rafmacalaba/datause-encoder-data huggingface dataset:
savings at the 5 percent level, both during and after the commitment period. The point estimates for both periods are just short of GHS 600, very close to total program savings of the median SSP participant (GHS 570).

In contrast, heavy overdrafters saved significantly less in other formal sector accounts, more than offsetting the additional savings stored with NVRB – as a result, point estimates on total savings are negative and not statistically significant. We reject equality of treatment effects on total savings for heavy and light overdrafters at the 1 percent level both during and after the commitment period.

Columns 6-8 of Table 4 show that this heterogeneity persists when accounting for debt. Only heavy overdrafters took on significantly more debt during the commitment period. As a result, treatment effects on savings net debt are large and negative for heavy overdrafters, while treatment effects on savings net debt are similar to treatment effects on total savings for light overdrafters. Here, we reject equality of effects by overdrafter status at the 5 percent level during and after the commitment period.

Given that SSP helped light overdrafters save more without taking on new debt, where did the additional money come from? Appendix Table A8 presents impacts on income and expenditure. Given the modest monthly changes needed to generate our point estimates, we cannot definitively identify consumption or income as the source of the new savings, although We do find some significant survey-based evidence that light overdrafters in the treatment group earn more. We view this result with caution, as we only collected income data in one follow-up survey. Table A8 also helps rule out the hypothesis that heavy overdrafters leverage SSP payouts and new debt to make lumpy investments in the post-commitment period: SSP had no significant impact on investment for either group.

# **3.5 Mechanisms and Discussion**

Overall, our results for heavy overdrafters are consistent with a model of partially-naïve hyperbolic discounting, e.g. where workers sign up for SSP expecting that it will help with their commitment problems, but fail to account for the fact that they will resort to costly borrowing to undo their commitment in the future. The behavior of light overdrafters is more difficult to rationalize. What is clear is that their response to SSP is not consistent with standard models of hyperbolic discounting, where savings outside SSP should weakly decrease during the commitment period. How, then, did SSP catalyze savings among light overdrafters?

One possibility is that SSP helped light overdrafters manage financial demands from extended family or community members. This type of “other control” problem could be 11
10

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and capital mobility plays a more direct, but equally inconclusive, role. Assuming away terms-of-trade and rental effects, one obtains Neary's (1988) result that (2.23) is negative and that the welfare effects of VERs are reduced because of capital mobility. Again, this is an issue to be settled empirically.

We have shown that terms-of-trade and rental rate valuation effects are sufficient to prevent one from signing the effects of 1..ternational capital mobility on the welfare costs of tariffs, QRs, and VERs. Terms-of-trade and rental effects are unlikely to be negligible, at least for a number of industrialized countries which have sizeable worldwide market shares. Thus we examine further the issue with a numerical application to the U.S. economy, evaluating the costs of protection with and without capital mobility for the year 1984.

# 3. An Application to the Welfare Effects of U.S. Import Restrictions

This section evaluates the aggregate welfare costs to the U.S. of QRs in autos, textiles and steel and of tariffs, using a static six-sector computable general equilibrium (CGE) model. 4/ The model is calibrated to 1984 data and is more thoroughly discussed in the appendix. Two forms of trade restriction are modelled: tariff protection with tax collection returned to the representative consumer; and QR protection. Two assumptions are made with respect to factor mobility: (1) no capital mobility, in which case capital owned by domestic and foreign residents are
Assuming an extreme case whereby households do not change their cigarette consumption because of an increase in cigarette prices (complete pass-through), there are small impacts in terms of consumption, poverty, and inequality. For instance, in a scenario in which prices increase by 50 percent, the average consumption loss would be 0.6 percent and similar across deciles. The official upper poverty rate would increase slightly, but this increase is not statistically significant. There is also no impact on consumption inequality measured using the Gini.

Assuming households reduce cigarette consumption because of higher prices, the impacts become even smaller. Indeed, in a high-response scenario in which elasticities are larger than −1 and include some substitution by bidis, the impacts translate into gains because households reduce their consumption of cigarettes more than proportionately. In this upper-bound case, poverty would fall by about a half percentage point, but the change is not statistically significant. There are no changes in consumption inequality.

The short-term consumption impacts are also small compared with the estimated gains because of savings in medical costs and the greater number of productive years of life. In particular, the increased number of productive years associated with a 25 percent rise in the price of cigarettes is on the order of 20 times more than the direct consumption impacts in a medium-response scenario and 45 times more in a higher-response scenario.

