rafmacalaba/datause-encoder-data huggingface dataset:
Following the nature of our dependent variable we employ a Probit (Logit) model estimated using standard maximum likelihood techniques. Our baseline model can be formally expressed as where _↵i_ is a constant term, **_✓_** and **_δ_** are vectors of coefficients, **X** _i_ is a matrix containing controls describing personal characteristics of the respondent, **Z** _i_ is a matrix of geographical dummies, and _"i_ is an error term. The coefficients of interests are _β_ 1, _β_ 2 and _β_ 3. The models are run twice, the first time specifying a probit regression while the second a logit regression. Following the classical approach of limited dependent variable regressions, we report (for the two main surveys: January 2011 and September 2013) the marginal effects of the three main regressors keeping all other variables at their mean values. Finally, in order to overcome the traditional issues of the _R_<sup>2</sup> in probit/logit models, for each regression we report two alternative measures of goodness of fit: the percent of correctly predicted (PCP) observations<sup>9</sup> and the ’receiver operating characteristic’ (ROC) curve that overcomes the arbitrarily PCP cutoff to classify the observations.

# **5. Results**

The results of our baseline regressions are reported in table 4 for the January 2011 survey, in table 5 for the four intermediate surveys, and in table 6 for the September 2013 survey. The marginals for the three main regressors are plotted in figures 4 to 6 for the January 2011 survey and in figures 7 to 9 for the September 2013 survey. In each table we report the results of model (1) allowing for different controls. As for the January 2011 and the September 2013 surveys, the model is run four times: the first one including our main regressors plus a constant term only, the second one controlling for the individual characteristics, the third one by entering a set of geographical dummies, and the fourth one by including all controls. On the other hand, for the four intermediate surveys (May 2011, August 2011, May 2012, and August 2012) we allow for

> 9 The procedure involves three steps: first, run the model and estimate _Y_ b _i_ , second classify as a ’1’ any observation with a predicted probability higher than 0.5; finally, the PCP measure is calculated as _PCP_ = (100 _· Correct Predictions_ ) _/N_ where a correct prediction arises if _Y_<sup>b</sup> _i_ = _Yi_ .

19
|**Table A2**<br>**. Attrition in Follow Up Survey**<br>OLS|
|---|
|**Panel A: Intent to Treat Effects**|
|Business Training (1=Yes)<br>-0.022|
|(0.019)|
|Lottery Winner (1=Yes)<br>-0.019|
|(0.019)|
|BT and LW<br>-0.008|
|(0.021)|
|R-Squared<br>0.03|
|P-value of t - test of ...|
|BT = BT and LW<br>0.45|
|LW = BT and LW<br>0.54|
|BT = LW<br>0.96|
|**Panel B: Intent to Treat Effects with Gender Interactions**|
|Business Training (1=Yes)<br>-0.003|
|(0.028)|
|BT x Female<br>-0.039|
|(0.039)|
|Lottery Winner (1=Yes)<br>-0.044*|
|(0.024)|
|LW x Female<br>0.052|
|(0.036)|
|BT and LW<br>-0.021|
|(0.027)|
|BT and LW x Female<br>0.028|
|(0.040)|
|R-Squared<br>0.03|
|P-value of t - test of ...|
|BT = BT and LW<br>0.48|
|LW = BT and LW<br>0.37|
|BT = LW<br>0.11|
|BT + BT x Female = 0<br>0.11|
|LW + LW x Female = 0<br>0.79|
|BT and LW + BT and LW x Female = 0<br>0.81|
|Mean of dependent variable<br>0.18|
|N. Observations<br>4,161<br>Note: The reported mean of the dependent variable is computed<br>using CO members not offered business training nor chosen as<br>winners of the lottery. The dependent variable takes value 1 if<br>observation is missing at follow-up. Regressions include stratification<br>variables as covariates (business ownership at baseline, gender,<br>eligibility of larger loan and branch dummies). Standard errors are<br>clustered at the CO level. The following symbols *,** and *** denote<br>significance at the 10, 5 and 1 percent level, respectively.|

Note: The reported mean of the dependent variable is computed using CO members not offered business training nor chosen as winners of the lottery. The dependent variable takes value 1 if observation is missing at follow-up. Regressions include stratification variables as covariates (business ownership at baseline, gender, eligibility of larger loan and branch dummies). Standard errors are clustered at the CO level. The following symbols *,** and *** denote significance at the 10, 5 and 1 percent level, respectively.
more prominent in smaller farmers. Figure 4.1 illustrates graphically the results from Table 4.2 and shows how beyond a certain point increasing the use of fertilizer has an adverse effect on yields.

