rafmacalaba/datause-encoder-data huggingface dataset:
16

# _Pulling the UHC index together_

Figure 1 summarizes the thinking underlying our UHC ‘mashup’ index. UHC has two dimensions, service coverage and financial protection. Within each dimension, we have two domains: prevention and treatment in the case of service coverage, and impoverishment and catastrophic spending in the case of financial protection. Each domain is captured empirically by an index. These indices are a weighted geometric average of a set of indicators; with the exception of the impoverishment indicator, these indicators are not simply the population mean but are instead the ‘achievement index’ which reflects not only the mean but also the degree of inequality in the indicator between the poor and better off. The two dimension indices are then weighted geometric averages of the relevant domain indices, and the UHC index is in turn a geometric average of the two domain indices.<sup>14</sup>

# **IV. Operationalizing the UHC index**

We turn now to the question of operationalizing the UHC index. If we want to be able to use the index to see how a country’s performance vis-à-vis UHC changes over time or how it compares with other countries’ UHC performances, we need to operationalize the index in such a way that it is applicable to multiple time periods and/or multiple countries. This affects our choices vis-à-vis weights, the catastrophic payment threshold, and the poverty line. But it also affects our choice of service coverage indicators, which need to be relevant and available across a

> 14 _SC_ and _FP_ could be weighted unequally, but in the absence of any indication that countries care more about service coverage than financial protection, or vice versa, we have chosen to weight them equally.

16
rather muted. Calling for more research, she tentatively concluded that this was because of other distortions in factor markets.

More recent exercises have had more liberalizations to consider and better data, and although they show mixed results the general tendency is still towards small effects. For example, Rama (1994), applying a model of monopolistic competition to a panel of 39 sectors in Uruguay over 1979-86 found a significant positive relationship between protection and employment in manufacturing, but no significant effects on real wages. Reducing the protection rate within a sector by 1 percent led to an employment reduction of between 0.4 and 0.5 percent within the same year. Harrison and Hanson (1999) suggest that an implication is that during the years concerned the labor market in Uruguay was fairly competitive, with significant employment reallocation between sectors after the reforms.

Revenga (1997), using plant-level data for Mexico, found a no reduction in overall firmlevel employment following reductions in tariff levels, but that reductions in quotas had a significant but relatively small impact: a reduction in quota coverage from 90 percent to 10 percent of output was associated with a 4-6 percent reduction in output and, via that, a 2-3 percent decline in employment. Tariff reductions appeared to affect wages, however, because Revenga concludes, tariff liberalization eroded rents and thus had no effect on employment and output decisions. Similarly small employment effects elsewhere in Latin America are reported by, for example, Marquez and Pages-Serra (1998) for Latin America and the Caribbean in general, Levinsohn (1999) for Chile and Moreira and Najberg (2000) for Brazil.

Milner and Wright (1998) explore industry level data on Mauritius and find a slightly more encouraging response to liberalization. After an initially adverse wage effect they find fairly strong long-run growth in wages and employment in the exportables sector (mainly of female labor producing clothes). But they also find, surprisingly, growth in the import-competing sector, which they attribute to Mauritius’ overall strong economic performance. In fact, Mauritius opened up via export promotion rather than import liberalization and, according to Subramanian (2001), owes its success to its institutions rather than its trade policy. Thus it is doubtful that its case is typical.

15
the nepotism hypothesis, referred workers experienced a shorter job search spell, have lower abilities and productivity.<sup>13</sup> We consider that implications on turnover and wages are theoretically unclear and depend on contexts. Column (5) in table 1 summarizes the predictions.

# **3 Data**

We use Bangladeshi matched employer–employee survey data collected in 2012 by the World Bank. The data are nationally representative of formal-sector employers and employees in manufacturing, commerce, finance, education, and public administration.<sup>14</sup> The survey randomly sampled establishments, and then sampled employees within the establishments. The sampling of establishments was stratified by industries and size.<sup>15</sup> Interviews were administered separately for employers and employees. Note that while the data represent the formal sector employees, they do not represent the whole labor force since the data do not include informal-sector employees or the unemployed labor force.

