wearing apparel<br>Industrial chemicals Petroleum refineries Iron and steel<br>Non-electrical machinery Electrical machinery Transport equipment<br>Source: TRAINS database<br>Weighted tariff rate (%)<br><!-- End of picture text --> Two characteristics of India’s catch-up strategy are as disappointing as Brazil’s. One, the structure of its industry is virtually unchanged over 25 years and the transformation of resourcebased low tech exports into dynamic medium and high tech exports has been slow (figures 7 and 14). As an example, in 2007, the share of high tech exports in India’s total exports was only 5 percent compared to 35 percent each in Korea and China. Since 2005-06, the emergence of sophisticated Electrical and Non-Electrical machinery exports seems to signal some progress. Two, export-driven growth has been weak. In 2000-04, the proportion of output exported by the top two industries in India was only 12-13 percent relative to 64 percent in Korea and 33 percent in China (table 1).

Trade liberalization was very gradual. In the 1990s, when the government opened up the economy to foreign competition, India was a decade too late in the catch up game. From prohibitive rates of 75-100 percent in 1990, tariffs were cut slowly to 20-40 percent by 1996-97, and were between 10-20 percent in 2006, the highest among the BICs (figure 15). Liberalization also entailed other reforms. Between 1987and 1995, the share of products subject to quantitative restrictions fell from 87 to 45 percent, import licensing was eliminated and export controls were relaxed. Only 33 percent of the regulated industries were exempt from licensing in 1985; the remainder was de-licensed in 1991. Delayed and limited foreign competition was not costless. It eroded Indian competitiveness especially in high tech industries at a time when China’s competitiveness in them increased.

The Indian government implemented a blend of distance shortening and industrial policies during the closed economy era of the license raj. However, the absence of competition, especially foreign, and lack of export-orientation led India’s industrial policies to have many 27
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32 approximated by the average redemption price less central bank fees; it corresponds to the 1988 average price of DES for each country. The parameter a is approximated by the difference between one and the percentage of years over which profit remittances were prohibited (under the last or current DES program) in the last ten years. For all countries we assume constant returns to scale, i.e., rk' equal to zero, and a real rate of return on capital of 6.8 percent. The 6.8 percent rate was obtained subtracting the US rate of inflation from the nominal average rate of return of US foreign direct investments for all Latin America in the year 1985 (see footnote 4 of Table 3). The evidence reported in Table 3 indicates that DES will raise secondary market prices in the cases of Brazil and Chile, and reduce them in the cases of Mexico and Argentina. Note, however, that the price impacts are very small and are sensitive to the assumptions made.

Table 3. Steady State Impact of DES on Sovereign Debt Prices l/.

||Est|imated|Parameter|Values(Z)||Price Impact|
|---|---|---|---|---|---|---|
||t /f<br>**t*/f**|<br>v<br>2/<br>&2/|<br>a|s/f 3/|<br>r 4/<br>-k|S<br>A|
|Argentina|3.2|57|75|20|6.8|- 0.015|
|Brazil|8.2|68|100|24|6.8|0.025|
|Chile|5.6|86|75|11|6.8|0.006|
|Mexico|6.2|87|.00|16|6.8|-<br>0.012|

Note: 1/ Based on inequality (27)

- 2/ Estimates were obtained from Larrain (1989), Sanguines (1989), and Rodriguez (1989).

- 3/ The sources for the stock of foreign direct investment are "IRM Directory of Statistics of International Investment and Production", Macmillan 1987; and 'Foreign Direct Investment in Selected Developing Countries in the Last Two Decades", Gyorgy Becsky, World Bank, draft
observed differences in the qualitative analysis. Particular attention is given to sex, marital status, and children variables, in order to further examine their role in constraining labor market choice, as suggested by the qualitative research.

The next section defines home-based work and lays out the challenges to analyzing the sector. Section III presents the findings from the small sample, ethnographic data. Section IV discusses the data, research methodology, and the mean characteristics of the samples. Section V uses the large samples from household surveys to empirically test the hypotheses. Section VI summarizes the findings and discusses public policies to increase the efficiency of the sector while protecting workers’ rights.

