rafmacalaba/datause-encoder-data huggingface dataset:
The estimates represent projected expansion in physical infrastructure (electricity production and telephone lines), and do not say anything about possible need for rehabilitation or upgrading or operations and maintenance expenditures. Hence both the physical and derived dollar value of required investments are likely to be lower-bound estimates.

To translate these forecast physical infrastructure into dollar values of required investment flows, we follow the simple approach earlier applied by Fay (2001) in estimating infrastructure financing needs in Latin America. We calculate the investment needs as the change in projected physical infrastructure multiplied by the cost of infrastructure as explained above (Table 3). Unit costs in Table 3 were inflation-adjusted over the period of projection for an average annual inflation rate of 4%.

**Table 4: South Africa – Infrastructure Investment Needs Forecast,** **<u>The Current Growth Scenario*, 2003-2010</u>**

||Avera<br>|ge Annual Investment Fl|ows|
|---|---|---|---|
||Inbillions ofUS$|Inbillions of rand|In% ofGDP|
|**Power:**||||
|Lower Bound:|0.29|1.87|0.11|
|Upper Bound:|0.52|3.37|0.20|
|**Telecom:**|1.98|12.90|0.75|
|**Total** (upper bound):|2.50|16.27|0.95|
|*Average annual grow<br>termprojection.|th projection (in %): 3|.6%. Source: IMF and W|orld Bank medium-|
|Source: The authors’|estimates.|||

Using this method, we predict that total value of the electricity generating stock employing the lower bound cost structure is going to increase from US$ 4.03 billion (R 26.21 billion) in 2002 to US$ 5.29 billion (R 34.38 billion at an exchange rate of R6.5/$1) in 2005, and then to US$ 8.13 billion (R 52.87 billion) in 2010. Therefore, required average annual investments --- changes in stocks --- in electricity over the period 2003-2010 are of the order of $ 0.29 billion (R 1.87 billion) (See Table 4, and Figures 6a,b).

16
# **West African ICT Common Market Project**

Another ECOWAS initiative is the ITU West African ICT Market Harmonization Project, which is being financed by the European Commission. The aim of the project is to promote policy development and regulatory reform in the region and to build human and institutional capacity in the field of ICT and regulatory reform through training, education and knowledge sharing. The first phase of the project was launched in 2004. A series of workshops were held under the coordination of WATRA.

The project took into account the ECOWAS vision – _to have a single liberalized telecommunications market, following the adoption of uniform legislative and regulatory frameworks, and the interconnection and integration of national networks_ . The project also builds on the recommendations and conclusions of the World Bank study on the Harmonization of Telecommunications Policies in ECOWAS<sup>26</sup> .

The project issued a final set of guidelines in the following categories:<sup>27</sup>

- establishing a national ICT policy and law;

- interconnection;

- licensing;

- numbering;

- radio spectrum management;

- Universal access and universal service.

# _Guidelines for establishing a national ICT policy_

ECOWAS has adopted the following guidelines relating to a model ICT Policy:

- ICT policy must give prime focus to the sector

- ICT Policy should address the following objectives:

–Increasing the benefits from information technology for the country

> 26 ITU. 2005: “West African Common Market Project: Harmonization of Policies Governing the ICT Market in the UEMOA-ECOWAS Space – Final Reports”.

> 27 The following text consists of edited parts of the guidelines from: ITU, 2005: “West African Common Market Project: Harmonization of Policies Governing the ICT Market in the UEMOA-ECOWAS Space – Final Guidelines”.

17
individual choice, amongst those eligible. This approach does not sit easily with what we know about many anti-poverty programs in developing countries, in which the choices made by politicians and administrators appear to be at least as important to the selection process as the choices made by those eligible to participate.

This speaks to the need for a richer theoretical characterization of the selection problem in future work. An example of one effort in this direction can be found in the Galasso and Ravallion (2005) model of the assignment of a decentralized anti-poverty program; their model focuses on the public-choice problem facing the central government and the local collective action problem facing communities, with individual participation choices treated as a trivial problem. Such models can also point to instrumental variables for identifying impacts and studying their heterogeneity.

