rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 6:** Sectoral productivity responses to pollution

<!-- Start of picture text --> Pollution on agriculture productivity Pollution on construction productivity Pollution on wholesale productivity<br>0.0 0.0 0.0<br>−0.1 −0.1<br>−0.1<br>−0.2<br>−0.2<br>−0.3 −0.2<br>−0.3<br>−0.4<br>1 KM 1−2KM 2−3KM 3−10KM >10KM 1 KM 1−2KM 2−3KM 3−10KM >10KM 1 KM 1−2KM 2−3KM 3−10KM >10KM<br>Pollution on manufacturing productivity Pollution on transport productivity<br>0.0 0.0<br>−0.2<br>−0.1<br>−0.4<br>−0.2<br>−0.6<br>−0.3 −0.8<br>1 KM 1−2KM 2−3KM 3−10KM >10KM 1 KM 1−2KM 2−3KM 3−10KM >10KM<br>Estimate with 95% CI Estimate with 95% CI Estimate with 95% CI<br>Estimate with 95% CI Estimate with 95% CI<br><!-- End of picture text --> In the Appendix we analyze sensitivity through the lens of a macroeconomic model that accounts for both intensive and extensive margins of labor supply. The results show that a higher sensitivity of labor productivity to pollution has a substantial impact on output. We demonstrate this by evaluating the shifts in the steady state values of output, pollution, and consumption (Figure 8). The steady state for output is lower under a higher elasticity with regards to pollution. However, pollution is also lower in steady state, reflecting a lower output overall. The elasticity thus acts as a wedge in the model. This suggests that steady state production should be lower in countries that have a higher share of their labor exposed to pollution. There are thus potentially strong economic benefits from reducing emissions. The reduction in emissions, e.g., through a carbon tax, would lower pollution, which has co-benefits in terms of higher output and consumption.

# **6.1 Robustness**

We design our robustness check around a propensity score matching dataset (see Heckman et al. (1997)). This allows us to compare treated areas (the ones that built power stations) with control areas (those that did not build power stations). These areas would share similar characteristics in terms of wages, pollution, and labor productivity. Using this data set we test the strength of our exogeneity assumption, as well as whether the labor productivity responses are purely attributable to productivity effects and not also due to changes in marginal costs.

The propensity score matching is done in two steps. First, a logit model is estimated to determine the conditional probability (in this case the score) for a city to build a power station given its pre-treatment characteristics (i.e., real wages, labor productivity, pollution levels, and multi-level effects such as year, province and sector characteristics). In the second stage, the scores for creating the matched control sector group are based on a nearest-neighborhood matching with a replacement procedure: 13
- 28 -

of the portfolio in the past 10 years while the share of mortgage and corporate bonds increased commensurately. Figure 4 indicates that a portfolio of corporate bonds would have generated returns exceeding the annuity rate by 100-120 basis points in 2002-2005. However, this strategy implies excessive concentration of risks in one asset class. Moreover, providers also have to pay for brokers’ commissions, cover their operating costs, make an allowance for several risks such a default risk, and generate an adequate return on equity. Therefore, both the MWRs and the spreads estimated for recent years indicate a situation that may not be sustainable.

An international comparison of MWRs estimated with a higher discount rate yields similar conclusions. As shown in Table 15, the average MWR for 2004 drops from 1.06 to 0.9 when it is computed with the corporate bond rate. However, MWRs for representative classes of annuities in Chile remain significantly higher than the corresponding averages for other countries, as shown in table 17. This suggests thin margins for Chilean providers on a present value basis, possibly making some providers unable to cover all costs and risks and still generate a positive profit margin. It is possible that the high MWRs observed in recent years reflect aggressive pricing strategies by some providers and that MWRs computed with the risk-free rate will eventually decline to levels closer to one with the ongoing industry consolidation.

