rafmacalaba/datause-encoder-data huggingface dataset:
weakness of reciprocity dynamics is sometimes argued to imply that the major source of potential gain for many small developing countries is the use of trade agreements as credibilityenhancing or lock-in mechanisms. But this rationale may be weaker in practice than often claimed in the literature for the same market access-related reason: because they are small and poor, credibility cannot derive from the threat of external enforcement of trade agreements by trading partners. Making the WTO DSU mechanism work for these countries requires that foreign governments deliberately pursue enforcement actions even if they have no market access incentive to do so. The likelihood that countries can pursue such “tough love” in a credible manner is low, and the political feasibility of this possibility appears to be very limited.

Credibility must therefore be sought in other instruments, such as greater and more effective transparency mechanisms. A major advantage of additional monitoring and analysis of developing country policies that affect trade is that this can be a valuable input into improved domestic policies. What is needed is that the constituencies in these countries see implementation of commitments as being in their interest. Often that will require a period of gradual learning about the benefits and the costs of different regulatory approaches, interactions with other countries and learning from their experience, and building up the required institutions needed to enforce the regulations that are developed. Rather than rely on binding commitments and the threat of the DSU, an approach that focuses on transparency and analysis of the effects of policies in such regulatory areas may do more to bolster ownership and identify where multilateral commitments can be beneficial. An additional argument in favor of this approach is the one stressed in this paper: the threat of formal DSU proceedings often may not be perceived as credible.

# **References**

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23
# **3.3 Analytical Approach**

NREGS participation can, in principle, yield three types of direct benefits. First, a transfer effect will directly increase income by either paying higher wages than those received in the market, especially for females, or providing employment at times when there is no demand from other sources. The size of the effect will depend on the extent to which supply to the casual labor market increases compared to the without-program situation. Second, some of the income gained can be channeled toward savings and investment to strengthen households’ resilience in the longer term, an effect that may be enhanced if wages are deposited into a savings account. Third, as NREGS aims to increase the productivity of resource use and small-scale works on participants’ own land that is eligible for program support, investment on marginal lands to enhance agricultural productivity can be another avenue for program impacts to materialize, though these would not directly benefit the landless.

We define direct beneficiaries as eligible households that had at least one member work under NREGS and use difference-in-difference (DID) and triple difference (DDD) methods, together with propensity score matching (PSM), to provide estimates of NREGS impacts on these beneficiaries. To illustrate the DID approach, let _t_ = 0, 1, 2 indicate year 2004, 2006, and 2008, respectively. Let _Tit_ = 1 if a household _i T C_ is treated at _t_ , and _Tit_ = 0<sup>otherwise. With</sup> _Yit_ as the outcome under treatment and _Yit_ the counterfactual outcome, the gain from being treated is ( _YiT_ 2 − _YiC_ 2 ) . Our interest is in the average effect of treatment on the treated (ATT), _E_ ( _Y_ 2 _T_ − _Y_ 2 _C_ | _T_ 2 = )1 , that is, the expected difference between actual and counterfactual outcomes, _Y_ 2 _T_ and _Y_ 2 _C_ , for treated households ( _T_ 2<sup>=</sup> 1 ). Since _Y_ 2 _C_ is unobservable, we cannot estimate ATT directly.

DID estimates, _E_ ( _Y_ 2 − _Y_ 1 | _T_ 2 = )1 − _E_ ( _Y_ 2 − _Y_ 1 | _T_ 2 = 0) , provide an unbiased estimate of ATT if the parallel trend assumption, _E_ ( _Y_ 2 _C_ − _Y_ 1 | _T_ 2 = )1 = _E_ ( _Y_ 2 − _Y_ 1 | _T_ 2 = 0) , holds. Defining the selection bias at _t_ as _Bt_ = _E_ ( _YtC_ | _T_ 2 = )1 − _E_ ( _YtC_ | _T_ 2 = 0) , the parallel trend assumption is equivalent to _B_ 1 = _B_ 2 , or selection bias being constant in 2006 and 2008. This condition will not hold if household characteristics or initial conditions affect subsequent changes of the outcome variables so that their distributions in the treatment and control groups differ from each other.

