rafmacalaba/datause-encoder-data huggingface dataset:
malnourished during the first round (with a z-score below -2 S.D.) and those who were not malnourished. The estimates in Table 5 show that changes in public expenditure have a statistically significant and positive impact on changes in the nutritional level only of initially well-nourished children. The coefficient estimate is slightly larger than the one for the whole urban sample (0.283), which attests to the importance of early circumstances. In rural areas, changes in public expenditure only have a statistically significant and negative impact on children with worse initial nutritional status. In both areas, the differential impact across the subsample is not statistically significant. Hence, initial conditions do not solely explain the results.

_The impact of public spending according to maternal mother tongue._ An “indigenous” mother tongue can be a barrier with regard to communicating with medical professionals as well as a source of lack of self-confidence. I define as “indigenous” a mother with a mother tongue other than Spanish (the official language) and as “not indigenous” a mother with Spanish as her mother tongue. In rural areas, even though there is a negative and statistically significant effect of changes in public spending for non-indigenous mothers, the differential impact across subsamples is not significant. In urban areas, the impact of changes in public expenditure is positive and significant only for mothers who are not indigenous. This impact is much stronger than for the whole urban sample (0.346). Furthermore, the differential impact of public spending across ethnic groups is statistically significant.

_The impact of public spending according to household wealth level._ Households with tighter budget constraints are less prone to dedicate time to child-enhancing activities. I define poor households as those with an initial household wealth score below the regional household wealth median<sup>27</sup> , which translates to an average income per capita of approximately $2 per day.

> 27 The regional household wealth median was calculated using the ENAHO surveys.

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<!-- Start of picture text --> Alternative delivery period<br>An alternative delivery procedure is available to buyers and sellers who have been matched by the exchange<br>subsequent to the termination of trading-in the spot month contract. If buyer and seller agree to consummate dehlvery<br>under terms different from those prescribed in the contract specifications, they may proceed on that basis after<br>submitting a notice of their intention to the exchange.<br>Exchange of futures for, or in connection with, physicals (EFP)<br>The commercial buyer or seller may exchange a futures position for a physical position of equal quantity by submitting<br>a notice to the exchange. EFPs may be used to either initiate or liquidate a futures position.<br>Quality specifidations<br>Pipeline specifications in effect at time of delivery.<br>Position limits.<br>12,000 contracts for all months combined, but not to exceed 750 in the delivery month or 7,500 in any one month.<br>Margin requirements<br>Margins are required for open futures and short options positions. The margin requirement for an options purchaser<br>will never exceed the premium paid.<br>Trading symbols<br>Futures: NG<br>Options: ON<br>Source: NYMEX<br><!-- End of picture text --> Market participants unable to accept price risk because of technology or demand constraints are willing to pay a premium to reduce the risk to an acceptable level. This demand can be served by a financial gas contract that transfers the risk to the issuer of the contract in exchange for a payment. Such price risk management is often intermediated by natural gas marketing companies or financial intermediaries.

Intermediation of price risk management benefits both marketers and their contractual partners. Marketers have lower risk aversion and better knowledge of markets<sup>and</sup> hedging strategies than most other participants in the gas market and can therefore provide better and less expensive risk hedging. Marketers sell market participants risk management services in the form of financial gas contracts, at a premium<sup>that reflects the</sup> risk of the transaction. They then combine the risks of individual financial contracts<sup>into</sup> one portfolio and minimize the overall risk by taking positions in physical and<sup>financial</sup> gas markets.