**Figure 4**

<!-- Start of picture text --> Annuity Rate and Interest Rates on Central Bank Bonds<br>and Corporate Bonds (% p.a.), 1993-2005<br>10%<br>9%<br>8%<br>7%<br>6%<br>5%<br>4%<br>3%<br>2%<br>1993 1995 1997 1999 2001 2003 2005<br>Annuity Rate (RV-04) Central Bank Bonds Corporate Bonds<br><!-- End of picture text -->

# **_6.2. Comparisons with Other Estimates for Chile_**

This section compares the MWRs estimated in this study with those estimated by James, Iglesias and Martinez (2005), who compute MWRs using data on quoted annuities from 4 insurance companies in March 1999 and March 2003. The MWRs are calculated using 3 alternative mortality tables, 2 different discount rates, and 2 different premium levels.
not particularly remunerative – incomes proxied by nightlights indeed seem to fall locally in spite of the population influx. This pattern is not entirely different from that documented by Jedwab (2014) for cocoa: the temporary prosperity brought by cocoa triggered the creation of small towns, but once cocoa production fell, these same towns attracted informal sector workers no longer employed in cocoa cultivation. What we observe is not identical but it shares one common feature, namely, that the _end_ of primary production is associated with an influx of people and a rise in informal employment.

The rest of the paper is organized as follows. In Section 2 we briefly describe the conceptual framework. We present the gold mining sector in Ghana and various data sources used in the paper in Section 3, together with descriptive statistics of the variables of interest. Section 4 outlines the empirical strategy. and Section 5 presents estimation results.

# **2 Conceptual framework**

The over-arching concept behind our analysis is central place theory: urban centers arise because certain economic activities benefit from agglomeration externalities. The sectoral mix of cities indirectly reveals, for a given point in space and time, where these externalities are strongest. For instance, in the 19th and early 20th century, most of the manufacturing activities were concentrated in cities. But since the 1950s, manufacturing moved out of urban centers to make room for finance and other services (e.g., Desmet and Fafchamps 2007). Agriculture, in contrast, requires a lot of land and thus tends to be displaced by urbanization – even though peri-urban agriculture often is the most productive because of its emphasis on high value crops and dairy production (Jacobs 1969). The urban and peri-urban share of agricultural employment, however, is low because of the importance of non-farm production (e.g., Fafchamps and Shilpi 2003). Based on this, we expect urban centers to be characterized by less employment in agriculture and more in non-primary sectors. Mining, by definition, follows sub-soil mineral deposits, the location of which often bears no relationship with the location of cities. Whether or not mineral deposits are exploited does, however, depend on accessibility and profitability. Formal mines create a significant amount of industrial employment, typically mostly for well-educated and highly specialized workers that migrate to the mining areas. However, miners may demand 5
TABLE A17: Impact of leaders’ exposure on individual intermediary outcomes: nutrition and stimulation

