rafmacalaba/datause-encoder-data huggingface dataset:
- Iterating until convergence, which is experimentally rather fast.

This procedure seems to produce stable results independent of the seed. The sign of factors and elasticities may be simultaneously changed from one trial to another, which has no relevance.

The number of dimensions to be retained is determined by procedures similar to the linear principal component analysis such as the scree plot.

# **8 Results: Latent Variables and Interpretation of Latent Factors**

The model of section 7 is implemented for one year (2010) starting with the 2-digit SITCS rev 3 level. Energy trade is excluded. Twelve principal components are computed in order to estimate the number of relevant dimensions.

# **_Number of dimensions in product space_**

As in PCA, the determination of the significant number of dimensions can be done by looking at the contribution to the increase of the log-likelihood _Ln_ or the reduction in unexplained information, at stage n, _I n_ .

By construction the informational content of each successive variable _J n_ = _I n_ − _I n_<sup>+</sup> 1 is decreasing hence the convex shape of the information curve and the decreasing one for the Js (see the scree plot in Fig 9). Table 5 below provides, for the first 12 component factors, the values of the contribution of component i as well as the cumulated contribution and the unexplained information in the matrix.

The number of dimensions relevant to the problem corresponds to the “bottom” of the scree where the relative information or improvement in the log-likelihood _J n_ + 1 brought by the next variable becomes significantly less than with the previous _J n_ , and could be considered as “noise”. Unfortunately, as often is the case, visual inspection (Fig 6) is not enough to identify the “bottom” of the scree. A more rigorous criterion is to compare the contribution with the one corresponding to a pure random “noise” in the data. In such a case, the contributions are in a geometric series with a ratio close to one. The noisy principal components are easily generated with computer algebra software for the same number of countries and products.

**_<u>Table 5. Improvement in information brought by the first 20 factors.</u>_**

|||Inform<br>|ation<br>||
|---|---|---|---|---|
||Unexplained<br>I|Contribution<br>J|Explained<br>100-I|“Noise” level|
|Factor|**100.00**||||
|1|**67.12**|32.88|32.88|4.36|
|2|**51.99**|15.13|48.01|4.01|
|3|**42.04 **|9.95|57.96|3.95|
|4|**35.12**|6.92|64.88|3.75|

21
countries with more flexible exchange rate arrangements and more frequent intervention in the FOREX market are able to generate an undervaluation of the currency. Fiscal policy is also effective while the probability of the size of RER undervaluation is small to medium whereas it becomes ineffective when the RER undervaluation is larger (say, more than 20 percent). Finally, export concentration — as measured by the Hirschman- Herfindahl index of export revenues— shows a positive and significant coefficient. This means that export pattern matters on the magnitude of RER undervaluation. The results on the ability of exchange rate flexibility to affect the magnitude of the undervaluation are mixed.

# _Dollarization Robustness Analysis_

Table 31 replicates the results from the baseline _Tobit_ regressions using two different measures of dollarization —and including some regressions without dollarization similarly as the _Probit_ analysis. The results are presented from lower to higher thresholds (10, 20 and 25 percent).

Compared to _Probit_ results most of results from _Tobit_ does not show overall significance although the coefficient of lagged RER misalignment as calculated from the Johansen estimates is always statistically negative significant in Table 31. The deposit dollarization is positive significant only when fiscal discipline is absent.

# _Can other policies generate persistent deviations?_

Table 32 reports a positive and significant coefficient for dollarization while the interaction term between RER misalignments and intervention is not significant. In sum, we find that the intervention may affect the persistence of the likelihood of undervaluation rather than the magnitude itself.

Analogously to the _Probit_ analysis, we test whether ― _de facto_ ‖ fixed or flexible exchange rate arrangements generate more persistent undervaluations (in terms of magnitude). Table 33 reports the regression results of our baseline regression with the interaction term between lagged RER misalignments and fine classification of exchange rate regimes by Reinhart and Rogoff (2004). The coefficient estimates for this interaction are negligible.

