rafmacalaba/datause-encoder-data huggingface dataset:
Table 5: Average Trade-Flow vs. Trade-Consumption Elasticities

|Dep.|Var. :|(1)<br>_m_<sup>_DD_</sup><br>_z,t_|(2)<br> ̃_q_<sup>_DD_</sup><br>_z,t_|Diference|
|---|---|---|---|---|
|_Panel A:_ All Goods|||||
|_τ _<sup>_DD_</sup><br>_z,t_||-8.9<sup>_∗∗∗_</sup><br>(1.1)|-7.7<sup>_∗∗∗_</sup><br>(1.1)|16%|
|_Panel B:_ Phaseout Cat|egories||||
|1 _{ Phased } × τ _<sup>_DD_</sup><br>_z,t_||-13.2<sup>_∗∗∗_</sup><br>(1.6)|-10.9<sup>_∗∗∗_</sup><br>(1.6)|21%|
|1 _{ Others } × τ _<sup>_DD_</sup><br>_z,t_||-6.6<sup>_∗∗∗_</sup><br>(1.4)|-6.0<sup>_∗∗∗_</sup><br>(1.3)|10%|
|Year FE||✓|✓||
|HS6 FE||✓|✓||
|Observations||15,153|15,153||

_Note:_ All estimates are obtained form equation 10 and by varying the dependent variable. Column 1 uses imports and column 2 uses our baseline measure of consumption of imports. Estimates in Panel B separate between the effect of _τzt_<sup>_DD_</sup> for good’s whose tariffs were phased-out (class B,C,C+) and the rest. Standard errors, in parentheses, are clustered at * ** *** HS-6 level, _p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01.

period lag in the reception and consumption of imports has the following representation: The results of estimating (4) for _h_ = 1 and _h_ = 7 under this version of _q_ ̃ are contained in columns 3 and 4 of Table 6. The estimates are very similar to the baseline results.

_Different Inventory-Sales Ratios._ - In the baseline measure we use a different inventorysales ratio, _kcz_ , for different exporter countries _c_ , but not for the different destination countries _i_ . To check whether our results are sensitive to this lack of data we impose a common _kz_ across all four directions of trade in our baseline measure. Again, as can be seen in columns 5 and 6, the estimates are very similar to the baseline results. Results are also robust to calculating _kcz_ using the average over HS10-district of entry HH indexes or different levels of aggregation of the HH index.

_Demand Shock._ - In our baseline we assume that the usage of inputs is a constant fraction of inventory holdings. Here, we extend the baseline measure of _q_ ̃ by including a demand shock, _νiczt_ . Under this extension the consumption of imports takes the following 21
|Region‐Year FE|yes|yes|yes|yes|
|---|---|---|---|---|
|Sector‐Year FE|yes|yes|yes|yes|
|Firm controls|yes|yes|yes|yes|
|Observations|140,594|140,594|140,594|140,594|
|R‐squared|0.215|0.216|0.169|0.170|

Note: The reported coefficients are beta coefficients, t‐values in parentheses. The explanatory variables are measured at time t, while the dependent variables are 3‐year changes between t and t+3. The explanatory variables show the share of HGFs in different industries at the same microregion and county, by using the 2‐digit input‐out matrix from OECD STAN. Firm controls are log employment, labor productivity and log capital per labor ratio. Standard errors are clustered at the county‐industry (2‐digit NACE)‐year level. The definition of HGF is the 3‐year Birch employment definition.

# 3.3 Robustness checks

To test the robustness of our results, we first investigate whether the measured relationships changed after the Great Recession in 2007 (Table 8). We find that HGF share among buyer industries matters in both periods, though somewhat more after the Great Recession. The results also indicate that HGF in supplier industries had been significant before the recession and has become less important afterwards.

The results are largely robust when controlling for autocorrelation (Table A3). The larger coefficient following the crisis comes from the 2009‐2012 period, a period of sluggish recovery. Firms with potential buyers who have been high growth even during the crisis, have increased their productivity rapidly.

