rafmacalaba/datause-encoder-data huggingface dataset:
-33

-

# Table 11: (Continued)

||LnlCPAGF 1/||Independe<br>LnICPAGF<br>2/|nt<br>Variables<br>LnICPAGN|F 1/|Ln I CPAGF 2/|
|---|---|---|---|---|---|---|
|Intercept|1.779<br>(0.854)||0.803<br>(0.600)|2.314<br>(0.729)||0.468<br>(0.376)|
|t-l|0.722||0.872|0.309<br>||0.453<br>|
||(0.192)||(0,189)|(0.173)||(0.162)|
|t-2|-0.111||-0.410|-0.038||-0.097|
||(0.259)||(0.258)|(0.170)||(0.176)|
|t-3|0.434||0.639|-0.029||0.168|
||(0.219)||(0.232)|(0.172)||(0.178)|
|t-4|-0,169<br>(0.222)|<br>|-0.152<br>(0.247)|0.295<br>(0.131)||0.364<br>(0.138)|
|f-5|-0.234<br>(0.176)|<br>|-0.124<br>(0.191)||||
|"rot_l|-0.262<br>(1.295)|<br>||-0.156<br>(1.540)|||
|TRBt-2|-2.765<br>(1.941|<br>)||-3.123<br>(2.719)|||
|TRot-3|1.952<br>(2.057|<br>)||4.038<br>(2.134)|||
|TRBt-4|2.574<br>(2.152|<br>)||-4.189<br>(1 .530)|||
|TRBt_5|-3.200<br>(1.764|)|||||
|Sample|1954-8|8|1954-88|1953-88||1953-88|
|h|36||35|36||36|
|R*2|0.811||0.708|0.866||0.806|
|s.c.|0.092||0.104|0.097||0.109|
|2<br>(1) X(m)<br>(2) X2||2<br>X(5)<br>X(2)|<br>*9.823<br>5.872||2<br>X(4)<br>X(4)|* 9.302<br> <br>*0.093|

1/ Corresponds to equation (2) 2/ Corresponds to equation (3) 3/ 4-...,t-n corresponds to the lag dependent variable for each commodity price Index. 4/ Chi-squared text for the causality of interest rates of prices. 5/ Chi-squared test for the causality of prices on inte est rates.
biased nature of the data. It is restricted in the sense that it doesn’t cover trade between all the bilateral region pairs. Rather, it only contains flows from regions where exporters and importers are situated to regions where ports are. Being based on shipments of exporting firms could bias estimates of trade costs as these firms are more productive than non-exporting firms. Under the assumption that regional distribution of exporters is not endogenous, one can still identify relative trade costs across regions. Since this may be the only data source available for many countries, it is still a useful venue for researchers to pursue. Recent examples are Van Leemput (2021) for India, Cosar and Demir (2016) for Turkey, Baldomero-Quintana (2020) for Colombia and Fan, Lu and Luo (2021) for China.

More recently, a number of countries have made available administrative databases that contain information about firm-to-firm transactions. While such transactions are not direct evidence for cargo shipments—even if they were related to freight, they do not contain information about the characteristics of the goods involved—they inform researchers about trade flows between locations. Such links can be very granular if the addresses of firms or establishments are also observed. Otherwise, they can be aggregated to a regional level to create an internal trade flow matrix. In a recent application using Turkish data, Coşar et al. (2021) estimate the impact of road capacity (i.e., lanes) and quality improvements. According to their results, a one-hour reduction in travel time between two districts increases bilateral trade between those districts by around 8.2 percent. This effect translates into an almost 1 million USD increase in trade flows for a typical supplier district over 10 years. If two districts were not trading prior to the road investment program, a one-hour travel time reduction increases the probability that they will start doing so by 11 percent.

In estimating the elasticity of transport costs to travel distances, a threat to identification arises from the fact that travel distances emanate from infrastructure placement, which may itself be endogenous to expected trade potential. Endogeneity bias may affect the ordinary least squares (OLS) estimates in either direction. If selective road investments prioritize routes with the highest trade potential, the OLS estimate in a regression of trade flows to driving distance will be biased upward. Since endogenous placement lowers driving distance where trade flows are higher, this bias generates the impression that trade flows are very sensitive to transport costs. On the other hand, if road investments follow distributional concerns and prioritize disadvantaged locations whose exports may still lag behind, the downward bias generates the impression that trade flows are not sensitive to transport costs.