Table 34 shows the results from the baseline regressions with two interaction terms: the lagged interaction between overvaluation and exchange rate regime and the lagged interaction of undervaluation and exchange rate regime. The coefficient of 30
debated GHS estimates and allows further disaggregation beyond the regional level due to its representativeness at the state level.

Firstly, findings concerning monetary child poverty based on the national and international poverty line are fourfold: First, results confirm the claim that children are more affected by poverty than adults. More specifically, the share of Nigerian children living in households with consumption levels below the poverty line is decreasing with child age but is on average 13 percentage points higher than the share of adults living below the poverty line. Second, rural children face much higher poverty rates than urban children. Third, looking beyond national averages the analysis shows extreme discrepancies in monetary child poverty between different regions in both sectors, whereby child poverty rates in the three northern regions are exceeding those in the southern regions. The largest gap is observed between rural children in the North West and North East and rural children in the South East and South South, where in the latest wave up to over 80 percent compared to around one quarter of children are estimated to live below the international poverty line, respectively. Fourth, in addition, the findings suggest an inequality enhancing trend of monetary poverty between the North and the South. Whereas poverty rates appear to be largely rising over the three waves in the north and in rural areas, the share of children living below the poverty line has been decreasing in the South and in urban areas. These estimates however only quantify the extent of poverty but not the intensity of poverty as the distance to the poverty line.

Secondly, the analysis presents multi-dimensional poverty estimates based on three individual level (education, food and health) and four household level severe deprivations (sanitation, water, shelter and information). With few exceptions the findings confirm the North-South and Rural-Urban poverty gap. Large inter-state heterogeneity in deprivations within each of the six geo-political regions, suggests the need for a more disaggregate analysis beyond the regional level. More in depth analysis of water and sanitation deprivation moreover show, that it is important to look beyond severe deprivations. Children in the North West for instance may not appear as drastically deprived, when solely considering severe water and sanitation deprivation, but deprivation levels are many times higher when considering protected water and improved sanitation deprivation instead. The descriptive analysis cannot provide evidence for a systematic evidence for girls having much lower school enrollment rates or systematically dropping out of school at a younger age. Rather there appear to be state specific findings, whereby in some few states instead boys appear to have lower enrollment rates over the full schooling age range, which might be a result of the higher opportunity costs of staying in school, when there are more jobs available. In any case, observed state specific gender differences are much smaller in magnitude than the 57
Table 2: Difference between Climate in Tanzania in the Late 20<sup>th</sup> and Early 21<sup>st</sup> Centuries as Determined by the Period Average and Standard Deviation Values of Bias‐Corrected Temperature and Precipitation by GCM.