## **Table 8 Before and after the Great Recession**

||**Laborpro**<br>|**Productivity (3**<br>**ductivity**<br>|**‐year change)**<br>**TF**<br>|**P**<br>|
|---|---|---|---|---|
||**before 2008**|**after 2007**|**before 2008**|**after 2007**|
|HGF Downstream share|0.026***|0.053***|0.035***|0.058***|
||(5.670)|(6.844)|(5.365)|(7.016)|
|HGF Upstream share|0.020***|0.007|0.014**|0.003|
||(4.756)|(1.028)|(2.159)|(0.452)|
|HGF intra‐industry share|0.009**|0.012**|0.025***|0.010*|
||(2.132)|(1.976)|(4.858)|(1.702)|
|Region‐Year FE|yes|yes|yes|yes|
|Sector‐Year FE|yes|yes|yes|yes|
|Firm controls|yes|yes|yes|yes|
|Observations|90,037|50,557|90,037|50,557|
|R‐squared|0.238|0.173|0.178|0.155|

Note: The reported coefficients are beta coefficients, t‐values in parentheses. The explanatory variables are measured at time t, while the dependent variables are 3‐year changes between t and t+3. The explanatory variables show the share of HGFs in different industries at the same microregion and county, by using the 2‐digit input‐out matrix from OECD STAN. Firm controls are log employment, labor productivity and log capital per labor ratio. Standard errors are clustered at the county‐industry (2‐digit NACE)‐year level. The definition of HGF is the 3‐year Birch employment definition. Small firms are firms with less than 5 employees We conduct further robustness checks to investigate whether the results are robust to including different sets of fixed effects to the regressions. Recall that our baseline regression includes region‐year 17
isolation and the defensive attitude of the Indians,” and similar reports on Ceylon state that it is the caste system and the “conservative pressure of religious forces,” and on Jamaica, “a lack of energy and cooperation.”<sup>15</sup> Assar Lindbeck summarized economic thinking about developing countries in the late 1950s as follows.<sup>16</sup> “The dominant vision among economists with regard to the basic mechanisms of the development process changed considerably during the course of the post-World War II period. The main visions during the 1940s, 1950s and early 1960s have usually been characterized as structuralist—ideas tied to names like Paul Rosenstein-Rodan, Ragnar Nurkse, Raul Prebisch, Hans Singer and Gunnar Myrdal. The term ‘structuralist’ then refers to the notion that the developing countries are characterized by genuine inflexibility in the allocation of resources, due to inelasticities of supply and demand for goods, services and factors, as well as of productive effort in response to changes in economic incentives, such as relative price signals. Based on this vision of the development process was the idea of binding constraints on economic growth, like the “two-gap” theory of a savings and balance-of-payments constraint, due to asserted weaknesses in the response of saving to changes in income and interest rates, and of exports, imports and long-term capital movements to changes in exchange rates, relative prices and rates of return on productive assets. From these views followed both a strong distrust of the price mechanism, and as a mirror image, considerable enthusiasm for comprehensive central planning of inputs, outputs and investment activity.

“Recommendations were prevalent regarding public regulations and interventions to increase public saving and investment, the latter often in the form of large-scale projects in basic industries in order to deal with indivisibilities and externalities. Heavy protection, via direct controls (i.e., licensing) and high tariff barriers were other important components of the package of policy recommendations. Although some of these policies could certainly be well defended in the early post-World War II period, many governments were not flexible enough to shift to more decentralized and less interventionist policies when the usefulness of the previous approaches eroded.”

> 15  Kapur, Lewis and Webb (1997), p. 146. 16

> This quote is taken from an evaluation of World Bank research carried out by Lindbeck (1984).

8
6 Bangladesh in terms of the depth of their poverty. And it is plain from at least casual observation that the poor are not all equally poor. So I shall also consider two alternatives to the headcount index, both of which are members of the Foster, Greer and Thorbecke (FGT) (1984) class of additively decompoEable poverty measures, each member of which is identified by a nonnegative parameter a. The headcount index is also a member of this class. The hree FGT measures used here are: (i) The headcount index of poverty given by the percentage of the population living in households with average consumptions below the poverty line; this is the FGT measure for a - 0. While this is a simple measure to interpret, it has the disadvantage that it is entirely insensitive to changes below the poverty line; for example, a poor person may become poorer, but measured poverty will not change.

(ii) The Doverty gaR measure, defined as the consumption deficit of the poor as a proportion of the poverty line divided by the population size; this is the FGT measure for a - 1. Thus, letting g=(z-y)/z denote the proportionate poverty deficit of a person with income or consumption y below the poverty line z, and setting g-O for the non-poor, the FGT poverty gap measure is simply the arithmetic mean of g over tne whole population. The measure is then also equal to the average poverty deficit of the poor times the headcount index of poverty.

