rafmacalaba/datause-encoder-data huggingface dataset:
2 ‘leakage’. Working with the government, we carried out the same intervention in a random subset of clinics within two different states in the country (Ekiti and Niger), allowing us to directly test not only the intervention, but also the generalizability of the results. Furthermore, we exploited survey data at the clinic level to examine which factors influence performance on expenditure tracking as well as the relative success of the intervention across environments.

We contribute to several strands of literature. First, our simple treatment effects add to literatures on incentives within bureaucracies, on preventing corruption, and on the possible scope for behavioral interventions in developing countries. We test whether our inexpensive social recognition intervention motivated individual bureaucrats to improve performance on one very specific and very simple aspect of their jobs: financial resource tracking. We find that the intervention did motivate bureaucrats in Ekiti, the originally better-performing state, but not in Niger.

Second, we contribute to the literature on interventions aimed at health outcomes and health systems in developing countries. The recent work of Dunsch, Evans, Eze-Ajoku, and Macis (2017), for example, relates closely to ours, in trying to improve practices within health systems in Nigeria. In alignment with the findings in that paper, we find larger effects among the simpler sub-tasks within the broader set of resource tracking measures we capture.

We also contribute to the growing literature on external validity in development economics. Though generalizability is not a challenge specific to randomized trials, this trial does present an opportunity to examine generalizability more directly than is often feasible. We test whether observable characteristics at the individual facility level, such as staff resources and local educational attainment, are predictive not only of levels and treatment effect heterogeneity within a single state, but also of the difference in treatment effects across states, holding constant the implementing organization. Among observable characteristics of facilities, resource tracking was marginally better (even in the absence of the intervention) in places with more educated client (patient) populations, for example. Only one variable (fraction of staff who are female) is predictive of treatment impact in Ekiti; it does not, however, explain the difference in treatment effects between states—if anything, it amplifies it. We interpret this to imply that social recognition can be an effective and relatively low-cost tool to motivate bureaucrats to complete a specific task such as record keeping, but that its effects are dependent on milieu and institutional structures in ways that are difficult to measure. Our contribution is thus not only an examination
Table la Tests of Stationarity (Jan. 1988-Dec. 1996)

|Variable|Dickey-Fuller<br>D-F (statistic)|Augmented D -F|
|---|---|---|
|(1) Spot Price (Levels)|-2.69|-2.08 (6 lags)|
|(2) Futures Price (Levels)|-2.32|-1.97 (6 lags)|
|(3) Spot Price (Differences)|-6.57**|-5.06**(6 lags)|
|(4) Futures Price (Differences)|-6.51**|.-5.25**(6 lags)|

Note: The critical value of the D-F statistic for 100 observationi **s** -3.45. ** indicates significance at the 95% level.

Table lb Test of Basis Risk (Jan. 1988-Dec. 1996)

|Regression|_a_|I<br>f<br>|I<br>R<sup>2</sup>|I<br>D-W|
|---|---|---|---|---|
|ASt =_a_+ flFt|-.02<br>(-.28)|.96**)<br>(21.41)|.81|2.37|

Notes:

1. ASt= St - St-,, AFt=Ft - Ft-I

# 2. D-W: Durbin-Watson Statistic

3. ** indicates significance at the 95% level. T-statistics are in parentheses.

4. The stationarity test involves the following regression: AXt = a + flXt-I + :P ?i AXt-i + et i=0 If **Si** = **0,** Vi this is referred to as the Dickey Fuller (D-F) test. If S<sup>5</sup> i X 0, Vi this is called the Augmented Dickey Fuller (ADF) test. The optimal lag length, p, is chosen using Akaike's Iformation Criterion (AIC); p is the value that minimize's AIC.

If _8_ is significant, the null hypothesis of nonstationarity is rejected. A significant D-F test statistic thus rejects the null, implying stationarity. Note that the spot and futures prices in levels are non-stationary but the spot and futures prices in first differences are stationary.