The results presented focus on changes in the price of cigarettes. The link with a specific reform and revenue impacts is outside the scope of this paper and will be addressed in future research. A reform in cigarette taxation that has been advocated by partner organizations for tobacco control would involve (1) the introduction of a specific excise tax replacing the current ad valorem system with a mixed system (a mix of ad valorem and specific excise taxes), (2) greater reliance on specific excise taxes, and (3) a gradual transition to a uniform structure.

This reform implies a greater increase in price for low-tier brands, which are largely consumed by lowincome smokers. This argument has been used in Bangladesh against the reform and in favor of the persistence of the multitiered tax structure. However, the HIES does not identify the type of cigarettes smoked by households so that one might directly address this concern. Nonetheless, the results presented here highlight that tobacco taxation is likely to have a small impact on overall consumption and poverty, even if, in some scenarios, the relative impact could be greater among poorer consumers because of the greater consumption of lower-tier cigarettes. This is the result of the combination of the smaller share of poor households consuming cigarettes and the higher response of these households to the change in prices.

# **References**

Ahmed, N. 2012. “Tobacco Taxation in Bangladesh: Administrative and Policy Constraints.” National Board of Revenue, Ministry of Finance, Dhaka. http://www.who.int/tobacco/economics/meetings/wctoh_2012_tob_tax_bangladesh.pdf.

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size from tax records, which we estimate for single-bookkeeping firms from our data. We observe earnings used for benefit calculation, allowing us to trace the impact of reported earnings on expected benefits.<sup>7</sup> We focus on the 2012 to 2017 period because in our data earnings before 2012 were smoothed within employment spells by employer-employee-year. We limit to individual-month observations with at most 5 sick days and no maternity payments. We adjust monthly earnings for sick days and to their full-time equivalent, using contractual work hours.

We observe the month of each birth between 2009 and 2017, defined by International Classification of Diseases (ICD-10) inpatient diagnosis codes in the health records. We focus on the first observed birth by each woman.<sup>8</sup> We restrict our sample to women who gave birth between ages 20 and 40 (90% of first childbirths in our data). We create a control group of women who did not give birth by 2017 and assign them a placebo childbirth date between ages 20 and 40 and years 2012 and 2017.

Our baseline sample consists of women working in the private sector who had a non-missing full-time equivalent wage of at least 90% of the monthly minimum wage each month between thirty to four months before giving birth or the placebo birth. Table 2 suggests that the two groups are broadly comparable, though women who give birth are somewhat more likely to have higher wages and to work in higher-skill occupations and in services.

# **4 Reporting Responses to the Contribution-Benefit Link**

## **4.1 Main Results on Reported Earnings**

In this section, we start by examining the evolution of reported earnings among expectant mothers. We document several patterns consistent with the formalization of previously underreported earnings to increase maternity benefits.

We estimate an event study regression following Kleven et al. (2019) to characterize the time pattern of reported earnings among expectant mothers relative to otherwise similar women who do not give birth: where _i_ indexes individuals, _t_ indexes calendar time, _Eit_ indicates event time, _Wit_ is the log of reported monthly earnings, **A** _it_ is a vector of age dummies, _γi_ captures individual fixed effects, _δt_ captures calendar month effects, and _Bi_ is a binary indicator for giving birth in the sample. Event time for women who did not give birth is defined by randomly assigning a placebo birth year and month. The coefficients of interest are the _βj_ , which capture the differential evolution of log monthly earnings for women who gave birth relative to women who did not. We set _−_ 13 <u>�</u> _j_ = _−_ 30<sup>_βj_= 0, thus the estimated coefficients show the deviation from the average (conditional)</sup>

> 7For a comprehensive overview of the administrative data, see Seb ̋ok (2019).

> 8We do not consider a childbirth as first if we observe a prior maternity payment receipt. While we do not have information on births before 2009, maternity benefit data are available for 2003-2017.

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If attention is restricted to national stock markets, several indices are generally available. Often the choice is between an 'all-share' index and one that is only made up of the largest or most frequently traded companies. In an analysis of the interval problem (see Section 6.2 and Appendices A1.3 and A3), the beta values of large companies (as the utilities tend to be) are found to have a smaller, upwards bias when calculated against an index consisting only of frequently traded shares. As is clear from Table 6.1 for AT&T, the large-security indices (Dow Jones Composite 65 and FTSE 100) produce lower beta estimates than their respective all-share indices. Economic theory suggests that largesecurity indices would produce more accurate beta estimates as they are less prone to price adjustment lags, and so, where possible, beta estimates in this paper have been derived from large-security national stock market indices.

