rafmacalaba/datause-encoder-data huggingface dataset:
# **Causality between External Debt and Capital Flight in Sub-Saharan Africa**

Hippolyte Fofack<sup>1</sup> The World Bank Group Revised September 8, 2009 JEL Classifications: B41, F32, F35, H63, O55

**Keywords** : Capital flight, external debt, Granger-causality test, revolving door hypothesis

> 1 An earlier version of this paper was presented at the Senior Policy Seminar on “Capital Flight from SubSaharan Africa: Implications for Macroeconomic Management and Growth”, jointly organized by the African Development Bank, the Association of African Central Bank Governors, the Bank of England, the Reserve Bank of South Africa and the World Bank in October 2007 in Pretoria, South Africa. I am grateful to participants to the Seminar for their valuable comments. I would also like to thank Pierre-Richard Agénor, Ibi Ajayi, Ibrahim Elbadawi, Stephany Griffith-Jones, Roumeen Islam, Raj Nallari, Benno Ndulu, Leonce Ndikumana and Yaw Nyarko for helpful comments and Nihal Bayraktar for excellent research assistance.
107 L"Layard and M4incer (1985) compares **estimates** of female labor supply parameters from various industrially advanced countries. A comparative study of seven Latin American countriec (ECIEL, 1982) in the 1970s replicated the family labor supply model specification proposed by Schultz (1980b) and reported large positive own-wage labor supply elasticities for married women and smaller negative husband cross-wage elasticities. The estimates of labor supply behavior for women living without husbands **were** less elastic, **as one** would expect because they tend to already be ore fully in the labor market and more self-sufficient. Man without wives, particularly at young and old ages, exhibit more positive labor supply responses to their own market wage rate, as do single women. Cogan (1980a) shows evidence that the lack of adjustment for sample selection appears to change very little the parameter estimates of the labor supply model for U.S. married women, tho deographic group for which the bias might be thought a Lrgori to be most serious. Returns to education for this group is changed from 8.5 to 8.8 percent, when corrected for sample selection bias (Cogan, 1980a: Tablo 2.1).

i/For example, do subsidized birth control information, services and supplies reduce the lifetime fertilLty of couples and also increasa the "quality* of their offspring? **Are** more accessible and higher quality (i.e., lower priced) public schools an effective means of increasing school enrollments (quality) and also reducing fertility? The answer to both related questions appears to be a qualified 'yes' in the few studies we have to date (Roscnzweig and Uolpin, 1982). Both types of policies are also likely to be associated with women taking on more market production activities and gradually increasing the market wage opportunities of women relative to men (Rosenzweig, and Schultz,
Table 2: Seeking company for clinic visits during the intervention period

|||**Sought co**|**mpany to visit **|**the ADC fro**|**m:**|
|---|---|---|---|---|---|
|||Husband/|Non-husband/|||
||Someone|MIL|MIL|Sister-in-law|Non-relative|
||(1)|(2)|(3)|(4)|(5)|
|Own Voucher|0.173***|0.040*|0.099***|0.053***|0.031*|
||[0.035]|[0.022]|[0.028]|[0.020]|[0.017]|
|BAF Voucher|0.356***|0.032*|0.291***|0.186***|0.103***|
||[0.039]|[0.019]|[0.038]|[0.032]|[0.028]|
|Observations|603|586|586|586|586|
|Endline control mean|0.034|0.016|0.022|0.003|0.009|
|p-value: Own = BAF|0.000|0.783|0.000|0.000|0.011|

_Notes:_ All columns include balancing controls (i.e., the baseline variables used to balance randomization– whether a woman is currently using family planning, her years of education, whether she wants another child, and the number of peers she mentions in the social networks module), other baseline covariates (i.e., baseline values of woman’s age, indicator variables for Hindu, SC-ST, OBC, whether the woman works, wears ghunghat, and had ever visited a family planning clinic, her mobility score, and her asset index) and village fixed effects. MIL denotes mother-in-law and BAF denotes bring-a-friend. Variable definitions are in Online Appendix. Robust standard errors are in brackets.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

