rafmacalaba/datause-encoder-data huggingface dataset:
# **Political economy concerns**

However, a note of caution can be useful. Better statistics may work (more effectively) under certain conditions. Improving a country’s statistical capacity equips this country with a better tool, but perhaps what is more important is how these tools are used for the public good and ensuring that these tools are not misused. Put differently, improving a country’s statistical capacity should not be considered separately as an end by itself, but should be examined together with other political economy factors such as governance (including government incentives) to maximize its beneficial impacts. Indeed, some recent theoretical evidence suggests that improved economic statistics may even inhibit government reform attempts, since with better statistics, politicians no longer can hide if their reform efforts are failing and thus face a higher risk of electoral losses (Binswanger and Oechslin, 2020).

Alternatively, policy makers and politicians may fear public or timely data dissemination for a variety of reasons, such as more accountability, increased public scrutiny (e.g., the data may present a simplistic perspective that does not do full credit to the complex policy-making process) or potential loss of immediate public support (e.g., policies may take more time than expected to work) (Taylor, 2016; Dargent _et al._ , 2018; Agrawal and Kumar, 2020) or not being able to secure more loans (Coyle, 2015). Similarly, for sustainable solutions, improving statistical capacity may not just include setting things right in the short term, but may also require more nuanced thinking about the varying degrees that official economic indicators can be manipulated in specific contexts, and how solutions can be tailor-made to address these challenges on a longterm basis. This process is appropriately named “many shades of wrong” by Aragao and Linsi (2022).

# **Future directions**

More optimistically, the world is expected to be moving toward an integrated national data system (INDS) framework, which enables the production of data relevant to development, and fosters the equitable and safe flow of data between government, individuals, civil society, academia, and the private sector (World Bank, 2021). In this process, countries at different levels of data (or resources) development can formulate their own best strategy to improve their statistical capacity. Specifically, at the basic level, countries can prioritize establishing the fundamentals of a national data system. Once the fundamentals are in place, countries can seek to initiate better 14
# _6.4 Effects on employment_

One of the ultimate goals that shall be achieved with the interventions under study is employment creation. Therefore, we included a whole battery of outcome indicators related to jobs and work in our surveys. Table 5 shows the results for the total number of employees, the number of paid wage workers, the number of unpaid workers and the number of days worked by the employees in a normal week. Overall, we do not find any significant effects on employment-related outcomes, neither for beneficiaries of cash grants nor for beneficiaries of matching grants, and whether we look at all firms or at already existing firms only, if at all, it seems that the short-term effects are slightly negative. The only exception is ‘days worked’; it seems that beneficiaries of cash grants increased the number of days they are working per week by 0.16 from 5.66 to 5.82 or roughly 3 percent. This is a relatively small effect. Digging deeper reveals that treated firms seem to use less household helpers and casual work. This could hint to a higher capital intensity in these firms. Yet, more generally, we believe that the time that elapsed between the investments that have been undertaken and the survey is still too short to expect substantial employment effects; if employment creation materializes, it will certainly take more time. Here as well, it must be assumed that the COVID-19 pandemic did not offer favorable conditions for an expansion of jobs.

21
7

# **_Direct agricultural distortions_**

Consider a small, open, perfectly competitive national economy with many firms producing a homogeneous farm product with just primary factors. In the absence of externalities, processing, producer-to-consumer wholesale plus retail marketing margins, exchange rate distortions, and domestic and international trading costs, that country would maximize national economic welfare by allowing both the domestic farm product price and the consumer price of that product to equal _E_ times _P_ , where _E_ is the domestic currency price of foreign exchange and _P_ is the foreign currency price of this identical product in the international market. That is, any government-imposed diversion from that equality, in the absence of any market failures or externalities, would be welfare-reducing for that small economy.