**Table 6.1: Daily asset beta estimates for AT&T against alternative indices**

|**NYSE All-share**|**Dow Jones Composite**65|**World**($)|**World**(£)|**FT All-share**|**FTSE 100**|
|---|---|---|---|---|---|
|0.85|0.72|0.42|0.35|0.20|**0.17**|

In the case of AT&T, the value of 0.72, calculated using the Dow Jones Composite 65 index, has been chosen as the best estimate of the true beta value.

# **6.2 Choice of interval length**

Another important issue is the frequency of the data used in calculating beta. Returns calculated over a daily, weekly, monthly or even longer period may be used in the calculation, but there is a trade-off between two problems. Literature on the interval problem (see Appendix Al.3 for a brief review) suggests that short-interval estimates are systematically biased, such that the beta values of highly traded securities are overstated while those of infrequently traded securities are understated. Use of longer intervals, on the other hand, lowers the number of data points available for the calculation and the 25
Figure 11: Specification Charts for Growing Degree Days (GDD) <!-- Start of picture text --> Ethiopia Malawi<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.1 .15<br>.1<br>.05 .05<br>0<br>* Weather Product * 0 * Weather Product * -.05<br>MERRA-2 MERRA-2 -.1<br>ERA5 -.05 ERA5<br>CPC CPC<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18<br>Niger Nigeria<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.04 .04<br>.02 .02<br>0 0<br>* Weather Product * -.02 * Weather Product *<br>MERRA-2 -.04 MERRA-2 -.02<br>ERA5 ERA5 -.04<br>CPC CPC<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18<br>Tanzania Uganda<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.15 .005<br>.1.05 0<br>0 -.005<br>* Weather Product * -.05 * Weather Product * -.01<br>MERRA-2 -.1 MERRA-2<br>ERA5 ERA5 -.015<br>CPC CPC<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18<br><!-- End of picture text --> _Note_ : The figure presents specification curves, where each panel represents a different country, with three different models presented within each panel. Each panel includes 18 regressions, where each column represents a single regression. Significant and non-significant coefficients are designated at the top of the figure. For each Earth observation product, we also designate the significance and sign of the coefficient with color: red represents coefficients which are negative and significant; white represents insignificant coefficients, regardless of sign; and blue represents coefficients which are positive and significant.

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# **Table F.2. Conflict and consumption, alternative lag structure**

|||||Event <br>|type<br>|Per<br>|petratort<br>|ype<br>|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|VARIABLES|||||||||
|Conflict events|-0.082***<br>(0.020)|-0.051**<br>(0.023)|-0.046*<br>(0.024)|<br>|||||
|Conflict events property||||-0.142**<br>(0.056)|||||
|Conflict events violence|||||-0.005<br>(0.084)|<br>|||
|Insurgents|||||<br>|-0.043<br>(0.028)|||
|Bandits/criminals|||||||0.052<br>(0.103)||
|Pastoralists/nomads||||||||-0.193*<br>(0.100)|
|Observations|2,125|2,084|2,084|2,084|2,084|2,084|2,084|2,084|
|R-squared|0.163|0.296|0.310|0.312|0.309|0.310|0.309|0.312|
|Number of households|717|717|717|717|717|717|717|717|
|Controls|NO|YES|YES|YES|YES|YES|YES|YES|
|Year FE|YES|YES|NO|NO|NO|NO|NO|NO|
|HH FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Region-roundFE|NO|NO|YES|YES|YES|YES|YES|YES|

_Note_ : Dependent variable is log consumption of household. Data used are from the three waves of the GHS and telephone survey for conflict. The conflict recall is one year. All regressions are conducted using weights. Controls include all household and geographical variables listed in appendix table C.1. Standard errors clustered at the local government area level. Significance: *** _p_ < 0.01; ** _p_ < 0.05; * _p_ < 0.1 xxii
# specifically for such sectors as manufacturing and agriculture.

Such trade cost estimates refer to bilateral trade. To obtain country and regional measures of multilateral trade costs, bilateral trade costs from the UNESCAP-World Bank database are aggregated using 2018 bilateral country export shares from the UNCTAD database. Regional and sectoral aggregates are obtained as unweighted averages of individual country measures.

# IV.2 Literature view

Trade costs and trade. A growing literature has documented evidence that lower trade costs raise trade growth (Anderson and van Wincoop 2003). A study of data for the period 1870-2000 found that declines in trade costs explain roughly 60 percent of the growth in global trade in the pre-World-War 1 period and around 30 percent of trade growth in the period after World War II (Jacks, Meissner, and Novy 2011). Studies of firm-level data have found that lower trade costs have encouraged firms to locate abroad (Amiti and Javorcik 2008), and to choose out-sourcing over in-sourcing and intra-firm rather than arm’s-length trade (s).