Intense competition among marketers drives premiums down toward the least cost<sup>of</sup> hedging risk. The increasing complexity of the gas market and competition among<sup>natural</sup> gas marketers have led to consolidation of the marketing segment as a number of marketing companies have emerged into several large marketing houses in<sup>order to</sup> reduce costs, diversify services in natural gas markets, and expand into the developing
<!-- Start of picture text --> Survey Year<br>0.179<br>2012 —}—<br>-0.106<br>2007 ——<br>Region<br>-0.028<br>Other urban —_—e<br>-0.124<br>Rural —+—<br>Individual characteristics<br>0.045<br>Higher education ——_<br>0.077<br>Secondary Education —_—e<br>oloo4<br>Primary Education<br>-0.168<br>Male +<br>-0.200 -0.100 0.000 0.100<br>Point estimate and 95% Cl<br><!-- End of picture text -->
57 America, for example, **as** such **as** half of the lifetime returns to schooling for the children of rural residents is realized by **the** increased likelihood that the children will migrate from **the** rural to **the** urban labor market (Schultz, 1988e). One could analyze this process lf one **knew whore** poople migrated fron, the costs they incurred in moving and where they received their schooling. Alternatively, if **the** individualVs current residential regional labor **mnrket is** held statistically constant in estinating a wage function, this procedure purges from the **estimate** of **the** return to schoolLng that component due to migration, to the extent that interregional migration toward higher wage regions occurs with increasing frequency for the better educated, as it clearly does in Latin America and the United States (Schwartz, 1976). Finally, regional labor market nominal **wage** differences may reflect compensating variation in price levels and reinforclng variation in **the** quality of subsidized public services, and thus not measure accurately real wage differences. Urban high wage regions havo more and better schooling, and regional shift variables in a wage function may also reflect this difference in the quality of schooling across workers (Behrman and Birdsall, 1983). Other than school and health services, most other prices ar% generally higher in urban high wage reglons, particularly housing. On balance, nominal regional wage dlfferences may exceed real **wage** differences, if public services are unimportant. Estimating the participation and wage functions with and without regions **as** explanatory variables should at least help to assess the impdrtance of migration ir. the estimation of school returns. **The** lack of information on migration in the Thailand survey data does not permit any further analysis of this issue **here. The** selection-corrected private rate of return estimates are contrasted
Note that _θ_ controls the thickness of the top end of the income distribution, which is a key determinant of income inequality; the smaller the value of the tail index _θ_ , the larger the proportion of high incomes, the higher the value of inequality. Under the assumption that top incomes are Pareto distributed, the mean top income level takes on the following form: This mean top income level features in the computation of the top income shares as well as the computation of the between-inequality components.<sup>8</sup>

# **2.2.2 Population underlying top income database is unclear**

Let us next address the challenge that emerges when the data underlying DB-2 are not necessarily representative of the whole population (i.e. households with incomes exceeding _τ_ ). Consider the possibility that DB-2 has “over-sampled” some and “under-sampled” other households among the top earners, such that DB-2 no longer yields a consistent estimator for _F_ 2( _y_ ) unless some corrective efforts are made. This is a rather realistic scenario as the data may constitute a series of transactions or listing prices rather than a proper sample drawn from the target population. For ease of exposition we will assume that DB-2 observes actual household incomes and not predictors of income, so that we may focus exclusively on the challenges presented in this section.

We will assume that the data are representative for selected sub-populations and that a representative “sample” can be obtained by anchoring DB-2 to some known population totals. Suppose that the target population can be sub-divided into _D_ districts with _d_ = 1 _, . . . , D_ indicating the district. The top income distribution for district _d_ will be denoted by _F_ 2 _,d_ ( _y_ ) = _Pr_ [ _Y ≤ y|Y > τ, district d_ ]. By extension, let _F_ 1 _,d_ ( _y_ ) = _Pr_ [ _Y ≤ y|Y ≤ τ, district d_ ]. Using this notation the complete income distribution for district _d_ , denoted _Fd_ ( _y_ ), satisfies:

where _λd_ = _Pr_ [ _Y > τ |district d_ ]. The density functions corresponding to _F_ 1 _,d_ ( _y_ ), _F_ 2 _,d_ ( _y_ ) and _Fd_ ( _y_ ) will be denoted by _f_ 1 _,d_ ( _y_ ), _f_ 2 _,d_ ( _y_ ) and _fd_ ( _y_ ), respectively.

> 8As an alternative to assuming a Pareto distribution for the top tail, and estimating the tail index parameter, one could also appeal to multiple imputation methods, see e.g. Douidich et al. (2015). This approach might in fact be more practical in case a more flexible functional form for _m_ ( _xh_ ; _β_ ) is being considered.

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in employment, the costs of protection may be greater than in the case with no wage distortions.

This latter result bears resemblance to the discussion by Eaton and Grossman (1986) who argue that one must be cautious in using profit-shifting as a basis for industrial policy because the existence of profits could be the basis for taxing rather than subsidizing an industry. The appropriate policy depends on how firms formulate expectations, something about which policymakers have little information. In a similar vein, we find that wage premia may result in higher costs of protection depending on the union's valuation of wages versus employment. More strikingly, we show that the "optimal' wage policy is a tax under IRTS and a market sttucture which allows for firm entry and exit. In sum, we show that once one recognizes the endogeneity of wage distortions, it becomes difficult to determine appropriate industrial policy on their basis and extra caution must be exercised under IRTS.

In terms of modelling innovation, this paper is the first to model endogenous union-induced labor market distortions in applied general equilibrium. It is also the first to investigate systematically the impact of labor market distortions under different market structure assumptions. Also in line with our focus on the labor market and the interaction of employment and wage distortions, we incorporate labor-leisure choice in a more satisfactory manner than previously.