|GCM<br>Name|GCM<br>Code|Percent Diff<br>Value in the<br>Average Va|erence in the<br>21<sup>st</sup>Century<br>lue in the 20<sup>th</sup><br>(%)|Average<br>from the<br>Century|Percent Dif<br>Deviation i<br>the Standa|ference in th<br>n the 21<sup>st</sup>Cen<br>rd Deviation<br>Century (%)|e Standard<br>tury from<br>in the 20<sup>th</sup>|
|---|---|---|---|---|---|---|---|
|||Bias‐|Bias‐|||||
|||Corrected<br>Average<br>Monthly<br>Growing<br>Season|Corrected<br>Average<br>Monthly<br>Growing<br>Season|Annual<br>Average<br>Grains<br>Yield|Average<br>Monthly<br>Growing<br>Season<br>Temp.|Average<br>Monthly<br>Growing<br>Season<br>Precip.|Annual<br>Average<br>Grains<br>Yield|
|||Temp.|Precip.|||||
|I|II|III|IV|V|VI|VII|VIII|
|bccr_bcm2_0|01|1.20 (0.27)|7.21|11.72|‐21.46|‐4.54|‐11.90|
|cccma_cgcm3_1|02|1.68 (0.38)|20.86|15.81|‐29.40|28.09|3.28|
|cccma_cgcm3_1_t<br>63|03|3.52 (0.80)|11.11|6.78|4.72|1.97|5.05|
|cnrm_cm3|04|3.52 (0.80)|1.99|3.17|43.29|24.37|34.21|
|csiro_mk3_0|05|1.17 (0.26)|3.38|10.28|37.60|14.45|18.72|
|gfdl_cm2_0|06|2.67 (0.60)|11.02|9.12|45.14|12.28|19.04|
|gfdl_cm2_1|07|1.72 (0.39)|0.12|7.46|‐14.89|‐19.68|‐17.91|
|giss_aom|08|3.82 (0.86)|3.14|2.78|‐8.07|‐28.34|‐22.84|
|giss_model_e_h|09|3.69 (0.83)|6.06|4.31|31.72|16.43|21.61|
|iap_fgoals1_0_g|10|1.70 (0.38)|0.32|7.59|‐6.40|‐7.60|‐2.74|
|ingv_echam4|11|2.13 (0.48)|1.89|7.00|‐8.90|7.47|5.07|
|inmcm3_0|12|3.53 (0.80)|11.12|6.76|9.87|6.87|‐23.27|
|ipsl_cm4|13|3.34 (0.76)|5.13|4.91|10.33|0.93|9.69|
|miroc3_2_hires|14|4.90 (1.11)|8.12|1.75|19.35|7.07|5.06|
|miroc3_2_medres|15|2.33 (0.53)|3.74|7.18|26.51|1.31|‐14.85|
|miub_echo_g|16|1.71 (0.39)|1.81|8.15|‐3.58|‐15.23|‐7.32|
|mpi_echam5|17|0.88 (0.20)|‐1.74|9.06|25.84|‐6.18|1.50|
|mri_cgcm2_3_2a|18|1.99 (0.45)|‐1.26|6.15|32.97|‐8.10|‐0.11|
|ncar_ccsm3_0|19|4.07 (0.92)|17.18|7.67|4.21|‐10.38|‐25.56|
|<br>ncar_pcm1|20|2.80 (0.63)|‐0.64|4.14|‐5.64|‐18.57|‐13.84|
|<br>ukmo_hadcm3|21|2.01 (0.45)|‐10.42|2.46|‐2.98|‐10.95|‐16.56|
|ukmo_hadgem1|22|3.20(0.72)|‐4.54|1.47|29.98|‐14.63|‐4.58|
|Average||2.62(0.59)|4.35|6.62|10.01|‐1.04|‐1.74|
|Average Absolute||2.62(0.59)|6.04|6.62|19.22|12.06|12.94|
|Sign Consistency||1.00|0.72|1.00|0.52|‐0.09|‐0.13|

Source: Authors’ estimates and processing of Meehl et al (2005) Note: Sign consistency is the ratio of the average to the average of absolute values and is bounded by ‐1 and +1. A value of 1.0 indicates that the models all agree that the variable in question will rise, and conversely for a sign consistency measure of ‐1.0. The numbers in parentheses in column III indicate the difference in growing season average temperature between the 20<sup>th</sup> Century and 21<sup>st</sup> Century in °C.

6
1

**Recent and Prospective Adoption of Genetically Modified Cotton: A Global CGE Analysis of Economic Impacts**

# **1. Introduction**

Cotton is important for many developing countries, either as a cash crop and/or as an input into their textile industry. It is receiving more attention of late for two reasons. One is because, thanks to genetic modification using modern biotechnology, new insectresistant and herbicide-tolerant cotton varieties are emerging that are proving to be more productive than traditional varieties of cotton. Over the decade following their first release, genetically modified (GM) cotton rose to account for 28 percent of all land sown to cotton globally in 2005 and to one-ninth of the world’s total area of GM crops. But the United States and China account for almost all of that, where the proportion of plantings that are GM are already more than four-fifths and two-thirds, respectively (Table 1).<sup>1</sup> The only other countries with high GM adoption rates by 2005 are Australia and South Africa, both with slightly more than four-fifths of their cotton areas under GM varieties. Apart from India and Mexico, where legal adoption began to take off only in 2003-04,