# **II. Defining home-based work**

Home-based work is a difficult empirical concept due to an absence of a single definition of the sector and a lack of data to aid in the development of one (Prugl 1997). The most general definition of a home-based worker is an individual who works in his or her home (WIEGO 2000, Arriagada 1998), encompassing the self-employed;<sup>4</sup> piece workers; salaried employees who work for a “middle-man” or a firm; or unpaid workers in a family enterprise. Refinements have led to a wide-range of concepts to identify a home-based worker, for example:

- _Degree of Subordination_ :  a worker whose “client” has control over the

worker and the production process (Prugl 1997, ILO 1995);

> reporting of violation of the laws and enforcement is judged to be low.

> 4 The self-employed may be defined as those who own their own firm but do not have any paid employees. The only employees would be family members who do not earn a wage.

4
# 1. Introduction

Jawaharlal Nehru once remarked, “I have long been convinced that a nation’s progress is intimately connected with the status of its women” (Parthasarathi, 1985). In the wake of successive waves of economic liberalization, the ‘condition’ of India—when thought of in terms of economic and human development—has improved dramatically. Yet, while the status of women has arguably improved in both the public and private spheres, their ability to access opportunities in this newly liberalized economy remains precarious. India’s female labor force participation (FLFP) rate has remained visibly low; the ILO (2013) ranks India’s FLFP rate as 121 out of 131 countries, one of the lowest in the world. In 2013, India had the lowest FLFP rate in South Asia, with the exception of Pakistan. Globally, only parts of the Arab world held a lower FLFP rates than India in the same year (Table 1).

**<u>Table 1: Estimated Female Labor Force Participation Rates for All Ages (%)</u>**

||1990|1995|2000|2005|2010|2013|
|---|---|---|---|---|---|---|
|India|34.8|35.4|33.9|36.9|28.6|27.0|
|Bangladesh|61.7|58.3|54.2|55.5|56.9|57.4|
|Sri Lanka|36.4|36.3|37.3|34.4|34.8|35.1|
|Nepal|79.8|80.0|81.7|80.5|79.9|79.9|
|Pakistan|13.4|12.5|16.0|19.3|23.9|24.6|
|China|72.7|72.2|70.7|66.5|63.5|63.9|
|Arab World|20.8|20.5|20.9|21.4|22.7|23.3|
|European Union|45.7|46.3|47.8|48.8|50.2|50.8|
|United States|56.4|57.8|59.0|58.3|57.6|56.3|

Source: ILOSTAT 2015 Moreover, the FLFP rate dropped from 49.0 percent to 37.8 percent in rural areas between 200405 and 2009-10 (NSSO, 2011), despite an impressive annual GDP growth rate of around 8.6 percent, and an annual population growth rate of 1.74 percent. The same pattern continued into the most recent round of the National Sample Survey (NSSO) in 2011-12. Among the Asian 2
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**Figure 4. The Earnings Distribution of Willingness to Pay for the Full Social Security Package**

<!-- Start of picture text --> Willigness to pay for full package of social security<br>(decile distribution)<br>180,000<br>160,000<br>140,000<br>120,000<br>100,000<br>80,000<br>60,000<br>40,000<br>1 2 3 4 5 6 7 8 9 10<br>Decile<br>Formal salaried Formal self-employed Informal Poor Informal Nonpoor<br>Willingness to Pay (COP$)<br><!-- End of picture text --> <!-- Start of picture text --> Source: ELS (2008)<br>Note: US$1 = COP$ 2,250<br><!-- End of picture text --> Differences in willingness to pay for the complete package of legal benefits in each category vary in a range between COP$ 51,000 (US$ 23) for the median poor informal worker to COP$ 129,000 (US$ 58) for the formal self-employed worker, with narrower differences between formal salaried and informal nonpoor workers (whose reported valuation ranges between COP $ 80,000 – 90,000; US$ 36–39). Somewhat surprisingly, informal nonpoor workers are those who reportedly are more willing to pay for the package in relative terms, 17.7% of their median labor incomes. Both informal poor and salaried workers would be willing to pay 14.7% of their median labor incomes, while formal self-employed report a willingness to pay for the whole package of 13% of their labor incomes. These results substantiate two important messages: first, there are wide differences in the willingness to pay for the full package of social security across types of workers; second, all types of workers are willing to pay less than 20% of their labor incomes to received the full package, even though, workers –regardless of their occupation and circumstances– typically report to regard highly social security programs (see Appendix 2 for reported answers on how workers “value” or appreciate social programs).

