rafmacalaba/datause-encoder-data huggingface dataset:
CAPE VERDE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE (figure 1a). While the overall impact of infrastructure on growth was positive, in a number of countries deficient power infrastructure acted as a significant break on growth, as did inadequate road infrastructure in a few cases.

**Figure 1. Infrastructure has contributed much to economic growth—but could contribute much more**

a. Infrastructure’s contribution to annual per capita economic growth in selected countries, 2003–07, in percentage points <!-- Start of picture text --> 2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>telecom electricity roads<br><!-- End of picture text -->

- b. Potential contributions of infrastructure to annual per capita economic growth in selected countries, in percentage points

<!-- Start of picture text --> 5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>-1.0<br>-2.0<br>telecom electricity roads<br><!-- End of picture text --> _Source:_ Calderón 2009.

Looking ahead, simulations suggest that if the countries of West Africa were able to improve their infrastructure to the level of the African leader—Mauritius—annual per capita growth rates would be 2.9 percentage points higher, on average, than they are at present. This impact would come mainly from improvements across the three main infrastructure sectors (roads, electricity, and telecommunications; see figure 1b). Overall, evidence from enterprise surveys suggests that infrastructure constraints are responsible for about 40 percent of the productivity handicap faced by African firms.

# The state of Cape Verde’s infrastructure

Cape Verde’s geography has contributed to the nation’s relatively high cost of infrastructure services and access limitations. A population of approximately half a million is dispersed across a nine-island 3
Table 2a: Shares of plants with financial loans by state

||||Total ce|ll count||S|tate GDP per capita||Total s|hare wi|th finan|cial loan|s|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1989|1994|2000|2005|2010|Average|in 2000-01|1989|1994|2000|2005|2010|Average|
|A & N Islands|382|558|597|225|273|407|26,469|0.34|0.28|0.14|0.09|0.12|0.19|
|Andhra Pradesh|10,666|17,883|19,386|9,356|12,451|13,948|18,320|0.09|0.11|0.08|0.09|0.11|0.09|
|Assam|2,738|4,087|6,575|3,559|3,225|4,037|13,545|0.02|0.10|0.07|0.10|0.03|0.06|
|Bihar|8,089|12,885|18,279|6,550|7,008|10,562|7,111|0.09|0.06|0.03|0.03|0.03|0.05|
|Chandigarh|321|557|445|216|311|370|52,520|0.20|0.30|0.09|0.20|0.05|0.17|
|Dadra & Nagar Haveli|265|327|508|548|482|426|n.a.|0.16|0.14|0.41|0.47|0.36|0.31|
|Daman & Diu|128|359|519|643|565|443|n.a.|0.13|0.29|0.53|0.32|0.37|0.33|
|Delhi|1,829|3,217|2,721|1,376|2,788|2,386|42,220|0.08|0.14|0.05|0.18|0.05|0.10|
|Goa|252|558|1,049|585|429|575|44,934|0.27|0.15|0.11|0.20|0.18|0.18|
|Gujarat|7,079|10,526|11,846|6,676|7,818|8,789|20,827|0.11|0.12|0.09|0.08|0.06|0.09|
|Haryana|2,281|3,432|5,248|3,561|4,258|3,756|26,568|0.16|0.20|0.12|0.15|0.08|0.14|
|Himachal Pradesh|1,505|2,223|4,026|1,984|2,016|2,351|24,690|0.35|0.10|0.10|0.09|0.06|0.14|
|Jammu & Kashmir|2,696|1,431|6,010|2,856|2,182|3,035|16,145|0.07|0.06|0.03|0.08|0.07|0.06|
|Karnataka|6,288|9,892|10,066|5,285|5,870|7,480|19,551|0.09|0.11|0.08|0.08|0.10|0.09|
|Kerala|5,631|7,104|11,081|5,064|6,260|7,028|22,549|0.11|0.12|0.17|0.18|0.13|0.14|
|Madhya Pradesh|6,461|7,691|14,745|6,024|9,033|8,791|12,459|0.16|0.08|0.05|0.04|0.02|0.07|
|<br>Maharashtra|11,982|13,938|21,732|10,639|10,724|13,803|25,228|0.17|0.18|0.13|0.16|0.10|0.15|
|Manipur|1,951|1,704|3,449|1,195|860|1,832|13,422|0.04|0.02|0.03|0.08|0.07|0.05|
|Meghalaya|532|673|1,069|598|427|660|16,440|0.02|0.02|0.01|0.02|0.01|0.01|
|Nagaland|349|1,079|1,584|759|759|906|16,805|0.04|0.04|0.05|0.15|0.13|0.08|
|Orissa|4,305|8,368|7,314|3,844|4,008|5,568|11,543|0.06|0.02|0.03|0.07|0.08|0.05|
|Pondicherry|388|327|756|636|672|556|37,985|0.24|0.36|0.16|0.21|0.14|0.22|
|Punjab|5,587|6,171|10,973|5,476|5,189|6,679|28,714|0.17|0.14|0.08|0.09|0.06|0.11|
|Rajasthan|5,283|6,536|11,125|5,968|5,932|6,969|14,504|0.14|0.09|0.06|0.07|0.04|0.08|
|Tamil Nadu|12,476|27,716|22,302|10,683|13,367|17,309|22,846|0.11|0.14|0.12|0.11|0.11|0.12|
|Tripura|1,601|2,615|3,725|1,839|1,801|2,316|16,143|0.08|0.10|0.01|0.15|0.02|0.07|
|Uttar Pradesh|16,070|25,196|28,552|11,877|15,642|19,467|10,874|0.11|0.07|0.05|0.06|0.02|0.06|
|West Bengal|10,664|20,986|18,657|8,153|10,920|13,876|17,607|0.07|0.09|0.04|0.07|0.03|0.06|
|Nationwide|127,799|198,039|244,339|116,175|135,270|164,324||0.10|0.09|0.07|0.08|0.06|0.08|

