rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2: The impact of various policy interventions on U5M levels**

|Country|U5M Base<br>values|Universal<br>primary<br>completion for<br>mothers|Elimination of<br>short birth<br>interval|Universal BCG<br>vaccination<br>coverage|Needed to<br>reach target|
|---|---|---|---|---|---|
|Guinea|155.2|-5.7|-12.4|-24.8|-77.9|
|Senegal|107.2|-23.7|-6.2|-7.6|-63.4|
|Mali|211.4|-48.8|-28.0|-31.1|-129.3|
|Niger|169.6|-44.1|-19.4|-19.4|-64.4|
|<br>Kenya|116.6|-11.5|-9.6|-26.9|-85.7|
|<br>Uganda|111.7|-16.8|-11.5|-15.9|-53.8|
|<br>Madagascar|89.2|-0.6|-14.3|-18.0|-34.6|
|Mozambique|144.4|11.5|-14.3|-20.2|-79.0|

Source: Authors’ calculation from DHS surveys and simulations.

With the current trends in terms of primary schooling in mind, the potential effects of expanding education of mothers look impressive for some countries. In Mali and Niger, the simulated reduction in the U5M rate exceeds 40 per thousand points. These results are mainly driven by very low base completion rates in combination with odds ratios of the primary education variable around 0.75. For Guinea, the odds ratio being close to one explains why the simulation yields only a small effect despite a major expansion in education. For Madagascar, the underlying mechanism is the same, although, in this case, the simulated expansion is also smaller. The case of Mozambique appears puzzling but can be explained by the fact that the estimation of the survival model for that country actually yields a small positive – albeit insignificant – impact of mothers’ education on child mortality. Kenya, Uganda, and Senegal represent intermediate case with quite substantial simulated reductions, the differences in outcomes mainly resulting from differences in base completion levels (and not in coefficients).

Before we proceed with further results, let us quickly reflect on the assumptions behind this simulation. The exercise assumes that mothers and children remain the same in all their observed characteristics (all explanatory variables of the specification reported in Table A5), but change their educational endowment to completed primary schooling if they were not educated. The corresponding coefficient however may be biased due to omitted variables, for example due to some type of ability bias. Another possible source of bias would arise if more educated mothers lived in communities with better the supply of health infrastructure, which we can only partially control for with our vaccination proxies. The fixed-effects estimations indeed suggest that there may be some bias of this type (Table A6). Finally, it has to be noted that most coefficients that eventually drive the effects of expanding education are not 36
Figure 10: Employment by sector in Roraima.

<!-- Start of picture text --> Agriculture Industry<br>−20 −10 0 10 −20 −10 0 10<br>Construction Trade<br>−20 −10 0 10 −20 −10 0 10<br>Transport Hospitality<br>−20 −10 0 10 −20 −10 0 10<br>25 16<br>14<br>20<br>12<br>15 10<br>8<br>10 6<br>24 50<br>22 45<br>20 40<br>18 35<br>16 30<br>14 25<br>8 16<br>7 14<br>6 12<br>5 10<br>4 8<br><!-- End of picture text --> Note: The y-axis is in thousand and the x-axis the distance in quarters from the first quarter of 2018. The data comes from PNADC and solid line shows the local linear fit while the dashed lines show the 95% confidence interval.

Overall, it seems that the labor market in Roraima, 27% of whose total employed is employed in the public sector, found it difficult to adjust to the population shock caused by the influx of displaced Venezuelans in the short-run and consequently unemployment rose along with a fall in employment.

Table A1 documents all the variables used in this study, their sources and their frequencies, while Table A2 shows the summary statistics of the different variables by pre-2018 and 2018 and after groups. In the labor market, all variables except formal employment and employment in agriculture and industry, are higher in 2018 and after than in pre-2018 period, suggesting an increasing trend overall. For the fiscal variables, expenditures in most categories declines, while revenues from most sources and fiscal balance show an uptick. Since the total number of observations are quite small (36), Table A3 and Table A4 shows the power calculations for one sample mean hypothesis test at different sample sizes and show that for most variables, the sample size have a power of more than 0.8, which adds support to the estimates that the paper displays.