Combining DID with PSM can address the bias from observables and time-invariant unobservables but not time-variant unobservables. Two rounds of pre-intervention data allows us to test if the parallel trend assumption holds for 2004–2006. The null hypothesis is _E_ ( _Y_ 1 − _Y_ 0 | _T_ 2 = )1 = _E_ ( _Y_ 1 − _Y_ 0 | _T_ 2 = 0) , or

10
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46
Gregorio Impavido

**Table 19: Disability Benefits Under LSS-73 and LSS-97**

||**LSS-73**|**LSS-97**|
|---|---|---|
|Sala<br>ry base|Ave<br>cont<br>rage of last 500 weeks of<br>ributions not indexed|Ave<br>cont<br>INP<br>rage of last 500 weeks of<br>ributions indexed to the<br>C|
|Basic pension|Following table in Art 167 of<br>LSS<br>-73.|35 p<br>f salary base<br>ercent o|
|Minimum pension|Min<br>ry of the Federal<br>Dist<br>(MX<br>imum sala<br>rict at the time of pension.<br>N 1,402 as of July 2004)|Minimum salary of the Federal<br>Dist<br>ed<br>to IN<br>July<br>rict as of July 1997 index<br>PC (MXN 1,551 as of<br>2004).|
|Pension indexation|INP<br>C (As per reform of 2001)|INP<br>C|
|SAR-92 (92-97) account|Bala<br>the form of<br>lump sum<br>nce payable in|SAR<br>ble in<br>the f<br>sum<br>92-97 balance paya<br>orm of lump|
|Old<br>CV<br>subaccount<br>age cash balance in R|Bala<br>f<br>lum<br>As per reform of<br>2002<br>nce payable in the form o<br>p sum (<br>)|Not<br>ble<br>applica|

**Source** : Fernandez Reyes (2004)

The increased relative generosity of the 1973 provisions and the fact that the IMSS granted the option of electing IV benefits under such provisions and opted for tightened eligibility and disability rules, changed the growth prospects of the market and premium growth decreased in the years since 2002 relative to previous years as shown in the next figure.

The only thing left to be explained now is why insured individuals would stop electing disability benefits under the LSS-97 provisions when, on average, these were designed to be more generous than the disability benefits provided under LSS-73, as mentioned before. The following table compares the disability pension that an insured individual would obtain under the provisions of LSS-73 and LSS-97. In order to compare the heterogeneous benefits under the two laws, the old age balance payable in the form of a lump sum from the RCV subaccount when individual elect the LSS-73 was transformed in IV annuity and added to the basic pension the individual would be entitled.

- 30 -
there is a primacy of institutions over trade in the most distorted economies—not only do bad institutions lower growth, but they also prevent trade from generating growth.

The paper is organized as follows. The next section surveys evidence on trade and growth and how regulations affect structural change following liberalization. Section III describes the data. Section IV and V present the cross-section levels and growth results, respectively. Section VI concludes.

# **II.  The Evidence on Trade, Growth, and Structural Change**

There is a large literature on the effect of openness on growth. The general conclusion is that impediments to trade lower growth (Dollar 1992, Ben-David 1993, Sachs and Warner 1995, and Frankel and Romer 1999). Rodriguez and Rodrik (1999), however, argue that the measures of openness used are poor measures of trade barriers or are highly correlated with other sources of bad performance. For example, they show that the results on openness and growth are not robust when controlling for other determinants of income. But, since the number of observations is limited, the Rodriguez-Rodrik (1999) results also have a shortcoming; there are too many highly-correlated explanatory variables in their regression, making it impossible to identify the separate effect of trade on growth.

There are alternative ways of looking for evidence that trade promotes growth. Since the gains are theorized to come through specialization, we would hope to see evidence that sectoral output and employment are influenced by the level of openness. While there is evidence that comparative advantage guides specialization, the evidence that the degree of specialization is influenced by openness is weak. Using data on production patterns in 20 OECD countries since 1970, Redding (2002) finds that world prices have been a major driver of specialization, but that 4
benefits of USD325 million to USD845 million every year. Similar effects have been documented in eastern China (Li et al., 2017). These reductions may be driven by changes in the amount of radiation reaching the surface or by deposition of particulate matter on the solar panels, further blocking sunlight.