What can be done to overcome this threat? To some extent, geodesic straight line distances capture the exogenous variation in travel distance due to geography but are not directly relevant for estimating how trade flows respond to actual transport 14
<!-- Start of picture text --> 0.4<br>0.35<br>0.3<br>y = 3E-05x 2 - 0.0062x + 0.3855<br>R 2 = 0.7469<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>0 20 40 60 80 100 120 140 160<br>Monthly per capita expenditure percentile<br>Unemployment Ratio<br><!-- End of picture text --> Source: Latvia Household Budget Survey 2000.

# **IV. Determinants and Correlates of Poverty in the Late 1990s**

In this section, we use a probit model in a standard form with binary outcomes to investigate the determinants of poverty in the late 1990s. This model falls within the family of generalized linear models, with the exception that the response is not continuous, but discrete. This discrete response is related to exogenous variables through a non-linear probit link function expressed as follows:

> where Φ −1 is the inverse of the standard normal cumulative distribution function (CDF). The transformation to the CDF is important for welfare inference because of its non-decreasing shape. In the general linear model, the response η is expressed as a function of linear predictor variables _x_ 1, _x_ 2 ,L,<sup>_x_</sup> _k_ .<sup>33</sup> This specification is very useful for studying events with binary outcomes, and can be quite appealing for investigating the determinants of poverty when the distribution follows a binomial process, with the response taking values 1 for non poor and 0 for poor households or individuals.<sup>34</sup> We assume that the observations are a random sample of unreplicated data with dichotomous response, accounting for the widespread nature of poverty illustrated notably by the relatively large poverty incidence. Hence, the continuous response derived directly from the aggregated household per capita

> 33 For further details on probit models, see Greene (1997).

> 34 This model can also be extended to model events with polychotomous outcomes, depending on the nature of the * +

> transformation imposed to the initial distribution of income, which is continuous and has the positive real line (<sup>_y_</sup> _h_ ∈ R ) as support.

23
# **Regression Table 3** : _Distribution effect on poverty near crises_

|Dependent variabl|e: Change|inpover|tyhead|count at|unchan|gedmea|n inco|me (serie|s of Ta|ble4)||
|---|---|---|---|---|---|---|---|---|---|---|---|
||Equation:|3.A||3.B||3.|C|3.D||3.|E|
|||Coeff.|t-Stat|Coeff.|t-Stat|Coeff.|t-Stat|Coeff.|t-Stat|Coeff.|t-Stat|
|Constant||-0.80|0.5|0.56|0.7|3.13|*2.5|3.03|*2.5|4.98|1.9|
|Latin America||2.26|1.3|1.66|1.7|2.27|*2.1|2.46|*2.4|2.80|*2.2|
|Transition||-0.01|0.1|0.132|0.2|||||||
|Institutions (Corrupt)||-0.102|0.4|-0.244|1.6|-0.839|**3.1|-0.901|**3.4|-0.73|**2.4|
|Type of crisis (Micro)||||||||1.91|1.5|||
|Initial Gini||||||||||-0.06|0.8|
|_Countries excluded_||_None_||_KEN, _|_KGZ_|_Trans, _|_KEN_|_Trans, _|_KEN_|_Trans_|_, KEN_|
|R-squared / NOBS||0.046|41|0.119|39|0.350|24|0.417|24|0.371|24|
|Adjusted R-squared||-0.031||0.044||0.288||0.329||0.277||
|S.E. of regression||4.1||2.2||2.3||2.3||2.4||
|Loglikelihood||75.0||95.3||57.7||59.0||58.1||

** and * indicate significance at the 1% and 5% levels, respectively.

24
fundamentally—and national accounts data from the World Development Indicators in local currencies. Each time we consider GDP per capita and GDP. We prefer GDP per capita throughout the paper since it is closer to a welfare relevant metric but expect to obtain similar results with GDP.

