rafmacalaba/datause-encoder-data huggingface dataset:
# **Annex 4: Data on existing assets used for capex estimates**

## **Table A4.1: State-owned installed capacity (MW) in the reference year**

|**Country**|**Hydropower**|**Natural**<br>**gas**|**Diesel and**<br>**HFO**|**Coal**|**Nuclear**|**Other**<br>**renewable**<br>**energy**|**Total Installed**<br>**capacity**|
|---|---|---|---|---|---|---|---|
|Benin|0|0|169|0|0|0|169|
|Botswana|0|0|0|732|0|1|733|
|Burkina Faso|32|0|253|0|0|0|285|
|Burundi|27|0|8|0|0|0|35|
|Cameroon|732|0|215|0|0|0|947|
|Cape Verde|0|0|107|0|0|8|115|
|Central African Republic|19|0|7|0|0|0|26|
|Comoros|0|0|27|0|0|0|27|
|Congo, Rep.|209|350|41|0|0|0|600|
|Côte d'Ivoire|604|100|0|0|0|0|704|
|Ethiopia|1,946|0|133|0|0|88|2,167|
|Gabon|170|174|93|0|0|0|437|
|Gambia, The|0|0|101|0|0|0|101|
|Ghana|1,580|898|0|0|0|3|2,480|
|Guinea|127|0|161|0|0|0|288|
|Kenya|820|60|221|0|0|515|1,615|
|Lesotho|72|0|0|0|0|0|72|
|Liberia|0|0|22|0|0|0|22|
|Madagascar|146|0|181|0|0|0|327|
|Malawi|351|0|0|0|0|0|351|
|Mali|53|29|156|0|0|0|238|
|Mauritania|0|0|170|0|0|0|170|
|Mauritius|61|78|361|0|0|1|501|
|Mozambique|2,184|7|91|0|0|0|2,281|
|Niger|0|0|64|0|0|0|64|
|Nigeria|1,958|5,911|29|0|0|0|7,899|
|Rwanda|40|0|28|0|0|0|69|
|São Tomé and Príncipe|2|0|23|0|0|0|25|
|Senegal|0|59|493|0|0|0|552|
|Seychelles|0|0|98|0|0|6|104|
|Sierra Leone|57|0|37|0|0|0|94|
|South Africa|2,061|2,426|0|37,754|1,940|100|44,281|
|Sudan|1,599|0|1,428|400|0|0|3,426|
|Swaziland|60|0|10|0|0|0|70|
|Tanzania|562|305|125|0|0|0|991|
|Togo|2|0|121|0|0|0|123|
|Uganda|0|0|0|0|0|0|0|
|Zambia|2,172|0|10|0|0|0|2,182|
|Zimbabwe|750|0|0|1,220|0|0|1,970|

_Source_ : Utility annual reports and other documents.

88
24

# **Table 12** Average Annual Real Earnings of **Employees by** Sector 1965-83 **(1975** Kwacha)(a

||1965|1970|1975|1980|1983|
|---|---|---|---|---|---|
|Agriculture, Forestry, Fishing<br>||||||
|Zambian<br>|327<br>|492<br>|453<br>|518|393|
|Non-Zambian|4410|6350|2524|2902|1215|
|Mining and Quarrying<br>||||||
|Zambian<br>|1570<br>|2179|1478|1668|1186|
|Non-Zambian|10034|10254|6784|5278|3604|
|Manufacturing<br>||||||
|Zambian<br>|924<br>|1133<br>|1179|1063|756|
|Non-Zambian|5836|7590|5657|5243|2972|
|Construction<br>||||||
|Zambian<br>|612<br>|860<br>|764|805|632|
|Non-Zambian|6549|9694|6868|3847|2850|
|Transport<br>||||||
|Zambian<br>|924|1710|1834|1308|1006|
|Non-Zambian|5743|6651|7377|5182|2662|
|Services(b)<br>||||||
|Zambian<br>|869|1119|1246|1146|884|
|Non-Zambian|5149|5535|4894|4278|2726|
|Total<br>||||||
|Zaambian<br>|890|1210|1140|1134|851|
|Non-Zambian|6476|7207|5572|4601|2897|

Notes: a - generally 4th quarter figures. Nominal earnings estimates for Zambians deflated by low-income CPI. Those for non-Zambians are deflated by the highincome CPI.

b - includes utilities, trade, hotels, finance and other services.