> 1 China’s adoption share is lower because insect infestations are low in the Western part of China where much of the crop is grown, so the gains from switching to current varieties of GM cotton are not yet sufficient to make the change. The drop in China’s GM cotton acreage in 2005 (see Table 1) paralleled a drop in its non-GM cotton acreage as farmers moved away from land-intensive to labour-intensive crops.
# **Land Institutions to Address New Challenges in Africa:**

**Implications for the World Bank’s Land Policy**

## **Klaus Deininger Aparajita Goyal**

JEL codes: R3, O18, O44, Q15, Q30 This paper draws on the Flagship Report on Land Governance prepared by the Office of the Chief Economist in the Africa Region. We thank Andrew Dabalen and Albert Zeufack for guidance and insightful discussions Daniel Ali, Rajul Awasthi, Richard Baldwin, Katherine Baldwin, Karol Bordeaux, Frank Byamugisha, Mark Cackler, Jonathan Conning, Issa Faye, Riel Franzsen, Rene Giovarelli, Ed Glaeser, Douglas Gollin, Vernon Henderson, Thea Hilhorst, Matthew Hunter, Heather Huntington, Remi Jedwab, Sandra Joireman, Adnan Khan, Jonathan Lindsay, Jennifer Lisher, Vivian Liu, William McCluskey, Joao Montalvao, Jorge Munoz, Janvier Mwisha, Njuguna Ndung’u, Rita Nikiema, Emmanuel Nkurunziza, Ed Parsons, James Robinson, Bekele Shiferaw, Iain Shuker, and Victoria Stanley for inputs and helpful discussions.
and public transport services in the community, which are suggested to be inserted into the household questionnaire or community questionnaire as a standalone section. Since the information on transport expenditures and ownership of vehicles can be captured by the separate modules of household non-food expenditures and durable goods in the typical LSMS surveys, they are not included in these prototype modules.

# **5.1. Transport Questions to Be Integrated into the Existing Modules of LSMS Surveys**

This section identifies a number of questions to integrate into existing modules of LSMS surveys based on questions found in different LSMS surveys. It also complements the usual questions with new questions, not reviewed in the rest of the paper, that relate to road safety, pollution, and climate change. First, injuries or deaths from road accidents affect the socio-economic opportunities of households. Introducing a question to know whether such a negative shock has affected the household can be used to quantify the gains for households from improving road safety. Second, transport is major source of CO2emissions and local pollution. More questions to understand how household members choose among modes of transport and make decisions about vehicle ownership is helpful to understand the gains and distributional impacts from policies aiming to reduce transport emissions. Household surveys are a good source of information to understand the trade-off between more mobility and the economic gains in terms of jobs and access to services, and the need to reduce CO2 emissions.

# **5.1.1. Ownership / durables section**

The durable section often includes a question on ownership of vehicles that has been harmonized across countries (see Section on harmonized datasets for an example). We suggest two additional questions on the age of the vehicle and the type of fuel used to better understand two trade-offs informative for the climate change and pollution literature. Buying an older vehicle might be cheaper but older vehicles often pollute more. In many countries, most owned vehicles are second-hand vehicles, often imported from richer countries where newer restrictive regulations are enforced. Question 2 can be used to produce aggregate statistics on the age fleet of vehicles in a country and across groups within country. We suggest a last question on fuel type to understand the penetration of electric and hybrid cars across and within countries.

Three questions are therefore suggested for the durable section:

1. Does your household own any of the following means of transportation? [Already included in many of the questionnaires]

- A. Car

- B. Truck

- C. Motorcycle

- D. Bicycle

- E. Boat

- F. Animal-drawn cart

2. [If owns a motorized mean of transport] What is the year of production of your vehicle?

