rafmacalaba/datause-encoder-data huggingface dataset:
proceeds, it is likely that China would experience some loss in its cultivated land both in terms of quantity and quality (Deang et al., 2013; World Bank and DRC, 2014). Figure 11b shows the result of a scenario in which we assume that China’s agricultural land areas and land bioproductivity (agricultural production potential) continues to decline at the rate found by Deng et al. (2013), 0.47 percent and 1.68 percent over the period 2000-2008, respectively (scenario 2).<sup>23</sup> Specifically, it is assumed that China loses its “effective” land at 0.27 percent annually (combination of land area and bio-productivity loss), given projected population growth and economic growth rates taken from Huang et al. (2013). In this scenario, as the CE production at each GDP level is evaluated at the projected level of China’s land endowment, the estimated CE production line in Figure 11b becomes flatter than that in Figure 11a. As a result, the changes in China’s CE consumption and production turn out to be comparable when China’s income reaches around $18,500 in PPP terms per person. Until China reaches that point, the gap between supply and demand increases more under scenario 2 than under scenario 1. The results of these scenarios highlight that sustainable land management appears to be a key determinant to ensure reasonable supply and demand balance for food in the future.

These are, of course, only hypothetical scenarios. If, for instance, China’s demand for food were to stabilize at a lower level than is assumed in these scenarios, then the gap between supply and demand might start to close at an earlier time. On the supply side, if China were to reduce its investments in agricultural productivity, or if climate change were to decrease its productivity, or if China were to lose agricultural land at a faster rate than that under scenario 2, the gap might increase further. Nevertheless, China is in a very different situation from a country

> 23 Between 1988 and 2000, China recorded a net increase in cultivated land of 1.9 percent, which nearly offset the decrease in average potential bio-productivity (-2.2 percent). During the period 2000-2008, area of cultivated land decreased by 0.47 percent while the potential bio-productivity is reduced by 1.68 percent (Deng et al., 2013; World Bank and DRC, 2014).

24
- Martorell, R. and J. Habicht (1986). Growth in early childhood in developing countries. Human Growth: A Comprehensive Treatise. F. Falkner and J. Tanner. New York, Plenum Press.

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Shorrocks, A. (1983). "Ranking income distributions." Economica 50: 1-17.

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World Bank (1999). Vietnam Health Sector Review. Hanoi, The World Bank,

- World Health Organization (1999). The World Health Report 1999: Making a Difference. Geneva, The World Health Organization.

_19_
poverty.<sup>22</sup> Second, another definition of metropolis is used, i.e. the population in cities with population of 750,000 or more in 2007 instead of 1 million or more at the time of the spell (Table 4, columns 5 and 6). This avoids discontinuous jumps as cities grow beyond one million during the period of the sample. A disadvantage of this definition is that even if a city has a population of more than 750,000 in 2007, it may not have been large ten years ago. Unlike in the base specification, metropolitization was also associated with poverty reduction, though the effects on poverty reduction from rural diversification were quantitatively at least 50 percent larger. This also held when GDP growth was excluded, to which we return below.

Third, to further examine whether definitional issues related to the delineation of the nonagricultural population affect the results, even though no systematic relation was observed in the differences between DHS and FAO estimates nor a correlation between the observed difference in the nonfarm employment shares from both sources and the change in poverty, equation (7) was re-estimated dropping those countries where the observed difference was largest. As observed in columns (7) and (8), doing so did not affect the core finding of superior poverty reduction from movements out of agriculture to the missing middle.

Finally, when augmenting the regressions with a quadratic term of the change rate of the sectoral shares to allow for nonlinearity in the impact of the sectoral transformations (Table 4, columns (9) and (10)), no effect of metropolitization on poverty is found. The findings on rural diversification and secondary town expansion remain robust, with a strong poverty reducing effect at first, which declines as the migration rate to the missing middle increases. In sum, the core findings are qualitatively robust against the use of alternative metrics and non-linear specifications. Looking across the different columns, they are also quantitatively quite similar to the benchmark results.