**Figure 4.1 Urea and rice yields in 2007 –2SLS model estimation**

<!-- Start of picture text --> Rice yields (kg/ha)<br>2SLS Model Estimation<br>Actual urea use (kg/ha)<br>0 200 400 600<br>0<br><!-- End of picture text --> _Source_ : World Bank staff calculations.

The analysis in this section supports the notion that urea usage has a positive impact on yields, with the exception of the larger farms, but it still leaves some questions unanswered. It does not answer whether the costs at which this improvement in yields is achieved outweigh the benefits. Neither does it discuss whether alternative public spending (on irrigation, improved R&D or extension services) may actually have a larger impact on productivity than the subsidization of fertilizer.

To assess the benefits of fertilizer subsidies in terms of rice production, one can look at the elasticities estimated in the model in Tables 4.2 and 4.3 and calculate the decline in urea usage and in yields that an increase in the price of the fertilizer would cause. A removal of the fertilizer subsidy and therefore an increase in prices from the average urea price paid by rice farmers according to the 2007 survey (Rp 1,524/kg) to the estimated market price of Rp 2,170.5/kg would translate into lower consumption of urea and a decrease of 6.3 percent in yields per hectare. (That is, assuming the effect of all other inputs is constant.) If one translates this decline to the total rice production for 2008, then, production would have been 3.8 million tons lower (from the estimated production of 60.3 million tons by BPS). At an average dried grain price of Rp 2,200/kg, the decline in production would have resulted in losses worth Rp 8.3 trillion. This decline is much lower than the cost of the fertilizer subsidy, budgeted at Rp 15.2 trillion in 2008. As such, the GoI could have saved Rp 7 trillion even after accounting for the production losses. Estimating the decline in yields resulting from an increase in urea prices using the linear model at the quintile level tells a similar story. If one assumes that a 1 percent increase in the urea price would result in a decline in urea usage of 0.35 percent, this would translate into a 5.1 percent decrease in rice production. In terms of the total rice production for 2008, it would represent 3.1 million tons less of rice and it would generate losses at around Rp 6.75 trillion. This figure is less than half the fertilizer subsidy budget for the year and shows how the benefits from higher production came at a very high cost The benefits of the fertilizer subsidy could be more widespread, for example other food crops and plantations are probably also benefiting from cheaper fertilizer. To the extent that the main policy

**17**
# **Table 8.B. Model comparison: Root mean squared error of nickel price forecasts**

(U.S. dollars per metric tonne)