Trade costs and productivity. A link between lower trade costs and higher productivity has also been substantiated. For advanced economies, one study found that a 1 percentage point lower tariff rate was associated with a 2 percent gain in total factor productivity during 1997-2007 (Ahn et al. 2019). Analyses of firm-level and sector-level data have shown similar results. Industries with larger declines in trade costs had stronger productivity growth; lower-productivity plants in industries with falling trade costs were more likely to close; and non-exporters were more likely to start exporting in response to falling trade costs (Bernard et al. 2007).

# IV.3 Patterns across regions and sectors

Despite a sharp decline in the past two and a half decades, recent data show that trade costs in EMDEs raise the prices of goods traded internationally to more than double the prices of goods traded domestically and that they remain about one-half higher than in advanced economies (figure 5). Among EMDE regions, average trade costs range from tariff equivalents of 96 percent in ECA to 142 percent in South Asia (SAR), with wide heterogeneity within regions. This heterogeneity is particularly pronounced in the Middle East and North Africa (MENA), where trade costs range from 86 to 136 percent among different countries. Trade costs have declined since 1995 in all sub-regions except East Asia and Pacific (EAP), with the fastest decline occurring in SSA. Within ECA, average trade costs of countries that are members of the European Union or geographically close to it are two-thirds of the average trade costs of other countries, which are less integrated into EU supply chains.

Trade costs remain particularly elevated in agriculture—about four-fifths higher than in manufacturing. Agricultural trade costs are particularly high in SSA where they stand at 270 percent tariff equivalent. Likewise, manufacturing trade costs are particularly
1980s and 1990s Crespi (2005) found that firms in the top decile have labour productivities that are 256 percent larger than those in the bottom decile, while Disney, Haskel, and Heden (2003) found a difference of “only” 155 percent.<sup>8</sup> <!-- Start of picture text --> −2 −1 0 1 2 −.5 0 .5 1 1.5 2<br>Log Ratio of Median Sectoral Labour Productivity to Economy−Wide Median Labour ProductivityLog Ratio of Plant Labour Productivity to Median Sectoral Labour Productivity within 6−digits Sector<br>(a) Heterogeneity Between Sectors (b) Heterogeneity Within Sectors<br>1<br>1.5<br>.8<br>1<br>.6<br>Density Density<br>.4<br>.5<br>.2<br>0 0<br><!-- End of picture text --> Figure 1: Firms Heterogeneity (Source: INEGI, Aguascalientes) Having shown some evidence on the importance of plant-level heterogeneity, we now want to show how after the implementation of NAFTA we observe a remarkable increase in industrial inequality with good firms getting better, and larger firms getting larger.

If we analyze the evolution of productivity<sup>9</sup> during the period 1993-2002 we observe two fundamental trends: a shift in the mean productivity, as Mexican firms became on average more productive, and an increase in the spread of the productivity distribution.<sup>10</sup> These trends emerge from figure 3 where

> 8The explanations for this heterogeneity can be divided into two mayor groups. Traditionally, researchers have pointed towards supply-side explanations such as technological efforts and uncertainty surrounding technological investments (Nelson 1981), management or ownership , human capital as well as complementary investments in organizational capital, and matching between skills and organisation (Bartelsman and Doms 2000). For example the findings that productivity ranking tends to be relatively stable seems to imply that managerial skills, or some other persistent productivity shocks, are important determinants of this heterogeneity (Baily, Hulten, and Campbell 1992). Another potential explanation, hard to pinpoint convincingly, points toward the existence of external economies as documented by Krizan’s (1995) findings that there is a positive correlation between plant level productivity and regional economic activities in various developing countries. More recently, Syverson (2004) suggested that also demand side reasons can be important to explain this heterogeneity, in particular product heterogeneity and their substitutability plays an important role. Finally, external to the firm, regulations and exposure to international competition can be important in explaining part of the observed heterogeneity (Tybout 2001, Tybout 2000, Bartelsman and Doms 2000).

9 We use as productivity index the value added labour productivity.

> 10We also test the hypothesis that the productivity spread got larger more formally by regressing the coefficient of variation, calculated within each narrowly defined sector at six digits, on a linear trend. We find that the coefficient on the linear trend estimated with a FE model is positive and statistically significant confirming that dispersion of

6