rafmacalaba/datause-encoder-data huggingface dataset:
advantage of labor market opportunities more broadly.

Even in a developing country like Ghana, with universal health insurance, lost labor income and the (uninsured) financial cost of treatment still appear sufficiently sizeable to prompt households to increase labor supply in response to illness shocks. Labor supply responses are particularly strong in low socio-economic status households, suggesting that as incomes rise, households have better access to alternative income smoothing mechanisms, which reduce the pressure on household members to work extra hours to cover income losses. For poor households that lack these smoothing mechanisms, however, flexible labor options are an important coping mechanism for dealing with unanticipated illness shocks.

# **References**

Akazili, James, Paul Welaga, Ayaga Bawah, Fabian S Achana, Abraham Oduro, John Koku Awoonor-Williams, John E. Williams, Moses Aikins and James F. Phillips. "Is Ghana’s pro-poor health insurance scheme really for the poor? Evidence from Northern Ghana." _BMC health services research_ 14.1 (2014): 637.

Akazili, James, John Ele-Ojo Ataguba, Edmund Wedam Kanmiki, John Gyapong, Osman Sankoh, Abraham Oduro and Di McIntyre. "Assessing the catastrophic effects of out-ofpocket healthcare payments prior to the uptake of a nationwide health insurance scheme in Ghana." _Global Health Action_ 10.1 (2017): 1289735.

<mark>Alhassan, Robert Kaba, Edward Nketiah-Amponsah, and Daniel Kojo Arhinful. "A review of the national health insurance scheme in Ghana: What are the sustainability threats and prospects?."</mark> _<mark>PloS one</mark>_ <mark>11.11 (2016): e0165151.</mark> Aragon, Fernando, Juan Jose Miranda, and Paulina Oliva. “Particulate matter and labor supply: evidence from Peru.” _mimeo_ , Simon Fraser University. 2016.

Asfaw, Abay, and Joachim von Braun. "Is consumption insured against illness? Evidence on vulnerability of households to health shocks in rural Ethiopia." _Economic Development and Cultural Change_ 53.1 (2004): 115-129.

Baldini, Massimo, Costanza Torricelli, and Maria Cesira Urzì Brancati. "Family ties: Labor supply responses to cope with a household employment shock." _Review of Economics of the Household_ (2017): 1-24.

34
these districts and were likely to have been mistaken for interface meetings. Nearly 95 percent of staff in NFA clinics have heard of the NFA intervention; conditional on knowing about the program, 84 percent report participating compared to 48 percent among control villages. Staff in CM and control clinics also appear to have heard about the NFA program, albeit at lower rates. Conditional on having heard about NFA, 13 of 84 control clinics report participating. Again, this likely reflects misperception and not implementation failures or contamination: control clinics cannot opt into the NFA competition, which requires the research team to rank each participating clinic. Moreover, the research staff did not detect any deviations from treatment assignment when monitoring implementation of the NFA treatment.<sup>19</sup> If clinic staff in control clinics are motivated by the mistaken belief that they are eligible for an award, this would attenuate our treatment effects.

# **3.5 Specifications**

## **3.5.1 Survey Outcomes**

The analysis plan specifies outcome variables, the construction of indices, and the specifications we estimate; and we flag and explain any subsequent deviations in Appendix Section B.1.

Our baseline survey included a smaller sample of households. Moreover, some outcomes are only defined for individuals who recently experienced health episodes. Some households that experienced health episodes at endline may not have also experienced episodes during baseline. For both reasons, controlling for a household’s baseline outcome would reduce the size and representativeness of our sample. We instead employ an ANCOVA-type model that controls for the village-level average at baseline, estimating: where _yivc,EL_ is the outcome of household (or individual) _i_ in village _v_ in clinic catchment _c_ at endline ( _EL_ ). (Models for community-level outcomes omit the _i_ subscript.) _αb_ represents the matched-triplet fixed effects. Treatment status, which is randomized across clinics, is denoted by the indicator variables 1( _CM_ ) _c_ and 1( _NFA_ ) _c_ . _Y vc,BL_ is the village-level average at baseline. When _y_ is a sub-component of an outcome family, we still use the family-level outcome to compute this baseline average.<sup>20</sup> We cluster our standard errors on clinic, the unit of randomization. We also estimate a variant of Equation 1 in which we pool the CM and

> 19 The NFA treatment involved 4 rounds of meetings. During the second round, we randomly selected half of all clinics and sent an enumerator to monitor the meeting.