16
James Manor based upon his work in India and Africa concludes considerable promise for decentralization in promoting citizen participation, increasing information flows between governments and citizens, enhancing transparency and accountability, integrating society with the state and reinforcing and invigorating democracy at the national level (see Manor, 1996). Recognizing this, India has recently amended its constitution to strengthen local government institutions (see Paul, 1996). The same results are reaffirmed by Garman and Haggard (1995) based upon a comparative study of Latin America. They conclude that "greater the political sensitivity of central level politicians to subnational political outcomes, the more decentralized the system is likely to be " (p.2).

**Table 3: Correlation of the Decentralization Index with Governance Quality Indicators** (sample size: 80 countries)

|**_Pearson_**|**_Correlation Coefficients_**|
|---|---|
|_Citizen_**_Participation_**||
|Political Freedom|0.599**|
|Political Stability|0.604**|
|**_Government Orientation_**||
|Judicial Efficiency|0.544**|
|Bureaucratic Efficiency|0.540**|
|Absence of Corruption|0.532**|
|**_Social Development_**||
|Human Development Index|0.369*|
|_Egalitarianism in Income_|0.373*|
|_Distribution_||
|_(inverse of Gini coefficient)_||
|**_Economic Management_**||
|Central Bank Independence|0.327*|
|Debt Management Discipline|0.263|
|(Inverse of Debt to GDP Ratio)||
|Openness of the Economy (Outward|0.523**|
|Orientation)||
|**_Governance Quality Index_**|0.617**|
|*significant at the 0.05% level (2-tailed test)<br>** significant at the 0.01% level (2-tailed test)<br>_Source:_Huther and Shah (1998)||

22
- 51 -

administrative efforts and costs; fourth (a peculiar Latin American problem), coping with yearly assessments becomes an intricate operation during periods of galloping inflation and rapid urban expansion and engenders interminable appeals by taxpayers and reluctance, if not active opposition, by local politicians.

4.08 As a result, the effective yield of property taxes in IAC countries remains minor, compared to that of alternative sources (see Table 4.8a). In Mexico overall receipts from property taxes are 3 percent at the local level and less than 4 percent at the State level. In Brazil these taxes account for 1.3 percent of State and for 7.3 percent of municipal receipts. The only significant Table 4.8a: PROPERTY TAX YIELD AS PERCENTAk.E OF TOTAL REVENUES IN LAC COUNTRIES at -Subnaticnal Governments-

||1977|1978|1979|1980|1981|1982|1983|1984|Average<br>-|
|---|---|---|---|---|---|---|---|---|---|
|ARGENTINA<br>Regional Government|NA|25.0|12.5|12.7|13.0|13.7|16.3|16.1|<br>15.6|
|BOLIMA<br>Local Government|NA|NA|NA|13.3|11.1|11.5|6.2|3.9|<br>11.8|
|BRAZIL<br>Local Government<br>Regional Government|11.3<br>1.6|10.8<br>1.4|13.4<br>1.5|13.3<br>1.4|11.7<br>1.4|11.4<br>1.7|8.4<br>1.7|7.3<br>1.3|<br>11.0<br> <br>1.5|
|COLOMBIA<br>Local Government|24.7|22.3|18.7|19.5|20.7|22.4|20.9|NA|<br>21.3|
|COSTA RICA<br>Local Government|31.5|33.4|32.7|23.9|27.1|25.4|18.5|19.9|<br>26.5|
|MEXICO<br>Local<br>Government|3.2|3.5|3.2|3.0|1.7|1.4|1.5|6.0|2.9|
|Regional<br>Government|10.0|9.2|8.3|5.4|3.1|3.2|2.6|NS|6.0|

----------------------------------------------------------------------

__-----__------------------_pI_ Total revenues include own revenues and grants.

SourCeIMF, Government FInance Statistics Yearbook X987.
Finally, the Controls category contains variables that provide information on the structure of the population (relative importance of rural versus urban), the degree to which this population is active, healthy, and educated, and the quality of its standard of living.