When the policy issue is whether to expand a given program at the margin, the classic estimator of mean-impact on the treated is actually of rather little interest. The problem of estimating the marginal impact of a greater duration of exposure to the program on those treated was considered in section 7, using the example of comparing “leavers” and “stayers” in a workfare program (Ravallion et al., 2005). Another example can be found in the study by Behrman et al. (2004) of the impacts on children’s cognitive skills and health status of longer exposure to a preschool program in Bolivia. The authors provide an estimate of the marginal impact of higher program duration by comparing the cumulative effects of different durations using a matching estimator. In such cases, selection into the program is not an issue, and we do not even need data on units who never participated. The discontinuity design method discussed in section 6 (in its non-parametric form) and section 8 (in its parametric IV form) is also delivering an estimate of the marginal gain from a program, namely the gain when the program is expanded (or contracted) by a small change in the eligibility cut-off point.

A deeper understanding of the factors determining outcomes in _ex post_ evaluations can also help in simulating the likely impacts of changes in program or policy design _ex ante_ . Naturally, _ex ante_ simulations require many more assumptions about how an economy works.<sup>64</sup> As far as possible one would like to see those assumptions anchored to past knowledge built up from rigorous _ex post_ evaluations. For example, by combining a randomized evaluation design

> 64 For a useful overview of _ex ante_ methods see Bourguignon and Ferreira (2003). Todd and Wolpin (2006) provide a number of examples, including for a schooling subsidy program, using the PROGRESA data.

58
_12_ _FanIly Planning Success Stories in Bangladesh and India_ Table 3. Measures of fertiHity In the Treatment and Comparison Areas: 1978 to 1985

|_Year_|_Genera_<br>_Theatment_<br>_Area_|_l Fertility_<br>_Compari-_<br>_son Area_|_Total Fer_<br>_Treatment_<br>_Area_|_tility Rate_<br>_C&-npart-_<br>_son Area_|
|---|---|---|---|---|
|1978|138|164|4.5|5.4|
|1979|15O|205|4.8|6.9|
|1980|156|195|5.1|6.7|
|1981|148|189|4.8|6.3|
|1982|_156_|192|5.0|6.3|
|1983|144|185|4.5|6.1|
|1984|128|160|4.0|_5.1_|
|1985|143|185|4.5|5.9|

Soumo: Dalk et al. (1988) <!-- Start of picture text --> Figure 1. Monthly total fertility rates for Matlab Treatment and Comparison Areas,<br>1974-84<br>12.00<br>10.00 _<br>8.00 I 3<br>-6.00<br>A<br>4.00_ VI<br>2.00I<br>0.00 I f I I 1<br>1974 *76 78 80 82 1984<br>Year by quarter<br>Pre-project period Project impact period<br>Key:<br>Treatment area<br>Comparisonarea<br><!-- End of picture text --> Souic: Philips at al. (1988). The author acknowledges his thdns to t editor, _Studis in Fan* Planning,_ for parmision to reprduco th figuro.
Dixit-Stiglitz (DS) aggregator of the MRPK across all activities.<sup>13</sup> The aggregator weights each activity by their capital shares, . jj While the rule of thumb is not derived from an optimizing framework, it is tt−1 tt−1 KK /KK constructed to allocate investment to more efficient and larger industries (as would be the case in an optimizing framework) and has two other appealing properties. First, across same-size industries, if activity is 1% more efficient than activity , it receives more investment (i.e., if → ii jj ii jj � μμ% � )). Second, investment is allocated so that KKtt−1 = KKtt−1 ii jj ii jj capital shares remain constant across sectors with the same marginal efficiency of tt tt tt tt ln (ii ii ) = μμ ln ( MMRRPPKK MMRRPPKK capital (i.e., if � � ). Moreover, this rule of ii jj ii jj ii jj thumb is simple enough to be solved in a spreadsheet. We usually calibrate tt tt tt tt tt−1 tt−1 . MMRRPPKK = MMRRPPKK →KK KK = KK KK μμ= 1

# _A. The LTGM-NR Default_

In this subsection, we describe the LTGM-NR-Default submodel which is a simplified version of the LTGM-NR-External-Balance (presented in subsection B) and designed to be more user-friendly. In the LTGM-NR-Default, investment is determined directly—like Model 1 in the standard LTGM—rather than indirectly via international and domestic savings. The simplifications fall into two categories, which are related to the determinants of private and public investment, respectively.