|**I**|**TT Leaders**<br>(1)|**S.e**<br>(2)|**P-Value**<br>(3)|**Mean**<br>**Control**<br>(4)|**Obs**<br>(5)|
|---|---|---|---|---|---|
|**_Nutrition_**<br>||||||
|**Nutrition index**|-0.14|( 0.08)|0.08|0.16|2,062|
|**_Beverages and soup_**||||||
|Soy Milk|-0.05|( 0.09)|0.60|0.44|2,062|
|Juice|-0.31|( 0.19)|0.11|4.05|2,062|
|Soup|-0.06|( 0.08)|0.44|0.76|2,062|
|**_Vegetables_**||||||
|Salad|0.06|( 0.04)|0.17|0.33|2,062|
|Potatos|-0.19|( 0.11)|0.07|1.29|2,062|
|Tomate|0.02|( 0.23)|0.95|3.17|2,062|
|Onion|-0.10|( 0.18)|0.57|2.28|2,062|
|Other Vegetables|-0.07|( 0.13)|0.59|1.75|2,062|
|**_Staples_**||||||
|Rice|0.06|( 0.17)|0.74|5.90|2,061|
|Beans|-0.10|( 0.10)|0.29|6.16|2,062|
|Bread|-0.05|( 0.25)|0.83|4.26|2,062|
|Tortilla|0.11|( 0.15)|0.45|5.82|2,061|
|Cookies|-0.24|( 0.12)|0.05|1.53|2,062|
|**_Proteins_**||||||
|**Protein index**|-0.23|( 0.09)|0.14|0.00|2,062|
|Milk|-0.32|( 0.24)|0.19|1.97|2,062|
|Eggs|-0.23|( 0.14)|0.11|2.03|2,062|
|Cheese|-0.55|( 0.24)|0.02|2.48|2,061|
|Meat|-0.22|( 0.12)|0.08|1.37|2,062|
|Breast milk|-0.18|( 0.34)|0.60|3.33|647|
|**_Stimulation_**||||||
|**Stimulation index**|0.02|( 0.06)|0.80|0.13|2,062|
|Buy a toys for child|-0.01|( 0.02)|0.71|0.89|2,062|
|Tell tales to child|0.02|( 0.02)|0.40|0.82|2,062|
|Read books to child|-0.01|( 0.03)|0.84|0.31|2,062|
|Have paper and pen|0.01|( 0.02)|0.73|0.84|2,062|

Every line corresponds to a separate estimation (equation 3) and shows the ITT estimates on aggregate indices or individual questions measuring investments in nutrition or stimulation as reported by the caregiver. Questions on food items measure how many days in the last 7 days the child was given the specific item. The nutrition index was calculated by summing the days over all food items. The protein index was calculated by summing the days over all protein items. The individual items about stimulation are answers to yes/no questions, and the index is obtained by summing over yes answers. The nutrition, protein, and stimulation indices are standardized using the mean and standard deviation for control households.65 All the regressions include controls for the stratification variables, child age and gender, enumerator fixed effects, a dummy variable for a set of households surveyed in 2016, as well as village fixed effects. Standard errors reported in parentheses.<sup>_∗_</sup> p<0.1,<sup>_∗∗_</sup> p<0.05,<sup>_∗∗∗_</sup> p<0.01.
Comparability over longer time periods, however, is more involved and described in the following Proposition.

# **Proposition 5: Comparison of the indexes over different time periods**

_Assuming a non-negative and non-increasing correlation for household consumption over time and household consumptions in each pair of periods follow a bivariate normal distribution (that is, yt and y0 follow a bivariate normal distribution with non-negative correlation coefficient_ ρt _with_ ρt ≥ρt′ _, where period t’ is more recent than period t), and given a fixed vulnerability line for the original period,_

- _i) if household consumption growth remains stagnant over time (i.e., yt and yt’ are identically distributed), then both the insecurity index P_<sup>_1_</sup> _and the vulnerability index P_<sup>_2_</sup> _are nondecreasing in time._

_ii) if household consumption growth is stronger than the decaying effect of household consumption correlation over time, then both the insecurity index P_<sup>_1_</sup> _and the vulnerability index P_<sup>_2_</sup> _can decrease in time._

# **Proof**

Appendix 1, Part A.

The first assumption about a specific form of economic mobility over time (i.e., nonnegative and non-increasing correlation for household consumption) put forward in Proposition 5 is commonly shown to be true with panel data, which is also the case with the panel data we use for the US and Vietnam, as will be shown later. The second assumption about normality with the distribution of household consumption is stronger than the stochastic relationship assumed in Assumption 1 but is rather standard, and renders the mathematical derivations more tractable.