Turning to the robustness against competing hypotheses, Table 5 examines whether the

> 22 Note that the effect of the change rate in the missing middle is also negative and larger than the effect of metropolitization when using percentage point changes in $1-day poverty, even though it is estimated with imprecision. When entered quadratically, the coefficient on the missing middle share is also statistically significant and negative, while there remains no statistically significant effect of metropolitization, pointing to the possibility of a non-linear effect, as examined further below.

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analysis, and the location effect is modeled at the level of the localities. The target populations each cover an average of 1042 households.

- 4) True poverty and inequality rates were calculated for the 20 target populations based on actual per capita expenditure.<sup>13</sup>

- 5) The poverty mapping methodology was applied to predict poverty and inequality rates for the 20 target populations, using first-stage models estimated with the pseudosurvey.

- 6) The entire procedure was repeated 10 times, drawing a new pseudo-survey for each round of analysis.

The output of this procedure is a set of poverty and inequality estimates and associated standard errors for 200 target populations. To examine the sensitivity of the estimates to the error specification, two different specifications are used for the secondstage analysis. In the first, both the location component and the household component of the error are modeled as Student’s t-distributions. For the second specification, a semiparametric approach is used for both the location and the household components. In this semi-parametric approach, instead of drawing from a t-distribution, the standardized residuals are drawn from the first-stage survey residuals. For both specifications, the household component of the error is modeled as heteroskedastic, with the predicted log per capita expenditure as the sole explanatory variables.<sup>14</sup>

> 13 The poverty line was set to 159 pesos, the per capita expenditure of the median household in the full set of households. This corresponds roughly to PROGRESA’s poverty-classification scheme; using discriminant analysis techniques based on household income, approximately 50% of households were initially classified as “poor” and thus qualified for PROGRESA.

> 14 Note that for the semi-parametric approach, it is the _standardized_ residuals that are drawn from the observed distributions in the survey. These standardized residuals, with mean zero and variance equal one, are drawn and multiplied by the square root of the relevant simulated variance (of the location or household effect) to produce simulated residual values.

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**Table 2. Tier 2 Light Duty Full Useful Life Exhaust Emission Standards**

[Emission Limits (g/mile)]

|**Bin no**|**NOx **|**NMOG**|**CO**|**HCHO**|**PM**|**Notes**|
|---|---|---|---|---|---|---|
|11|0.9|0.28|7.3|0.032|0.12|(1)|
|10|0.6|0.156 (0.230)|4.2 (6.4)|0.018 (0.027)|0.08|(2,3,4)|
|9|0.3|0.090 (0.180)|4.2|0.018|0.06|(3,5)|
|8|0.2|0.125 (0.156)|4.2|0.018|0.02|(2,6)|
|7|0.15|0.09|4.2|0.018|0.02||
|6|0.1|0.09|4.2|0.018|0.01||
|5|0.07|0.09|4.2|0.018|0.01||
|4|0.04|0.07|2.1|0.011|0.01||
|3|0.03|0.055|2.1|0.011|0.01||
|2|0.02|0.01|2.1|0.004|0.01||
|1|0|0|0|0|0||

(1) Bin 11 is only for MDVPs and is available up to and including the model year (2) Bin deleted at the end of 2006 model year (2008 for HLDTs) (3) The higher temporary NMOG, CO, and HCHO values apply only to HDLTs and expire after 2008.

(4) Optional temporary NMOG standard of 0.280 g/mile applies for qualifying LDT4s and MDVPs only.

(5) Optional Temporary NMOG standard of 0.130 g/mile applies for LDT2s only.

(6) Higher temporary NMOG standard is deleted at the of 2008 model year. Source: CONCAWE, 2006 Under the Tier 2 program, manufacturers select a set of full useful life standards from the same row also called “emission bin” or “bin” for a given test group of light duty vehicles (LDVs) and light duty trucks (LDTs). The way it works is that, under the “emission bin” approach, manufacturers select a set of emission standards (a bin) to comply with, as a result of which test groups are obliged to meet all standards within that particular bin. For example: If a manufacturer aims for Bin 5 for its light duty diesel vehicles and cannot meet the target, the higher bins in that case allow a safety factor. It is the manufacturer’s responsibility now to offset the higher bin models with similar volumes of lower bin vehicles (CONCAWE, 2006). In addition, the Tier 2 vehicles are obliged to meet the requirements of one of the available “emission bin” and a full life NOx standard of 0.07 g/miles (CONCAWE, 2006).