> 20 This decision is motivated by two features of our data: first, some sub-components are only measured at endline; second, for some sub-components and villages we have no data to compute the average (e.g., if there were no recent births). To improve precision through the inclusion of a prognostic pre-treatment covariate, we include the average family-level outcome. This represents a slight deviation from the analysis plan, but does not affect affect any of our conclusions.

18
financial markets) has often been advocated from Keynesian standpoints either in order to offset people's preference for liquidity or to permit Governments to finance their budget deficits at low costs. Moreover, old and nec-etvucturalists mainly emphasized the stagflationary effects of tight monetary policy and high interest rates.

Leaving auide theoretical justifications, loan rate ceilings have been extensively used in many LDCs in conjuction with import restrictions to encourage industrialization through import-substitution. McKinnon (1973) and Shaw (1973) challenged this orientation, pointing out that interest rate ceilings associated with administrative allocation of credit were likely to have adverse effects on sa7ings and growth. The main drawbacks attributed to financial repression were that it discourages risk-taking, prevents lending institutions from charging risk premia, promotes low-yielding direct investment as a substitute for depositing money in a bank and encourages the adoption of capital-intensive techniques. Those advocating domestic liberalization rely on McKinnon-Shaw-type arguments: they expect interest rates in the official credit market to be pushed up so as to make domestic capital markets more appealing to household savings and to eauate thB price of capital with its true marginal cost.

However, concentrating on the level of interest rates as the ultimate objective of domestic financial markets liberalization3 can be partially misleading. **UndArtnkInn a** complete financial reformu requires, am.ong other things, establishing reliable deposit-taking institutions, reducing loan market segmentation (which produces market outcomes unsatisficing on efficiency as well as on equity grounds), allowing for a more indirect Government intervention in 3 This is more often done by those who oppose it. The World Development Report 1989 contains a well developed survey of the requirements for financial development.

22
per capita incomes at the 25th and the 75th percentiles, a revenue increase that would lead a median income municipality to spend 1 R$ of it on education would lead the richer municipality to spend only 0.67 R$, and the poorer municipality to spend 1.29 R$. On the other hand, a revenue increase that would lead a median income municipality to spend 1 R$ of it on infrastructure would lead the richer municipality to spend 1.10 R$, and the poorer municipality to spend only 0.91 R$. Figure 5 illustrates how this varies across municipalities at each decile of income. Once again, the income gradient is steeper than the inequality gradient, but both strongly influence a municipality’s public spending patterns. The results are similar in columns (3) and (7), where I use mean instead of median income.<sup>38</sup> As a partial test of the exogeneity of the instruments, I estimate overidentified models with four excluded instruments: simulated revenue, and its interactions with median income, average income, and the Gini. The estimates of _θ_ and _δ_ are almost identical. The Hansen J test statistics of each model, distributed _χ_<sup>2</sup> 1<sup>,indicatethattheinstrumentsareappropriately</sup> uncorrelated with the disturbance process. I consistently fail to reject the null hypothesis that the instruments affect investment only through their effect on revenue and its interactions with income and inequality.<sup>39</sup>

# **6.4 Contrasting the collective choice and political power channels**

These results can be explained by either of two existing theories, or channels: a collective choice channel, and a political power channel. If both matter, then the estimates reflect their combined impact. In this section, I attempt to apportion the effects between the two channels by focusing on municipalities where the political power channel is suppressed. In these municipalities, the effects are likely due to the collective choice channel.