**<u>Table 2.</u>** <u>Descriptive statistics of the original data for the MENA and OECD samples</u>

|SC dimension|Variable<br>|<br>|MENA<br>|<br>||OECD<br>||
|---|---|---|---|---|---|---|---|
|||Obs.|Mean|Sd.|Obs.|Mean|Sd.|
||pp<br><br>|266|−0.82|1.11|532|0.61|0.70|
|Protection (P1)|rp<br><br>|266|−0.38|0.81|532|1.24|0.49|
||rp<br><br>|266|−0.36|0.77|532|1.21|0.67|
||ff<br><br>|263|13.40|8.05|532|36.02|14.72|
|Provision (P2)|mf<br><br>|265|103.86|44.85|532|117.80|19.74|
||ff<br><br>|249|5.92|5.90|531|27.94|9.67|
||ifpi<br>+<br><br>|244|44.07|79.33|438|187.47|821.48|
||pvp<br><br>|266|−1.09|0.46|532|1.09|0.46|
|Participation (P3)|mp<br><br>|266|−0.37|0.75|532|1.17|0.82|
||di<br><br>|247|3.36|1.15|494|8.13|0.98|
||rp<br><br>|266|25.63|17.03|532|22.71|11.08|
|Controls|iu<br>|266|10.38|7.14|532|7.62|4.11|
|+This variable h|hdi<br>as been div|209<br>ided by10|0.71<br>00.|0.11|418|0.87|0.05|

Table 2 exhibits some descriptive statistics of the variables in the samples of the MENA and OECD countries, where the latter sample will be used as a benchmark in upcoming econometric analysis. To handle certain data irregularities, we imputed missing values between any two reported values using the annual growth rate. As documented in historical analysis of the SC of MENA countries over the past half-century, we also observe that, on average, MENA countries have lower quality SC than OECD SC. This disparity is particularly evident in the Participation dimension compared to the other two dimensions. We will later return to this point.

**_Rescaling and reducing dimensionality of the data_** We construct indices for the three dimensions of an SC, , , and using PCA, which is one of the most commonly used methods to calculate composite indices for large datasets. It is PP1 PP2 PP3 known to reduce the dimensionality of the data, which enhances parameter parsimony, and eases parameter interpretation while at the same time minimizing the loss of information

15
# **Table A.4: Comparison of different equivalence scales**

|**W**|**ihts**|**Per**|**Square-**|**Modified**|**RUS**<br>**Povert**|**Est**|**imated s**|**cales**|**Sch**<br>**(2**|**warze**<br>**003)**|**Fa**<br>**(2**|**lter**<br>**006)**|**Borah**<br>**(20**|**_et al._**<br>**18)**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**g**|**it**|<br>**t**|**OECD***|**y**||**_FE_**|||**_FE_**||**_FE_**|||
|||**capa**|<br>**roo**||**Line****|**_POL_**|<br>**_OLS_**|**_BUC_**|**_POL_**|<br>**_BL***_**|<sup>**_POL_**</sup>|<br>**_OLS_**|**_POL_**|**_BUC_**|
||1|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|
||2|2.00|1.41|1.50|2.00|1.55|1.32|1.33|1.34|1.28|1.43|1.48|1.31|1.26|
|**_Adults_**|3|3.00|1.73|2.00|3.00|2.01|1.55|1.56|1.59|1.47|1.77|1.86|1.53|1.44|
||4|4.00|2.00|2.50|4.00|2.41|1.74|1.77|1.78|1.63|2.06|2.18|1.71|1.58|
||5|5.00|2.24|3.00|5.00|2.78|1.90|1.93|1.98|1.76|2.31|2.47|1.87|1.70|
|**_1 Adult_**|1 Child|2.00|1.41|1.30|1.90|1.47|1.27|1.28|1.30|1.24|1.40|1.43|1.29|1.24|
|**_2 Adlt_**|1 Child|3.00|1.73|1.80|2.90|1.85|1.47|1.48|1.52|1.41|1.70|1.78|1.49|1.41|
|**_us_**|2 Children|4.00|2.00|2.10|3.80|1.95|1.51|1.54|1.59|1.47|1.85|1.90|1.59|1.51|
|**_3 Adl_**|1 Child|4.00|2.00|2.30|3.90|2.17|1.62|1.64|1.69|1.55|1.95|2.04|1.65|1.55|
|**_uts_**|2Children|5.00|2.24|2.60|4.80|2.17|1.62|1.65|1.71|1.56|2.04|2.11|1.71|1.62|
|**_4 Adl_**|1 Child|5.00|2.24|2.80|4.90|2.45|1.75|1.78|1.84|1.66|2.17|2.28|1.79|1.66|
|**_uts_**|2Children|6.00|2.45|3.10|5.80|2.36|1.71|1.75|1.82|1.65|2.21|2.29|1.82|1.71|
|**_5 Adults_**|1 Child|6.00|2.45|3.30|5.90|2.72|1.87|1.90|1.97|1.76|2.37|2.51|1.91|1.76|

**Note:** Household types whose population share is at least 1%. Children are defined as individuals aged below 16 years (OECD and Rosstat def.) *First adult has weight 1.0, every further adult 0.5, children 0.3.

