rafmacalaba/datause-encoder-data huggingface dataset:
of potential program effectiveness<sup>16</sup> . The total effect of targeting on the enrollment of hectares at above median risk of deforestation is captured by the area under the dashed lines in Figure 5. These comparisons show that while WTA targeting enrolls the most hectares, it enrolls the least area at high risk of deforestation. The highest enrollment of hectares at high risk is under pure risk targeting, followed by targeting on risk per WTA.

Finally, the figure includes the mean simple assets index for enrolled properties under each scheme. Mean assets are slightly lower under both risk and risk per WTA targeting than under the other two schemes indicating more targeting to poorer communal properties. Appendix A.2 statistically examines shifts in the average values of deforestation risk and poverty for enrolled properties and demonstrates that the wealth levels of beneficiaries from a risk or risk/WTA targeting strategy are statistically significantly lower than those of the current program, while the average levels of deforestation risk are higher. In summary, the simulation results indicate that by combining referendum-based WTA with measures of deforestation risk, it is possible to retarget the program to achieve more avoided deforestation at less social cost and without adverse distributional consequences.

# 7 Using WTA estimates to set prices

The other lever for changing program cost-effectiveness is to adjust the payment scheme. This section presents two possible approaches to adjust the payments offered within the six zones described in Table 1. In both cases, the payment schedule offers a single price for forest in each zone, which in effect maintains the system currently used by the government but changes the amounts offered.

The first approach, which we call “adjusted payments”, sets payment rates so that the supply of forest derived using our WTA estimates is equal to the area enrolled in each zone type under the present scheme. We then exploit our community-level median WTA estimates as best guesses of the payments that would induce participation of land submitted by the communities that we inter-

> 16Since deforestation tends to occur in small increments in Mexico it is difficult to predict precisely where it will be located. While another approach to assessing environmental effectiveness would be to use the exact predicted deforestation rate for each parcel (i.e., multiplying our risk index by the area enrolled from each community), we feel that this is likely to be inaccurate and somewhat misleading, since the areas at lowest risk are highly unlikely to contribute any avoided deforestation at all.

35
We start our analysis by first comparing the employment rate of children age 10-13 and age 14-17 with all adults i.e., age 18 and above.<sup>14</sup> The difference-in-difference estimate helps to determine if there is a significant difference in the probability of working for children age 10-13 and also 14-17 in the period May-December 1991. Marginal effects from the estimated logit model for all Pakistan are reported in table 3 which also reports the marginal effects for the models estimated for the male and female population separately.<sup>15</sup>

**Table 3: Marginal Effects from Logit Estimates of Work – all Pakistan (individuals 10-13** **<u>years and 14-17 years vs. all adults)</u>**<sup>16</sup>

||Total|Male|Female|
|---|---|---|---|
|May-Dec.|0.002<br>(0.009)|-0.026<br>(0.014)|0.020<br>(0.012)|
|Age 10-13|-0.476**|-0.658**|-0.193**|
||(0.021)|(0.027)|(0.027)|
|Age 14-17|-0.267**<br>(0.020)|-0.429**<br>(0.024)|-0.067*<br>(0.027)|
|May-Dec * Age 10-13|0.008<br>(0.025)|0.018<br>(0.030)|0.006<br>(0.032)|
|May-Dec * Age 14-17|0.010<br>(0.023)|0.057*<br>(0.028)|-0.038<br>(0.031)|
|Observations|23769|12448|11321|
|Pseudo R<sup>2</sup>|0.08|0.22|0.02|
|LogLikelihood|-15138.3|-6083.6|-7225.6|

Robust standard errors in parentheses *significant at 5%; ** significant at 1% The results suggest that there was little change in the relative and absolute probabilities of work over the two time periods. Children age 10-13 as well as 14-17 are less likely to be involved in work, however, the magnitude of the marginal effect at around 48 percentage points is much larger for the age group 10-13. The models estimated by gender show similar results,

> 14 In this case we can not use controls in the logit model because the controls are different for children and adults, so we estimate the regression without controls.

> 15 Since our data sets are not random samples we tried using the weights (provided with the data sets) for our analysis and the results appeared quite robust to whether weights are used or not. So all our analysis uses unweighted logit analysis. For details about using weights for regression analysis see Deaton (1997) and Angrist and Krueger (1998). In the models where we use sub-samples of particular age and occupation groups, we were unable to use the weighted analysis because of small sample sizes, since weighted analysis requires at least two or more PSU in each strata.