Source: Meesook et al, 1986: Appendix Table 3
In low-income countries, where there was less institutional capacity, the complete absence of a formal policy or regulation was not uncommon. The most number of subsector-modes that were not covered by any specific regulation or policy appeared in Lao PDR, followed by Cambodia, Myanmar, and the Philippines, and it was a phenomenon mostly observed in modes 1 and 2. The absence could have had a restrictive impact, since it reduced transparency and predictability of the policy regime and increased the potential for discretion. But in many of these cases, in practice the supply of a service was allowed, with Vietnam a notable exception in this respect. Some other dimensions of the regulatory environment that the survey covered are described below.

# **5 DID ASEAN MEMBERS BECOME MORE OPEN BETWEEN 2008 AND 2012?**

As noted above, the surveys were conducted in both 2008 and 2012 for six ASEAN countries: Cambodia, Indonesia, Malaysia, Philippines, Thailand, and Vietnam. Over this period, the data shows that six countries’ policies on average became more liberal but the change is very small (Figure 3). To identify the policy change, we need to look at the subsector mode level, because the country level score is a weighted aggregation of subsector–mode scores. For example, Indonesia’s overall STRI increased by 7 points between 2008 and 2012. Even though the restrictiveness index went down in 5 subsector–modes, it went up in 3 subsector-modes, including the relatively important retail sector.

# **Figure 3: ASEAN Members country level STRI: 2008 and 2012**

15
# **Table S4.16.** ITT effects on other ISFM-related practices, non-FREG sample.

|||||Non-FREG<br>|sample<br>||||
|---|---|---|---|---|---|---|---|---|
||Ado<br>ur<br>|pted<br>ea<br>|Ad<br>green m<br>|opted<br>anuring<br>|Ado<br>intercr<br>|pted<br>opping<br>|Ado<br>forage<br>|pted<br>crops<br>|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|T1 (extension)|0.067|0.039|0.031|0.024**|0.062|0.056*|-0.022|-0.007|
||(0.041)|(0.024)|(0.021)|(0.012)|(0.043)|(0.029)|(0.033)|(0.026)|
|Unadjusted p-value|0.101|0.100|0.139|0.047|0.154|0.053|0.498|0.805|
|Adjusted p-value|0.293|0.290|0.407|0.155|0.446|0.175|0.912|0.997|
|T2 (extension + video)|0.075*|0.071**|0.028*|0.024**|0.063|0.047|0.005|0.010|
||(0.043)|(0.028)|(0.016)|(0.010)|(0.051)|(0.035)|(0.034)|(0.027)|
|Unadjusted p-value|0.076|0.011|0.074|0.021|0.216|0.185|0.880|0.713|
|Adjusted p-value|0.227|0.035|0.236|0.072|0.577|0.515|0.999|0.988|
|Test T1 >= T2 (unadjusted p-value)|0.430|0.130|0.562|0.506|0.495|0.590|0.233|0.285|
|Test T1 >= T2 (adjusted p-value)|0.839|0.364|0.944|0.915|0.911|0.957|0.608|0.694|
|Endline control mean|0.8|19|0.|017|0.2|24|0.1|73|
|Additional controls|No|Yes|No|Yes|No|Yes|No|Yes|
|(Pseudo)R-squared|0.012|0.265|0.025|0.293|0.005|0.130|0.001|0.105|
|Observations|1,606|1,606|1,606|1,606|1,606|1,606|1,606|1,606|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ Average marginal effects (AME) of probit models. FREG stands for ‘Farmer research and extension group’. FREG- and non-FREG samples consist of actual FREG respectively non-FREG farmers in treatment mws and their matched controls. Additional control variables identical to those listed in notes of Table 2. Bonferroni-adjusted p-values taking into account correlations between outcomes. One-sided equality tests of T1 and T2 are Wald tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

23
result from guerrilla actions still serving some redistributive motive for the poorest fringe. Another possible explanation is that the poorest profit from abandoned property in the presence of an attack on law and order. Note however that this regression only looks at participation (yes/no) and not values, and therefore captures only the behavior of a very small fraction of the households we are looking at.