Notes: See Table 1a. Table provides state-level estimates of the share of manufacturing plants with financial loans. GDP per capita is in 1999-2000 Rupees. Shares use sample weights for population-representative values.
# 10c. Indirect effects

|Quintile|Gasoline|LPG|Diesel|Electricity|All|
|---|---|---|---|---|---|
|Quintile 1|-1.8|-2.8|-5.7|-1.8|-12.1|
|Quintile 2|-3.3|-5.4|-9.7|-3.1|-21.5|
|Quintile 3|-4.7|-7.8|-13.4|-4.4|-30.3|
|Quintile 4|-6.4|-11.3|-18.0|-6.2|-41.9|
|Quintile 5|-9.9|-21.9|-30.5|-12.0|-74.3|
|Total|-5.2|-9.8|-15.5|-5.5|-36.0|

_Source:_ WB staff calculations using SUBSIM.

Figure 5: Impact of Reforms on Households’ Expenditures (as % of household total expenditures) <!-- Start of picture text --> Gasoline Diesel LPG Electricity<br>Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Average<br>0<br>-1<br>-2<br>-3<br>-4<br>-5<br>-6<br>-7<br>-8<br>% of total household expenditures<br><!-- End of picture text --> _Source:_ WB staff calculations using SUBSIM.

Estimates of the impacts on poverty and inequality show an increase of almost 3 percentage points in the incidence of poverty (2.69), which represents an increase of 17% in the incidence of poverty. Table 11 shows a Gini coefficient increase of 0.61 percentage points, or a 1.7% increase in the pre-reform levels of inequality. A large portion of those changes in poverty result from direct effects, both in poverty and in inequality, and LPG is the largest contributor to poverty and inequality deterioration.

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rather than using it as a medium of exchange. He argued that, during seasonal times in Egypt, currency is mainly used for transaction purposes, but in off peak periods, currency will be held also as an asset. Second, contrary to the case in developed economies, the quarterly adjustment is conducted slowly. Third, the estimated long run elasticity of M1 with respect to the interest rate exceeded the values found by other studies in developed economies. El-Sheikh suggested that the narrow asset menu available in Egypt might be the reason behind this finding. Finally, assuming there is no change in the structural behavior of the asset holder following the studied period, the Egyptian government should refer to money demand in Egypt when designing monetary policy.