# _Price-distorting trade measures at the national border_

The most common distortion is an ad valorem tax on competing imports (usually called a tariff),<sup>_t_</sup> _m_ . Such a tariff on imports is the equivalent of a production subsidy and a consumption tax both at rate<sup>_t_</sup> _m_ . If that tariff on the imported primary agricultural product is the only distortion, its effect on producer incentives can be measured as the **_nominal rate of assistance_** to farm output conferred by border price support **_(NRABS_** ), which is the unit value of production at the distorted price less its value at the undistorted free market price expressed as a fraction of the undistorted price:4 The effect of that import tariff on consumer incentives in this simple economy is to generate a **_consumer tax equivalent (CTE)_** on the agricultural product for final consumers: (2) _CTE_ =<sup>_t_</sup> _m_ The effects of an import subsidy are identical to those in equations (1) and (2) for an import tax, but<sup>_t_</sup> _m_ in that case would have a negative value.

> 4 The _NRA_ thus differs from the producer support estimate (PSE) as calculated by the OECD, in that the

> PSE is expressed as a fraction of the distorted value. It is thus _tm_ /(1 + _tm_ ) and so for a positive<sup>_t_</sup> _m_ it is smaller than the _NRA_ and is necessarily less than 100 percent.
skilled workers in agriculture decreased moderately. In China, substantial increases are observed in all industries except agriculture, utilities and other services.

Table 3a: Proportion of skilled workers by selected industry group over time <u>(Workers with positive earnings)</u>

|_Industry_||% Uppe|r Secondary a|nd above||
|---|---|---|---|---|---|
||Indones|ia<br>Philipp|ines<br>T|hailand|Vietnam|
||1996 2007|Change<br>1988 2006|Change<br>1990|2004 Change|1992 2006 change|
|Agriculture|6.2 16.1|9.9<br>29.6 41.0|11.4<br>2.4|5.6 3.2|8.6 7.7 -0.9|
|Mining|32.4 40.0|7.6<br>54.6 47.7|-6.9<br>25.0|33.3 8.3|46.9 47.0 0.1|
|Manufacturing|33.5 40.1|6.6<br>59.5 77.8|18.3<br>16.4|28.9 12.5|21.4 22.6 1.2|
|Utilities|72.4 79.9|7.5<br>89.9 98.0|8.1<br>52.8|57.6 4.8|65.0 88.1 23.1|
|Construction|20.3 29.9|9.6<br>52.9 66.0|13.1<br>9.9|13.8 3.9|17.7 20.0 2.3|
|Trade|54.3 56.3|2.0<br>60.4 77.9|17.5<br>17.5|28.5 11.0|29.5 43.3 13.8|
|Transp/Comm.|36.2 45.7|9.5<br>67.4 77.5|10.1<br>21.0|30.4 9.4|31.1 61.5 30.4|
|Finance/Business|86.4 84.1|-2.3<br>94.9 96.5|1.6|-|70.0 81.6 11.6|
|Public Admin.|67.9 80.8|12.9<br>92.1 91.2|-0.9|-|-|
|Other Services|-|92.3 91.8|-0.5<br>42.8|67.0 24.2|64.3 78.6 14.3|
|_Mean *_|-|||-|-|
|_Mean (1)_|44.2 55.5|11.3<br>49.6 65.9|16.3<br>17.7|28.8 11.1|29.1 36.7 7.6|
|_Mean(2)_|9.0 19.1|10.1<br>19.2 27.3|8.1<br>6.1|12.1 6.0|6.7 13.0 6.3|

# Table 3a continued

|_Industry_|% Upp|er Secondary and|above|
|---|---|---|---|
|C|ambodia|China|Mongolia|
|1997|2007 Change|1999 2005 Change|1998 2007 change|
|Agriculture<br>6.2|2.3 -3.9<br>|64.5 68.0 4.5|69.4 63.7 -5.7|
|Mining|-|n/a|-|
|Manufacturing<br>17.2|16.4 -0.8<br>|49.0 60.3 11.3|81.2 81.7 0.5|
|Utilities|-<br>|57.5 57.6 0.1|82.2 77.1 -5.1|
|Construction<br>10.4|14.1 3.7<br>|48.4 74.1 25.7|80.4 70.0 -10.4|
|Trade<br>13.2|23.9 10.7<br>|46.2 61.0 14.8|80.6 81.1 0.5|
|Transp/Comm.<br>16.4|16.9 0.5<br>|55.4 62.4 7.0|89.6 83.0 -6.6|
|Finance/Business<br>54.5|52.4 -2.1|n/a|-|
|Public Admin.<br>49.4|53.4 4.0<br>|74.3 86.2 11.9|90.8 91.3 0.6|
|Other Services<br>51.4|67.7 16.3<br>|66.8 69.3 2.5|85.4 88.3 2.9|
|_Mean *_<br>26.9|24.5 -2.4|-|-|
|_Mean (1)_<br>10.5|10.0 -0.5|56.4 66.5 10.1|84.9 82.2 -2.7|
|_Mean(2)_<br>0.02|0.033 1.3|21.0 24.3 3.3|58.2 32.8 -25.4|