> 16 We use an upper age-limit of 70 years for our analysis.

18
**Table 6e—Market Shares of Output Produced in COMESA in Sectors with FDI (in percentage).**

||Kenya|Tanzania|Uganda|Rwanda|COMESA|SADC|USA|EUR|China|ROW|
|---|---|---|---|---|---|---|---|---|---|---|
|**Business Services**|||||||||||
|Air Transport|0|0|0|0|5|0|2|92|0|1|
|Communication|0|0|0|0|15|40|0|19|0|26|
|Insurance|32|0|0|0|32|17|12|3|0|4|
|Business Services nec|0|0|0|0|78|0|6|15|0|0|
|Financial Services nec|0|0|0|0|50|0|26|24|0|0|
|Transport nec|0|0|0|0|66|0|6|20|0|7|
|Water Transport|0|0|0|0|40|0|13|0|0|47|

# **Source: Authors’ calculations based on data in Fukui and Lakatos (2012) and appendices D and E.**

**Table 6f—Market Shares of Output Produced in SADC in Sectors with FDI (in percentage).**

||Kenya|Tanzania|Uganda|Rwanda|COMESA|SADC|USA|EUR|China|ROW|
|---|---|---|---|---|---|---|---|---|---|---|
|**Business Services**|||||||||||
|Air Transport|0|0|0|0|0|32|68|0|0|0|
|Communication|2|0|0|0|0|53|5|28|0|13|
|Insurance|3|0|0|0|1|81|6|6|0|3|
|Business Services nec|0|0|0|0|0|80|6|2|0|13|
|Financial Services nec|0|0|0|0|0|70|12|18|0|0|
|Transport nec|0|0|0|0|0|74|3|0|0|23|
|Water Transport|0|0|0|0|0|21|62|0|0|18|

# **Source: Authors’ calculations based on data in Fukui and Lakatos (2012) and appendices D and E.**

67
week-of-sample by two-degree grid cell level; when I instead impute length distributions at the one-week-of-sample by one-degree grid cell level, I find that the closures policy increases juvenile catch by 49% (delta method standard error is 5.7%).<sup>45</sup> Figure C1a displays the resulting length distribution data for all sets. I calculate the number of individuals caught by non-SNP sets with each set’s length distribution and tons caught.

For example, suppose the reported percentage juvenile for a non-SNP set is 20%, the individuals-weighted average percentage juvenile among SNP sets in the two-week-of-sample by two-degree grid cell is 10%, and the average length distribution for the two-week-of-sample by two-degree grid cell is as follows: 2% of individuals are between 11 and 11.5 cm, 8% of individuals are between 11.5 and 12 cm, 60% of individuals are between 12 and 12.5 cm, and 30% of individuals are between 12.5 and 13 cm. Then the imputed length distribution for the set is: 4% of individuals are between 11 and 11.5 cm, 16% of individuals are between 11.5 and 12 cm, 53.33% of individuals are between 12 and 12.5 cm, and 26.67% of individuals are between 12.5 and 13 cm. This length distribution implies that the average weight of individuals caught by this set is 12.1 grams. If the set caught 50 tons of anchoveta, then it caught 4,132,685 individual anchoveta, of which 826,537 are juvenile.<sup>46</sup> Next, I match all sets to landing events in order to correct the length distribution, percentage juvenile, and number of individuals caught by all sets. Unlike vessels in the electronic logbook data, the closures policy does not give third-party inspectors an incentive to misreport percentage juvenile because the regulator does not use landings data to determine closures during my study period.

Vessels report when each fishing trip begins and ends in the electronic logbook data. For each landing event by a vessel, I record the vessel’s most recent preceding electronic logbook fishing trip and the sets that occurred on the trip. I matched 93.1% of sets to landing

> level in the above procedure. 58 non-SNP sets (.04%) occur during a two week period without any SNP sets. I record the length distribution values, number of individuals caught, and number of juveniles caught as missing for these 58 sets.

> 45Whenever I create grid cells (of any resolution), I create a grid over Peru’s Exclusive Economic Zone. Peru’s Exclusive Economic Zone boundaries are from Flanders Marine Institute (2012).