17
half as profitable, and has slightly lower capital adequacy but similar asset quality when compared to the average private bank. Average size, as measured by total assets, is 41 billion USD for private banks, and 97 billion USD for state-owned banks. Profitability, as measured by average Return on Assets (ROA), is 1.1 percent for private banks, and 0.5 percent for state-owned banks. Asset quality, as measured by the share of non-performing loans over gross loans, is remarkably consistent across the subsamples, i.e., at 9.1 percent and 9.9 percent for private and state-owned banks, respectively. Finally, the average Tier 1 Capital ratio stands at 16.6 for private banks and 15.5 for state-owned banks.

# Latent Variables

State-owned banks are supposed to target credit constrained firms or firms that generate positive externalities. This section describes the construction of three indexes aimed at capturing the presence of credit constraints, innovation, and green activities.

The Enterprise Survey contains several variables that indicate the potential presence of credit constraints. For instance, one question asks whether access to finance is an obstacle; another question asks whether access to finance is the major obstacle faced by the firm (hence, unlike the first question, this question focuses on the relative importance of credit constraint); yet another question asks if the firm is credit constrained (the possible answers are fully constrained, partially constrained, and unconstrained) and a further question (included in a different set of surveys) asks if the firm is partially credit constrained. Finally, the survey includes a variable that measures the share of investment financed with bank loans.

Each of these variables is likely to be correlated with some latent continuous variable that measures whether a firm is credit constrained. We assign numerical scores to each of these questions and use factor analysis to extract this latent variable.<sup>7</sup> One problem with applying factor analysis to the variables listed above is that not all questions are asked in all surveys. Moreover, there are missing answers for certain firms, even when a given question is asked. Such missing values lead to a substantial loss of observations. We address this issue by using multivariate imputation to fill the missing values. Specifically, we use the Stata command “mi impute mvm” which accommodates arbitrary missing-value patterns by using an iterative Markov chain Monte Carlo method.

> 7 For the categorial variables we assign scores that increase with the tightness of credit constraints (hence, for the first question, we assign 0 to firms that respond that access to credit is not an obstacle and 1 to other responses). For the percentage of investment that is financed with bank loans, we multiply the answer by -1. Hence, the potential range of the answer is [-100,0].

8
determinants in _X_ by forecasting their future values using univariate forecasting models and assuming that they evolve following past trends. For the 20 countries in Latin America and Caribbean we cite results from Loayza et al. (2005). Next we discuss the panel data, the estimation methodology, and the forecasting models.

# _3.1 Data_

Loayza _et al_ . (2005) estimate cross-country regression (4) using cross-country, time-series panel data, spanning the period 1961-99. The following variables are part of their panel.

Growth per capita (d _lny)_ is the log difference of real GDP per capita, represented in 1995 PPP-adjusted US dollars and constructed using Summers and Heston (1991) and World Bank (2002). For the calculation of a period’s growth rate, the base corresponds to the final year of the previous period. Initial per capita GDP ( _lny_ ) is the initial value of the ratio of total GDP to total population<sup>15</sup> and is computed using data in Summers and Heston (1991) and the World Bank (2002). Initial output gap ( _lny-lny_<sup>_T_</sup> ) is the difference between the log of actual per capita GDP and the log of potential (trend) per capita GDP at the start of the period. The trend per capita GDP is identified using the Baxter-King filter.

The regression includes the following steady state growth determinants in _X’_ (equation 4): education, financial depth, trade openness, government burden, public infrastructure, and governance.

Education is measured as the ratio of total secondary enrollment to the population of the age group that officially corresponds to that level of education. This “flow” variable – used as proxy for human capital in Barro (1991), Mankiw, Romer and Weil (1992), and Easterly (2001) – captures more closely current education policies and human capital investment than “stock” measures related with the educational attainment of the adult

> 15 GDP is in 1985 PPP-adjusted US$.