First, private investment is assumed to be an exogenous fraction of GDI, which is our analogue of the standard assumption in a Solow-Swan model. Second, public investment as a share of GDI responds to fluctuations in resource revenues according to the following rule: (13) gg gg RR RR where are cyclical government resource revenues (as a share GDI)—i.e., iitt = ıı̅tt + θθ(zztt −zz̆tt ) + εεtt RR RR the deviation of actual resource revenues tt tt from their “structural” or long-run zz −zz̆ RR values (discussed below). The parameter tt is the marginal propensity to invest zz RR tt zz̆ θθ

> 13 Note that equation (10) does not apply for the non-resource sector (0) but equations (11) and (12) do. Investment in the non-resource sector is determined residually as 0 −∑ . However, the normalization of equation (10) by D ensures that it also holds for the non-resource sector.  NN ii tt tt ii=1 tt II = II II

> MMRRPPKKtt 11
IMF and WB (2000, box 2). For the purpose of this paper we consider three basic types of risks: (i) foreign currency (FX) risk, (ii) re...nancing (roll-over) risk, and (iii) interest rate risk. Risk type (i) addresses the desired currency composition of the debt portfolio, i.e. the relative weight on domestic currency versus foreign currency debt. Further, the currency composition of the foreign currency debt itself can be also addressed. Risk type (ii) addresses the desired maturity structure and redemption pro...le of the debt. Risk type (iii) deals with the desired proportion of ‡oating as opposed to ...xed interest rate debt or, in some cases, the price-indexed debt.<sup>6</sup> Another attribute of a debt management strategy that will be considered in this paper is whether such a formal document is made public. The debt management strategy is considered as public if it is published either in the annual report of the debt management body, or made available on the respective website.

This paper aims to summarize and analyze the results of a survey on debt management strategies conducted by the Banking and Debt Management Department of the World Bank. We will focus on three main ...ndings related to the debt management strategies across countries. Namely, (i) whether a public debt management strategy exists in a given country, (ii) whether it is made public, and (iii) in which form it is presented, i.e. either in the form of guidelines or a strategic benchmark. We will analyze the distribution of the latter characteristics over di¤erent regions, income groups and levels of indebtedness using graphical analysis. Moreover, we will use regression analysis to investigate to which extent selected economic indicators can explain the characteristics of public debt management strategies across countries. In expectation of the results we would like to set forth the following hypotheses. Namely, that increasing income levels increase the incidence of strategies; that increasing levels of indebtedness show a positive but likely non-linear (humpshaped) relationship with the incidence of debt strategies; and that countries facing larger shocks show lower incidence of debt strategies. To our knowledge this paper

> 6Regarding the ful...llment of a strategy one can also think of specifying the pace at which the strategic benchmark should be reached, as the latter represents another level of the cost-risk tradeo¤. More speci...cally, in order to move the current debt portfolio structure faster towards the benchmark’s structure the debt managers would have to proportionally relax their cost considerations as both restructuring of the debt portfolio or hedging can be costly.

4
In terms of scale, the track record of microfinance is encouraging. At the beginning of the essay, we noted a recent count of 133 million microfinance customers at the end of 2006 (Daley-Harris, 2007). Multiply that by five to get a rough total of the number of people affected through family members’ access to credit, and we find that microfinance reaches on the order of over half a billion poor and low-income people worldwide.