9
**StiumlatinS Agricultural Growth and Rural Developeent In**

|||Sub-Saharan<br>Africa<br>Table<br>of Contents||
|---|---|---|---|
|I.|INTR|ODUCTION|1|
|II.|PARA|METERS OF AGRICULTURE DEVELOPMENT IN AFRICA|4|
||(a)|Environment and Ecological Constraints|4|
||(b)|Demographic and Social Factors|14|
||(c)|Farming Systems and Land Tenure|17|
|III.|ENLA|RGING THE ORBIT OF GROWTH|21|
||(a)|The Dimensions of Rural Poverty|21|
||(b)|Approaches to Poverty Alleviation|25|
|IV.|TECH|NOLOGICAL INTERVENTIONS|31|
||(a)|Targetting Input-Intensive Technology|32<br>|
||(b)|Improvement of Traditional Systems|35<br>|
||(c)|Adaptive and Applied Research and Extension|39|
||(d)|The Role of Irrigation|42|
||(e)|Draft and Mechanical Power|45|
|V.|ECON|OMIC POLICY AND INSTITUTIONAL CHANGES|47|
||(a)|The Policy Regime|47|
||(b)|Role of the Private Sector|58|
|VI.|FARM|/NON-FARM LINKAGES|60|
|VII.|INTE|RNAL MIGRATION|67|
|VIII.|CONC|LUSION|69|

The authors are grateful to Mr. G. Edward Schuh, then Director<sup>of the</sup> Agriculture and Rural Development Department, and Xavier de la<sup>Renaudiere,</sup> then Director of the Special Office of African Affairs, for their<sup>help and</sup> encouragement. The authors would also like to acknowledge the assistance provided by their colleagues in AGR and the African Region and the research support provided by Mr. Boyd Gilman.
significant heterogeneity. The census includes data on demographics, labor market (including employment, industry and salary), and access to infrastructure, among other. Using cartographic data on communities and former homeland boundaries we identified which communities were located inside and outside the former homelands. In the next section we document the extent to which the data are consistent with the historical accounts of the apartheid.

The geographic community definitions changed from 1996 to 2011. We use official cartographic data to match communities over time. For the analysis presented in the paper, we match the 2011 data to the 1996 data by the nearest centroid.<sup>15</sup> We allow for multiple matches, and therefore there is not a one-to-one correspondence between local communities.<sup>16</sup> Our final sample is comprised of almost 16,000 communities, observed both in 1996 and 2011. Appendix Table A1 provides summary statistics on these data.

Our main variable of interest is the growth of income per capita at the community-level. This variable is defined as the difference in the natural logarithms of population-weighted income per capita between 2011 and 1996. Since data on income are grouped in categories (e.g. no income, 1 to 4800 rand a year, and so on), we take the mid-point of each category.<sup>17</sup> All income values are expressed in December 2012 prices (obtained from Statistics South Africa). To provide complementary evidence on the specific mechanisms we emphasize, we also use data from the 10% sample of the 1996 population census.

# **5 The legacy of apartheid: Size and evolution of local development gaps**

We document the evolution of development differentials across communities located justinside and just-outside the former homelands. Communities are grouped in one-kilometer bins with respect to the minimum linear distance to the former homeland border. Our aim is to verify the extent to which the data are consistent with numerous historical accounts pointing to a tragic legacy of apartheid through the various repressive mechanisms discussed above. The use of these descriptive diagrams focusing on communities located just-inside and just-outside the former homelands makes it possible to minimize

> 15The centroid of the community is defined as the central point (i.e. latitude and longitude) of the community polygon.