The remainder of the paper is organized as follows. In section 2, we describe how we model labor market distortions with and without labor union activity. We also describe how we model economies of scale and how we represent pticing practices. Section 3 describes the data and benchmarking for the counterfactual simulations arnd reports on the welfare costs of VERs under CRTS and no wage distortions. Section 4 evaluated the distortionary costs of
<!-- Start of picture text --> EXTRA HEALTH EXPENDITURE<br>In equivalent additional household tax<br>Base Low case Middle case High case<br>1400<br>1200<br>1000<br>800<br>600<br>400<br>200<br>0<br>in billions of 2007 USD<br><!-- End of picture text -->

# **_Cumulative economic cost_**

To summarize the cumulative economic cost, we derive the present values (PVs) of the differences for three key economic variables between the AMR scenarios and the baseline, using four alternative social discount rates.<sup>14</sup> These PVs summarize the cost of different degrees of inaction against AMR, as reflected by the three scenarios. In practice, different views and social discount rates are employed by countries and multilateral agencies. The annex in Go et al. (2013) surveys some of the discount rates and their implications for welfare results. In the current analysis, we employ three rates: A low rate of 1.4%, similar to the environment study by Stern (2007), which yields higher PVs for the economic losses caused by AMR, thus spurring societal actions that may protect future generations; an intermediate rate of 3.5%, which is the rate used by HM Treasury (2003); and a high rate of 5.5% (as in Nordhaus 2007). To provide a higher extreme for the PV of the AMR costs, we also compute the results for a rate of zero.

Table 5 summarizes the present values for the cumulative economic impact in terms of losses in GDP and trade, as well as additional health costs. Using a discount rate similar to the one used in climate change for long-term shock, the losses (present value) during the period 20152050 may add up to 85 trillion dollars in GDP and 23 trillion dollars in global trade relative to a world without AMR. For an intermediate discount rate of 3.5%, the corresponding figures are still high at 54 and 14 trillion, respectively. In comparison, O’Neill (2016) estimates an economic cost of at least 100 trillion USD, which would match the high case and a discount rate between 0% and 1.4% in this analysis. The O’Neill number comes from the Taylor et al. (2014) study for one of

> 14 A social discount rate, which may differ from market rate, is typically used to derive a net present value as a summary measure of the impact of projects with streams of economic benefits and costs that are uneven over time.

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destination of exports toward a richer market will lead a firm not only to charge a higher output price on average, but also to pay higher _input prices_ on average. Empirically, using detailed information on output and input prices at the firm level and real-exchange-rate changes as a source of exogenous variation in the composition of destination markets, we show that increases in the average income level of export destinations lead Portuguese firms to charge more for outputs and pay higher prices for material inputs, controlling for export share, average destination distance, and total sales. We argue that although differences in mark-ups across countries may explain the output price patterns, it is difficult to reconcile the effects on input prices with models that do not allow a role for quality choice. We interpret the results as supportive of the hypothesis that increased exporting to rich countries leads firms to raise the average quality of goods they produce and to purchase higher-quality inputs.

This paper is most closely related to a recent study by Brambilla, Lederman, and Porto (2012). Using trade-transactions data linked to a firm-level panel survey from Argentina, the authors analyze the effect of the Brazilian devaluation of 1999, which led Argentinian firms to reduce exports to Brazil and increase the share of exports to other destinations — principally the U.S. and Europe. They find that increased exports to richer countries led to higher skill composition and higher wages at the firm level, while increased exports _per se_ had no such effect. The authors interpret the results as supportive of the argument that firms sell higher-quality, skill-intensive varieties in richer countries to appeal to richer consumers there. Relative to the Brambilla et al. paper, the current study makes three main contributions. First, we have access to output price data, including prices of products sold domestically, and we can show how average output prices at the firm level respond to exogenous shifts in destinations. Second, we have access to input price data, and we can show that average material input prices respond similarly. This is a particular advantage because labor may be a special input: there are a number of mechanisms that might lead to rent-sharing within a firm — e.g. collective bargaining, fair/efficiency wages — and these might generate a relationship between wages and mark-ups (and hence destination markets), even in the absence of quality effects. This concern is arguably less salient for material input prices, since arm’s-length supplier-purchaser relationships are less subject to the social pressures and institutional idiosyncrasies that characterize employment relationships in Argentina and elsewhere.<sup>5</sup> Third, we are able to distinguish between the “Washington apples” and income-

> 5Kugler and Verhoogen (2012) consider at some length the possibility that material input suppliers bargain for higher input prices when output prices are higher, but find little evidence for such a mechanism in cross-sectional data in Colombia.