When those who were previously unable to receive credit become able to receive it through microfinance, there is ample reason to expect substantial economic and social effects. This presumption is backed up by success stories and systematic evidence of high returns to capital for businesses run by the poor. McKenzie and Woodruff (2006), for example, show high returns to capital for male-owned “micro” businesses in Mexico. But rigorous empirical evidence based on credible control-treatment evaluations remains scant for microfinance, and the same researchers find much weaker, near-zero evidence on returns to capital for female microentrepreneurs in a randomized controlled trial in Sri Lanka.

With the institutional data that is the focus of this paper, we can not add to the debate on whether subsidies to microfinance are welfare-enhancing. For that, we would need data on net social and economic benefits. As summarized in Armendáriz and Morduch (2005) and World Bank (2007), the existing studies of the effects of microfinance yield inconclusive results, ranging from the assertion of substantial reductions in poverty among microfinance borrowers in Bangladesh to zero effects in Northern Thailand. At a first pass, the mere fact that customers continue borrowing from year to year and maintain high loan repayment rates is a sign that they value the microfinance services offered, but even that evidence in itself offers only modest reassurance. After all, continued borrowing from year to year is also consistent with being trapped in debt. The evidence also gives no sense of net benefits; and it ignores places, like subSaharan Africa, in which customer drop-out rates can be high. The sharpest study so far investigates customers of a very atypical microlender in South Africa that specializes in highinterest consumer loans. Karlan and Zinman (2006) focus on a group of loan applicants that had initially been rejected. The researchers convinced the lender to reconsider the marginally rejected applicants and to reverse the loan decision for some, using a randomized algorithm.

22
Worsening of certain aspects of the business environment over the period 2001 to 2004 for firms in leather (NACE 19), paper (NACE 21), office, accounting, and computing machinery (NACE 30), radio, television, and communication equipment (NACE 32), and, motor vehicles (NACE 34) led to significant negative impacts on productivity growth. Decreases in the indicator for labor quality negatively impacted log TFP growth in office, accounting, and computing machinery (−13.8 percent), radio, television, and communication equipment (−7.3 percent), and paper (−2.0 percent). Decreases in the indicator for labor market flexibility negatively impacted log TFP growth in office, accounting, and computing machinery (−7.1 percent), motor vehicles (−4.0 percent), and radio, television, and communication equipment (−3.5 percent). Decreases in the indicator for competition negatively impacted log TFP growth in leather (−6.1 percent), radio, television, and communication equipment (−5.8 percent), and paper (−3.1 percent). Lastly, the decrease in the indicator for financial development negatively impacted log TFP growth in motor vehicles (−8.3 percent).

# **5. Conclusions**

This paper provides new evidence on the impact that changes in the business environment have on firm productivity, and contributes to the literature in two important respects. First, a unique dataset is constructed by merging information from two large databases in order to address shortcomings of earlier studies, namely reverse causation and unreliable TFP estimates. Second, in order to mitigate the problems of multicollinearity in the full model regression, a new set of robust indicators is constructed using principal component analysis on quantitative variables from the BEEPS manufacturing dataset to summarize the following five distinct aspects of the business environment: (a) infrastructure quality, (b) financial development, (c) governance, (d) labor market flexibility, and (e) labor quality. Regression analysis is based on production function estimates that correct the bias arising from the simultaneity between inputs and productivity. Furthermore, the paper exploits cross-cell (defined by country, subnational location, and firm size) variation in the changes of the business environment indicators across years to determine their effect on firm-level productivity, thereby circumventing the shortfalls of previous studies that focus only on a single year of business environment variables.

Results indicate that successful efforts to improve the business environment has a strong positive impact on firm productivity, even after controlling for unobserved firm, industry, sub30
to spend more time hauling water on average (e.g., Northern, Upper East, Volta) is reduced in the restricted sample. This is not surprising knowing that the average distance between matched clusters was higher in these regions (Table 1). Some statistics on the distribution of the time to the water source in both samples are given in Table E.2 (Appendix E).

In general the estimated coefficients are of the same sign and of similar magnitude in the two models, but fewer coefficients are significant in the restricted sample. In particular the coefficient for hauling time greater than 20 minutes is found not significant at usual levels of significance. This result is probably driven by the lack of variability of hauling times, which itself is a consequence of the removal of the poorest clusters from the restricted sample.