## **Correlation of error terms: The** ρ **’s**

The ρ ’s reported at the bottom of Results-Table 4 indicate the correlation between the individual error terms of both probits. We see that the errors are strongly and significantly correlated. We would expect this result to be picking up omitted variables in the decision for fixed and mobile assets, such as ability, regional traditions etc. Idiosyncratic measurement error in the survey is another possibility. Other things equal, people with fixed assets are also likely to have mobile assets. So, the ρ may be picking up the complementarity between both kinds of assets. If you have a house, a mixer and other small household goods are useful. Likewise, if you have cattle, you might want to have a bike or motorbike to look after them.

# **CONCLUSION AND POLICY IMPLICATIONS**

This paper presented a theoretical model for investment in two kinds of assets, fixed and mobile, which predicted that the assets would react differently to different kinds of violence. Fixed assets would go down to the benefit of mobile assets in the presence of the guerrilla and paramilitaries, because they cannot be carried away in case of displacement – a favorite weapon of the aforementioned, and the reverse would happen in the presence of common delinquency, which is associated with theft. Empirical results from Colombian micro-data largely confirm this model. Tobit and OLS regressions 40
THE REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE finance and some non-OECD finance for transport and power, whereas external financiers play a much more prominent role in the other resource-rich countries (figure 12).

**Table 9. Annualized spending on infrastructure in Congo from all sources (yearly average 2004–07)**

|||US$ million per year<br>||
|---|---|---|---|
||O&M|Capital expenditure||
|Sector|||Total|
||Public sector<br>Public s|ector<br>ODA<br>Non-OECD<br>financiers<br>PPI<br>Capital exp.<br>total||
|ICT|6|6<br>0<br>0<br>18<br>24|30|
|Power|62|32<br>2<br>31<br>0<br>65|128|
|Transport|89|139<br>17<br>12<br>0<br>168|258|
|WSS|28|19<br>0<br>1<br>0<br>20|48|
|Total|185|196<br>19<br>44<br>18<br>278|463|
|Source: Briceño<br>ODA = official d<br>**Figure 11. Pu**|-Garmendia and others, 2008, N<br>evelopment assistance from OE<br>**blic funding for infrastructu**<br>Sub-Saharan Africa|ote: Public sector includes budget and off-budget funding.<br>CD member countries; PPI = private participation in infrastructure.<br>**re is high in Congo**||
|nonfragil|e low-income countries|||
|m|iddle-income countries|||
||resource-rich countries|||
|fragil|e low-income countries|||
||CEMAC|||
||Benin|||
||Congo, Rep. of|||
||Cameroon<br>Nigeria|||
||Congo, Dem. Rep. of|||
|||0<br>2<br>4<br>6<br>8<br>10<br>12||
|||**% of GDP**||
||capital expe|nditure<br>operations and maintenance||

_Source_ : Briceño-Garmendia and others 2008.

28
Most notably, defaults are significantly less likely in fast-growing countries, and expropriations are significantly less likely under right-wing governments.

We began our data description with the observation that expropriation and default events are correlated across countries in the long run: over the period 1970-2004, countries that expropriate at least once are likely to also default at least once. In fact, the simple crosscountry correlation between the average number of default events and the average number of expropriation events per year is 0.30. This raises the following question: To what extent are the reasons for the coincidence of these two forms of sovereign theft well-captured by the parsimonious set of explanatory variables we have considered so far? One way to answer this question is to compute the correlation across countries of the residuals of the specifications in the last two columns of **Table 2** . Naturally, this correlation is lower, at 0.25, than the correlation of average expropriations and average defaults, at 0.30. However, this correlation is still far from zero, suggesting that there are other country characteristics that account for the high coincidence of default and expropriation across countries in the long run that remain to be identified.