20
this result. He studied the determinants of infrastructure privatization using probit regressions with panel data covering 155 developing countries for the years 1984-1998. He concludes that ‘ _infrastructure privatization is more likely when the shadow cost of public funds falls._ ’ Auriol and Tuske (2005) find similar results while focusing on the telecommunication industry. The probability of privatization of the fixed access and long distance segment decreases with country risk rating. When controlling for firms’ characteristics, the privatization probability increases with the level of total telephone subscribers, the number of main telephone lines in the largest city, the percentage of digital main lines, and the level of annual telecom investment. This suggests that efficient firms are sold more often than less efficient ones which contradicts the idea that governments use privatization to improve firms’ efficiency.<sup>33</sup> On the other hand the macro-economic argument seems empirically supported. Developing countries’ governments that are concerned with solving critical financial problems regard privatization as a useful fiscal instrument.

# **6 Conclusion**

The present paper shows that countries’ privatization decisions non-trivially depend on their development stages. Such an argument is particularly decisive in developing economies constrained by bad macro-economic conditions and poorly performing tax systems. On the one hand, for low-profit natural monopolies, privatization always dominates regulation for intermediate and large values of opportunity costs of public funds. Privatization is indeed Pareto improving when it leads to the creation of an infrastructure that would otherwise not have existed. Numerical simulations suggest that the ‘intermediate’ values compatible with the model, are in the range of those of developing countries. On the other hand, for more profitable natural monopolies, the privatization decision is a non monotone function of the opportunity costs of public funds. Indeed developing countries plagued with financial problems are tagged with bad country risk ratings and are unable to attract international capital flows. Privatization does not yield large divesture proceeds. Since public firms are sold at a discount, governments in need of cash should preferably keep

> 33On the other hand it is consistent with the idea of maximizing the sale revenue.

33
# **Table 10. Distribution of Firms with Foreign Stakeholders by Region, Percent**

||**Northwest**|**Central**|**South**|**Volga**|**Ural**|**Siberia**|**Far**<br>**East**|**North**<br>**Caucasus**|
|---|---|---|---|---|---|---|---|---|
|Water|0.9|0.5|1.2|3.5|1.8|1.9|1.5|1.0|
|Construction|9.9|8.3|12.0|9.9|9.1|10.4|10.6|7.3|
|Distribution|31.1|34.9|33.5|34.9|32.4|33.5|38.6|32.3|
|Transportation|10.8|4.4|16.1|6.3|7.2|6.0|13.7|11.5|
|Travel|2.1|1.2|3.7|1.9|3.4|2.2|4.2|7.3|
|ICT|5.7|7.1|3.2|7.4|4.6|8.8|3.3|4.2|
|Financial|4.8|11.3|5.0|5.6|7.7|3.3|2.0|1.0|
|Real estate|17.8|14.4|12.0|11.9|15.4|10.4|6.8|17.7|
|Professional|9.8|12.5|7.3|9.5|9.1|11.3|11.7|6.3|
|Administrative|4.4|2.8|2.0|3.7|2.7|3.8|5.3|1.0|
|Other|2.7|2.6|4.1|5.3|6.6|8.4|2.2|10.4|

Source: RUSLANA.
undermines the cost-effectiveness of emissions reduction. In Phases 1 and 2, the cost per ton of abatement of CAT-R2 is 120 percent and 60 percent higher than that of CAT-NO, although allowance prices are lower. The presence of the RPS targets results in an inefficiently high renewable share, particularly in Phases 1 and 2.