Baliamoune-Lutz and Haughton (2004) tested Friedman’s hypothesis with application to Egypt. The research question was whether increased variability in the growth of the money supply causes velocity to decline. They used annual data from the period 1960–99. The monetary aggregates M1 and M2 are decomposed into anticipated and unanticipated components, and the variability of money growth is computed as the standard deviation of five years of monetary growth rates. Applying Johansen and Juselius’ (1992) co-integration tests provided evidence that in the case of Egypt there is a statistically significant long-run relationship between the variability in money growth and velocity for both M1 and M2. However, while increased variability in the growth of M2 is found to be associated with lower velocity, supporting Friedman’s velocity hypothesis, increased variability in the growth of M1 seems to have no influence on velocity, possibly because the definition of M1 has changed over time. The findings also suggested that anticipated movements in M2 volatility are not neutral, in the sense that they do affect velocity. The main contribution of this research is that in the short run, discretionary monetary policy in Egypt is somewhat circumscribed and constrained. However, if the Central Bank of Egypt were to make its decisions more transparent and pre-announce its policies, then velocity would be more predictable and monetary policy would be more potent and effective.

In an attempt to investigate whether Egypt should implement an inflation-targeting approach to pursue its monetary policy, the Central Bank of Egypt’s annual report for the year 2010 examined the stability of the money demand function. Using monthly data over the period July 1997–June 2009, this exercise starts by estimating the long-run co-movement vector based on the Johansen method. The model incorporated data on real GDP, 3-month Treasury bill rate, annual inflation rate, and an impulse dummy to account for the exchange rate regime change in 2003, while 315
the sites. We further discuss the potential for Delphi results to be helpful for benefit transfer, in the final section below.

Our Delphi exercise could, in principle, serve several objectives. First, and most obviously, it provides estimates of population WTP which can be held up against estimates from the population SP surveys that we intend to undertake. A large discrepancy between the Delphi exercises and the population surveys, absolutely and/or relatively across the countries for which we will have estimates from both, may signal a need for further analysis and refinement of our overall valuation approach. Second, note that our Delphi exercise provides us with average population WTP assessments for some countries where population surveys will, and will not, be done. We do not have the resources to carry out population surveys in all major countries. Our expert surveys will then give us useful relative WTP assessments for these two groups of countries. Such relative assessments could be more useful than the absolute WTP numbers from our Delphi exercise, as concluded also by León et al. (2003). Even persons who view themselves as experts are likely to have limited knowledge of true WTP levels in their own countries, for the particular value object in question here, as population data on these levels are generally lacking.

Thirdly, the distribution of experts’ WTP predictions could serve as a guide to planning more efficient experimental designs (Ferrini and Scarpa 2007). Finally, as part of the Delphi exercise we also sought all experts’ comments on various aspects of design of those population SP surveys we intend to undertake. Several leading environmental valuation experts participated in our exercise, and their feedback provided us with highly useful information. Most importantly, the expert group as a whole confirmed some of our beliefs about the most difficult problems we will face in conducting these surveys; and also led us to reevaluate certain design choices and raised some new issues.

# **2. Our Delphi Exercise**

# **2.1 Expert Participants**

Some key data concerning the distribution of participating experts are given in Tables 1 (experts from other countries than Europe) and 2 (European experts). Overall, 49 experts from 21 different European countries; 98 experts from other OECD countries (the U.S., Canada, Australia and New Zealand); and 70 experts from 12 (lower- and middle-income) Asian countries 5
Africa, among firms that are credit constrained, and among firms that perceive access to finance as an obstacle. Our analysis shows that firm size and age are significantly correlated with firm growth. Distinguishing between different types of ownership, we find that firms tend to have higher growth if they are an exporter, are part of entities with multiple establishments, are foreign owned, or are privately owned.