5
Figure B12: Urban/rural poverty rates and GDP <!-- Start of picture text --> (A) Official definition<br>Urban Rural<br>80 80<br>MWI<br>60 MWI 60 NER<br>UGA AGO<br>40 40 TZALSO<br>TZA<br>NER UGA TCD BFA<br>ETH ETHGNB<br>20 AGO 20 GIN SEN GHACIV<br>TCD BFA LSO MRT<br>GNB GIN SEN GHACIVMRT GAB<br>GAB<br>7 7.5 8 8.5 9 9.5 7 7.5 8 8.5 9 9.5<br>Log of GDP per capita Log of GDP per capita<br>R2 = 0.1358 R2 = 0.3075<br>(B) DOU<br>Urban Rural<br>80 80<br>MWI<br>60 MWI 60 NER<br>UGA AGO<br>40 TZA 40 TZALSO<br>NER UGA TCD BFA<br>ETH ETHGNB<br>20 AGO 20 GIN SEN GHACIV<br>TCD BFA LSO MRT<br>GNB GIN SEN GHACIVMRT GAB<br>GAB<br>7 7.5 8 8.5 9 9.5 7 7.5 8 8.5 9 9.5<br>Log of GDP per capita Log of GDP per capita<br>R2 = 0.3159 R2 = 0.3363<br>(C) DB<br>Urban Rural<br>80 80 MWI<br>60 60 NER<br>UGA AGO<br>40 NERMWI 40 TZALSO<br>TZA TCDETHBFA<br>ETH GNB<br>20 AGO 20 GIN SEN GHACIV<br>TCDGNBBFAUGAGINLSO SEN GHACIVMRT GAB MRT GAB<br>7 7.5 8 8.5 9 9.5 7 7.5 8 8.5 9 9.5<br>Log of GDP per capita Log of GDP per capita<br>R2 = 0.3285 R2 = 0.3290<br>Poverty rate in urban areas Poverty rate in rural areas<br>Poverty rate in urban areas Poverty rate in rural areas<br>Poverty rate in urban areas Poverty rate in rural areas<br><!-- End of picture text --> Source: International Urban Poverty Database.

Note: GDP per capita is measured in PPP (constant 2017 international $). For the DOU and DB methods, GHSPOP 1km is used. Urban areas include the categories “Urban center” and “Urban cluster” for the DOU method, and the categories “Core” and “Suburb” for the DB method. Poverty is measured using the $2.15 poverty line.

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Table 9:Trends in Head-count Ratio for Rural Poor by States "1973-74 to 1986-87

||||||Annual Gro|wth Rate|s (%)||
|---|---|---|---|---|---|---|---|---|
||Head-c|ount Rat|io(%)||73-74|77-78|83|73-74|
|States|||||to|to|to|to|
||73-74|77-78|83|86-87|77-78|83|86-87|86-87|
|AN '4RA PRADES<br>|52.1|41.9|29.5|27.1|-5.3|-6.2|-2.4|-4.9|
|ASSAM|56.8|64.4|45.1|42.3|3.2|-6.3|-1.8|-2.2|
|BIHAR|69.2|67.1|68|53.3|-0.8|0.2|-6.7|-2|
|GUJARAT|50|44.6|29.2|32.2|-2.8|-7.4|2.8|-3.3|
|HARYANA|. 372|30.3|20.5|22.6|-5|-6.9|2.8|-3.8|
|JAMMU-KASHMIR<br>|53.9|46.5|25.5|27|-3.6|-10.3|1.6|-5.2|
|KARNATAKA|56.6|50.8|38.7|39.5|-2.7|-4.8|0.6|-2.7|
|KERALA|60.9|52.5|40.1|35|-3.6|-4.8|-3.8|-4.2|
|MADHYA PRADES|65.1|64.3|51|49.3|-0,.3|-4.1|-1|-2.1|
|MAHARASHTRA|61.2|66.4|47.7|46.5|2.1|-5.8|-0.7|-2.1|
|ORISSA<br>|78.6|74.6|58.4|56.7|-1.3|-4.4|-0.8|-2.5|
|PUNJAB|29|18.4|15.4|15.3|-10.8|-3.2|-0.2|-4.8|
|RAJASTHAN<br>|46.5|38.6|36|31.7|-4.5|-1.3|-3.6|-2.9|
|TAMIL NADU|60.6|60.5|55.8|44.8|0|-1.5|-6.1|-2.3|
|UTTAR PRADESH,|59.9|51.3|49|38.6|-3.8|-0.8|-6.6|-3.3|
|WEST BENGAL|75.6|71.3|65|50.8|-1.5|-1.7|-6.8|-3|
|ALL INDIA|60.5|56.2|47.7|41.5|-1.8|-2.9|-3.9|-2.9|