> 16In Appendix Table A2, we present results for different matching techniques and find that the main findings remain unchanged across definitions.

> 17For example, category 1 to 4800 rand would take the value of 2400.5, and so on. The maximum category of income is truncated at its value. We count all missing values as zero when calculating the community’s per capita income. In Appendix Table A3, we show that our main results are robust to imputing missing values by the community average instead.

13
based on estimates by the US Department of Energy.<sup>41</sup> The annual depletion of wealth in the crude-oil sector is estimated to roughly US$125 million.

# **<u>Updated Figures for Adjusted Nets Savings in 2013</u>**

**After formulating estimates for the different types of national wealth (natural, produced and intangible) and for annual wealth depletion in each natural-resource category, the Adjusted Net Savings (ANS) rate for 2013 can be calculated.** The results of the analysis are presented in Figure 5, below.

**Figure 5: Adjusted Net Savings and Gross National Savings as a Share of GNI, 1997-2013**

<!-- Start of picture text --> 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013<br>Adjusted Net Savings/GNI Gross Savings/GNI<br><!-- End of picture text -->

**The analysis finds that while Mauritania’s gross savings rate has been consistently positive, its national wealth stock is nevertheless in decline.** Its 2013 ANS rate is slightly negative, and though currently above its recent average, this nevertheless indicates that the sustainability of Mauritania’s national wealth is in jeopardy. Over the past 6 years the country’s ANS rate has been consistently negative, with the exception of 2006, when domestic savings was boosted by a temporary boom in the oil sector.

**The ANS methodology indicates that Mauritania’s stock of national wealth fell by 3.9 percent in 2013.** Countering this trend will require expanding the reinvestment of resource rents in produced and intangible

> 41 The Energy Information Administration, US Department of Energy; <u>http://www.eia.gov/tools/faqs/faq.cfm?id=367&t=6</u>

17
11 An easy, although admittedly weak, test of how the regulators distributed the efficiency gains is the correlation between efficiency and average tariff changes. 17 Two main outcomes can be expected from this analysis. First, either tariff and efficiency gains are highly negatively correlated, in which case, the adoption of the price cap achieved a fair outcome for users but this can explain the requests for renegotiation by the operator. Second, there is no correlation and this suggests that incentive based regulation did not achieve much for the users. The more interesting story is the reason for why there is no correlation. One explanation could simply be that the regulator was unable to deliver on its mandate and the operator appropriated the gains (Table 6 below provides some evidence on that). Another is that the government may have hijacked part of the gains for fiscal purposes through tax increases.

To illustrate the point, the Argentina case is analysed here as a representative of the 1990s in the region. Table 5 summarizes the evidence for Argentina and shows that the increasing use of the privatised sector as a tax handle may provide a good explanation as to why operators were not happy with the price cap system.

**Table** 5: **Comparing annual real tariff levels to efficiency changes since privatization**

||**Electricity**<br>**Distribution**<br>|**Gas**<br>**Distribution' **<br>|**Water Distribution**<br>.|**Telecoms**<br>-<br>**e**|
|---|---|---|---|---|
||**-f**<br>.<br>9,<br>,|**icienc~ych~nge**<br>,<br>.<br>U,, . , ,.|**donot**wiaef**togohandi **<br>.. ,,diff,~|**n:hand**<br>1.;'|
|**Annual averagetax**<br>**inclusivetariff**|_-0.75%_**%**|**-0.8%**|**+1.75%**|**-0.6%**|
|**change**|||(forAguasArgentinas)||
|**Possible**<br>**approximation of**<br>**annualefficiency**<br>**Gainstobeusedin**<br>**tariffrevision**|**1%**<br>(shift)|**2.9%**<br>(shift+<br>average<br>catchingup)|**6.1%**<br>(shift+averagecatching<br>upfor4watercompanies)<br>**3.7%**<br>(withoutMendoza)<br>**1.9%**<br>(forAguasArgentinas)|**3.9%**<br>(shift+catchingup)<br>[set at2%between91<br>and96aspartofcap]<br>(inMendoza)|
|**IndirectTax**|20-57%|20-30%|20-30%|40-50%|