We restrict the sample as follows. We include only three industries: manufacturing, commerce, and finance, because the other industries: education and public administration, consist mostly of government or government-aided organizations for public services.<sup>16</sup> The three industries account for 48% and 71% of the establishments and employees, respectively, in the Bangladesh formal sector (Nomura et al., 2013). We exclude seasonal and day laborers and part-time and contract workers, and focus only on full-time permanent employees,<sup>17</sup> who constitute about 90% of the original sample. We also restrict the sample to males, who account for nearly 90% of the original sample. Lastly, we restrict the sample to those aged 40 and below, who constitute about 90% of the original sample. The reason for excluding older employees is that information at the timing of recruitment, such as initial salary, is crucial for our analysis and may be less accurate for older employees since they tend to have been recruited a longer time ago. After the above restrictions are applied, our sample has 339 establishments and 3,396 tions of nepotism and does not have predictions relevant to our study.

> 13In a Bangladeshi context, Mahmood and Absar (2015) suggests that there exists nepotism favoring relatives and friends in a labor market.

> 14See Nomura et al. (2013) for details about the data.

> 15The size is defined by the number of employees and consists of small (10–20), medium (21–70) and large (71+).

> 16Most schools in Bangladesh are government-aided privately-managed schools. The recruitment of full-time teachers is regulated by the government.

> 17Permanent workers are those who have an indefinite period of employment.

8
|Grain Storage Project(02)|William L. Cuddihy|
|---|---|
|Grain Storage Project(02)|William L. Cuddihy|
|Grain Storage Project(02)|William L. Cuddihy|
|Grain Storage Project(02)|William L. Cuddihy|
|Grain Storage Project(02)|William L. Cuddihy|
|Grain Storage Project(02)|William L. Cuddihy|
|Seed Development Project(02)|Maxwell L. Brown|
|Seed Development Project(02)|Maxwell L. Brown|
|Seed Development Project(02)|Maxwell L. Brown|
|Seed Development Project(02)|Maxwell L. Brown|
|Seed Development Project(02)|Maxwell L. Brown|
|Seed Development Project(02)|Maxwell L. Brown|
|Seed Development Project(02)|Maxwell L. Brown|
|Seed Development Project(02)|Maxwell L. Brown|
|Seed Development Project(02)|Maxwell L. Brown|
|Timber Transport and Distribution Project||
|Timber Transport and Distribution Project||
|Timber Transport and Distribution Project||
|Timber Transport and Distribution Project||
|Timber Transport and Distribution Project||
|Timber Transport and Distribution Project||
|Timber Transport and Distribution Project||
|Irrigation Ye-U Rehabilitaion and Modernization Project||
|Irrigation Ye-U Rehabilitaion and Modernization Project||
|Irrigation Ye-U Rehabilitaion and Modernization Project||
|Irrigation Ye-U Rehabilitaion and Modernization Project||
|Irrigation Ye-U Rehabilitaion and Modernization Project||
|Irrigation Ye-U Rehabilitaion and Modernization Project||
|Irrigation Ye-U Rehabilitaion and Modernization Project||
|Irrigation Ye-U Rehabilitaion and Modernization Project||
|Gas Development and Utilization Project|Rebecca C. Sekse|
|Gas Development and Utilization Project|Rebecca C. Sekse|
|Gas Development and Utilization Project|Rebecca C. Sekse|
|Gas Development and Utilization Project|Rebecca C. Sekse|
|Gas Development and Utilization Project|Rebecca C. Sekse|
|Gas Development and Utilization Project|Rebecca C. Sekse|
|Gas Development and Utilization Project|Rebecca C. Sekse|
|Nature Reserves Management Project|Susan S. Shen|
|Nature Reserves Management Project|Susan S. Shen|
|Nature Reserves Management Project|Susan S. Shen|
|SICHUAN GAS DEV. CON|Shohreh Beiglari-Mahan|
|SICHUAN GAS DEV. CON|Shohreh Beiglari-Mahan|
|SICHUAN GAS DEV. CON|Shohreh Beiglari-Mahan|
|SICHUAN GAS DEV. CON|Shohreh Beiglari-Mahan|
|SICHUAN GAS DEV. CON|Shohreh Beiglari-Mahan|
|Montreal Protocol Ozone DepletingSubstances Phase Out Project|LilyUyHale|
|Montreal Protocol Ozone DepletingSubstances Phase Out Project|(0LilyUyHale|
# **<u>Table A.18. The district fixed-effect RDD-DD regressions of individual-level outcomes using a poverty rate bandwidth of 10%</u>**