A drawback of these cross-sectional results is that they can tell us little about the dynamics over time of the two types of events, which we have seen in the previous section to be potentially very interesting. We remedy this deficiency in **Table 3** , which reports a similar set of regressions using the full panel of 1,560 country-year observations covering the same set of 126 developing countries. In this table, the dependent variables are dummies taking the value one for the onset of default events and expropriation events, and zero otherwise for non-default and non-expropriation country-years. We report results using simple linear probability models, i.e. OLS regressions of these dummies on various explanatory variables.<sup>7</sup> One further difference with the results from the previous table is that we lag the FDI and debt stock measures, as well as the CPIA and growth variables, by one year to avoid capturing any

> 7 Conceptually more appropriate probit regressions deliver similar results.  However, as discussed in more detail below, the interpretation of within-country results is more straightforward using the linear probability model.

12
Table 11 Irrigation infrastructure investment costs as a percent of the regional GDP discounted at 6% (%)

|**Scenario**|**Year**|AFR|EAP|ECA|LCR|MNA|SAR|WLD|
|---|---|---|---|---|---|---|---|---|
||2020|0.43|0.19|0.05|0.10|0.13|0.37|0.07|
||2030|0.36|0.13|0.04|0.08|0.11|0.27|0.06|
|Invest|2040|0.31|0.11|0.04|0.07|0.09|0.22|0.06|
||2050|0.27|0.10|0.04|0.07|0.08|0.19|0.05|
||2020|0.72|0.29|0.18|0.19|0.16|0.53|0.11|
||2030|0.68|0.20|0.17|0.19|0.14|0.37|0.10|
|MaxInvest|2040|0.65|0.17|0.15|0.19|0.12|0.29|0.09|
||2050|0.58|0.15|0.14|0.18|0.10|0.25|0.09|

Table 12 Cumulative irrigation infrastructure investment costs as a percent of the cumulative regional GDP from 2010‐2030 and 2030‐2050, calculated as an average of the ratio of costs to GDP undiscounted (%)

|Scenario|Year|AFR|EAP|ECA|LCR|MNA|SAR|WLD|
|---|---|---|---|---|---|---|---|---|
|Invest|2010‐2030<br>2030‐2050|0.36<br>0.16|0.13<br>0.05|0.04<br>0.03|0.08<br>0.04|0.10<br>0.04|0.27<br>0.08|0.06<br>0.03|
|MIt|2010‐2030|0.68|0.20|0.17|0.19|0.13|0.37|0.10|
|axnves|2030‐2050|0.45|0.07|0.09|0.16|0.05|0.10|0.06|