(iii) The distributionally sensitive FGT measure, whereby, instead of weighting the various poverty deficits of the poor equally (as in the previous measure) they are weighted by the deficits themselves. The resulting measure is then simply the mean of the squared proportionate poverty deficits i.e., a = 2. This measure satisfies the main axioms ftcr a desirable poverty measure
Table A2: Sample Size

|**Birth cohort**|||**Numb**|**er of co**|**horts b**|**y year**|||**Number of**|**individuals**|
|---|---|---|---|---|---|---|---|---|---|---|
|**(years)**|**1995**|**1997**|**1999**|**2001**|**2003**|**2005**|**2007**|**2009**|**Unweighted**|**Weighted**|
|1936-38|14|18|18|18|18|18|18|18|29237|10034004|
|1939-41|14|18|18|18|18|18|18|18|42596|14354638|
|1942-44|14|18|18|18|18|18|18|18|54768|18299629|
|1945-47|14|18|18|18|18|18|18|18|75225|25036860|
|1948-50|14|18|18|18|18|18|18|18|95509|32201858|
|1951-53|14|18|18|18|18|18|18|18|117188|39522084|
|1954-56|14|18|18|18|18|18|18|18|139507|47440640|
|1957-59|14|18|18|18|18|18|18|18|159606|54468349|
|1960-62|14|18|18|18|18|18|18|18|175293|60275779|
|1963-65|14|18|18|18|18|18|18|18|191295|66839428|
|1966-68|14|18|18|18|18|18|18|18|188610|66279462|
|1969-71|14|18|18|18|18|18|18|18|198280|68779300|
|1972-74|0|18|18|18|18|18|18|18|178815|62720401|
|1975-77|0|0|18|18|18|18|18|18|165060|57798437|
|Total|168|216|216|216|216|216|216|216|1467114|503532031|

Source: Author’s calculations based on data from Latin American household surveys, 1995-2009, biannual.

28
Table 6: Changes in farm distribution by production size and changes in planted coffee area, 19921999

|_Production_|_Distribution_<br>_p_|_of coffee farms_<br>_roduction (%)_|_by size of_|_Land_|_under cof_|_fee in acres_||
|---|---|---|---|---|---|---|---|
|_size group_|_1992_|_1995_|_1999_|_1995_||_1999_||
|1|31.0|33.3|29.7|_0.3_<br>0.6|_0.9_|_0.4_<br>0.5|_0.5_|
|2|22.1|15.8|22.3|_0.5_<br>0.8|_1.2_|_0.6_<br>0.7|_0.8_|
|3|22.8|23.6|23.1|_0.6_<br>0.8|_1.1_|_0.9_<br>1.0|_1.1_|
|4|14.3|15.6|15.1|_1.0_<br>1.3|_1.7_|_1.2_<br>1.5|_1.8_|
|5|9.9|11.6|9.9|_1.5_<br>2.0|_2.6_|_2.5_<br>2.9|_3.3_|
|total|100.0|100.0|100.0|_1.0_<br>1.0|_1.2_|_1.0_<br>1.1|_1.2_|

Source: Authors’ calculations.

Note: 95 % confidence intervals in italics (standard errors corrected for survey design). Production size groups are defined according to coffee production quantity. Production size group 1 includes all coffee farms that produce less than 0.5 times median coffee production in the respective year. Production size group 2 (3, 4, 5) produces between 0.5. and 1 (1and 2, 2 and 4, more than 4) times median production in the respective year. This procedure helps to account for changes in survey design as well as external shocks (above all weather shocks) that affect overall production levels.

Regarding the evolution of coffee production, 1992 and 1995 should not be compared because of large differences in the survey design, while 1995 and 1999 are more readily comparable. The data show a large and significant expansion of coffee production across all farm sizes (Table 7), which may at least partly explain why coffee households could raise their living standards in a phase of falling prices. In combination with the rising share of coffee farmers, this dramatic production increase also provides an indication that farmers have indeed responded to the coffee price boom, with the delay in production reflecting the time that has to pass between planting and harvesting.

22
- Ingole, V., et al. 2022. "Mortality risk attributable to high and low ambient temperature in Pune city, India: A time series analysis from 2004 to 2012." _Environmental Research_ 204: 112304.