We perform a final robustness check by excluding the last round of observations (2008). As discussed earlier, there was a significant increase in the proportion of girls attending school in this year (Table 2). It is thus important to check that our main findings are not driven (mainly) by what happened at the end of the period. Estimated coefficients of all variables are similar as the ones shown in Table 4. The coefficients of the time variables are found negative and significant. For hauling time greater than 20 minutes, the estimated parameter is now somewhat greater in absolute value, -0.20 (whereas it was -0.14 when the 2008 data were included), not statistically different from the parameter estimated on the full sample.

# **7. Conclusion**

Using four rounds of the DHS from Ghana, we provide some new insights into the relationship between households’ water access and girls’ school attendance in this African country. Our main methodological innovation is to build a panel of clusters based on GPS coordinates. The latter is combined with the use of panel data techniques suited to the analysis of fractional response data allows us to control for endogeneity of infrastructure placement but also for unobservable cluster-specific effects. As far as we know our paper is the first to find evidence of a statistically significant relationship between time to the water source and girls’ school attendance for an African country. Our results indicate that a 50% reduction in the time to haul water would increase the proportion of girls 5 to 15 attending school by 2.4 percentage points on the average, with stronger effects for rural communities. Household composition is also found to be an important determinant of girls’ school attendance.

We also find evidence of “threshold” effects, in the sense that the discouraging effect of higher water hauling costs on girls’ schooling is particularly salient when this cost is relatively high at the outset (above 20 minutes). We argue that this could be explained 18
cannot practice law, but can only provide legal consulting services, and accounting firms must be 100 percent locally owned.

Only in engineering services does Tunisia receive a relatively liberal score. Tunisia has been trying to encourage FDI, and accordingly provides a relatively unrestrictive regulatory regime for both the domestic and foreign engineers required for large projects.

The index measures of regulatory restrictions in banking services originate from one of a series of studies carried out by the Australian Productivity Commission (McGuire and Schuele 2000). Figure 7 shows how Tunisia compares against the worst performing developed and developing countries for which recent banking estimates are available. The scoring is based on the template shown in Table 7.

These index measures in banking explicitly distinguish between regulations that affect only foreign banks (the foreign index), and those that affect domestic and foreign banks equally (the domestic index). Thus the difference between the domestic and foreign index measures the pure margin of discrimination against foreign banks.

Tunisia’s banking restrictions against foreign banks are not as severe as those in Malaysia, China, Vietnam and Brazil. But they are marginally worse than those in Morocco, and significantly worse than those in South Eastern Europe. Tunisia’s score reflects in part the restrictions on both lending and raising funds that are faced by foreign banks. Tunisia also maintains non-discriminatory restrictions, including the authorization process that is required of both domestic and foreign banks.

# **4. Reform options**

_Reform strategies_ It appears that the regulatory restrictions affecting Tunisia’s services sector are generally relatively severe, compared with those in comparable emerging economies. Liberalizing these restrictions could help to harness the export potential of its emerging services, such as ICT-enabled services and the professions, allowing Tunisia to be better positioned as a location for the outsourcing of the back-office functions of European companies. Greater use of business-to-business (B2B) ecommerce within Tunisia could also improve the competitiveness of Tunisia’s own manufacturing and services sectors. Finally, liberalizing the restrictions in key backbone services such as banking, telecommunications and air passenger transport could boost competition and investment in these sectors, for further gains to the economy as a whole.

Given Tunisia’s relatively uncompetitive position in services relative to other emerging economies, there is little reason for Tunisia to wait for other countries to reciprocate in liberalizing their services. France, its key client economy within Europe, is already significantly more liberal than Tunisia. And Tunisia lags behind many of the emerging countries that would compete with it for outsourced services. For the professions, banking and telecommunications, it would make sense for Tunisia to liberalize unilaterally, although there is also scope for reform on a bilateral basis in the professions.

6