57
|Reallocation<br>(6)<br>.014<br>(.016)<br>-.044<br>(.065)<br>-.028<br>(.011)_∗∗_<br>-.012<br>(.012)<br>-.0009<br>(.0004)_∗∗_<br>.002<br>(.0008)_∗∗∗_<br>.007<br>(.017)<br>.008<br>(.022)<br>14962<br>.017<br>ates market-share-<br>variance between<br>r dummies. Each<br>e clustered at the|
|---|
|Reallocation<br>(5)<br>-.013<br>(.022)<br>.051<br>(.065)<br>-.019<br>(.011)_∗_<br>-.011<br>(.013)<br>.0001<br>(.0003)<br>-.001<br>(.0007)<br>-.017<br>(.015)<br>.004<br>(.022)<br>14962<br>.016<br>“Aggregate” indic<br>n” indicates the co<br>vel, and include yea<br> standard errors ar<br>(2004).|
|Average<br>(4)<br>-.049<br>(.034)<br>-.526<br>(.121)_∗∗∗_<br>.045<br>(.017)_∗∗∗_<br>.002<br>(.020)<br>-.00009<br>(.0007)<br>-.0008<br>(.001)<br>.014<br>(.020)<br>.003<br>(.032)<br>14962<br>.081<br>each column.<br>d “Reallocatio<br>te-industry le<br>all years, and <br>and Burgess|
|Average<br>(3)<br>-.065<br>(.039)_∗_<br>-.550<br>(.122)_∗∗∗_<br>.054<br>(.015)_∗∗∗_<br>.010<br>(.019)<br>.0003<br>(.0006)<br>.0003<br>(.001)<br>-.012<br>(.019)<br>-.019<br>(.032)<br>14962<br>.082<br>en at the top of<br>roductivity, an<br>lyses at the sta<br>dustry across <br>ased on Besley|
|Aggregate<br>(2)<br>-.035<br>(.031)<br>-.571<br>(.112)_∗∗∗_<br>.018<br>(.015)<br>-.010<br>(.020)<br>-.001<br>(.0005)_∗_<br>.002<br>(.001)<br>.021<br>(.019)<br>.012<br>(.033)<br>14962<br>.079<br>e names are giv<br>eighted mean p<br>xed-effects ana<br> in the state-in<br>are dummies b|
|Aggregate<br>(1)<br>-.078<br>(.029)_∗∗∗_<br>-.499<br>(.108)_∗∗∗_<br>.035<br>(.014)_∗∗_<br>-.0008<br>(.019)<br>.0004<br>(.0005)<br>-.0007<br>(.001)<br>-.030<br>(.018)_∗_<br>-.014<br>(.034)<br>14962<br>.079<br>Dependent variabl<br>ge” indicates unw<br>pecifcations are f<br> number of frms<br>and “pro-worker”|
|Final Goods Tariff<br>Input Tariff<br>FDI Reform<br>Delicensed<br>Pro-worker X Final Goods Tariff<br>Pro-worker X Input Tariff<br>Pro-worker X FDI Reform<br>Pro-worker X Delicensed<br>Pro-employer X Final Goods Tariff<br>Pro-employer X Input Tariff<br>Pro-employer X FDI Reform<br>Pro-employer X Delicensed<br>Obs.<br>_R_2<br>Each observation is a state-industry.<br>weighted mean productivity, “Avera<br>market share and productivity. All s<br>observation is weighted by the total<br>state-industry level. “Pro-employer”|

47
between our indicator of business climate and<sup>the political and financial risks indicators</sup> reported in the preceding section.

**Table 4. Econometric results: sensitivity**<sup>**of business climate to policy variables**</sup>

**(T-statistics in parenthesis)**

||Panel d|ata a|Cross C|ountry|
|---|---|---|---|---|
|Dependant variable|FDI business<br>climate|FDI inflows|FDI business<br>climate|FDI inflows<br>|
|Economic growth|0.123<br>_(1.90)_||0.101<br>(1.71)|0.587<br>_(1.96)_<br>|
|Trade openness|0.163<br>_(2.43)_|2.812<br>(3.23)|0.172<br>_(1.94)_|1.812<br>_(1.50)_<br>|
|Illiteracy rate|-0.209<br>_(-0.39)_|1.097<br>(1.09)|0.139<br>_(1.33)_|0.489<br>_(0.80)_<br>|
|Telephone lines|-0.0404<br>_(-0.51)_|-0.407<br>(-0.42)|0.0129<br>_(0.15)_|-0.144<br>_(-0.46)_<br>|
|Urban population|-0.978<br>_(-1.21)_|-0.228<br>(-1.26)|-0.0937<br>_(-0.49)_|-0.525<br>_(-0.63)_<br>|
|GDP||0.91<br>(3.97)||1.415<br>_(4.28)_<br>|
|Natural resources<br>||0.92<br>(7.04)||1.214<br>_(3.89)_|
|AdjFR|0.08|0.433|0.04|0.56|
|Number of observations|236|236|29|29|

a Fixed-term effects were used for our panel data<sup>regressions.</sup> We estimated equation (2) for the panel data<sup>of 29 countries over the period 1990-</sup> 1997. Alternatively, we proceeded with cross-country<sup>regressions using the average</sup> values of the selected variable during the<sup>same period. The panel data regression</sup> includes fixed-term effects because the results<sup>from testing the homogeneity of such</sup> effects indicate that the changes in the FDI<sup>business climate include critical time-</sup> correlated elements common to all countries.