|||||Dependen<br>|t variables<br>||||
|---|---|---|---|---|---|---|---|---|
|Explanatory variables|Self-<br>employed<br>farm work<br>(yes=1,<br>no=0)|Self-<br>employed<br>non-farm<br>work<br>(yes=1,<br>no=0)|Having<br>wage job<br>(yes=1,<br>no=0)|Log of<br>monthly<br>wage (all<br>workers)|The annual<br>number of<br>healthcare<br>visits|The annual<br>number of<br>outpatient<br>healthcare<br>visits|Currently<br>attending<br>school<br>(children<br>aged 6-17)|Log of<br>education<br>subsidy for<br>students|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Program * Post-program period|-0.1299**|0.0480***|0.0419|0.5341*|0.2424*|0.2608**|0.0122|1.1550**|
||(0.0522)|(0.0142)|(0.0493)|(0.2805)|(0.1397)|(0.1300)|(0.0400)|(0.4450)|
|(Poverty rate – 50) * Post-program|0.0113***|-0.0040***|-0.0062**|-0.0304*|-0.0250*|-0.0248**|0.0020|-0.0119|
|period|(0.0033)|(0.0014)|(0.0024)|(0.0158)|(0.0128)|(0.0121)|(0.0025)|(0.0282)|
|Program * (Poverty rate – 50) * Post-|-0.0067|0.0029*|0.0050|-0.0040|0.0232*|0.0225*|-0.0047|0.0153|
|program period|(0.0041)|(0.0015)|(0.0034)|(0.0207)|(0.0134)|(0.0127)|(0.0029)|(0.0333)|
|Age|0.0262***|0.0043***|0.0099***|0.1107***|-0.0056*|-0.0050*|0.1609***|0.3477***|
||(0.0012)|(0.0004)|(0.0005)|(0.0054)|(0.0030)|(0.0030)|(0.0076)|(0.0393)|
|Age squared|-0.0003***|-0.0000***|-0.0001***|-0.0015***|0.0003***|0.0002***|-0.0084***|-0.0177***|
||(0.0000)|(0.0000)|(0.0000)|(0.0001)|(0.0001)|(0.0001)|(0.0003)|(0.0018)|
|Male|-0.0728***|-0.0072***|0.0914***|1.1042***|-0.1599***|-0.1471***|0.0328***|0.0889***|
||(0.0064)|(0.0027)|(0.0047)|(0.0553)|(0.0211)|(0.0200)|(0.0081)|(0.0262)|
|Ethnic minorities (yes=1, Kinh=0)|0.1575***|-0.0863***|-0.0237**|-0.1006|-0.2153***|-0.2013***|-0.1091***|0.7050***|
||(0.0191)|(0.0088)|(0.0114)|(0.1125)|(0.0675)|(0.0657)|(0.0184)|(0.0889)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.2074***|0.0229***|-0.0880***|-0.6223***|0.6915***|0.6096***|0.2490***|-0.9925***|
||(0.0268)|(0.0077)|(0.0132)|(0.1374)|(0.0545)|(0.0527)|(0.0417)|(0.2224)|
|Observations|174,774|174,774|174,774|174,774|117,124|117,124|65,879|65,879|
|R-squared|0.187|0.063|0.172|0.155|0.099|0.093|0.206|0.337|

Note: This table reports district fixed-effect RDD-DD regressions of individual-level outcomes. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

68
- 25 -

# Tbkle 7 S1Qected_banking system indicators (1983-87)

||1983|1984|1985|1986|1987|
|---|---|---|---|---|---|
|Lending interest rates (%)a<br>|15.9<br>|11.3<br>|11.0<br>|<br>7.6<br> <br>|7.7<br>|
|Spreads<br>(%)b<br>|11.2|8.6|6.6|<br>6.0|5.9|
|Return<br>on equity<br>**_(%)C_**<br>Foreign liabilities/total<br>|2.8|6.9|<br>7.4|<br>7.2|13.7|
|liabilities(%)<br>|25.6|23.4|21.2|19.9|17.0|
|Total liabilities/capital||||||
|& Reserves|16.9|23.4|23.7|20.8|17.5|
|Growth in loans &||||||
|investments<br>(%)d<br>|13.1<br>|19.1|25.8|-1.8|-3.7|
|Past due loans/total loans(%)<br>Risky loans sold to Central<br>|<br>8.4<br>|8.9<br>|3.5<br>|<br>3.8<br>|2.7<br>|
|Bank/total loans<br>|10.0|10.7|26.5|31.6|30.6|
|Risky loans sold+past due||||||
|loans/capital & reserves (%)|158.1|186.3|231.2|240.6|215.6|

Source: Central Bank Bulletin and Superintendency of Banks (Informacion Financiera).