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19 required for a BOAL to be formed were (1) that it be a working unit, (2) that the value of its product could be separately calculated, and (3) that selfmanagement rights could be exercised (Article 320). 7hus the industrial economy was carved up into a multitude of small self-managed units, often themselves departments of larger operating entities. The government used fairly ad hoc taxes and subsidies to redistribute income among these units, thus keeping the weaker ones afloat and preserving employment. Incentives in this extreme version of worker democracy under "soft budget constraints" ran counter to efficiency and growth, as workers tended to be more concerned with increasing wages and benefits--and the government with preserving employment-than with preserving and enhancing the productive capital stock of the firm.'9 The Yugoslav Enterprise Law adopted in 1988,60 which took effect January 1, 1989, represented a radical departure from the past. It introduced modern company forms into Yugoslavia and provided equal treatment for privately-owned and socially-owned firms. Together with the new Foreign Investment Law<sup>8</sup> " that took effect the same day, it also provided greatly expanded avenues for foreign investment and similar treatment with domestic investment. It repealed most of the Law on Associated Labor (except Article 196 dealing with labor relations) and called for the BOALs to be consolidated in larger units and reorganized into stock companies (Article 192). It also downgraded the powers of workers' councils (Article 131), and 1990 amendments to the law did away with the requirement of obligatory establishment of workers' councils in joint stock and limited liability companies.82 The Yugoslav Enterprise Law is still the currently applicable law in Slovenia,83 although lawmakers have prepared a draft of a new law that is 79 Vodopivec (1990).

80 Zakon o podjetjih (Company Law], Yugoslav Official Gazette, No 77 (1988), most recent amendment Yugoslav Official Gazette, No 61 (1990).

**81** Zakon o tujih vlaganjih [Law on Foreign Investment], Yugoslav Official Gazette, No 77 (1988).

# **82** Yugoslav Official Gazette, No 46 (1990), Article 131.

83 The August 1990 amendments to the Law on Transfer and Use of the Social Capital (Zakon o prometu in razpolaganju z druibenim kapitalom, Yugoslav Official Gazette, No 46 (1990)] dealing with the distribution of shares to workers were aplicable only by special permission of the Agency for Privatization (Constitutional Law for Execution of Constitutional Amendment XCVI, Official Gazette RS, No 37 (1990) and No 4 (1991), Article 5]. Thus, they were virtually rejected in late 1990 in the expectation that the issue would be dealt by a new privatization law.
climate variables (Giesbert and Schindler, 2012; Diffenbaugh and Burke, 2019; Azzarri and Signorelli, 2020). In particular, Rentchler _et al._ (2022) combine different data sources on flood, population, and poverty to provide more granular assessment of exposure to flood risks and poverty.

Combining various data sources is especially valuable for providing critical weather information in remote and inaccessible areas where ground-based networks are limited.<sup>9</sup> While satellites can capture a broad spectrum of data simultaneously, satellite data comes with its own set of limitations. For instance, these records typically cover a shorter time span than stationbased data, owing to the relatively recent deployment of satellite technologies. Furthermore, the data require complex processing to account for non-climatic factors, such as variations in satellite orbits and the need for instrument calibration.

 Do studies analyze data that are more disaggregated than the country level? While most existing studies analyze country-level data, using such data has certain drawbacks. Country-level data can mask significant disparities within countries, such as regional variations in weather conditions, exposure to natural disasters, and levels of poverty and inequality. This issue can be particularly pronounced in developing countries, which are often located in tropical areas with diverse weather conditions and disaster risks. Consequently, important intracountry variations can be obscured when data is aggregated at the national level. In response to this limitation, a growing number of studies have begun to exploit subnational data. These studies observe more pronounced effects of climate change on economic growth and poverty at this more granular level (Damania _et al._ , 2020; Kalkuhl and Wenz, 2020; Dang _et al._ , 2023b). This shift towards more localized analyses underscores the need for more disaggregated,

> 9 Temperature and rainfall data are primarily sourced from ground-based weather stations, which are valued for their long-term records and high precision (Jones and Olken, 2010; Dell _et al._ , 2012). However, station-based data often has uneven geographic distribution, resulting in coverage gaps particularly in poorer countries or regions where there is limited infrastructure to support these stations (Dell _et al._ , 2014).

16