Table 3: Clinic visits for family planning services during the intervention period

||||**Vis**|**ited the A**|**DC with:**|
|---|---|---|---|---|---|
||**Visited the**<br>**ADC**<br>(1)|**Visited any**<br>**clinic**<br>(2)|Alone<br>(3)|Husband/<br>MIL<br>(4)|Non-husband/<br>MIL<br>(5)|
|Own Voucher|0.208***|0.179***|0.036*|0.046*|0.065**|
||[0.035]|[0.047]|[0.018]|[0.026]|[0.027]|
|BAF Voucher|0.190***|0.125***|0.052**|0.034|0.079***|
||[0.033]|[0.045]|[0.021]|[0.024]|[0.028]|
|Observations|606|604|606|516|516|
|Endline control mean|0.015|0.193|0.009|0.018|0.022|
|p-value: Own = BAF|0.702|0.336|0.510|0.712|0.692|

_Notes:_ All columns include balancing controls (i.e., the baseline variables used to balance randomization– whether a woman is currently using family planning, her years of education, whether she wants another child, and the number of peers she mentions in the social networks module), other baseline covariates (i.e., baseline values of woman’s age, indicator variables for Hindu, SC-ST, OBC, whether the woman works, wears ghunghat, and had ever visited a family planning clinic, her mobility score, and her asset index) and village fixed effects. MIL denotes mother-in-law and BAF denotes bring-a-friend. Variable definitions are in Online Appendix. Robust standard errors are in brackets.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

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13 by the IMF board. Based on Nardo et al. (2005) and OECD (2008), we use the centeredreduced normalization, or Z-score approach which consists of transforming a given variable X characterized by its mean μ and standard deviation σ, into an index or Z score expressed as follows: If X is normally distributed, then Z follows a centered-reduced normal distribution, with a zero mean and a unit standard deviation. With this standardization, all reform variables are expressed in the same unit, namely the standard deviation, and can, therefore, be meaningfully compared in terms of effects.<sup>13</sup> For each class of reforms described above and for each period, we compute a normalized reform index by country as follows: ��� is the total number of successful reforms (met or met with minor delay) in country c during the last review by the IMF board at year _t_ . N���� and ��� are, respectively, the average number of reforms met or met with minor delay and the standard deviation of the number of successful reforms for all countries at a given year. The index takes the value 0 if the number of reforms corresponds to the average number of successful reforms. All the indexes of reforms are computed using the entire sample of the MONA data set i.e., the 94 countries.

We also build an average index of reforms by averaging indexes of different reforms (fiscal, financial, trade, and real sectors). The average index of reforms is computed using equal weights for each reform and ranges between -1.6 and 3.5 with high values corresponding to a higher intensity of successful reforms on average. Compared to the existing literature, our reform index focuses on reforms “truly” implemented as we select successful reforms based on performance criteria in countries under IMF programs. Moreover, the database allows

> 13 One matter of concern related to this approach is the sensitivity of the transformed Z variable to the presence of outliers. In the robustness section, we address this issue by using the min-max approach.

(continued...)
essence, the “marginal utility” of formal jobs relative to informal jobs goes down with time – while the marginal disutility linked to the effort invested in finding and keeping jobs remains constant (given the shape of the utility function). The main reasons for this are higher consumption levels and higher asset accumulations. In our setting, therefore, it becomes optimal to reduce efforts in finding/keeping formal sector jobs.

In terms of retirement, the model predicts that around half of the high earners would retire between ages 55 and 60. Low income individuals, on the other hand, tend to retire later – between ages 60 and 65. This is also consistent with the analysis of cohorts presented in Section 4 and the micro-data analyzed in World Bank (2008). But again, the variation in retirement patterns can be considerable. Some individuals can retire as early as 53, others can delay retirement until 70 (see bottom two panels of **Figure 5** ).

Figure 5: Probabilities of Contributing to INSS and Retiring <!-- Start of picture text --> Earnings = 100% Average Earnings=50% Average<br>1.0 1.0<br>0.9 0.9<br>0.8 0.8<br>0.7 0.7<br>0.6 0.6<br>0.5 0.5<br>0.4 0.4<br>0.3 0.3<br>0.2 0.2<br>0.1 0.1<br>0.0 0.0<br>35 40 45 50 55 60 65 35 40 45 50 55 60 65<br>Age Age<br>1.0 1.0<br>0.9 0.9<br>0.8 0.8<br>0.7 0.7<br>0.6 0.6<br>0.5 0.5<br>0.4 0.4<br>0.3 0.3<br>0.2 0.2<br>0.1 0.1<br>0.0 0.0<br>45 50 55 60 65 45 50 55 60 65<br>Age Age<br>Probability of Formal Work Probability of Formal Work<br>Probability of Retirement Probability of Retirement<br><!-- End of picture text --> _Note:_ The dark lines with dots give the “average” path for the cohort. _Source:_ Simulation model.