The intuition behind Proposition 5 is that, given these assumptions, if household consumption growth remains stagnant over time, both the insecurity index and vulnerability index are likely to be larger the longer time interval is considered. However, if household consumption growth is strong enough and can offset the decaying effect of the correlation of household consumption over time, we will see the opposite situation where households are better off and thus the indexes are likely to be smaller (i.e., households are less susceptible to falling 15
considerable movements in and out poverty (Figure ). Among the non-poor, there is a greater stability from one period to the next, but still close to a fifth of the non-poor in one period fall into poverty in the next. These results indicate that poor households have the ability to make temporary escapes from poverty, but find it difficult to maintain their new status.

Figure 10a: Distribution of the poor and nonFigure 1b: The poor, the vulnerable and the poor by poverty status in the earlier period, middle class 2009-15 <!-- Start of picture text --> 90<br>60<br>80 54.3<br>70 18 15 47.8 48.3 49.0<br>60 16 50<br>50<br>40<br>3020 68 32 52 16 57 14 64 10 40 30.0<br>10 15 11 11 30 34.0 33.1 25.7<br>0<br>19.1<br>26.0<br>20<br>11.7 21.0<br>Poor<br>10 Vulnerable<br>2009 2011 2013 2015<br>Middle Class<br>Poor in previous period<br>0<br>Non-poor in previous period<br>2009 2011 2013 2015<br>Percentage<br>Non-Poor Poor Non-Poor Poor Non-Poor Poor Non-Poor Poor<br><!-- End of picture text --> _Source_ : Authors’ calculations based on 2009, 2011, 2013 and 2015 WMS and following Lopez-Calva and Ortiz-Juarez <u>(2014) methodology.</u> This concept of poverty transitions, in particular the notion of the risk of falling into poverty, can be taken a little bit further to come up with a more precise definition of the vulnerable and the middle-class populations. Vulnerability is typically defined using heuristic constructs such as “population clustered just above the poverty line” or population just above the poverty line but below some arbitrary multiple of the poverty line. Likewise, the definition of the middle class is often based on arbitrary absolute or relative thresholds such as between $2 to $4 dollars a day (Duflo and Banerjee 2009) or $2 to $20 dollars a day (ADB 2010) or $2 to $13 dollars a day (Ravallion 2010) or those between 75 percent to 125 percent of any society’s median income (Birdsall et all 2000) or some hybrid of the two (Birdsall 2007). But having panel data such as the data we have for Georgia allows us to do better.

Following Lopez-Calva and Ortiz-Juarez (2014), we define households whose probability of falling back into poverty is sufficiently low as belonging to the middle class. Specifically, we use the value of 10 percent as the cutoff. This translates into lower threshold of 257 GEL/month per 16
The taskforce does not provide specific proposals in its report to operationalize its recommendations. Numerous questions remain to be resolved, including how resources should be managed and allocated, what countries will be eligible, and what the role of the WTO and the various development institutions should be. Whatever is agreed on the allocation mechanisms and modalities, much of the aid for trade agenda at the country level in our view revolves around improving the quality and cost of services. The agenda goes far beyond technical assistance to help countries make market access commitments – the focus of much current assistance (as illustrated for example by the language on technical assistance for services negotiations in the Hong Kong ministerial declaration). It spans helping countries overcome the political economy factors that impede liberalization; improving domestic regulation; adopting measures that spread the benefits of liberalization to poor and disadvantaged households; support for the pursuit of regional cooperation; and measures that are preconditions for obtaining and exploiting better market access in export markets. What follows briefly discusses each of these areas.

# **_Political economy: redistributing rents and dealing with adjustment costs_**

Poor policies in many countries often reflect standard political economy forces: those who gain (or are not hurt) from current policies are more economically and politically powerful than those who lose. In the case of telecommunications, for example, the incumbent provider may confront an administered price structure (with artificially high international prices and artificially low local prices). Liberalization will require tariff rebalancing to allow the incumbent to compete on the international segment. The resultant increase in local call prices is likely to be resisted by the politically vocal urban consumers, though the prospect of more competitive mobile telephony may dilute such opposition. Putting in place transparent and credible compensatory measures (e.g. voluntary retirement schemes, access to cheaper mobile telephony) could help persuade the incumbent’s employees and urban consumers to accept reform.