- a. Real interest rate charged for short-term loans (compounded monthly).

- b. Gross spread: Difference between short-term lending and deposit rates.

- c. Net profit figures for 1984-87 include the income used to repurchase the risky portfolio sold to the Central Bank.

- d. In real terms, adjusted using CPI.
tertiary education, and from 0.55 in 1950 to 1.08 in 2010 for secondary education. The country with the largest increase in the share of females in tertiary education has been Saudi Arabia, from 0.16 to 2.42 over the period 1950-2010. In contrast with tertiary and secondary education, we observe significantly less variation over time for primary completion, with an increase of the mean female-to-male ratio from 0.82 to 1.03 on average in our baseline estimation sample.

We estimate the model parameters for two alternative levels of educational attainment _s_ ̄: enrollment in tertiary education and secondary school completion. In our model, _P_ ( _·_ ) and _R_ ( _·_ ), the theoretical equivalents of _xit_ and _yit_ , are jointly determined by _Gzf_ and _Gzm_ , and by the scholastic performance threshold _z_ ̄. Unlike _z_ ̄ _t_ , _xt_ is observable, and the two distributions are assumed to be fully characterized by the four-parameter vector _{μm, σm_<sup>2</sup><sup>_, μf,σ_</sup> _f_<sup>2</sup><sup>_}_.Fortertiaryeducation,</sup> estimates are obtained by fitting the total enrollment rate and the female-to-male ratio among individuals that enrolled in tertiary education. For secondary school completion, estimates are derived by fitting the secondary school completion rate and the female-to-male ratio among individuals that completed secondary education. The baseline estimation is conducted over the entire period of 1950-2010, with observations available every five years from the Barro-Lee database.

We do not estimate our model for primary school completion for both conceptual and methodological reasons. As highlighted in Section 2, primary school completion has been universal for many countries over the past decades, where primary schooling has been mandatory. This implies that primary school completion in many countries is the result of institutional features instead of optimal decisions of investment in schooling. This is incompatible with our conceptual framework which assumes that educational attainment is determined by the optimization of individual intertemporal utility functions. In addition, these features generate limited variation in completion rates and gender ratios over time and across countries to estimate our model parameters. As a result, the model estimation using primary completion rates is unlikely to yield reliable and precise estimates.

20
FHS term would be negative: more egalitarian attitudes and perceived norms would be associated with a lower female household share of labor. Table 1 shows the result of these regressions, presenting only the coefficient for the FHS term. Full results are available in the Appendix.

Table 1: Female Household Share, Attitudes, and Perceived Norms

||Attitudes|Pooled PN|PN Men|PN Women|PN Neighbors|
|---|---|---|---|---|---|
|Female Household Share|_−_0_._009|_−_0_._128*|_−_0_._212**|_−_0_._052|_−_0_._114|
||(0_._075)|(0_._050)|(0_._066)|(0_._073)|(0_._082)|
|Num.Obs.|888|888|888|888|888|
|Covariates Included|Yes|Yes|Yes|Yes|Yes|

+ p _<_ 0.1, * p _<_ 0.05, ** p _<_ 0.01, *** p _<_ 0.001 _Note:_ The table shows the coefficient estimated for the variable indicating the share of household tasks undertaken primarily by women (female household share) from weighted OLS regressions using models taking as the left-hand side variable, respectively, the personal attitudes score (1), the pooled perceived norms score (2), the perceived norms of men score (3), the perceived norms of women score (4), and the perceived norms of neighbors (5). Covariates are included in all models; full results are in the Appendix.