Overall, the results emphasize the significance that individual preferences have in determining behaviors – and therefore the impact of alternative policies. It is thus important to understand what is the marginal effect that various parameters have on outcome variables of interest, in this case contribution densities and retirement ages. To do this we estimated stepwise 21
In Iran and Morocco, the railways variable also influences TFP growth, which is consistent with the result of the panel regression in column 1, Table 3 above. This finding can be explained by the fact that railways in Morocco and Iran are relatively much developed as opposed to other countries part of this sample.

Only in Morocco does the telephone variable influence TFP growth significantly.

In Algeria, Israel, any Jordan we do not find any influence over time of infrastructure variables on TPF growth. However, again, the panel regressions above do suggest that the electricity variable has influenced differences in TFP growth levels between countries.

**Table 4: Individual country regressions**

**Endogenous variable: TFP growth Hicks neutral**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|Algeria|Egypt|Iran|Israel|Jordan|Morocco|Syria|Tunisia|
|WDI: Growth rate of Mobile and fixed-line tel.<br>subscr.|<br>-8.028|0.0679|32.39|-1.858|7.295|-6.285*|6.313|19.22|
||(23.82)|(2.203)|(29.42)|(4.388)|(7.625)|(3.179)|(12.09)|(12.74)|
|WDI: Growth rate of electricity prod, net of<br>losses|10.61|0.707|82.30|-12.14|7.785|7.158|21.89|39.62*|
||(13.81)|(4.324)|(47.16)|(6.567)|(13.38)|(16.92)|(14.40)|(20.18)|
|WDI: Growth rate of kms rail|-16.41|-2.616|80.66**|37.03|-29.23|-129.0**|-19.88*|-6.800|
||(15.70)|(1.796)|(37.07)|(16.17)|(23.95)|(48.73)|(10.89)|(8.040)|
|Constant|-0.581|1.727**|-11.07*|2.245|-3.632|1.649|-1.399|-3.852|
||(2.163)|(0.672)|(5.972)|(1.081)|(2.856)|(1.491)|(2.520)|(2.758)|
|Observations|20|15|20|5|20|20|20|17|
|R-squared<br>|0.118<br>Robust stand<br>*** p<0.0|0.077<br>ard errors<br>1, ** p<0.|0.223<br>in parenth<br>05, * p<0.|0.824<br>eses<br>1|0.047|0.145|0.189|0.279|

The next section revisits these results, using standard growth regression techniques.

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on how much of an exogenous revenue shock they convert into education spending. Appendix Table A.3 presents first and second stage IV results, which use the same excluded instruments. I compare this table with the baseline results. Once again, _θ_ 1 and _δ_ 1 are robustly negative. Comparing the coefficients _θ_ 1 and _δ_ 1 to the mean of education spending, and doing the same for the baseline results, one sees that the magnitude of the effects changes very little.<sup>43</sup> This supports the findings of the constrainedness sub-sample analysis.

This analysis also has limitations. Simulated revenue may affect public pre-primary investment through channels other than education spending, violating the exclusion restriction.<sup>44</sup> However, it is meaningful that both of these constrainedness analyses confirm the main results.

# **7.3 Correlates of inequality and income**

I treat inequality and median income as broad concepts that summarize important dimensions of society. However, my findings may be less interesting if income and inequality are simply picking up the effect of some variable correlated with them that also conditions how revenue is spent. For example, they may be correlated with higher initial enrollment in public pre-primary (‘saturation’ from the start, and less room for growth), or with urbanization (creating more economies of scale in provision, and thus more room for enrollment growth).

To ensure that this is not the case, I individually enter four new interactions with revenue into the model: the 1997 enrollment rate in public pre-primary education, and the year 2000 fraction of the municipality that was urban, labor force participation rate, and index of racial fractionalization.<sup>45</sup> Due to problems of weak instruments, I cannot enter all of these in the same specification. However, as Appendix Table A.4 shows, their individual inclusion has negligible effects on the main point estimates and their statistical significance. My findings seem to come from what income and inequality imply for the number of people wanting public pre-primary education, and from their political influence, rather than one of these more mechanical channels.

> 43The same analysis, applied to pre-primary quality, also reveals almost no change in the size of the effects.

> 44E.g., investment in roads and public transit might boost enrollment by reducing the cost of getting to school.