Similar forces play out in other sectors. In Zambia,<sup>12</sup> a country that being landlocked confronts higher transportation costs than many coastal countries, high costs are partly due to restrictions that Zambia imposes on air and road transport. While these

> 12  What follows draws on detailed analyses in Mattoo and Payton (2007).

16
|||The Va|lue end Dest|Table<br>ination<br>of<br>(1981|1 (Cont<br>Individu<br>, 1983 and|inued)<br>al<br>Caribbean<br>Count<br>1985)|ryls<br>Exports|||
|---|---|---|---|---|---|---|---|---|---|
|||||Shar|e of All E|xports Destined F|or:|||
|||Value of Total|All|of|which||Centrally|All|of which|
|Caribbean Country|Year|Exports (S111)|Developed|EEC(I0)|Japan|<br>United Stales|<br>Planned|Developing|Latin America/Caribbean|
|Haiti 2/|198)|619.2|25.8|5.6|0.0|19.4|11.1|63.1|22.1|
||1983<br>|154.0<br>|94.8<br>|14.5|-|77.3|0.0|5.2|2.7|
||1985|180.0|95.2|10.9|0,1|81.8|0.0|4.8|2.3|
|Jamaica 3|1981|_r,_;7|77.3|19.3|0.6|39.2|1.5|21.2|11.8|
||1983|7i,.S|77.9|21.9|1.1|33.5|1.6|_29,s_|18.0|
||1985|564.0|87.1|21.3|1.2|44.3|0.0|12.9|11.0|
|St. Kitts-Nevis|1981<br>|<br>47.9<br> <br>|93.9|17.5|0.2|71.0|0.0|6.;|5.3|
||1983|<br>49.3|93.3|8.9|-|82.0|0.0|6.7|6.3|
||1985|<br>110.1|97.4|6.6|--|89.2|0.0|2.6|2.5|
|St. Lucia|1981<br>|<br>36.4<br> <br>|49.1|41,4|-|7.3|0.0|50.9|48.5|
||1983|<br>49.2|67.8|67.7|-|0.0|0.0|32.2|22.1|
||1985|<br>62.7|91.0|90.8|-|0.0|0.0|9.0|9.0|
|St. Vincent|1981|<br>32.2|66.1|61.2|-|0.0|0.0|33.9|29.0|
||1983<br>|<br>41.4<br> <br>|46.1<br>|45.9|--|0.0|0.0|53.9|5193|
||1985|<br>45.6|63.5|-63.2|--|0.0|0.0|36.5|35.2|
|Surin<br>ame|1981|<br>517.5|92.3|30.8|4.4|38.5|0.0|7.7|2.8|
||1983|<br>418.0|91.0|53.7|4.2|17.4|0.0|9.0|3.4|
||1985|<br>296.2|94.5|48.6|3.2|21.7|0.0|5.5|0.7|
|Trinidad and Tobago|1981|<br>3,760.8|75.2|14.4|-|58.9|-|24.8|17.6|
||1983|<br>2,352.7|73.6|11.8|0.4|59.8|--|26.4|15.5|
||1985|<br>2,160.9|77.6|13.7|-|61,9|0.3|22.1|15.0|
|ALL ABOVE COUNTRIES|I/<br>1981|<br>9,497.6|77.3|13.6|0.7|45.6|0.9|21.8|13.3|
||1983|<br>9,486.4|67.1|10.5|O.|51,2|0.3|32.6|;.9|
||1985|<br>5,827.4|86.2|18.6|1.8|59.6|0.1|13.7|9.0|
on internal funds to finance their working capital and fixed assets. Due to the issue of data availability, any analysis of other potential measures of access to finance is constrained (for example, rejected loan applications are available only in the post-treatment period in our data sample).