**Working-age adult has weight 1.0, pensioner 0.8, children 0.9 *** Fixed Effects Binary Logit

39
could further help through educational programs, including language and cultural training. In the meantime, the government should strengthen labor standards compliance programs, with perhaps rewards and penalties schemes designed to encourage and deter companies.

Most government (except one) did not express concern about the immigration impact. Face-toface discussions with government officials in at least two host countries further revealed that this concern was more related to the construction projects that tended to bring sizable number of Chinese workers into the country, although it was also acknowledged that these workers would usually leave the country once the construction projects were complete. There was some concern about individuals who might pretend to be “investors” but were actually small traders, who were unwanted by the countries. The merits or the lack of it regarding “traders” notwithstanding, it was clear that manufacturing firms were usually less of an immigration concern to the governments. On the contrary, it was recognized that factories did tend to employ local workers for their own interest but that, in the early operational stages, they did need to bring in some experienced workers to provide on-the-job training to local workers.

# 4. Impact and policy implication for China, both the government and firms

The Chinese government initiated its “going global” policy at the beginning of the new millennium to promote its overseas investment activities and has since kept heat on this new strategy. Among the expected benefits from outbound investments, Chinese leaders have emphasized access to more markets, the acquisition of natural resources, and enhancement of technology and management skills – all of which would boost the country's economic growth. Most recently, leading officials cited the importance of OFDI as an effective way to release the pressure of excess foreign currency reserves, investment providing an attractive alternative to buying foreign sovereign bonds and serving as a step toward making the Chinese RMB a convertible currency. In his report to the 18th National Congress of the Communist Party of China in November 2012, President Hu Jintao called on Chinese enterprises to speed up their global activities by enhancing their international capacity and reputations. Following that, official spokesmen vowed publically to further ease and improve the systems involved to ensure that momentum continues.<sup>21</sup> <mark>However, interviews with government agencies suggested that, up to now, government efforts to promote OFDI have put the priority on SOEs and a small number of large private companies. Private outbound investment, especially the small and medium sized firms such as the many coastal manufacturers investigated by this study, has received limited attention. In fact, at the provincial level, government officials interviewed had mixed views on letting coastal</mark>

> 21 _<mark>China Daily</mark>_ <mark>, “Chinese firms' push into global market,” by Li Jiabao, 2012-12-04; http://usa.chinadaily.com.cn/business/2012-12/04/content_15982777.htm</mark>

39
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Bibliography and references

This country report draws upon a wide range of papers, databases, models, and maps that were created as part of the Africa Infrastructure Country Diagnostic (AICD). All of these can be downloaded from the project Web site: <u>www.infrastructureafrica.org. For papers go to the document page</u> (www.infrastructureafrica.org/aicd/documents), for databases to the data page (www.infrastructureafrica.org/aicd/tools/data), for models go to the models page (www.infrastructureafrica.org/aicd/tools/models), and for maps to the map page (www.infrastructureafrica.org/aicd/tools/maps ). The references for the papers that were used to compile this country report are provided in the table below.

## General

AICD (Africa Infrastructure Diagnostic). _Africa’s Infrastructure: A Time for Transformation_ .

- www.infrastructureafrica.org.

- Foster, Vivien, and Cecilia Briceño-Garmendia, eds. 2009. _Africa’s Infrastructure: A Time for Transformation._ Paris and Washington, DC: Agence Française de Développement and World Bank.

## Financing

- Briceño-Garmendia, Cecilia, Karlis Smits, and Vivien Foster. 2009. ―Financing Public Infrastructure in Sub-Saharan Africa: Patterns and Emerging Issues.‖ AICD Background Paper 15, Africa Region, World Bank, Washington, DC.

## Growth

- Calderón, César. 2009. ―Infrastructure and Growth in Africa.‖ Policy Research Working Paper 4914, World Bank, Washington, DC.

- Escribano, Alvaro, J. Luis Guasch, and Jorge Pena. 2010. ―Assessing the Impact of Infrastructure Quality on Firm Productivity in Africa.‖ Policy Research Working Paper 5191, World Bank, Washington, DC.

- Yepes, Tito, Justin Pierce, and Vivien Foster. 2009. ―Making Sense of Africa’s Infrastructure Endowment: A Benchmarking Approach.‖ Policy Research Working Paper 4912, World Bank, Washington, DC.