Source: Authors tabulation based on Delfin and Casarin (2001), Benitez, et al. (2001), Rossi (2001), Estache and Trujillo (2002)

**17** A second test is to check on the changes in the tariff structure across user and consumption types and to see how each one compares to the evolution of efficiency. If each category gets a similar cut of the efficiency gains in terms of reductions in tariffs, the perception will be that the distribution was fair--even if differentiated distribution of gains may simply reflect improvements in allocative efficiency, in which there is a better mach between cost and tariff per user and usage type.
**-** **<u>Table 1: Comparing household and HH head indicators between non refugee hosting and refugee hosting districts (excluding Kampala)</u>**

|||**_I_**|**_n the base wa_**<br>|**_ve=2009_**|**_/10_**||||**_In the last_**<br>|**_wave=20_**|**_12_**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Mean (Non-**<br>**hosting)**|**Obs.**|**Mean**<br>**(Hosting)**|**Obs.**|**Mean Diff.**|**Std.**<br>**Error**|**Mean (Non-**<br>**hosting)**|**Obs.**|**Mean**<br>**(Hosting)**|**Obs.**|**Mean Diff.**|**Std. Error**|
|HH welfare(consumption<br>aggregate per adult equivalent)|56666.446|1835|50487.684|257|6178.762*|3471.519|60848.386|2008|53033.680|271|7814.706|7793.058|
|Rural locations|0.823|2026|0.890|273|-0.067***|0.024|0.835|2026|0.890|273|-0.055**|0.024|
|HH size|6.239|2026|5.883|273|0.357*|0.201|-|-|-|-|-|-|
|Age of HH head|46.089|2023|46.179|273|-0.090|0.980|47.518|2025|47.744|273|-0.226|0.964|
|HH head male|0.720|2026|0.758|273|-0.038|0.029|0.687|2026|0.736|273|-0.050*|0.030|
|HH head female|0.280|2026|0.242|273|0.038|0.029|0.313|2026|0.264|273|0.050*|0.030|
|**_HH head education level_**|||||||||||||
|No formal education|0.198|1908|0.198|263|0.000|0.026|0.194|1979|0.209|268|-0.015|0.026|
|Didn't complete primary|0.429|1908|0.426|263|0.003|0.033|0.411|1979|0.403|268|0.008|0.032|
|Completed primary|0.266|1908|0.262|263|0.004|0.029|0.280|1979|0.272|268|0.008|0.029|
|Secondary and above|0.106|1908|0.114|263|-0.008|0.020|0.114|1979|0.116|268|-0.001|0.021|
|**_HH head marital status_**|||||||||||||
|Married monogamously|0.559|2021|0.571|273|-0.012|0.032|0.537|2025|0.546|273|-0.009|0.032|
|Married polygamously|0.185|2021|0.260|273|-0.076***|0.025|0.195|2025|0.271|273|-0.076***|0.026|
|Separated(divorced/widowed)|0.237|2021|0.165|273|0.072***|0.027|0.248|2025|0.176|273|0.073***|0.027|
|Never married|0.019|2021|0.004|273|0.016*|0.008|0.020|2025|0.007|273|0.013|0.009|
|**_HH main source of income_**|||||||||||||
|Subsistence farming|0.535|1946|0.618|262|-0.083**|0.033|0.566|1856|0.630|262|-0.064**|0.033|
|Commercial farming|0.028|1946|0.000|262|0.028***|0.010|0.012|1856|0.008|262|0.004|0.007|
|Wage employment|0.160|1946|0.149|262|0.011|0.024|0.129|1856|0.160|262|-0.031|0.022|
|Non-agric. self-employment|0.205|1946|0.179|262|0.025|0.026|0.213|1856|0.168|262|0.045*|0.027|
|Remittances & Others|0.072|1946|0.053|262|0.019|0.017|0.080|1856|0.034|262|0.046***|0.017|