Ideally, I would randomly assign some municipalities to determine public investment without preferring any citizen over another. This would effectively turn off the political power channel. If the effects of income and inequality were the same in treated and untreated municipalities,

> 38Results shown in Appendix Table A.4 indicate that the findings are substantially similar when I use logged values of revenue and the outcome variables. Also, the addition of political and agricultural controls has virtually no effect on the coefficients interpreted in this analysis, or their standard errors.

> 39A table of these results is available upon request.

26
# **Table A9: Accounts together versus accounts separate**

|Dependent Variable|**Labor Prod**|**uctivity (logs)**|
|---|---|---|
|Sample|**Business and Household**<br>**Accounts Separate**|**Business and Household**<br>**Accounts Together**|
||**coef/se**|**coef/se**|
|Total duration of water shortages last month (in days)|-0.111***|-0.014*|
||(0.043)|(0.008)|
|Log of total number of employees in regular month|-0.722***|-0.491***|
||(0.206)|(0.113)|
|Largest owner has secondary education or higher: Y/N|0.407*|0.186|
||(0.233)|(0.144)|
|Manager experience (years)|-0.001|0.020**|
||(0.005)|(0.010)|
|Largest owner has a job in formal sector or looked for one over<br>last 2 years Y/N|-0.272|0.229|
||(0.200)|(0.155)|
|Largest owner started another business in the last three years<br>Y/N|-0.143|-0.069|
||(0.089)|(0.108)|
|Firm uses electricity for activity Y/N|0.141|0.163|
||(0.136)|(0.111)|
|Firm uses machinery in its current operations Y/N|0.339**|-0.092|
||(0.150)|(0.126)|
|Firm uses vehicles in its current operations Y/N|0.460***|0.397***|
||(0.143)|(0.135)|
|Manufacturing firm Y/N|0.277**|-0.009|
||(0.138)|(0.064)|
|Produces or sells under contract from another business or<br>person Y/N|0.554***|0.167|
||(0.135)|(0.137)|
|Firm uses bank account to run business Y/N|0.396**|0.212**|
||(0.176)|(0.088)|
|Firm has a loan at this time Y/N|0.258|-0.126|
||(0.167)|(0.145)|
|Firm pays informal payment to remain unregistered Y/N|-0.963**|0.143|
||(0.392)|(0.201)|
|Firm experienced crime last month Y/N|0.185|-0.055|
||(0.379)|(0.202)|
|Firm pays for security Y/N|-0.218|-0.090|

47
change in the elasticity of distance on trade using bilateral trade data at the aggregate and industry level. We also discuss the decomposition of the average elasticity into the part due to a change in the composition of trade and a part due to change in the elasticity among industries.

To illustrate the aggregation problem arising from a gravity model, we use a simple example with varying industry distance effects. Assume we can write a gravity equation for imports in product _k_ , by country _i_ from country _j,_ at time _t_ as where Atk is a constant that reflects how important that product is in consumption and varies by product and by time, γ _k_ is a product-specific component of the effect of distance on trade, and δ _t_ is a time-specific component (γ _k_ >0, δ _t_ >0). The wide variation in average distances by industry shown above suggests that the elasticity of trade to distance does in fact vary substantially by product (a fact which we will confirm below). We assume that the time-specific component δ _t_ is common across products for ease of exposition, though we relax this assumption in the estimation. If the effect of distance is decreasing over time then δ _t_ will fall over time.

> Summing equation (2) over all products yields total trade between countries _i_ and

_j_ , Thus, the log of aggregate bilateral trade can be written as a function of income and distance as follows: 11
shows the results for each sector including and omitting domestic trade flows. The general conclusion from these results for average, intra-regional, and with the rest of the world RTA effects is that omitting domestic trade flows underestimates the trade effect. This comes from the fact that trade agreements divert some domestic flows toward international trade. This is an intuitive result, already seen in the literature (Yotov, 2022b), since RTAs are expected to increase competition in the domestic economies, with foreign firms entering the market, and increasing the productivity of firms and improving the reallocation of resources. This is part of the objectives of signing a trade agreement. Results suggest that South Asian RTAs have worked in this line contributing to increase competition in domestic economies. This seems to have been the case with RTAs signed with the rest of the world, but even more within the region.