– **Table 5** Robustness: different data sets for GDP and GDP per capita

||_GD_|_P per capita s_|_eries_|_G_|_DP series_||
|---|---|---|---|---|---|---|
||PWT7<br>_rgdpch_<br>(1)|PWT9<br>_rgdpna/pop_<br>(2)|WDI<br>_gdplcu/pop_<br>(3)|PWT7<br>_rgdpch × pop_<br>(4)|PWT9<br>_rgdpna_<br>(5)|WDI<br>_gdplcu_<br>(6)|
|||_Panel (a) Str_|_uctural breaks _|_estimated with s_|_ize = 0.10_||
|Executive Constraints ( <sup>�</sup><br>_INS_0)<br>|-0.209***<br>(0.064)|-0.255***<br>(0.077)|-0.170**<br>(0.069)|-0.181***<br>(0.046)|-0.163***<br>(0.045)|-0.161***<br>(0.051)|
|Fractionalization ( <sup>�</sup><br>_ELF_15)<br><br>|0.018***<br>(0.004)|0.020***<br>(0.005)|0.009*<br>(0.005)|0.011***<br>(0.003)|0.009***<br>(0.002)|0.008***<br>(0.002)|
|�<br>_INS_0_×_ <sup>�</sup><br>_ELF_15|-0.003**<br>(0.001)|-0.004***<br>(0.001)|-0.005**<br>(0.002)|-0.003***<br>(0.001)|-0.004***<br>(0.001)|-0.003***<br>(0.001)|
|Exits|48|43|47|59|63|53|
|Spells|58|54|59|62|64|56|
|||_Panel (b) Str_|_uctural breaks _|_estimated with s_|_ize = 0.20_||
|Executive Constraints ( <sup>�</sup><br>_INS_0)<br>|-0.197***<br>(0.048)|-0.239***<br>(0.062)|-0.175***<br>(0.060)|-0.122***<br>(0.036)|-0.109**<br>(0.046)|-0.142***<br>(0.042)|
|Fractionalization ( <sup>�</sup><br>_ELF_15)<br><br>|0.014***<br>(0.004)|0.014***<br>(0.005)|0.009**<br>(0.004)|0.008***<br>(0.003)|0.006**<br>(0.003)|0.007***<br>(0.002)|
|�<br>_INS_0_×_ <sup>�</sup><br>_ELF_15|-0.004***<br>(0.001)|-0.003**<br>(0.001)|-0.004***<br>(0.002)|-0.004***<br>(0.001)|-0.003***<br>(0.001)|-0.003***<br>(0.001)|
|Exits|67|61|61|72|76|75|
|Spells|79|74|75|75|77|78|

_Notes_ : All models include region FEs, the US real interest rate, the log of initial GDP, and a constant. The standard errors are clustered at the country level to account for repeated spells.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 _Source_ : Authors’ analysis based on data from the Penn World Tables version 7.0, Penn World Tables version 9.0, and the World Development Indicators. Additional data come from the Polity IV project, Desmet et al. (2012), and the Federal Reserve Economic Data (FRED) database.

Our main findings are virtually unaffected by the choice of data sets and preference for a particular national income series. Table 5 takes our preferred specification, including the mean-centered interaction term and region fixed effects, but replaces the dependent variable with the duration estimated by running our break search algorithm on these different data sets. Columns (1) to (3) focus on GDP per capita, while columns (4) to (6) report results for GDP. As usual, we consider two type I error rates in panels (a) and (b). All results are qualitatively (and often even numerically) similar. Note that the underlying list of slumps 30
# **3.2 Flooding and relative wealth**

Between 2000 and 2018 (table 1), there were an average of 2.2 floods in urban areas and 1.5 floods in rural areas in our sample. Table 3 presents the results of the regressions on flood exposure and RWI. First, we regress RWI in a dummy for whether the location experienced a flood between 2000 and 2018. In urban areas, locations that have experienced a flood between 2000 and 2018 have a lower RWI than locations that have not experienced any flood in that period. The difference is small and statistically significant without state FEs; it is not significant when state FEs are included. In contrast, in rural areas, flooded locations have a significantly higher RWI than nonflooded locations. The difference is small, less than 0.1 standard deviations of RWI or the approximate difference in wealth between two neighboring sub-districts in Dhaka or New Delhi, but it is statistically significant with and without state FEs.

To examine the intensive margin of exposure to floods, we regress RWI on the number of floods experieced between 2000 and 2018, restricting the sample to locations that experienced at least one flood in this period. Note that these “ever-flooded” locations were on average affected by multiple floods during this period, experiencing an average of 6.6 floods in urban areas and 6.2 in rural areas, which suggests that these locations are generally flood prone. Among these everflooded locations, in _urban_ areas, a higher number of flood events during 2000-18 is associated with a significantly lower RWI (table 3).<sup>4</sup> This result for South Asia is consistent with findings from other settings, that the poor are more exposed to flooding in urban areas (Hallegatte et al. 2020; Gandhi et al. 2022). In contrast, in _rural_ ever-flooded locations, the relationship between the number of flood events during 2000-18 and RWI is weak and statistically not significant.