- Jafino, B. A., B. Walsh, J. Rozenberg, and S. Hallegatte. 2020. “Revised Estimates of the Impact of Climate Change on Extreme Poverty by 2030.” _World Bank Policy Research Working Paper_ 9417. Washington, DC: World Bank.

- Jonkman, S. N., et al. 2009. "Loss of life caused by the flooding of New Orleans after Hurricane Katrina: analysis of the relationship between flood characteristics and mortality." _Risk Analysis: An International Journal_ 29(5): 676-698.

- Kazianga, H., and C. Udry. 2006 "Consumption smoothing? Livestock, insurance and drought in rural Burkina Faso." _Journal of Development Economics_ 79(2): 413-446.

- Kim, J., A. Lee, and M. Rossin-Slater. 2021. “What to Expect When It Gets Hotter: The Impacts of Prenatal Exposure to Extreme Temperature on Maternal Health”. _American Journal of Health Economics_ 7(3): 281-305.

- Kishore, A., and D. Spears. 2014. "Having a son promotes clean cooking fuel use in urban India: Women’s status and son preference." _Economic Development and Cultural Change_ 62(4): 673-699.

- Korkeala, O. 2012. “The effect of weather shocks and risk on schooling and child labour in rural Indonesia.”

- Kovats, S., K. L. Ebi, and B. Menne. 2003. _Methods of assessing human health vulnerability and public health adaptation to climate change_ . No. EUR/03/5036776. World Health Organization.

- Lee, Y., B. Haile, G. Seymour, and C. Azzarri. 2021. “The heat never bothered me anyway: Gender‐specific response of agricultural labor to climatic shocks in Tanzania.” _Applied Economic Perspectives and Policy_ 43(2): 732-749.

- Maccini, S., and D. Yang. 2009. “Under the weather: Health, schooling, and economic consequences of early-life rainfall.” _American Economic Review_ 99(3): 1006-26.

- Mahajan, K. 2017. “Rainfall shocks and the gender wage gap: Evidence from Indian agriculture.” _World Development_ 91: 156-172.

- Maitra, P., and A. Tagat. 2019. Labour supply responses to rainfall shocks. _Manuscript_ . Available at SSRN: https://ssrn.com/abstract=3449144.

- Marchetta, F., D. E. Sahn, and L. Tiberti. 2019. “The Role of Weather on Schooling and Work of Young Adults in Madagascar.” _American Journal of Agricultural Economics_ 101(4): 1203–1227.

24
is 4.84 and in Lower middle income countries, 4.68.

# **4.2 Correlates**

We now explore some correlates of the labor mobility costs. We do this by plotting simple bivariate non-parametric regressions between various country characteristics and the size of the estimated mobility costs. We organize this description around four groups of correlates: country well-being, features of the labor market, education, and other constraints. No causality is implied by this analysis, only simple correlations. Data on these correlates are from the World Development Indicators, and they represent averages for the period 1995-2007.

Figure 3 describes the correlation between the mobility costs and some measures of well-being. Richer countries, in terms of per capita GDP, tend to show lower mobility costs (top left panel). However, there is no obvious correlation between the growth rate of per capita GDP and the size of those costs (top right panel). We don’t observe any statistical correlation between _C_ and inequality (measured by the Gini coefficient), but there is a very strong positive correlation with both the poverty head-count and the poverty gap.

In Figure 4, we plot _C_ against the structure of employment in agriculture, industry, and services. The correlation is strongly negative with agriculture and strongly positive with industry and services. Furthermore, the top panel of Figure 5 documents a positive correlation between the mobility costs and self-employment, while the bottom panel reveals a positive correlation with the proportion of individuals in vulnerable employment conditions. The implications are that countries that are more highly specialized in non-primary sectors tend to show lower labor mobility costs, as do countries with large self-employment and low job quality.