## Information and communication technologies

Almeida, Henrique. 2009. ―Angola Sells 80 pct of Telecoms Group Movicel.‖ _Reuters_ , July 30. http://in.reuters.com/article/2009/07/30/angola-movicel-idINLU58752720090730.

44
**Figure 5: Second principal component**

<!-- Start of picture text --> 15.0<br>10.0<br>5.0<br>0.0<br>-5.0<br>1960 1970 1980 1990 2000 2010 2020<br>Range of variation Component 2<br>Percentage points<br><!-- End of picture text --> Source: Own calculations.

Regarding the third principal component, mainly associated with the male labor force participation rate ( _m_lfp_r_ ) and women’s political participation, we observe a substantial initial impact on the _f_lfp_r_ , with an average coefficient of 5.1pp between 1960 and 1977, mainly due to the positive relationship between these two variables, as we will discuss later on (Figure 6). From 1978 to 2005, the effect remained positive but decreased to an average of 4.5pp, indicating a weaker positive relation between these two variables. Female participation continued increasing at a lower rate than men's, but towards the 2000s, it stagnated. However, since the beginning of the 21st century, we have seen a change in the trend of this component’s effect on the _f_lfp_r_ .

As discussed below, two important laws were enacted to promote women’s political participation: Law 581 of 2000 and Law 1475 of 2011. Law 581, also known as the “quota law,” stipulated that women must hold 30% of decision-making positions in the public sector. This law has increased employment opportunities for women in the public sector (Pachón and Aroca, 2017; Wills, 2007). Law 1475, on the other hand, mandates that for elections to collegiate bodies, lists with five or more candidates for popularly elected corporations must be composed of at least 30% of one gender. In the case of Parliament, the quota law has risen the number of female candidates and the number of elected women, as highlighted by Iregui-Bohórquez et al. (2021).

11
Table 1: List of Circumstances

| Respondent’s sex:|- Medium (3-4 ISCED-97)|
|---|---|
|- Male|- High (5-6 ISCED-97)|
|- Female| Father/mother occupational status:|
| Respondent’s country of birth:|- Unknown or dead father/mother|
|- Respondent’s present country of resi-|- Employed|
|dence|- Self employed|
|- European country|- Unemployed|
|- Non-European country|- Retired|
| Presence of parents at home:|- House worker|
|- Both present|- Other inactive|
|- Only mother<br>- Only father<br>- Without parents<br>- Lived in a private household without<br>any parent| Father/mother main occupation (based on<br>International Standard Classifcation of Oc-<br>cupations, published by the International<br>Labour Ofce ISCO-08):<br>- Managers (I-01)|
| Number of adults (aged 18 or more) in re-<br>spondent’s household|- Professionals (I-02)<br>- Technicians (I-03)|
| Number of working adults (aged 18 or more)<br>in respondent’s household|- Clerical support workers (I-04)<br>- Service and sales workers (including|
| Number of children (under 18) in respon-<br>dent’s household|also armed force) (I-05 and 10)<br>- Skilled agricultural, forestry and fshery<br>|
| Father/mother country of birth and citizen-<br>ship:|workers (I-06)<br>- Craft and related trades workers (I-07)|
|- Born/citizen<br>of<br>the<br>respondent’s<br>present country of residence|- Plant and machine operators, and as-<br>semblers (I-08)|
|- Born/citizen of another EU-27 country|- Elementary occupations (I-09)|
|- Born/citizen<br>of<br>another<br>European<br>country|- Father/mother did not work, was un-<br>known or was dead (I-0)|
|- Born/citizen of a country outside Eu-<br>rope| Managerial position of the father/mother:<br>|
||- Supervisory|
| Father/mother education (based on interna-<br>ilSddClififEdi|- Non-supervisory|

| Father/mother education (based on interna-|
|---|
|tional Standard Classifcation of Education|
|1997 (ISCED-97)):|
|- Unknown father/mother|
|- Illiterate|
|- Low (0-2 ISCED-97)|

-  Tenancy status of the house in which the respondent was living: - Owned - Not owned

types we divert from the occupational list given in Table 1 by re-coding occupations into three categories: highly skilled non-manual (I-01–I-03), lower-skilled non-manual (I-04–I-05 and I-10), skilled manual and elementary occupation (I-06–I-09 and father/mother unknown or dead). This 13