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Democratic Republic, Peru, United States, and Vietnam. Vakis, Rigolini, and Lucchetti (2016) apply this method to measure chronic poverty in 17 countries in LAC between circa 2004-2012.

The purpose of this paper is to introduce a non-parametric adaptation of the parametric point estimate developed by Dang and Lanjouw (2013). The method builds on the upper and lower bound mobility estimates developed by Dang et al. (2014). By taking a weighted average of the residuals obtained from estimations of the income models in the first and second rounds of data, we can predict a point estimate—as opposed to two bounds—of the unobserved income in the first round of data for all households surveyed in the second round. The paper validates this method by estimating intra-generational income mobility using cross-sectional data and by comparing it with true mobility estimates using actual panels in Chile, Nicaragua, and Peru.<sup>2</sup> The proposal in this paper presents some strengths compared to mobility estimates computed by other studies in the literature. First, the key assumption required for the parametric approach developed by Dang et al. (2013, 2014) is the normality of the error terms in the underlying regressions. However, normality tests reject this assumption in Vietnam and Indonesia (Dang et al. 2014). By introducing a non-parametric approach, the method proposed in this paper does not assume any distribution of the error terms in the underlying income regressions.

Second, this non-parametric adaptation of the method predicts a point estimate of the unobserved income in the first round of data for all households surveyed in the second round, instead of just providing joint probabilities of mobility in and out of poverty as in the parametric approach. In doing so, the method allows estimating the income growth of every household between two rounds of surveys as if we had an actual household-level panel dataset.

Third, the paper performs a sensitivity test to study the robustness of results to changes in two underlying parameters—i.e., the number of repetitions that need to be run to obtain mobility estimates and the weights used for averaging residuals.

Fourth, validations in this paper are based on a panel that follows the same households for a period of five years. This allows us to validate the mobility estimates for periods of different lengths of time that include the same households. Validations are done for two periods; one that covers one year and another one that covers five years.

Finally, unlike previous studies, we use harmonized micro data that allow us to validate poverty dynamics using the same underlying income models and variables as the ones frequently

> 2 Like in Cruces et al. (2015), the term “true” mobility refers to mobility estimates using actual panel data.

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kilometers of roads within a circle of 200 km radius across localities for the 1986-2014 period. It can be seen that accessibility according to this variable has increased on average by 19.1 percent.

## **Access to external markets**

Finally, to account for access to external markets, we also construct the minimum travel time and minimum travel cost (using the Pérez Cervantes and Sandoval Hernández, 2015 formula) to one among six major Mexican ports (documented by inegi) and to one among forty-four entry ports to the u.s. (documented by the u.s Department of Transportation, see us dot, 2014). Table 12 in Appendix A provides the mean and median minimum travel time and minimum travel cost to these ports and u.s. border entry ports. Over the 1986-2014 period, access to international markets slighlty improved on average as we measure a decrease in the average minimum travel time (by 8 percent) and in the average minimum travel cost (by 3 percent) to a u.s. border entry port. The same is true for the average minimum travel time and travel cost to a major port (which respectively decreased by 6.1 and 1 percent).

# **. 3 5 Trends in industrial concentration and specialization**

## **3.5.1 Trends in industrial concentration: 2004 – 2014**

We examine how industrial concentration evolved between 2004 and 2014. Although several measures are available in the literature, we resort to the Ellison and Glaeser concentration index (see Ellison and Glaeser, 1997), henceforth denoted eg. Using locality-level plant data and both the 4-digit and 6-digit naics industrial classifications, we calculate eg indexes for all industries and for manufacturing only, for the years 2004, 2009, and 2014 (see Appendix B for the formula and details).

Table 3 reports our results, which leads to the three following comments. First, industries have become more geographically concentrated over the 2004-2014 period. This can be seen in the observed increase in the mean value of the eg index from 0.381 in 2004 to 0.430 in 2014 (a 10.3 percent increase). Second, manufacturing industries are on average more concentrated than overall industries in Mexico. The mean value of the manufacturing eg index is on average 12 to 15 percent higher than the mean value of the eg index calculated for all industries taken together. Third, applying commonly agreed upon thresholds in the literature, about 98-100 percent of industries (for overall industries and for manufacturing) can be said to be concentrated. The fraction of concentrated manufacturing industries (98 percent) is greater than the one reported for Canada (75 percent) in Behrens and Bougna (2015) and is roughly similar to the one reported for the u.s. (97 percent), France (95 percent), and the u.k. (94 percent) formulas and is made to reduce endogeneity issues (see the econometrics section below).

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