||Manufa|cturing|Agric|ulture|Ser|vices|
|---|---|---|---|---|---|---|
||(1)<br>trade|(2)<br>trade|(3)<br>trade|(4)<br>trade|(5)<br>trade|(6)<br>trade|
|RTA|0.01<sup>_∗∗∗_</sup>|0.10<sup>_∗∗∗_</sup>|0.03<sup>_∗∗∗_</sup>|0.09<sup>_∗∗∗_</sup>|0.01|0.05<sup>_∗∗∗_</sup>|
||(4.13)|(11.97)|(2.91)|(6.12)|(1.40)|(7.80)|
|RTA_×_ SAR|0.03<sup>_∗∗∗_</sup>|0.10<sup>_∗∗∗_</sup>|-0.02|0.01|-0.06<sup>_∗∗_</sup>|0.22<sup>_∗∗∗_</sup>|
||(2.92)|(6.00)|(-0.48)|(0.33)|(-2.00)|(11.13)|
|RTA_×_ SAR-intra|0.12<sup>_∗∗∗_</sup>|0.20<sup>_∗∗∗_</sup>|0.27<sup>_∗∗∗_</sup>|0.36<sup>_∗∗∗_</sup>|-0.07<sup>_∗∗_</sup>|0.14<sup>_∗∗∗_</sup>|
||(5.62)|(5.70)|(5.84)|(7.10)|(-2.08)|(4.09)|
|Domestic trade|NO|YES|NO|YES|NO|YES|
|_N_|928746|933660|928746|933660|928746|933660|

**Note:** the variable RTA measures the world average effect of trade agreements. The SAR effect is measured in addition to the average, and SAR-intra is in addition to the SAR effect. * _p <_ 0.10, ** _p <_ 0.05, *** _p <_ 0.01.

Table 7: The role of domestic trade flows and trade diversion Another area in which this paper has focused is to disentangle the heterogeneous effects of RTAs within the region and with the rest of the world. I carry out two more analyses to understand the different effects that have already been seen in the previous results. The first one is to estimate the specific effects of the South Asian Free Trade Area (SAFTA) agreement. This agreement is important since it is the core of the intra-regional trade agreements, with only a few country-pairs (like India- Sri Lanka) having an RTA in place with another South Asian country before SAFTA. I estimate not only SAFTA’s effect, 14
hold exactly, but we need them to be a reasonable approximation. The next sub-section then discusses what to do in cases when this assumption doesn’t hold.

The assumption that the autocorrelations are equal between all points in time is one that many economists may have second thoughts about for both theoretical and statistical reasons. On the theoretical side, if we think that a large source of the period-to-period variation in economic outcomes like profits, income or consumption comes from temporary shocks, we should expect the impact of these shocks to die out over time, so that observations closer apart in time will be more highly correlated than those further apart in time. Statistically, constant (non-zero) autocorrelations imply that although the data generating process is covariance-stationary, it is non-ergodic for the mean and thus a law of large numbers will not apply to the mean of as the time dimension approaches infinity.

However, while such an assumption may therefore not be appropriate globally over all horizons, it may be a reasonable assumption within the likely time horizons of many applications. That is, while we might not believe the correlation between business profits this month and next month is the same as that between this month and 10 years from now, the correlation between this month and next month may be close to that between this month and two months from now. For example, in a local neighborhood of a particular time horizon, a reasonable data generating process for might be , where is i.i.d. noise. Thus, for example, within a few months around the one year after an intervention horizon we might expect profits for a particular firm to be higher or lower depending on some longer-term shock , and then in addition from month to month the firm gets idiosyncratic demand shocks which cause its profits to move around further.