_Two sample t-test with unequal variances between refugee hosting and non-hosting districts. Significance level: *** p<0.01, ** p<0.05, * p<0.1_ 14
<!-- Start of picture text --> Bessler, D. A., and J. L. Kling. 1990. "Prequential Analysis of Cattle Prices "<br>Bickel, J. E. 2007. "Some Comparisons among Quadratic, Spherical, and Logarithmic Scoring Rules."<br>": National Bureau of Economic Research.<br>Interpretation of Changes in the Macroeconomic Effects of Oil Prices."<br>Brier, G. W. 1950. "Verification of Forecasts Expressed in Terms of Probability."<br>Brown, T. A. 1974. "Admissible Scoring Systems for Continuous Distributions."<br>Clark, T. E., M. W. McCracken, and E. Mertens. 2018. "Modeling Time-Varying Uncertainty of Multiple-<br>----------, 1984. "Present Position and Potential Developments: Some Personal Views: Statistical Theory:<br>The Prequential Approach."<br>De Finetti, B. 1937.<br>Diebold, F. X., J. Hahn, and A. S. Tay. 1999. "Multivariate Density Forecast Evaluation and Calibration in<br>Estrella, A., and G. A. Hardouvelis. 1991. "The Term Structure as a Predictor of Real Economic Activity."<br>Fama, E. F. 1981. "Stock Returns, Real Activity, Inflation, and Money."<br>Geweke, J., and C. Whiteman. 2006. "Bayesian Forecasting."<br>Giordani, P., and P. Sdderlind. 2003. "Inflation Forecast Uncertainty."<br>Gneiting, T. 2011. "Quantiles as Optimal Point Forecasts."<br>Gneiting, T., F. Balabdaoui, and A. E. Raftery. 2007. "Probabilistic Forecasts, Calibration and Sharpness."<br>Gneiting, T., and A. E. Raftery. 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation."<br>Gneiting, T., A. E. Raftery, A. H. Westveld III, and T. Goldman. 2005. "Calibrated Probabilistic Forecasting<br>using Ensemble Model Output Statistics and Minimum CRPS Estimation."<br>Gneiting, T., and T. L. Thorarinsdottir. 2010. "Predicting Inflation: Professional Experts versus No-Change<br><!-- End of picture text -->
4 details). The fixed year effects account,for influences (such as world fuel prices) not fully captured by GDP and affecting all countries equally in a given year. The fixed country effects are time-invariant parameters representing the unique characteristics of countries (such as climatic factors) that affect emissions independently of GDP. F-tests report that a1 are jointly significantly different from zero, as are the d,. The parameter estimates imply a diminishing MPE relationship between per capita emissions and per capita GDP. The turning point and subsequent downturn in per capita emissions occurs at currently out-of-sample levels of per capita income (around $36,000), but these levels are reached by developed countries during the planning horizon. As will be seen later, the diminishing MPE has profound distributional implications for the future.

Future emissions trajectories by country are predicted by incorporating GDP forecasts and population projections into (1). For per capita GDP projections, also following Holtz-Eakin and Selden (1995), a univariate time-series was fitted on the historic data. The GDP model implies convergence in growth rates in the very long run.' Population projections are provided by the World Bank (1994).

# 2.2. _Global emissions_

Figure I shows the predicted path of global carbon emissions, population (left scale), and gross world product (right scale) to 2100 obtained by summing the country-level projections.8 Global emissions grow to more than double their 1992 levels by 2050, and to slightly less than four-fold by 2100, in line with the Intergovermnental Panel on Climate Change IS92a reference scenario projections (IPCC 1992). In 1993, emissions and population are roughly equal, with a worldwide emissions average of one metric ton per person per year. By 2100, global per capita emissions more than double. These global business-as-usual emissions exceed the cumulative limits for stabilization of CO2 concentrations at

> 7Convergence, the notion that economic growth rates and/or real GDP levels per capita will tend to equalize across countries, was bome of the Solow growth model and remains an unresolved topic of debate in economics. See for example, Islam (1995) for a review of previous work and some recent estimates.

8These figures are scaled-up so they represent the world, not merely the sample of 135 countries.