|||||**Bivariate**|||**Machine**|
|---|---|---|---|---|---|---|---|
|**Approach**<br>|**Horizon**|**Consensus**|**Futures**|**correlations**|**BVAR**|**OEM**|**learning**|
|Consensus|3|1442.5|...|...|...|...|...|
|Futures|3|*|877.7|...|...|...|...|
|Bivariate correlations|3|||989.6|...|...|...|
|BVAR|3|*|*|*|2763|...|...|
|OEM|3||||*|958.8|...|
|Machine learning|3|*|*|*||*|2327.3|
|Consensus|6|1699.8|...|...|...|...|...|
|Futures|6|*|828.2|...|...|...|...|
|Bivariate correlations|6|*|*|3134.4|...|...|...|
|BVAR|6|*|*||2933.8|...|...|
|OEM|6|*||*|*|741.8|...|
|Machine learning|6||*|||*|2380.1|
|Consensus|9|2044.1|...|...|...|...|...|
|Futures|9|*|793.7|...|...|...|...|
|Bivariate correlations|9||*|2064|...|...|...|
|BVAR|9|*|*|*|3307.3|...|...|
|OEM|9|*||*|*|762.6|...|
|Machine learning|9||*|||*|2634.6|
|Consensus|12|2336.7|...|...|...|...|...|
|Futures|12|*|769.3|...|...|...|...|
|Bivariate correlations|12||*|2519.9|...|...|...|
|BVAR|12|*|*||3662.1|...|...|
|OEM|12|*|*|*|*|1220.5|...|
|Machine learning|12||*|||*|2883.9|
|Consensus|15|2528.9|...|...|...|...|...|
|Futures|15|...|...|...|...|...|...|
|Bivariate correlations|15|...|...|...|...|...|...|
|BVAR|15|*|...|...|4032.9||...|
|OEM|15||...|...|*|1600.7|...|
|Machine learning|15||...|...||*|3052.6|
|Consensus|18|2755.7|...|...|...|...|...|
|Futures|18|...|...|...|...|...|...|
|Bivariate correlations|18|...|...|...|...|...|...|
|BVAR|18||...|...|4015.5|...|...|
|OEM|18||...|...|*|1783.7|...|
|Machine learning|18||...|...||*|3161.7|
|Consensus|21|2886.1|...|...|...|...|...|
|Futures|21|...|...|...|...|...|...|
|Bivariate correlations<br>BVAR|21<br>21|...|...<br>|...<br>|...<br>31963|...<br>|...<br>|
|OEM|21||...<br>...|...<br>...|.|...<br>1999.8|...<br>...|
|Machine learning|21||...|...|||3208.3|
|Consensus|24|3011|...|...|...|...|...|
|Futures|24|...|...|...|...|...|...|
|Bivariate correlations|24|...|...|...|...|...|...|
|BVAR|24||...|...|3376.5|...|...|
|OEM|24||...|...||2233|...|
|Machine learning|24||...|...|||3173.2|

_Source_ : World Bank.

_Note_ : The forecast period is 2015Q1-2022Q1. Forecasts for OEM are only available at the semi-annual frequency; forecasts for all other approaches are available at the quarterly frequency. Diagonal entries indicate average RMSE estimates for the respective approach. * indicates significant difference between the forecasts of any pair of approaches, at the 5 percent significance level according to an F-test (RMSE).

47
oping countries. Bureaucrats appointed to the district by national ministries are responsible for managing national fiscal transfers to deliver public services across sectors–health, education, agriculture, roads, water, environment and natural resources. Following a wave of political decentralization across the developing world in the 1990s, Uganda promulgated The Local Government Act of 1997 to provide space for locally elected politicians in the functioning of the district bureaucracy. District councils, consisting of councilors elected from sub-district constituencies and headed by a directly elected chairperson, have legislative functions over matters devolved to the local council by the national state. Most importantly, these locally elected politicians are expected to perform unique roles of oversight or monitoring the bureaucracy, and of facilitating service delivery, as a liaison between local citizens and the state (Grossman and Michelitch, 2018; Raffler, 2017). An original survey was undertaken between September and December 2015 in 75 of Uganda’s 112 districts (at the time) to measure the selection traits of these district bureaucrats and politicians who oversee the implementation of national programs.<sup>8</sup> Figure 1 provides a map of our study area.

In each District, all the following _elected politicians_ were interviewed: the District Chairperson or Municipality Mayor; three District/Municipality Woman Councilors and three District/Municipality Directly Elected Councilors. In addition, all _non-elected contenders_ for the position of District Chairperson were listed and approached for interviews. In total 770 district politicians and political contenders were interviewed. Among _bureaucrats_ , all District Chief Administrative Officers (CAO), deputy CAO and the Resident District Commissioners (RDC) were interviewed. An extensive roster of senior officers were also included on the sample to be interviewed<sup>9</sup> ; whenever they refused to be interviewed or the position was vacant, they were replaced by other members of the same department. A total number of 1,357 bureaucrats are included in the final sample.

> 8Annex 9.8 provides the list of surveyed districts

> 9They are the Chief Finance Officer, Senior Finance Officer (Expenditure), Senior Accountant (Accounts), District Planner, District Education Officer, Senior Education Officer, Senior Inspector of Schools, District Internal Auditor, District Production Officer, Senior Agricultural Officer, District Health Officer, Assistant District Health Officer (Environmental Health), and Assistant District Health Officer (Maternal Child Health/Nursing).

9
development is thus assumed to be associated with a better institutional framework including a debt management strategy and its public availability. We used also a quadratic of this variable in the model to capture possible non-linearities, however, it appeared to be insigni...cant and is not reported in the estimation results. The measure of GDP per capita is the PPP converted gross domestic product from the Penn World Tables (Heston et al, 2006).