Finally, we report “Leakage Rates” at the bottom of Table 4. The potential relocation of production and emissions to foreign countries that are not bound or less bound by emissions regulations is an important policy concern for countries implementing carbon pricing in traded sectors. The leakage rate is defined as the rise in emissions abroad relative to the domestic reductions achieved by the climate policy. Its magnitude is assessed using China’s import and export data and the average emissions intensity of imported and exported goods. We find that emissions leakage, across all cases, is generally small when compared to China’s emissions reduction.<sup>21</sup> Prior literature has demonstrated that TPS can mitigate emissions leakage due to their implicit output subsidies (Fischer & Fox, 2007; Holland, 2012). Our results confirm this: leakage rates under DEPS are approximately a quarter of those observed under the CAT system.

# **2. Relative contributions of overlapping policies**

While Table 4 focused on the stated overlaps, Figure 2 explores the contributions of different policy components to cost reductions under the DEPS, by comparing the impacts of the scenarios S2 and RN, as well as R2. In all three cases, the benchmarks are set to meet the same renewable energy and national emission targets.<sup>22</sup> Here, we focus on Phase 3, when IEP applies to a significant portion (25%) of electricity consumption.

Individually, both IEP and the implicit electricity tax under the RPS improve the costeffectiveness of the DEPS, with the most benefits coming from the RPS-introduced endogenous electricity taxes. The difference in cost per ton of abatement between the R2 and RN cases under the DEPS highlights the impacts of IEP, which helps reduce the cost by 8%. The difference between the RN and SN cases highlights the impact of RPS-introduced endogenous electricity taxes, which reduce the cost by 17%. The difference between the SN and NO cases reveals the

> 21 We also compared scenarios holding global emissions constant. Since the leakage rates are relatively small, controlling for emissions leakage has little influence on the results and does not affect policy rankings. For this reason, we focus on the cost per ton of domestic emissions reduction in discussions in the following sections.

> 22 Table C1 in the Appendix presents the full results in all periods.

35
Maginn, D. L. Tuttle, D. W. McLeavey, & J. E. Pinto, Managing Investment Portfolios: A Dynamic Process (págs. 230-327). Hoboken, New Jersey, USA: John Wiley & Sons.

Solnik, B. H. (1974). Why Not Diversify Internationally Rather Than Domestically? _Financial Analysts Journal_ . https://doi.org/10.2469/faj.v30.n4.48 Solnik, B. H. (1995). Why Not Diversify Internationally Rather Than Domestically? _Financial Analysts Journal_ . https://doi.org/10.2469/faj.v51.n1.1864 Thomas, A., Spataro, L., & Mathew, N. (2014). Pension funds and stock market volatility: An empirical analysis of OECD countries. Journal of Financial Stability, 11, 92-103.
**<u>Table 8: Impact of knowledge and corruption on changes in quality of service delivery</u>**

||Teaching|Significant im<br>Buildings|provement in ..<br>Facilities|Health|
|---|---|---|---|---|
|Predicted knowledge on reporting|0.357***<br>|0.278***<br>|0.286***<br>|0.157***<br>|
||(6.68)|(4.13)|(4.88)|(3.54)|
|Mean overall corruption in village|-0.103***|0.023|0.007|-0.028|
||(3.12)|(0.61)|(0.20)|(1.10)|
|Village-level expenditure (log)|0.043***|0.047***|0.033***|0.006|
||(5.52)|(4.86)|(3.92)|(0.87)|
|Rural|-0.028***|-0.004|-0.003|0.018**|
||(2.89)|(0.37)|(0.26)|(2.32)|
|East|-0.056***|0.026*|-0.016|-0.015*|
||(5.10)|(1.88)|(1.34)|(1.74)|
|North|-0.106***|-0.098***|-0.117***|-0.057***|
||(8.43)|(6.05)|(8.46)|(5.54)|
|West|-0.004|-0.033**|-0.018|-0.015|
||(0.36)|(2.20)|(1.37)|(1.58)|
|Observations|7,620|7,620|7,620|9,018|
|Loglikelihood ratio|-3,238.22|-4,350.84|-3,614.35|-3,132.85|
|Absolute value of z-statistics in paren|theses||||
|* significant at 10%; ** significant at|5%; *** signific|ant at 1%|||

24