Ultimately it is therefore an empirical question as to whether this assumption seems reasonable over the periods of typical economic experiments, and especially over short-term neighborhoods of a few months around a particular horizon of interest. Figures 1A, 1B, and 1C investigate this assumption for microenterprise profits and for household expenditure. Figure 1A shows autocorrelations for monthly profits from the Sri Lankan microenterprises in de Mel et al. (2008) which were measured at three month intervals, and uses data from similar microenterprises measured monthly in de Mel et al. (2009) to show the autocorrelation in monthly profits at the one month and two month horizons. A horizontal line is drawn at 0.38, which is the average 10
based quality-choice channels. In the Argentinian case, richer destinations (primarily the U.S. and Europe) also tend to be further away than poorer destinations (primarily Brazil and other countries in South America). In the Portuguese case, the correlation is reversed: richer destinations (e.g. U.K, Sweden) tend be closer, and poorer destinations (e.g. Brazil, Angola) further away. This provides a stronger basis for separate identification of the effects of income and distance. These contributions strengthen the income-based quality-choice interpretation, consistent with the broader argument in Brambilla, Lederman, and Porto (2012) (as well as in Verhoogen (2008)). In addition to the papers cited above, our paper is also related to a growing recent literature on the role of product quality in trade, including Schott (2004), Hummels and Klenow (2005), Sutton (2007), Choi, Hummels, and Xiang (2009), Hallak and Schott (2011), Baldwin and Harrigan (2011), Fajgelbaum, Grossman, and Helpman (2011), Eckel, Iacovone, Javorcik, and Neary (2011), Crozet, Head, and Mayer (2012), Johnson (2012), Hallak and Sivadasan (2013), Amiti and Khandelwal (2013), Markusen (2013), Caron, Fally, and Markusen (forthcoming), Di Comite, Thisse, and Vandenbussche (forthcoming), Iacovone and Javorcik (2012), Harrigan, Ma, and Shlychkov (2012), Kneller and Yu (2008), and Gervais (2013).

Although we focus on Portugal, a middle-income country, we believe that our findings have implications for our understanding of the upgrading process in developing countries as well. In particular, the results reinforce the idea that raising the quality of outputs requires raising the quality of inputs (Verhoogen, 2008; Kugler and Verhoogen, 2012). This in turn suggests that increasing exports to high income destinations may require the upgrading of entire complexes of suppliers and downstream producers, not just of exporters. The particular empirical setting has the advantage that it allows us to identify cleanly a causal relationship between destination income and material input prices, but the basic findings seem likely to apply more broadly.

# **2 Data**

The analysis in this paper draws on two main datasets, both collected by the _Instituto Nacional de Estat ́ıstica (INE)_ , the Portuguese national statistical agency:

1. Customs data on firm-level international trade transactions, which are collected separately for European Union (EU) partner countries ( _Estat ́ısticas Correntes do Com ́ercio Intracomunit ́ario_ [Current Statistics on Intra-community Trade]) and non-EU partners ( _Estat ́ısticas_

4
decreasing with age: the turning point is at age 41. An individual is significantly more likely to apply if they are not married, are literate in English, do not have more than Form 4 schooling, have not had a health complaint in the last 6 months, do not drink kava or alcohol frequently, come from a smaller household and have a relative in New Zealand. There are more applicants among our Tanna sample than the samples from Ambrym and Efate/Port Vila. Most of these effects continue to hold when we look separately by gender. However, being married only reduces the likelihood of applying for women, and not men. Since there are far fewer female RSE applicants, we find fewer variables significant in this case, and that having more than primary education, rather than English literacy, is significantly associated with being an applicant. Women from richer households (those with higher per capita income and more cattle) are less likely to apply.

Column 4 of Table 3 examines selection of workers among the group of RSE applicants in our sample, while column 5 considers selection among male applicants. There are too few female applicants and workers to consider selection separately among females. We see males are more likely to be selected than females, conditional on other observable characteristics. Those with relatives in New Zealand are more likely to be selected, while few applicants from Ambrym had been selected at the time of our survey there. Individuals from households with more assets are marginally more likely to be selected, as are males with lower schooling levels, and males who do not regularly consume kava or alcohol. The point estimate on English literacy is large and positive, but not statistically significant.

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