> 46Recall from Section II that vessels do not underreport tons caught.

35
processing algorithms to identify crash reports [20,21], developing an improved geoparsing algorithm to extract data on crash time and location [22–28], and ground truthing the results. The paper also contributes to a broader literature that uses machine learning methods for road safety analysis [29–31].

This study innovates on three fronts and demonstrates the value of using social media to expand data availability. (1) Geospatial Twitter data analysis usually uses the approximately 1% of tweets that have a geolocation tag <mark>[32–34]</mark> ; we improve this by using a machine learning geoparsing algorithm to leverage the 99% of tweets that do not contain a geotag. (2) To our knowledge there are no other studies that physically validate the locational accuracy of tweets in real time. Among verified tweets, 92% were found to be valid crashes, demonstrating the validity of crowdsourced crash data. (3) The work created an essential resource by generating one of the first real-time maps of RTCs in an African city (Nairobi). We identify 52,228 crash reports and geolocate those with enough information provided in the text (32,991 of them). In a context where there is no systematic georeferenced data on crashes to support policy planning, the process outlined here could be used to capture these data for cities all over the world that need this essential resource.

Overall, the method expands the coverage of road crashes that can be used to analyze road safety and to prioritize policy action around the locations where crashes occur more often. This is especially useful in country contexts where the only data available for analysis are aggregated statistics on total fatalities in the country, with no detailed breakdown of location or time. Crowdsourced data can help act as an additional input that can be used by policymakers in better understanding the situation. By using a clustering algorithm to identify and rank crash locations, we find that the top 15% of crash clusters (66 of 435) account for half of all crashes. Knowing that a small portion ( _<_ 1%) of the road network hosts 50% of RTCs in the crowdsourced data can help reduce an intractable problem to a more manageable one. This analysis shows the potential for using these data to complement road safety diagnostics and to guide investments and planning in road safety in Kenya and in other contexts, especially those with similar data deficiencies and with sufficient social media density like India and the Philippines [35].

The approach can be extended to other events reported on social media, whether related to disaster relief, crime, personal safety, urban mobility, or road maintenance. The work on disaster relief and response makes prominent use of geoparsing of tweets [36–43]. Geoparsing of tweets that lack geolocation information could enable more comprehensive crime analytics [44–46]. Improved algorithms can lead to faster and better geolocation of events, which would help urban planners and policy makers improve responses and better target interventions.

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**Figure 6. Change in Exports Receipts (in percent)**

<!-- Start of picture text --> ‐14.8 ASS<br>‐30.7 Nigéria<br>‐2.2 Faible Revenu<br>‐9.9 Guinée Equatoriale<br>‐28.3 RDC<br>‐12.5 Cameroun<br>‐37.7 Botswana<br>‐50.0 ‐40.0 ‐30.0 ‐20.0 ‐10.0 0.0<br><!-- End of picture text --> Source: calculated using data from UNCTAD database.

10. **The collapse in export receipts dragged down GDP growth, hence underlying the vulnerabilities related to product concentration in DRC’s exports.** Economic growth rates plummeted as export incomes fell. For instance, for six consecutive years prior to the 2009 crisis, DRC’s economy grew by an average rate of 6 percent, while in 2009 growth dropped to 2.8 percent. Dependency on exports and, more importantly, high product concentration have been the main drivers of the negative growth impact of the collapse in the prices and volumes of exports (see Box 1).

# **Box 1: The Relation between Export Dynamics & GDP Growth – A Matter of Dependency or of Concentration** ?

Although export dependence increases an economy’s susceptibility to external shocks, the size of the loss in trade revenue depends on each country’s mix of exports and main trading partners (i.e., on its degree of export concentration). This, then, explains the difference in the size of loss that different countries and regions experience. It explains why East Asia, despite being the most export-dependent region but one with the most diversified export portfolio, lost the least amount in export revenues compared to regions like SSA that, despite being relatively less exposed, had an export basket that was highly concentrated. _“The reliance of most countries on a narrow range of commodities as well as a narrow range of markets, makes African export earnings extremely vulnerable to volatility in these markets”_ (Third World Network, TWN, 2010).