Figure 6 describes the correlation with educational variables. The top panels and the bottom-left panel show that the labor mobility costs are inversely correlated with the educational attainment of the labor force. This can be more clearly seen in the bottom-left panel, where the negative correlation of _C_ with the share of the labor force with tertiary education is evident. In the bottomright panel, we plot _C_ against the pupil to teacher ratio in secondary education. The graph shows a positive correlation, thus suggesting the countries with lower education quality (higher pupil-teacher 11
As Hausman and Rodrik (2003) have emphasized, different countries’ exports reflect a wide range of differences in trade regimes, as well as idiosyncratic factors that lead apparently similar countries to have very different product mixes at the finer levels of disaggregation. The top 25 exports of China and India for 2004, at the six-digit level of the Harmonized system (HS), which account for 58.4 percent of India’s merchandise exports, and 38.4 percent of China’s turn out to be almost mutually exclusive sets. Only one product—refined petroleum - enters both lists, accounting for over 9 percent of India’s exports and 0.9 percent of China’s. A notable feature of China’s list is the prominence of computer and electronic equipment products under Chapters 84 and 85. These two chapters (which also include nonelectronic equipment) alone accounted for almost 42 percent of China’s exports in 2004, up from 16 percent in 1994. In India, three HS products under Chapter 71 (diamonds and jewelry) and refined petroleum under Chapter 27 likewise accounted for 28 percent of total exports.

# **Methodology and Simulation Design**

The preceding discussion of trade patterns provides valuable background, but does not allow us to assess the implications of high growth in China and India and increased competition between China and India as India implements reforms that speed up its integration into global production networks. To do this, we use a modified version of the standard GTAP model.<sup>4</sup> The model emphasizes the role of intersectoral factor mobility in determining sectoral output supply. Product differentiation between imported and domestic goods, and among imports from different regions, allows for two-way trade in each product category, depending on the ease of substitution between products from different regions. Factor inputs of land, capital, skilled and unskilled labor, and in some sectors a natural resource factor, are included in the analysis. The model includes the explicit treatment of international trade and transport margins, a “global” bank designed to mediate between world savings and investment, and a relatively sophisticated consumer demand system designed to capture differential price and income responsiveness across countries.

The constant returns to scale version of the GTAP model was adjusted to incorporate China’s duty exemptions—which have been a key reason for the rapid integration of China into global production

> 4 This applied general equilibrium model is documented comprehensively in Hertel (1997) and in the GTAP Data Base documentation (Dimaranan 2006).

8
agricultural emissions (Steinfeld et al., 2006). In terms of livestock production, the relative importance of ruminants globally is likely to decline in the future, coupled with a move towards high productivity, and Steinfeld et al. (2006) do not anticipate increases in enteric fermentation in the future (methane emissions from animal manure are much lower). However, as with CO2, there are considerable system and regional differences. Recent estimates by Herrero et al. (2008) indicate that methane emissions from African cattle, goats and sheep are likely to increase from their current level of about 7.8 million tonnes of methane per year in 2000 to 11.1 million tonnes per year by 2030, largely driven by increase in livestock numbers. Again, there are considerable differences in methane emission per tropical livestock unit (TLU, 250 kg bodyweight), depending on production system and diet, from 21 (less productive systems) to 40 (more productive systems) kg per TLU per year.

The third important greenhouse gas is nitrous oxide, a powerful, long-lived gas (its global warming potential is 310-times greater than CO2 over a 100-year time horizon) (UNFCCC, 2007). Atmospheric concentrations are some 16% above the levels in preindustrial times. Nitrous oxide also has impacts on stratospheric ozone depletion. Ecosystem sources (mostly soil micro-organisms in a wide variety of environments) account for about 90% of all emissions (MA, 2005). Increased emissions are driven largely by fertilizer use, agricultural nitrogen fixation, and atmospheric nitrogen deposition. Livestock activities contribute substantially in two ways: in the use of manure and slurry as fertilizers, and through the use of fertilizers to produce feed crops. These account for some 65% of global anthropogenic emissions (75-80% of agricultural emissions). Emissions of N2O originating from animal manure are much higher than any other N2O emission caused by the livestock sector, and these emissions are dominated by mixed crop-livestock systems (Steinfeld et al., 2006).

Livestock contribute substantially to global emissions of greenhouse gases (Table 9), which have direct impacts on the climate via warming of the atmosphere, with consequent, complex impacts on rainfall amounts and patterns.

For the future, agricultural N2O emissions are projected to increase by 35-60% by 2030, because of increased nitrogen fertilizer use and increased animal manure production (Bruinsma, 2003). If methane emissions grow in direct proportion to increases in livestock numbers, global livestock-related methane may increase by 60% by 2030 (Bruinsma, 2003). This increase could be moderated by changes in feeding practices and manure management, 52