> 45Urbanization is as classified by IBGE (2000). Labor force participation is the fraction of the adult population that is economically active. Racial fractionalization is an ethnolinguistic fractionalization (ELF) index (an HHI), computed using data on the population in each of four categories: black or brown, white, asian, and indigenous.

34
reform (1978-2000) this trend continued, and the fall exceeded one-third. 25 Yao also estimated that, between 1953 and 1989, per capita arable land and crop area declined, respectively, by 2.08% and 1.69% per year. 26 Should more rural labor move out of the agricultural sector and more machinery be used in farming, China’s agricultural productivity could expect a major improvement. 27 However, considering the current industrial structure and land tenancy system, this would be a very difficult and slow process. Furthermore, based on 2000 data, China’s manufacturing labor productivity was only 92% of that in India and less than 5% of that in Brazil, France, Japan, and the United States (figure 2).

**Table 5.  Productivity Comparison in Agriculture, Industry, and Services (1999)**

<u>(Related GDP per person employed in industry, US$)</u>

||China|U.S.|U.K.|Germa<br>ny|Japan|Finland|Korea|Malays<br>ia|Brazil|Chile|Thailan<br>d|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Agriculture|512|53657|62117|41332|25902|34835|10439|3733|3795|7059|885|
|Industry|2954|81077|60168|64556|80860|65977|32138|13534|19827|18244|8506|
|Services|1751|66494|48329|58205|64294|52199|16959|7365|13771|12349|5773|

Source: IMD, The World Competitiveness Yearbook 2000, Lausanne, Switzerland, pp461-463.

**Table 6. Cereal Yields (Selected Countries)**

<u>(kilograms per hectare)</u>

|Year|China|United<br>States|France|Japan|India|S Korea|Malaysia|Brazil|
|---|---|---|---|---|---|---|---|---|
|1976–81|3,027|4,151|4,700|5,252|1,324|4,986|2,828|1,496|
|1999–2001|4,869|5,824|7,088|6,147|2,321|6,500|3,075|2,825|

Source: The World Bank, World Development Indicators 2003, pp. 128–131.

# **Table 7.  Rural Labor Density and Agricultural Machinery Comparison (1999/2000)**

> 25 Knight, John and L. Song, “Chinese Peasant Choices: Migration, Rural Industry or Farming”, _Oxford Development Studies_ , V31, No.2, June 2003.

> 26 Yao, Shujie, “Agriculture’s Role in Economic Development: The Case of China”, from Cook, Sarah, Shujie Yao and J. Zhuang, ed., _The Chinese Economy Under Transition,_ MacMillan (London) and St. Martin’s (New York), 2000. Also Yao, S., _Economic Reforms and Grain Production in China_ , MacMillan, London, 1994.

27 A household survey in Zouping County, Shandong Province, collected between 1990 and 1994, shows that, when controlling for household off-farm employment, the effect of per capita land on incomes increases by approximately 10%. This result may be due to the effect of moving labor out of agriculture. As labors move off the land, other resources may be more efficiently allocated and the returns to land also increase. Controlling for employment, a one _mu (Chinese measure, equaling 1/15_<sup>_th_</sup> _of a hectare)_ increase in per capita land leads to a 36% increase in per capita household income. See Cook, S., “Employment, Enterprise and Inequality”, from Cook, Sarah, Shujie Yao and J. Zhuang, ed., _The Chinese Economy Under Transition,_ MacMillan (London) and St. Martin’s (New York), 2000.

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for adequacy in health and environment requiring simultaneous access to improved levels for each component of infrastructure as well as access to adequate level for each component of health services. In this case the “chain is only as strong as its weakest link” in the sense that as long as there is very low access to an adequate level in any one of the components of the cluster (e.g. sanitation at the community level) then overall access to adequate health and environment is forced to be very low, in spite of considerably higher access to adequate levels in the other components of health and environment. The preceding argument is also valid for the determination of whether a child has access to adequate care or adequate food security. As long as each one of the clusters consists of numerous indicators measuring the different dimensions of child care or food security, adequate care or adequate food security overall will be determined by the level of adequacy in one of the sub‐components or the “weakest link.” In other words, the analysis following takes it as a given that the three clusters—food, care, environment and health— and the various sub‐components of each of these clusters work in tandem to produce nutritional outcomes.