The estimated results of our panel regression<sup>indicate that GDP growth rate and</sup> trade openness have been positively and significantly<sup>correlated with the investment</sup> climate in Africa (table 4).13 The positive<sup>impact of trade openness seems to confirm the</sup> arguments that trade liberalization leads to a more general<sup>reduction in administrative</sup> 12
ownership of 33 "strategic" enterprises. Of the 207, it selected only ten for privatization by 1995. Five have actually been privatized or brought to the point<sup>of</sup> sale. Furthermore, five r ,.)rity-owned public enterprises, which accounted for the bulk of government transfers, were to be restructured, including the railways,<sup>port</sup> authority, posts and telegraphs, the tea development agency, and, again, the National Cereals and Produce Board. This was a modest beginning. And, it is unclear<sup>that all</sup> the 33 enterprises to be retained in the public sector have "strategic" importance. But the greatest source of skepticism is the inclusion of the National Cereals and Produce Board in this list-that it is being targeted for yet another round of "restructuring."

# Civil service **reform**

In 1990, the mainstream civil service employed more than a quarter<sup>of million</sup> people-or about a quarter of urban wage employment. In the 1980s civil service employment growth slowed to an average of 4.8 percent p.a. after a spurt of 10 percent p.a. during the 1970s. Several policy measures have reflected the strong pressures to create jobs-guaranteed employment for university graduates and the tripartite arrangements of 1964, 1970, and 1979-81, which called for employment increases in the public sector by 15 percent, 10 percent, and 10 percent, respectively. Further, ad hoc absorption of staff from the agencies of the East Africa<sup>Community,</sup> the census operations of 1979, and a large number "works paid" employees hired under development projects have increased employment. As may be expected, the 48
the same survey firm revisited 93% of the sampled households.<sup>4</sup> The follow-up sample, which forms the main sample for the analysis in this study, includes 6,452 households with 6,914 tested children at the age ���3.91), of 2 (��184), 3 (��2271), 4 (��2459), 5 (��1965) and 6 (��35). The average number of followed up households per village is 21.2 and the average number of eligible children per household is 1.07. Using the follow-up sample instead of baseline allows to control for baseline test scores in regressions and to observe outcomes during an age period which is critical for the development of language and early numeracy skills.

Upon arrival at households’ home, the enumerator first identified the main caregiver and the eligible children,<sup>5</sup> and administered the household and caregiver surveys as well as completed the anthropometric measures and development tests. The child’s testing session lasted an average of 31 minutes (�50 �30).

# **Measures**

We developed and adapted the battery of child tests in close cooperation with the researchers of the Measuring Early Learning Quality and Outcomes project (MELQO; UNESCO, 2017) and the local survey firm. The survey firm recruited interviewers based on their familiarity with data collection and their experience with young children. To ensure that children and parents correctly understood the questionnaire and that the instruments were reliable, the research team pretested every instrument at least three times before collecting data on the

> interventions were randomized between villages and still at an early stage during the time of the first follow-up. We therefore ignore the interventions in this analysis. All our regression estimates are robust to the inclusion of treatment group variables to account for the randomized treatment.

> 4 The attrition rate of 7% can almost entirely be explained by seasonal and permanent relocation of households. The study does not follow up on households that move beyond the boundaries of sample villages.

> 5 The caregiver is defined as the direct relative that takes care of the child most of the time. In our sample, the primary caregiver is most often the mother (52% of the children), followed by the grandmother (35%), father (6%), grandfather (2%), aunt or uncle (3%), or an older sibling (1%).

8