Model based approaches to estimating the impact of air pollution on GDP growth are an alternative to the quasi-experimental approach employed by many of the papers above and that we employ. Model based approaches start with measured GDP and attempt to aggregate all of the costs of air pollution and then subtract these costs from measured GDP and calculate a pollution inclusive measure of growth. This approach is well represented by Mohan et al. (2020) who estimate how incorporating damages from air pollution changes GDP levels in 163 countries. Their approach models the damages from pollution that occur across the economy – what they term gross environmental damage (GED) – and subtracts those damages from GDP to arrive an estimated “environmentally-adjusted value added” (EVA). This approach provides an estimate of the extent to which economic activity is drawing down stocks of natural capital (i.e. clean air) and is important for assessing whether economic growth is occurring sustainably. They find that over the last several decades India’s EVA growth has been approximately 50 basis points lower than measured GDP growth.

Our approach differs as we do not attempt to explicitly estimate the full set of damages that air pollution can cause individually or in aggregate. Rather, we assume that the damage caused by year-to-year changes in air pollution will manifest as year-to-year changes in GDP. That implies that measured year over year GDP growth will be lower in years with higher levels of air pollution than it would have been absent the damages from air pollution.<sup>7</sup> Note that this measure will be inclusive of intra-annual adaptation measures taken to reduce the impact of air pollution on economic activity (e.g. shifting work from high pollution to low pollution days). Crucially, to the extent that air pollution has long-term effects on GDP growth (i.e. if it reduces future growth by reducing human capital formation in school aged children today) we will not detect these effects with our approach.

# **3 Data**

## **3.1 GDP data**

We compile real district GDP using officially published reports by state governments and CEIC (a data platform that compiles data from various official sources). The series is

> 7Note, we are not estimating the effect of pollution on long-run average growth rates, rather our outcome is the year-to-year change in GDP in percentage terms.

7
<!-- Start of picture text --> “‘ J 5<br><@<br><!-- End of picture text --> <!-- Start of picture text --> Legend<br>(MMMM Existing Coverage (Sep 06)<br>I Market Efficiency Gap<br>HBMN SustainabilityUniversal Coverage Gap Gap<br>Unpopulated Area<br>{| Water<br><!-- End of picture text -->

<!-- Start of picture text --> 7 *s §<br>«@<br><!-- End of picture text --> <!-- Start of picture text --> Legend<br>(DM Efficiont Market Gap<br>= cowesea<br>L_|snporsite1 a<br><!-- End of picture text -->
# **Supplementary Information**

# **SI-1 Materials and Methods**

## **SI-1.1 Data**

We use data on the tracks of every Atlantic basin hurricane that has struck the continental United States from 1988 to 2018.<sup>1</sup> These data are based on information tracked by the NOAA Storm Events database and the National Hurricane Center. They provide us with information on the track, rainfall totals, and wind speeds associated with the storms, as well as the counties that experienced a flood event as a consequence of each storm.

We define counties as exposed to a hurricane in three ways. In our primary analysis, a county is considered exposed if it experienced a flood warning within one day of the track of the storm passing through it OR if it experienced winds greater than 21 m/s in the same time period. In alternative analyses, we use the flood warning definition and the wind speed definition individually to assign exposure. In Figure SI-1, we show the counties effected by hurricanes under these alternative definitions in our sample. We choose 21 m/s as our wind threshold because this is (a) the wind speed at which NOAA indicates structural damage to buildings will begin to occur and (b) approximately the lower bound for wind speed for a storm to be considered a tropical storm. It is substantially below the minimum wind speed on the Saffir-Simpson scale for a Category 1 hurricane. We note, however, that our results are robust to using higher wind speed thresholds and that, as one increases the wind speed threshold to the Category 1 threshold, the set of impacted counties collapses to those that would be included based on the flooding definition. We show in the supplementary materials that our results are robust to using only those counties that meet this flooding threshold.

We supplement these data with data from FEMA on the total payments made to individuals for every disaster declared by FEMA since 1954.

Our migration data come from the IRS Statistics of Income’s county-to-county migration flows. The IRS publishes data on the number of migrants leaving each county and their destination based on aggregated tax return data for each year from 1991 to 2019.

From these data sets, we assemble a balanced panel that lists all migration to and from all counties in the United States and the number of storms each county experienced from 1992 to 2017.

19