Table 10:Trends in Head-count Ratio for Rural Ultra Poor by StaTEs "1973-74 to 1986-87

||||||AnnualGro<br>|wthRate<br>|s(/)||
|---|---|---|---|---|---|---|---|---|
||Head-co|unt Rati|o(%)||73-74|77-78|83|73-74|
|States|||||to|to|to|to|
||73-74|77-78|83|86-87|77-78|83|86-87|86-87|
|ANDHRA PRADESH|30.3|24.1|13.9|12.1|-5.6|-9.5|-3.9|-6.8|
|ASSAM|28.5|33.5|19|19.9|4.1|-9.8|1.3|-2.7|
|BIHAR|50|47.2|48.4|34.7|-1.4|0.5|-9.1|-2.8|
|GUJARAT|27.3|26.9|11.6|19.4|-0.4|-14.2|15.8|-2.6|
|HARYANA|19.3|14.4|8.6|8.1|-7.1|-8.9|-1.7|-6.5|
|JAMMU-KASHMIR|30.2|22.4|8.8|11.7|-7.2|-15.6|8.5|-7|
|KARNATAKA|38.9|34|24.1|23.3|-3.3|-6.1|-1|-3.9|
|KERALA|43.1|37.1|22.6|17.7|-3.7|-8.6|-6.7|-6.6|
|MADHYA PRADESH|48.2|46.9|31.2|28.5|-0.7|-7.1|-2.6|-4|
|MAHARASHTRA|41|48.3|28.7|29.2|4.2|-9|0.5|-2.6|
|ORISSA<br>|65|58|37.5|36.6|-2.8|-7.6|-0.7|-4.3|
|PUNJAB|14.6|7.8|6.2|5.1|-14.5|-4.1|-5.4|-7.8|
|RAJASTHAN|27|23.7|21.8|16.6|-3.2|-1.5|-7.5|-3.7|
|TAMIL NADU|39.8|43.1|38.8|28.3|2|-1.9|-8.6|-2.6|
|UTTAR PRADESH|40|31.8|30.3|21.4|-5.6|-0.9|-9.5|-4.7|
|WEST BENGAL|59.4|54.3|45.7|27.1|-2.2|-3.1|-13.9|-5.9|
|ALL INDIA|41.2|38|29.6|23.8|-2|-4.4|-6|-4.1|
decisively rejected. To address these two issues simultaneously, we implement the nonparametric covariance matrix estimator proposed by Driscoll and Kraay (1998) in an IV framework as in Driscoll (2004), which calculates standard errors corrected for spatial correlation, autocorrelation (to the third order), and heteroskedasticity using an extension of Newey and West’s nonparametric variance-covariance estimator.

Estimation results for equation (3), considering all 223 river basins with cropland and including only our climatic controls as well as controlling for basin fixed- and yearspecific effects, are given in the first column of table 4 for large dams only. Accordingly, the precipitation proxy, the standardized precipitation index ( _SPI_ ), is significantly positive, confirming that greater rainfall increases cropland productivity. Somewhat surprisingly, the evapotranspiration index is marginally insignificant (at the 5 percent level), although in the latter specifications, it is significant. With regard to the river flow variable, Asante et al. (2008) note that river flow series generated from the GeoSFM are particularly suitable for describing river flow anomalies. In the second column, we experiment with including the dummy variables low river flow event and high river flow event, _RFL_ and _RFH_ , respectively, indicating when stream flow was one standard deviation below and one standard deviation above its basin-level mean (as calculated over the 1950–2000 period), respectively. As shown, we now find that years in which river flow was below its long-term mean induced a significant fall in cropland productivity, whereas positive anomalies had no impact.

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Segou, Gao, and Mopti. In terms of welfare status, the share of producers in the bottom four quintiles is similar at 40 percent to 50 percent, while it drops to 26 percent in the top quintile. The average income from rice in the population as a whole is substantial, at 206,000 FCFA for producers, which translates to an average income of about 81,500 FCFA per household in the population as a whole. While households in the bottom four quintiles have a similar probability of being rice producers, the amount produced among producers is as expected higher among richer households, with producers in the fourth quintile selling about two and a half times more rice than producers in the bottom quintile.

Table 4 provides the same data for millet and sorghum. The share of producers in the population is even higher, at close to 54 percent, with this time a much higher probability of production in the poorest three quintiles. Millet and sorghum production tends to be more concentrated in the areas that are not major producers of rice, although households in Tombouctou, Mopti and especially Segou have a high probability of being producers. On average, sales among producers are a bit lower to rice sales among rice producers, at about 173,000 FCFA per household. But because the proportion of millet and sorghum producers is larger, average income from millet and sorghum in the population as a whole (including for autoconsumption) is slightly higher for millet and sorghum than for rice. A big difference however between the two crops is that most of the rice produced in the country is sold, while most of the millet and sorghum produced is used by household for their auto-consumption.