More specifically, East Asia, with a highly diversified export basket, experienced a drop by 10 percent in its export revenues in 2009, while SSA countries with higher export concentration ratios saw a drop by 15 percent (and DRC by 28 percent) of their export earnings in the same year. Since the loss in export revenues directly affects economic growth, a greater loss in export earnings causes a greater drop in economic growth. For instance, growth rates for the East Asia region declined slightly by 1 percent (from 8.5 percent to 7.5 percent) between 2008 and 2009. On the other hand, growth in SSA plummeted from 5 percent to just 2.1 percent. Similarly, DRC’s economic growth dropped from 6.2 percent in 2008 to 2.9 percent in 2009.

Source: World Bank Staff.

7
through the following steps: (a) estimate the models using OLS and extract the residuals; (b) estimate the unconditional variance parameters _σv_<sup>2and</sup><sup>_σ_</sup> _ε_<sup>2,usingHenderson’s</sup> method III (see Henderson, 1953; and Searle et al., 1992); (c) estimate the conditional variance _σε,ah_<sup>2;andfinally(d)constructthecovariancematrixΩ=</sup><sup>_E_</sup> � _vv_<sup>_T_</sup> + _εε_<sup>_T_</sup> _|x_ � = _σv_<sup>2</sup><sup>_In_+ diag(</sup><sup>_σ_</sup> _ε,ah_<sup>2),andcomputethefeasibleGLSestimatorfor</sup><sup>_β_.</sup>

While the regression coefficients purely capture correlations and not causal effects, it is instructive to verify what predictors stand out and inspect the signs of their effects. As one would expect, higher (lower) quality materials used for roof, floor, and walls as well as a larger (smaller) number of rooms are positively (negatively) correlated with (log) household expenditure. Household assets, particularly, owning a car, fridge, cooker, and tv, all report positive regression coefficients. Furthermore, we observe that per capita household expenditures tend to be higher (lower) for smaller (larger) households, and for households with a more (less) educated household head.

# **4.1.2 SEM approach: Village level data with village poverty rate as dependent variable**

A similar procedure is adopted to build the model for village level poverty using variables derived from public remote sensing data as predictors which are grouped into: (i) population density and land size, (ii) night time lights, (iii) road connectivity and distance to cities, borders and rivers, (iv) climate and environment, and (v) agricultural suitability variables. Among these categories of variables, night time lights, market access (i.e. road connectivity), and local climate and environment report the highest correlations with village poverty with similar in-sample goodness-of-fit levels. The final model has 15 explanatory variables.

We estimate the model by maximum likelihood. The distance decay parameter _τ_ is set to _τ_ = 0 _._ 54, which maximizes the in-sample goodness-of-fit (measured by the correlation between predicted and observed poverty rates). It turns out that, except for _ρ_ , the parameters (as well as their conditional covariance matrix) are not very sensitive to the choice of _τ_ . The estimation results are reported in Table 4, where poverty is measured as a percentage. Details on working out the asympototic variance-covariance matrix of the parameter estimators are given in Appendix 1.

While also here the regression coefficients capture correlations and not causal effects, it will nevertheless be instructive to inspect the signs of the correlations. Lower poverty rates are associated with higher levels of population density, higher values of night time lights and higher shares of cultivated land that can be irrigated through rainfall. In contrast, higher poverty rates are associated with increased remoteness (i.e. larger distances from major cities), higher levels of ruggedness, and higher rates of forest loss.

22
precipitation observations from ground stations (see Mendelsohn et al 2006 for a detailed explanation). Soil data were obtained from the FAO digital soil map of the world CD ROM. The soil data was extrapolated to the district level using GIS (Geographical Information System). The data set reports 26 dominant soil types.

# **4. Empirical results**

In practice, farmers can own more than one type of animal. In this analysis, we focus on the five primary species that generated the most livestock income to each farmer: beef cattle, dairy cattle, sheep, pigs and chickens. Altogether these five animals generated 90% of the total revenue from livestock.

In Table 1, we estimate the probability each species is selected using a multinomial choice model. The dependent variable is the (1, 0) choice of whether each species was chosen. Because every farmer must choose one animal, one choice had to be omitted. The choice of chickens has been left out of the regression as the base case. The regressions are explaining how the independent variable affected each choice relative to choosing chickens. The probability of choosing each animal was assumed to be a function of summer and winter temperature and summer and winter precipitation. Other explanatory variables included a dummy variable for the Andean region, three soil variables, a dummy variable for gender, and a dummy variable for a computer.