Using Zimbabwe as an example, although there are 22% of children adequate in food, 39% adequate in care, and 10% adequate in environment and health combined (Figures 5a, 6a and 7a), many children (47%) are not adequate in any component and only a few (17%) are adequate in more than one component (Figure 8a). As expected, from the earlier analysis, children in resource‐ rich households are more likely to be adequate in two of the three facets (17% vis‐à‐vis 11%) and in all three components (4% vis‐à‐vis 0%) than children in resource‐poor households (Figure 8b). The same general patterns appear to hold for Nepal (see Figures 8c and 8d).

<!-- Start of picture text --> Figure 8a Figure 8b<br>Adequacy status in Zimbabwe (2010) Adequacy status in Zimbabwe (2010)<br>By wealth<br>53<br>47<br>44<br>29<br>23 18 17<br>8 6 15 7 0 11 0 8 9 4<br>2<br>Resource-poor Resource-rich<br>None Food only None Food only<br>Care only Environmental health only Care only Environmental health only<br>Two of three adequacies All three adequacies Two of three adequacies All three adequacies<br>Source: Author estimates. Source: Author estimates.<br>100<br>100<br>80<br>80<br>60 60<br>40 40<br>20<br>20<br>0<br>Percentage of children who meet criteria 0 Percentage of children who meet criteria<br><!-- End of picture text --> 34
<!-- Start of picture text --> Map 2. Buildup of Takoradi over time<br><!-- End of picture text --> <!-- Start of picture text --> Map 2a. Buildup of Takoradi in 1975 Map 2b. Buildup of Takoradi in 1990<br>-2 -1.9 -1.8 -1.7 -2 -1.9 -1.8 -1.7<br>Longitude Longitude<br>Map 2c. Buildup of Takoradi in 2000 Map 2d. Buildup of Takoradi in 2014<br>-2 -1.9 -1.8 -1.7<br>Longitude -2 -1.9 -1.8 -1.7<br>Latitude given in Mollweide projection Longitude<br>6.3 6.3<br>6.2 6.2<br>Latitude Latitude<br>6.1 6.1<br>6 6<br>6.3 6.3<br>6.2<br>Latitude 6.2<br>6.1 Latitude<br>6.1<br>6<br>6<br><!-- End of picture text -->
|**Table 5. Lagged drivers of sending apermanent w**|**ork migrant, h**<br>|**ousehold level**<br>|<br>||||||||
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|VARIABLES|_Pric_|_es_|_Sho_|_cks_|_Insec_|_urity_||_Service_|_availability_||
|Log of avg. monthly real price of maize|0.09***|0.19**|||||||||
||(0.03)|(0.08)|||||||||
|Log of avg. monthly real price of matoke|-0.03|-0.08***|||||||||
||(0.02)|(0.02)|||||||||
|Log of avg. monthly real price of cassava|-0.01|-0.03|||||||||
||(0.01)|(0.02)|||||||||
|HH shock: agriculture (pests/disease/drought)|||0.01|0.00|||||||
||||(0.01)|(0.01)|||||||
|HH shock: death of HH member|||0.01|-0.01|||||||
||||(0.01)|(0.02)|||||||
|HH shock: theft or fire|||0.02|0.04***|||||||
||||(0.01)|(0.02)|||||||
|Log of # of fatalities near HH|||||-0.00|-0.00|||||
||||||(0.00)|(0.00)|||||
|Elementary school within one hour of HH|||||||-0.02*|-0.01|||
||||||||(0.01)|(0.01)|||
|Health center/clinic within one hour of HH|||||||||0.01|-0.00|
||||||||||(0.01)|(0.01)|
|Constant|-0.50***|-0.54|-0.18***|-0.01|-0.16***|0.00|-0.17***|0.05|-0.18***|0.01|
||(0.19)|(0.41)|(0.02)|(0.05)|(0.03)|(0.05)|(0.03)|(0.06)|(0.03)|(0.06)|
|Household fixed effects?|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|Observations|8,345|8,345|8,316|8,316|7,837|7,837|6,919|6,919|7,261|7,261|
|R-squared|0.09|0.59|0.09|0.59|0.09|0.58|0.09|0.61|0.09|0.59|

_Source: Authors’ calculations with UNPS. Regressions include all lagged covariates reported in Table 3 and year fixed effects. Region fixed effects are included in the specification without household fixed effects. Robust standard errors, clustered at the enumeration area level, are in parentheses. Significance levels are reported as follows: * p<0.10, ** p<0.05, *** p<0.01._ 13