Valuation parameters _β’s_ can be retrieved for each type of worker on the basis of the willingness to pay for the complete legal package of benefits (reported in Table 4) and the statutory amounts for social benefits (reported in Table 3).<sup>7</sup> As a result, the calculated parameters are as follows: βf = 0.48; βc = 0.42; βp = 0.48 and βn = 0.57. In all of these cases, the factor of valuation is below par, which means that workers value each dollar transferred to them by the social protection system well below the cost of its provision. Interestingly, formal self-employed workers value the most the full

> 7 The reported willingness to pay, WTP, for the full legal package, (P+S+V+E+F-Cf), can be expressed as WTPj = βj (P+S+V+E+F-Cf), so we retrieve βj  for each j-type of worker as: βj= WTPj / ((P+S+V+E+F-Cf)

15
Appendix Table A6: Aggregate Measures by Follow-up Round

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||Knowledge of<br>Remittance<br>Costs<br>Round 1|Knowledge of<br>Remittance<br>Costs<br>Round 2|Knowledge of<br>Remittance<br>Costs<br>Round 3|Budgeting and<br>Financial<br>Planning<br>Round 1|Budgeting and<br>Financial<br>Planning<br>Round 2<br>|Budgeting and<br>Financial<br>Planning<br>Round 3|Savings<br>Outcomes<br>Round 1|Savings<br>Outcomes<br>Round 2|Savings<br>Outcomes<br>Round 3|
|Migrant-Only Training (A)|-0.008|0.036|0.094**|-0.011|0.001|0.008|-0.027|0.022|0.155|
||(0.035)|(0.038)|(0.042)|(0.038)|(0.036)|(0.033)|(0.093)|(0.103)|(0.096)|
|Family-Only Training (B)|0.041|0.042|0.055|0.028|0.017|0.043|-0.022|0.116|0.022|
||(0.035)|(0.038)|(0.042)|(0.039)|(0.038)|(0.033)|(0.093)|(0.104)|(0.101)|
|Migrant and Family Training (C)|0.047|0.106***|0.086**|0.046|0.064*|0.065*|0.144|0.259**|0.158|
||(0.037)|(0.038)|(0.042)|(0.041)|(0.038)|(0.034)|(0.091)|(0.106)|(0.098)|
|R-squared|0.009|0.023|0.039|0.007|0.010|0.012|0.012|0.020|0.013|
|Sample Size|392|376|365|392|376|365|392|376|365|
|Number of Clusters|392|376|365|392|376|365|392|376|365|
|Mean of Dependent Variable in Control Group|0.382|0.455|0.508|0.433|0.350|0.398|-0.010|-0.016|-0.020|
|Test: A-B = 0**(p**-value**)**|0.171|0.877|0.332|0.294|0.662|0.326|0.957|0.380|0.189|
|Test: A-C = 0 (p-value)|0.139|0.058|0.841|0.146|0.099|0.123|0.057|0.030|0.976|
|Test: B-C = 0 (p-value)|0.858|0.085|0.440|0.663|0.239|0.545|0.065|0.189|0.188|
|Test: A = B = C = 0(p-value)|0.315|0.044|0.119|0.451|0.315|0.210|0.174|0.070|0.224|

This table presents regression analysis for aggregate measures of Knowledge of Remittance Costs, Budgeting and Financial Planning, and Savings Outcomes, by follow-up survey round. Standard errors in parentheses. The bottom half of the table presents p-values for equality of coefficient tests. Statistically significant coefficients are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).
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