We find that the firms are on average more than two decades old; they have approximately 40 employees; their direct exports account for less than 10 percent of their sales; and they are mostly owned by domestic private individuals. In our baseline regressions, we use the pre-treatment levels of all the firm-level explanatory variables to address potential concerns of reverse causality.

The bank systems in our sample are characterized by relatively high returns on equity, good capitalization, and a reasonably low share of nonperforming loans. However, these averages mask a great deal of variation among the banks. We examine in the following section whether Basel III implementation affects this variation. We present summary statistics for the whole banking industry in table 1. Some of our regressions use overall bank systems data, while in other regressions we identify the supply-side effects of the SME financing market more accurately by employing the matched firm-bank data.

Table 1: Summary statistics

||Observations|Mean|St. dev.|Min|Max|
|---|---|---|---|---|---|
|_SME financing: Dependent variable_||||||
|Access to finance as a constraint|31,014|3.43|1.31|1|5|
|_SME financing: Other variables_||||||
|Line of credit or loan|32,156|0.36|0.48|0|1|
|Working capital financed by banks (%)|29,249|16.71|26.81|0|100|
|Fixed assets financed by banks (%)|9,448|18.81|33.16|0|100|
|Internal financing of working capital (%)|26,189|70.04|33.64|0|100|
|Internal financing of fixed assets (%)|10,884|67.88|39.68|0|100|
|Collateral-to-loan ratio|6,096|1.47|1.13|0|5.95|
|Collateral value required|12,212|0.74|0.44|0|1|
|_Main explanatory variable_||||||
|Basel Dummy Gest|33,449|0.14|0.26|0|1|
|_Firm explanatory variables_||||||
|Employees|33,449|41.80|49.38|0|249|
|Age|32,020|18.32|14.43|0|187|
|Foreign owners share (%)|32,864|6.27|22.32|0|100|
|Government owned share (%)|32,794|0.39|4.82|0|98|
|Direct exports share (%)|32,918|6.46|20.41|0|100|
|Annual sales paid after delivery (%)|31,195|43.97|38.19|0|100|
|Annual employment growth (%))|29,246|5.63|113.85|-96.57|1,266.67|

13
The CHFS provides detailed information on demographic characteristics, household assets and liabilities, household insurance and social protection, and household income and expenditure. More importantly, the CHFS has detailed and comprehensive information on household liabilities, such as debts incurred for agricultural production, for use in own businesses, for car and housing purchases, and to make school tuition payments. Furthermore, the survey covers not only borrowing from banks but also debts owed to informal sources such as relatives, friends, and informal financial organizations. We also have information on whether the maturity of specific debts exceeds one year.<sup>10</sup> Equally importantly, the CHFS also has detailed information on household expenditures, including those made for food, clothing, housing, transportation, medical services, entertainment, and education. Based on these details, we construct a measure of total consumption per capita, which is the summation of all household consumption expenditures divided by the number of household members. We construct a measure of current or daily consumption per capita, which includes expenditures for food, clothing, daily necessities, housekeeping, transportation, rent, and communications, and another for long-term consumption per capita, which includes expenditures on health care and education. Current and long-term consumption are intended to capture the maintenance of daily life and long-term investment, respectively. See Table 1 for a list of our variables and their definitions.

We rely on two indicators of informal credit usage. First, _Informal Loan_ is an indicator that a household had at least one loan from an informal source at the time of the survey. Second, we construct a variable capturing the nominal amounts in RMB of _Informal Loans_ . We construct two indicators of usage of formal credit in analogous fashion.

We have several groups of variables that characterize a household’s basic characteristics, including wealth, education, depth of social networks, and extent of political access. For basic household characteristics, we rely on the age of the household

> 10 The survey asks about many different loans. For some loans, the maturity is directly inquired about. For others, we can make inferences based on the time a debt was incurred, and whether it has a remaining outstanding balance. If the loan has an outstanding balance and was incurred at least a year earlier, we classify it as a long-term debt.

8