Indebtedness of government - increases in this variable, de...ned as the ratio of total government debt to GDP, should result in an e¤ort to consolidate government ...nances and adopt a debt management strategy. One can also expect that if this indicator reaches high levels the government may give up on debt management and focus on debt renegotiations. Although debt renegotiations could be seen as a part of the debt management strategy we do not include them in our indicator yi: Therefore, inclusion of a quadratic of government indebtedness into our PROBIT model can be justi...ed. As in the case of the GDP per capita the quadratic term appeared to be insigni...cant and is not reported in the presented estimation results. The total debtto-GDP ratio was obtained from the GDF & WDI Central database of the World Bank and the EIU database.

Government share of GDP - this variable is used to approximate the importance of public sector (government) in economic performance of a country. One may expect that a larger share of government on real GDP would result in a greater e¤ort to stabilize government ...nances in the sake of greater macroeconomic stability. Similarly, if government actions are important for an economy the public will require higher transparency and accountability from the government. Existence of a public debt management strategy is thus deemed to represent increased e¤orts of the government to stabilize its ...nances and meet the requirement of the public for higher transparency and accountability. The measure used here is the government’s share on real GDP from the Penn World Tables (Heston et al, 2006).

Degree of government debt concessionality - this variables is used to capture the percentage of government debt ...nanced by means of concessional resources, e.g. from multilateral and bilateral donors. We assume that the higher the concessional share of government debt the lower the incentive for the government to adopt a strategy 17
natural log of a country’s population size and government consumption as a share of GDP; the latter is the most widespread measure of government size (Adsera and Boix, 2002; Alesina and Wacziarg, 1998; Rodrik, 1998).

Tax compliance is also related to the government’s ability to effectively detect and punish tax avoiders, tax evaders, and tax arrears. Although an imperfect measure of states’ deterrent capacity, Afrobarometer includes two survey questions on perceptions of government enforcement and monitoring capacities. One question probes respondents on how often ordinary people who break the law go unpunished. The other probes respondents on how often officials who commit crimes go unpunished. This latter question is also a measure of perceived government fairness - the extent to which a government implements the law evenly across all social groups.

# **6.2.5 Procedural Justice**

I include two indicators of procedural justice. The first probes respondents on how often people are treated unequally under the law. The next taps citizens’ perceptions of the government’s treatment of their ethnic group. Specifically, respondents were asked how often their ethnic group is treated unfairly by their government.<sup>9</sup>

# **6.2.6 Donor and Non-State Actor Provision of Services**

I include a measure of who citizens believe is responsible for collecting income taxes. I expect citizens who perceive tax collection to be the responsibility of non-state actors to be less likely to be willing to pay taxes to the state’s tax department than citizens who perceive tax collection to be the responsibility of the state.

Perceptions of the effectiveness of donor and non-state actor provision of services are assessed using the following items. Respondents were probed on how much they believe the following non-state actors and donors do to help their country: the United Nations; international donors and NGOs; international businesses and investors; China; and the United States.

> 9 I also include Freedom House’s political liberties and civil rights ratings for the 19 countries in the sample. These two variables should capture the relative equality of influence in making policy. They indicate whether citizens are able to express their voice without fear of repression and whether elections are free and fair. Neither of these variables are significant at the p _<_ 0.05 level.

14
<!-- Start of picture text --> :<br>BStoM MtoS<br>00.00%<br>90.00%<br>80.00%<br>70.00%<br>60.00%<br>50.00%<br>40.00%<br>30.00%<br><!-- End of picture text --> <!-- Start of picture text --> 10.00%0.00% a [| a<br>-10.00%<br>-20.00%<br>CHN IND BRA MEX USA<br><!-- End of picture text -->
(Figure 3).<sup>5</sup> Despite these improvements, Sudan’s labor force participation rate lags behind many neighboring and post-conflict African countries such as Chad (71 percent), Eritrea (81 percent), and Ethiopia (82 percent). The employment-to-population ratio in Sudan is the lowest (41 percent) among the average share of Sub-Saharan Africa and low-income countries (63 and 68 percent, respectively) (WDI 2020, data for 2019).