# 3 **. Combining consumption and production data to assess poverty impacts**

The impact on poverty of the change in food prices is the result of the combined impacts on the consumption and production sides. We first provide in this section estimates of the impact of changes in the price of rice, millet and sorghum, corn, wheat, as well as cereals as a whole. We measure likely impacts of food price increases on three poverty measures. The headcount index of poverty is the share of the population with a level of consumption per equivalent adult below the poverty line. The poverty gap takes in addition into account the distance separating the poor from the poverty line (while giving a zero distance to the non-poor). The squared poverty gap takes in addition into account the square of that distance (and thus inequality among the poor; for an introduction to the concepts of poverty measurement, see Coudouel et al., 2002).

We carry the simulations in a very simple way. First, for rice, millet and sorghum, and corn producers, we measure the additional income or the loss in income obtained from the sale of
depending on methodology. Elliott (2017) estimates that 62 percent of workers in OECD countries use skills that computers are close to replicating. Frey and Osborne (2013) estimate that 47 percent of U.S. employment is at high risk of automation in the coming decades. Deloitte (2014) finds that 35 percent of workers are in occupations at high risk of automation in the U.K. Manyika (2017) finds that few occupations are fully automatable; about 60 percent of occupations have at least 30 percent of activities that could be automated. Arntz, Gregory and Zierahn (2016) find that only 9 percent of U.S. workers are at high risk of being displaced by automation. For developing countries, Chang and Huynh (2016) estimate the percent of workers in occupations at high risk of automation in East Asian developing countries ranges from 44 percent in Thailand to 70 percent in Vietnam; Asian Development Bank (2015) estimates that a substantially lower proportion are at risk. Hallward-Driemeier and Nayyar (2018:135) applying the method by Arntz, Gregory and Zierahn (2016) estimate 2 to 8 percent of workers are at high risk in low and middle-income countries.

The implications of technology more generally for employment have been studied extensively in the economics literature. This work is motivated by the reduction of the labor share in the U.S. and European labor markets (Karabarbounis and Neiman 2014; Oberfield and Raval 2014) as well as job polarization or the shift from mid-range wage employment to low and high range wage employment (Katz et al. 2006; Autor 2010; Autor and Dorn 2013; Beaudry et al. 2013; Goos et al. 2014). These outcomes are often attributed to skill-biased technological change (Katz and Murphy 1992; Acemoglu 2002); though, some attribute them to other factors including demographic change (Cortes, Jaimovich and Siu 2016) or institutions (Acemoglu and Robinson 2015; Berkowitz, Ma and Nishioka 2017). In developing countries, Maloney and Molina (2016) find little evidence of job polarization.

The “canonical model” of skill-biased technological change involves two labor inputs, low and high skill workers, with technology augmenting one of them (Acemoglu and Autor 2011). For example, if technology augments high-skill workers, then high-skill wages will rise relative to low-skill, and job polarization arises with the addition of a middle-skilled group. This canonical model motivates the pessimism about automation, with automation substituting low or middle skill workers and complementing high-skill workers (e.g., Hemous and Olsen 2014).

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time, the objectives of investments shifted to give greater emphasis to upgrading worker skills and to increased access to vocational training for disadvantaged groups.

3.23 The percentage distributions of the 505 objectives stated for 121 components across regions, modes and country income levels are shown in Table 13. Data were collected on two kinds of objectives. The first, "objectives for clients," includes those which are directly related to various outcomes of training for students. The second, "objectives for systems development," includes objectives related principally to institutional development for the VET system. 1/ 3.24 In interpreting these data, it is important to recognize that the percentages reflect the share of all obiectives of a given type that fall into a particular category, and not the proportion of components with a given type of ob~jective. For example, the data indicate that 41% of the objectives for African components were classified as "manpower expansion" objectives. All but one Africa component had such an objective. Thus the data serve as overall indicators of the degree of emphasis given to objectives of a given type. To take yet another example from Africa, the data indicate equal and primary attention to manpower expansion and improving the qu&lity of training. Upgrading worker skills received about 10% of the emphasis, and improved access for disadvantaged groups about 05%.