Contrary to expectations, I find that female household share is not significantly correlated with personal attitudes. That is, respondents who report living in homes where women are responsible for a higher share of household tasks do not report more conservative personal attitudes, on average, than those living in households where women are responsible for a lower share of tasks. However, FHS is negatively correlated with the pooled perceived norms score ( _p <_ 0 _._ 05). Respondents in homes where women are responsible for a higher share of household tasks perceive other Moroccans’ attitudes as more conservative, compared to respondents where women are responsible for a lower share of tasks. This relationship is driven by respondent perceptions of men’s attitudes, which are negatively correlated with FHS ( _p <_ 0 _._ 01). FHS is not associated with respondents’ perceptions of women’s attitudes or their neighbors’ attitudes. The same relationship holds when the future perceived norms scores are used to predict current FHS (see Appendix 17
When tariffs are close to zero, the trade elasticity is close to 15, and it rises to about 18.5 at the highest tariff levels.

Our model’s long-run trade elasticity of 15–18 is larger than those recovered from our empirical approaches. As reported in Table 2, the elasticity estimated in the pooled cross section of U.S. imports from China is 6–7. This estimate, however, is obtained from the entire historical sample of U.S.-China trade, which includes both short-run and long-run responses to trade reforms, and therefore understates the true long-run elasticity. Using panel methods, we find a slightly higher trade elasticity of 8 to 8.5 using the error-correction model and an elasticity of about 10 when using the NNTR gap in the 1970s. Our model, which is calibrated to reproduce these estimates, indicates that even these dynamic methods understate the true long-run response of trade to permanent, deterministic changes in trade policy.

# **5 Conclusions**

We study, empirically and theoretically, China’s integration into the U.S. market after the long-standing embargo on Chinese goods was lifted in 1971. We find the dynamics of integration are consistent with substantial uncertainty about the future path of tariffs in the late 1970s and early 1980s. Compared with the earlier period, our results imply much less policy uncertainty in the late 1990s in the lead-up to China being granted permanent normal trade relations in 2000. The substantial drop in the probability of NTR reversal in the mid 1980s, combined with the gradual trade expansion common to trade liberalizations, suggests much of the growth in trade in high NNTR-gap products in the 1990s was due to the prior liberalization and not the future outlook on trade policy.

Our approach to estimating a forward-looking path of trade policy leverages unique aspects of U.S. policy toward China in which the change in trade policy is known and heterogeneous across products, but the likelihood and timing is unknown but common across products. Our analysis could be extended to consider more contemporary events such as Brexit, the U.S.-China trade war, safeguards, and domestic content requirements, as well as traditional protectionist measures such as antidumping duties. In all these cases, the size and timing of the reforms are uncertain, but by interpreting trade flows through a dynamic 29
# **1. Introduction**

While poverty is typically defined by whether someone has sufficient daily income or consumption to meet basic needs, many development organizations view wealth creation through asset ownership as ultimately the pathway out of poverty. For example, a core element of a poverty-reduction program in Bangladesh, run by BRAC (the largest nongovernmental development agency in the world) and replicated in over 20 countries, is an asset transfer program targeted to poor households. Banerjee et al. (2015) present evidence from randomized controlled trials from several countries that had asset transfer programs similar to the BRAC program as the core element, and show that the effects on poverty reduction are significant and long-lasting.<sup>1</sup> Owning more assets increases household wealth, and greater wealth can improve well-being in many different ways. One path is through increased investment in human capital, which can break cycles of poverty and help to extract people from chronic poverty. A large body of evidence has established that having more physical assets results in greater investment in children’s education, particularly in richer countries (Chowa et al., 2013; Conley, 2001; Deng et al., 2014; Elliott et al., 2011; Elliott and Sherraden, 2013; Huang, 2011, 2013; Kim and Sherraden, 2011; Loke, 2013; Shanks, 2007; Zhan and Sherraden, 2003).<sup>2</sup> There is also a fairly extensive body of research on the ‘asset-child education’ relationship in developing countries. Deng et al. (2014) and Filmer and Pritchett (2001) construct a measure of wealth based on assets and examine child education outcomes; others, like Chowa et al. (2013) and Cockburn and Dostie (2007), construct measures of asset ownership and examine education outcomes. Chowa et al. (2013) find that Ghanaian children in households that own at least one of five assets – TV, refrigerator, electric iron, electric or gas stove, and kerosene –

> 1 Their experiment had six elements, including asset transfer, training, and short-run support, but they consider the asset transfer to be the core component of the program. They found that the positive effects continued three years after receipt of the asset transfer, and the positive effects are seen in all six countries where the experiment was carried out (Ethiopia, Ghana, Honduras, India, Pakistan, and Peru).

> 2 For a survey of the literature, see Elliott et al. (2011).

2