# **3.3 Firms and climate shocks in India**

**Exposure to heat.** Table 4 present summary statistics of our merged firms and weather dataset for India. Firms in urban areas are generally larger, with 2.3 workers as compared to 1.8 in rural areas. There is also larger variation in firm size in urban areas. The average temperature experienced by firms in India is about 30 degrees Celsius in both urban and rural India. The average number of

> 4 Among ever flooded locations, when the urban and rural samples are pooled, places with more flooding have lower wealth. This suggests that the exposure gradient within urban areas on wealth is strong enough to offset the urbanrural wealth gradient.

10
availability of natural resources. For these two reasons, Vietnam's true manufacturing export potential may be somewhat over-estimated in the above calculations.

On the other hand, the above results are obtained with a cross-country regression based on 1990 rather than 1997 data. If anything, this is likely to lead to an underestimation of Vietnam's true manufacturing export potential since there tends to be an increase in world demand for manufactures over time (see Wood and Berge, 1997). Indeed, it is unlikely that all the increase in East Asian countries manufacturing exports between 1990 and 1997 (see table 14) can be fully accounted for by corresponding changes in resource endowments.

# _d) The value offuture manufacturing exports_

The above calculations have shown that there is scope for Vietnam to increase its manufacturing exports. But by how much? The above section has focused only on one aspect of Vietnam's exports, namely its commodity composition. It has shown that given Vietnam's resources, manufactures should quickly be able to reach around 63% of total exports. What does this suggest with respect to the absolute value of manufacturing exports?

Traditional trade theory generally assumes that economies are efficient. This means that production is located on the production possibility frontier, such as for example on point B in figure 2 below. In such a case, any increase in the production of manufactures must occur at the expense of primary production. So, for example, a movement along the production possibility frontier from B to point A, leads to an increase in manufacturing production but to a fall in primary production. Such a shift occurs as a result of an increase in relative prices of manufactures. And, as already emphasized, it assumes that the initial allocation of resources is optimal. It would be difficult to argue that this model accurately reflects the situation in Vietnam.

If countries start with an allocation of resources that is sub-optimal, production that takes place within the production possibility frontier. If Vietnam's current production is taking place at a point such as C in figure 2, a better allocation of resources could increase both manufacturing and primary production. However, if Vietnam's initial allocation of resources is such that production takes place on point D, there is no scope for increasing primary production through a better allocation of resources. The dotted line in figure 2 represents the production possibility frontier of a country that has reached zero marginal productivity in primary production. Any additional worker allocated to agriculture, while raising the number of people among whom agricultural incomes have to be shared, do not contribute any additional output. This happens when the amount of land is not large enough to absorb more than a given share of a country's workforce. In such a case, a better allocation would entail a movement from D to A along the dotted line, increasing manufacturing production while leaving primary production unchanged.

15
requirements. Individual member states have also provided generous excise tax concessions without limit, and Germany for example, has provided tax exemptions of €0.4704/ ($0.64) per liter of biodiesel and €0.6545 ($0.88) per liter of ethanol prior to new legislation in 2006 (Kojima, Mitchell and Ward, 2007; Global Subsidies Initiative 2008). These strong incentives and mandates encouraged the rapid expansion of biofuels in both the EU and U.S.

**The EU began to rapidly expand biodiesel production after the EU directive on biofuels** (2003/03/EC) entered into effect in October 2001 stipulating that national measures must be taken by EU countries aimed at replacing 5.75 percent of all transport fossil fuels with biofuels by 2010. This led to an increase in biodiesel production from 0.28 billion gallons in 2001 to 1.78 billion gallons in 2007 (FAPRI 2008). Rapeseed was the primary feedstock, followed by soybean oil and sunflower oil. The combined use of vegetable oils for biodiesel was 6.1 million tons in 2007 compared with about 1.0 million tons in 2001.

**The U.S. expanded its biodiesel production following legislation passed in 2004** which took effect in January 2005, providing an excise tax credit of US$1.00 per gallon of biodiesel made from agricultural products. This contributed to an increase in biodiesel production in the U.S. from 0.03 billion gallons in 2005 to .44 billion gallons in 2007 and used 3.0 million tons of soybean oil and 0.3 million tons of other fats and oils. These two policies encouraged the rapid expansion of oilseeds production for biodiesel and contributed to the surge in vegetable oils prices, with annual average soybean oil prices rising from $354/ton in 2001 to $881 per ton in 2007. Monthly soybean oil prices rose to $1,522/ton in June 2008. Since oilseeds are close substitutes and prices highly correlated, this led to similar increases in other oilseeds prices.