The model is significant according to three tests of global significance. Most of the individual coefficients are significant. The second column for each choice describes the chisquared statistic, χ 2 (a measure of statistical significance). P-values show the probability the coefficient is zero. Positive coefficients imply that the probability of choosing the animal increases as the corresponding variable increases.

6
# _The Macroeconomics ofAdjustment in Sub-Saharan African Countries Results and Lessons_

# Ishrat Husain,

After a prolonged period of economic stagnation, many African countries committed themselves in the mid-1980s to a series of adjustment policies aimed at restoring economic growth. Now, almost a decade into adjustment, Africa's economic climate still remains unclear and uncertain, with the overall results modest relative to original expectations.

Although some countries have enjoyed a resurgence of growth, the economic performance of the region as a whole has been disappointing, raising troubling questions about the extent and efficacy of policy reform efforts. Previous studies of structural adjustment have focused on cross-sectional and aggregate performance of a group of counties that have taken adjustment loans from the World Bank and the IMF. Few empirical studies have actually measured the extent to which policies have, in fact, been implemented by the countres themselves and then related changes in policies to subsequent economic performance.

To fill this gap, a recent World Bank study<sup>2</sup> eamined in depth how much adjustment has taken place and how successful it has been in African countries. This study-Africa Adjustment Study (AAS)-compares the policies and performance of 29 Sub-Saharan African countries during two periods: from 1981 to 1986, when most African countries were in economic crisis, and from 1987 to 1991, when these counties adopted structural adjustment programs. This study was supplemented by case studies of seven countries' Burundi, Cote d'Ivoire, Ghana, Kenya, Nigeria, Senegal, and Tanzania-that undertook adjustment programs during the mid-1980s. The period covered by these case studies ends, for most counties, in 1991. The countries were chosen to capture a variety of characteristics and initial conditions. In all seven countries, adjustment programs addressed such distortions as an overvalued exchange rte, high current account and fiscal deficits, low factor mobility, restrictions on domestic and foreign trade, distorted pricing for tadables, and inefficient public services.

**IThc** author is **the** Chief Economist, Africa Rcgion, **The** World **Bank. The views expressed** in this **paper are** personal **and** should not **be** attributed to **the** World Bank, **i;s** Management or the Board of Dircctorx.

> **2 Adjustment in Afica: Reforn, Results and the Road Ahcad, World Bank, Oxford Univcrsity Press,** 1994.

> 3 Husain, 1. and Faruqec. R., 1994, Lessons **from** County **Case** Studics, World **Bank** Regional and Sectoml Studies.
percent, respectively. However, after the price shock, banks that were more exposed increased their exposure to the sector, reaching 30 percent by mid-2016, while the share of lending to the energy sector by banks that were less exposed was around 8 percent. The data thus suggests that the increased share of bank lending to the energy sector that followed the drop in oil prices (Figure A1) was driven by banks with greater exposures.

Furthermore, panels B and C of Figure 2 show that although both types of banks had similar trends in their CDS spreads and stock prices, these trends diverged after the energy price drop. Normalizing the CDS spreads of both groups of banks to their values in August 2014, we find that through mid-2016, the banks with high exposure saw their CDS spreads reach 100 basis points, whereas the remaining banks reached only 50 basis points. Similarly, the stock prices of banks with high exposure declined by around 12 percent through mid-2016, whereas the stock prices of the banks with low exposure increased 10 percent. All in all, this descriptive evidence suggests that the financial conditions of banks with higher exposure to the energy sector became relatively worse following the collapse of energy prices.

We run equation 1 to test more formally the impact that exposure to the energy sector had on the banks’ balance sheets after the collapse of energy prices.

Our five outcomes of interest ( _yb,m_ ) at the bank-month level correspond to the exposure to the energy sector, total lending, CDS spreads, stock prices, and delinquency ratio. We regress these outcomes on the interaction of the August 2014 exposure to the energy sector of bank _b_ — _ExposureEnergyb,Aug14—_ and a dummy variable— _Postm_ —that equals one from September 2014 onward. We also include fixed effects at the bank and month levels, with standard errors double clustered at the bank and month levels.

To study the impact of the energy price shock on loans to energy-related firms by banks with varying exposure to the distressed sector, we use our loan-level data and run the regression summarized in equation 2.

11