3.25 African components had, on average, fewer objectives, reflecting the relatively small size of components and the greater proportion of secondary vocational school components with relatively simple designs. Components in LAC, Asia and EMENA had more objectives, with marginal differences between them overall. Each of these regions invested more heavily in relatively more complex nonformal modes. On this particular indicator, LAC also gave more emphasis to systems development objectives.

3.26 As noted, nonformal components addressed the largest number of objectives, both for clients and for systems development. Postsecondary components were least complex in terms of objectives; interestingly, within this smaller number of objectives these components gave a somewhat higher ratio of attention to systems development (.45).

1/ The list of objectives was developed from the VET literature and from preliminary analysis of a set of SARs, and refined through pre-testing of the questionnaire.
Table 1: Descriptive statistics on projects and conflict

|Number of World Bank projects evaluated|6,404|
|---|---|
|**Projects started in countries at peace**|**79%**|
|The country was always at peace|65%|
|The project was implemented before the outbreak of the war or after the post confict period|35%|
|**Projects started in a country at war**|**7%**|
|Peace is settled during the project|70%|
|War resumes during the project once peace is settled|3%|
|**Projects started in a post-confict country**|**14%**|
|War resumes during the project|25%|

Source: authors’ calculations using PRIO database on conflicts and IEG database on WB projects.

might be linked to the gradual institutionalisation of evaluation started in the mid-1970s (OED, 2003).

# **3 Econometric model**

We estimate the probability that World Bank projects will be successful depending on a set of projects ( _Pj_ ) and country characteristics ( _Cj,i_ ), but also on factors relating to the history of civil war of countries ( _Warj,i_ ), where _j_ ( _j_ = 1J) denotes projects and _i_ ( _i_ = 1I) denotes countries.

We estimate the probability of success of World Bank projects and explore whether projects in post-conflict situations follow a different pattern. To do so, we estimate a model of the following form: We consider that _Successj,i_ equals 1 whenever the outcome of World Bank projects is assessed by the IEG as successful or very successful.

_Cj,i_ is a set of characteristics of country _i_ averaged over [ _t_ ; _t-3_ ] where _t_ is the starting date of project _j_ . Hence, for a project that started in 2000, each country variable, _Cj,i_ , is calculated over the period 1997-2000. This way of controlling for country characteristics means that for two projects occurring in the same country, _Cj,i_ may end up being very different according to the period when 6
inundation due to climate change. Of this, 5,088 km (94%) will be exposed to an additional inundation depth of up to 0.5 m and 29 km (less than 1%) to more than 1 meter.

The total cost for raising embankments to offset the additional risk due to climate change is Taka <u>6,727 or US$ 96 million</u> **<u>.</u>** This cost has been calculated using cost estimates available from the BWDB for earth work (Taka 125/ m<sup>3</sup> ), compaction (Taka 60/ m<sup>3</sup> ), and turfing (Taka 25/ m<sup>2</sup> ). Table 9 provides the itemized cost of height enhancement of affected embankments for various inundation depths. As expected, the bulk of the costs is for the required earthwork. (See Table 9.)

# <u>Drainage within Coastal Polders</u>

Bangladesh has 123 coastal polders (i.e., areas with a dyke) to protect low lying coastal areas against flood and salinity intrusion. Water regulators are used to prevent drainage congestion in coastal polders.<sup>29</sup> The higher precipitation level during the monsoon and the rising sea level under the climate change scenario is expected to increase drainage congestion in coastal polders.

For this analysis, trial simulations were conducted with 4 coastal polders to determine the average number of additional vents necessary for draining water if the water level inside the polder increases due to climate change conditions and creates drainage congestion. Each simulation consisted of adjusting the length of opening under the climate change scenario to reduce the water depth inside each polder to the level under the baseline scenario. The adaptation measure is the additional length of opening needed to ensure same drainage conditions under both scenarios. Additional numbers of vents are computed by dividing the length of opening with 1.52 m (i.e. width of each vent). The 4 trials indicate 7, 17, 24, and 51 additional vents are needed to reduce water levels under the climate change scenario down to the levels under the baseline scenario.

Estimates further indicate a total of 1,475 additional vents, each costing Taka 20 million will be required to drain the expected extra monsoon flood water from the polders identified to face

> 29 At present, not all existing water regulators in coastal polders are operational. The costs of rehabilitating existing structures or adding new ones to meet the drainage needs under the baseline scenario are not part of adaptation cost to climate change.

16
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