# **_Labor market indicators by gender and age_**

Women’s economic activity lags substantially behind that of men, even though their increased participation in the workforce is a key driver behind the overall increase in the labor force participation rate in Sudan **.** The country’s increase in the labor force participation rate between 2009 and 2014 (from 48 to 53 percent) was mainly due to the substantial increase in female participation rate from 24 percent in 2009 to 33 percent in 2014. This positive change took place in both urban and rural areas. However, while the female labor force participation rate increased by nearly 10 percentage points (compared to only 2 percentage points among men), it remains less than half the male rate: the labor force participation rate is 76 percent among men compared to 33 percent among women (World Bank 2019b). In addition, among the third of women who are economically active, few women actually work: they are more than twice as likely as men to be unemployed (19 percent and 8 percent, respectively).

## **Figure 3. Labor force participation rates and unemployment rate by sex, quintile, locality, and year**

<!-- Start of picture text --> (a) Labor force participation rate (b) Unemployment rate<br>80% %<br>60%<br>%<br>40%<br>%<br>20%<br>0% %<br>By gender By quintile By locality By gender By quintile By locality<br>2009 2014<br>2009 2014<br>All Women Men B20% B40% T20% Rural Urban All Women Men B20% B40% T20% Rural Urban<br><!-- End of picture text --> _Source:_ World Bank 2019c.

Despite the increase in all regions, female labor force participation rates were below 20 percent in the Northern, Eastern, and Central regions. In contrast, more than half of all working-age women in Darfur

> 5 There are differences in the design of the labor modules between the two surveys: the recall period changed from 7 days in 2009 to 10 days in 2014, and the order in which questions were asked changed, with implications for skipping patterns.

10
||2010|2030|Percentage<br>Increase<br>p.a.|GDP<br>loss %<br>wrt BAU|%<br>reduction<br>in CO2<br>wrt BAU|
|---|---|---|---|---|---|
||**BAU**<br>|||||
|GDP US$Bn.<br>2010|3,763|13,820|6.89|||
|CO2: Mn. Tons|1,563|2,770|1.77|||
|PM10: Mn. Tons|8.68|16.81|1.94|||
|**10%**|**Reduction Vi**|**a a PM tax**|||20%|
|GDP US$Bn.<br>2010|3,763|13,774|6.87|0.33||
|CO2: Mn. Tons|1,563|2,180|0.79|||
|PM10: Mn. Tons|8.68|15.12|1.03|||
|**10%**|**Reduction Via**|**a Coal Tax**|||10%|
|GDP US$Bn.<br>2010|3,763|13,751|6.86|0.5||
|CO2: Mn. Tons|1,563|2,499|0.9|||
|PM10: Mn. Tons|8.68|15.24|1.03|||
|**30**|**% Reduction V**|**ia PM Tax**|||60%|
|GDP US$Bn.<br>2010|3,763|13,723|6.85|0.7||
|CO2: Mn. Tons|1,563|1,108|0.4|||
|PM10: Mn. Tons|8.68|11.86|1.02|||
|**30%**|**Reduction Vi**|**a Coal Tax**|||30%|
|GDP US$Bn.<br>2010|3,763|13,672|6.83|1.07||
|CO2: Mn. Tons|1,563|1,939|0.7|||
|PM10: Mn. Tons|8.68|11.84|1.02|||

The results in summary are:

- i. With the different tax regimes for a 10 percent particulate emission reduction we have a lower GDP but the size of the reduction is modest. With a PM10 tax conventional GDP is about US$46 billion lower in 2030, representing a loss in growth of 0.3 percent with respect to BAU. The impact on GDP is greatest if we seek to achieve the PM target via a coal tax.

- ii. For a 30 percent particulate emission reduction the conventional GDP is about US$97 billion lower in 2030 representing a loss of 0.7 percent.  The scenario suggests that even a substantial reduction in emissions can be achieved without compromising much on GDP growth rates if supported by adequate least cost policy measures. Again the coal tax performs worse with a GDP loss of 1.07 percent.

- iii. It should also be noted that the Green Growth Plus scenario assumes, in addition to the taxes, some increase in investment towards cleaner technologies. Such investments are associated with an increase in pollution control techniques, modernization of the existing capital and/or use of less

15