**Land use changes due to expanded Figure 6. U.S. Maize and Soybean Area biofuel’s feedstock** **<u>(million hectares planted)</u>** 40 **production have** Maize **been large and have** Soybeans **led to reduced** 35 **production of other crops.** The U.S. 30 expanded maize area 23 percent in 2007 in response to high 25 maize prices and rapid demand growth 20 for maize for ethanol 2000 2001 2002 2003 2004 2005 2006 2007 production. This Source: DECPG calculations based on USDA data. expansion resulted in a 16 percent decline

in soybean area (Figure 6) which reduced soybean production and contributed to a 75 percent rise in soybean prices between April 2007 and April 2008.

10
group experiences a penalty relative to the reference group. Finally, β3 < 0 indicates that there is an _additional_ penalty for being both female and a member of a minority group. The _cumulative_ effect for ethnic minority women is captured through the sum of all three effects.<sup>26</sup> We focus on three educational outcomes. In the first version of the model, EDUi is a _binary outcome_ indicating whether individual i aged 12 years or above is literate or not, so we use a probit estimator and report average marginal effects. Due to data availability constraints, this model is run for only Bolivia, Mexico, Peru and Sierra Leone. In the second model, EDUi is an _ordered categorical outcome_ representing educational attainment for individual i aged 25 and above, i.e. did not complete primary education, completed primary school, completed secondary school, and completed university. We estimate this model for all of the countries in the sample using an ordered probit regression and report average marginal effects for primary school completion and secondary school completion.<sup>27</sup> To assist in the interpretation of the results, we also complement the second model with a set of probit regressions, after collapsing the four educational attainment categories into two binary variables (the first variable is equal to one if individual i completed primary school or higher and zero otherwise; the second variable is equal to one if the individual completed secondary school or higher and zero otherwise).

Since the dependent and explanatory variables are all categorical, the interpretation of the model becomes analogous to the difference-in-differences model, where education outcomes of a “treatment group” are compared against those of a benchmark group. In this model, the benchmark category is _males in the reference_ group (where Gi = 0 ∧ ETHi = 0). In Equation (1), β3 is an indicator of whether there is a differentiated effect of ethnicity on educational

> 26 Due to non-linearity, the individual and combined impacts of being a female, being a minority, and being both are captured by the marginal effects, rather than by the coefficients. However, since f(⋅) is strictly monotonic, the regression coefficients have the same sign as the marginal effects.

> 27 We limit our analysis to primary and secondary school completion because these two outcomes are more compatible with an age cut-off. While it is reasonable to assume that most individuals who will complete primary and secondary school will have done so by the time they are 25, a similar assumption for university completion may not be as well founded. Nevertheless, it is worthwhile to mention that an analysis of university completion rates achieved results in the same direction as primary and secondary school completion, but with smaller magnitudes given the low level of university completion in countries studied in this paper.

14
Rogoff (2010), for example, show in a panel of high-income and developingcountries that high levels of sovereign debt lead to lower GDP growth in subsequent years. A recent debate has emerged, also, on the effectiveness of fiscal policy when debt levels are high, or conversely whether fiscal contractions may at times be expansionary, as claimed, for example by Alesina and Ardagna (2010). The IMF (2010) critiqued this result and concluded that fiscal contractions, while perhaps necessary at times, come at a short-run cost.

In an important contribution, Favero and Giavazzi (2007) show that not only may current SVAR estimates be biased due to the omission of debt as a control variable, but also that its inclusion in a VAR analysis is non-trivial. After all, the debt stock relates to the endogenous VAR variables-government expenditure, taxes, GDP, interest rates, and inflation—in a highly non-linear way. So while it may be problematic to omit debt from the regression, it is also inappropriate to include debt as an exogenous control variable.

To fix ideas, consider augmenting (2) with a variable d;, measuring the debt-to-GDP ratio, as suggested by Favero and Giavazzi (2007): K AY; = S- (CeYi-k + Dedi_x| + Bur,

<!-- Start of picture text --> (3)<br>* 1<br>Y;.<br>4<br><!-- End of picture text --> k=1 where the estimation is for an individual country, and where D,; are J * 1 vectors representing the feedback from debt to endogenous variables in Y;. As mentioned, d; is itself an endogenous variable that relates to current and past values of the endogenous variables. Favero and Giavazzi (2007) describe the following debt-accumulation identity: us ~~(1+~~ <u>Ap;)</u> ~~1+2= Ay~~ ‘ ~~excep—~~ <u>Yt</u> ~~exp—T;~~ 21