The paper is organized as follows. The next section reviews the literature and shows how this paper relates to it. Section 3 presents an overview of the data and describes the sample used. Section 4 examines the most binding constraint of the business environment. Section 5 examines the effect of access to finance on employment growth in the full sample and by region, while section 6 looks at determinants of access to finance. Section 7 investigates differences in the effect of financial access variables on employment growth, and section 8 looks at the relationship between firm size and financial constraints. The last section concludes.

# **2. Literature Review**

Understanding the firm growth process is important for designing appropriate policies for job creation and pro-poor growth. Many studies are devoted to understanding the determinants of firm growth, especially employment growth. Most of these studies focus on the manufacturing sector and large firms (Evans 1987a, 1987b; Hall 1987; Dunne, Roberts, and Samuelson 1989). They find that firm age and firm size are important in the analysis of firm growth.

## **2.1 Effect of the Business Environment on Firm Growth**

A number of recent studies use World Business Environment Survey data (a firm-level data set covering 4,000 firms across 54 countries) to study the effect of the business environment on firm growth. Using subjective, firm-level data on the business environment, some of these studies show the importance of finance, corruption, and property rights (Batra, Kaufmann, and Stone 2003; Ayyagari, Demirgüç-Kunt, and Maksimovic 2006). Others examine the relationship between the business environment and firm growth in individual countries or a small group (Dollar, HallwardDriemeier, and Mengistae 2005 in Bangladesh, China, India, and Pakistan; Fisman and Svensson 2007 and Reinikka and Svensson 2002 in Uganda; Bigsten and Söderbom 2006 in Africa).

Other studies also assess the effect of different dimensions of the business environment on firm growth. Some focus on the importance of access to finance for firm development and growth using subjective data (Rajan and Zingales 1998; Galindo and Micco 2007). Others investigate the impact of employment regulations on firm creation and growth (Djankov and others 2002; Klapper, Laeven, and Rajan 2004).

Several papers have emphasized the importance of financing obstacles. Using firm-level data, Demirgüç-Kunt and Maksimovic (1998) provide evidence on the importance of the financial system and legal enforcement on firm growth. Rajan and Zingales (1998) present supporting evidence on 3
MOZAMBIQUE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE grew from 14 percent in 2000 to over 80 percent in 2008,<sup>3</sup> taking Mozambique above the level of countries in the same income group. Mobile telephone penetration has gone up from less than 1 percent in 2000 to over 20 percent in 2008 compared to just 0.4 percent for fixed-telephone penetration in 2008. Mobile growth between 2005 and 2008 was around 40 percent a year, about the same as the Sub-Saharan average (table 16).

**Table 16. Benchmarking ICT indicators**

||**Low-income country**|**M**|**ozambiqu**|**e**<br>**Sub-Saharan Africa**|
|---|---|---|---|---|
||Unit<br>**2008**|**2000**|**2005**|**2008**<br>**2008**|
|GSM coverage|% population under<br>signal<br>56|14|70|83<br>56|
|International bandwidth|bits/person<br>24|0.2|1.9|14<br>34|
|Internet|users/100 people<br>4.6|0.1|0.5|3.6<br>6.5|
|Landline|subscribers/100 people<br>4.6|0.5|0.4|0.4<br>1.5|
|Mobile phone|subscribers/100 people<br>28.5|0.3|8.4|22.1<br>33.3|
||**Low-income country**|**M**|**ozambiqu**|**e**<br>**Middle-income country**|
|_US dollars_|**2008**|**2005**|**2008**|**2010**<br>**2008**|
|Price of monthly mobile ba|sket<br>10|10.7|10.9|9.8<br>11.8|
|Price of monthly fixed-line|basket<br>9|15.4|14.7|13.2<br>11.6|
|Price of 20-hour Internet pa|ckage<br>—|32.9|26.7|24<br>—|
|Price of monthly fixed broa|dband<br>102.4||99|63<br>100.1|
|Price of a call to the United|States per minute<br>—|0.4|0.4|0.3<br>0.8|
|Price of an inter-Africa call|per minute<br>—||0.5|0.5<br>1|

_Source:_ AICD 2006. GSM = global system for mobile communications. — = Not available.

The development of the mobile market has been part of the Mozambican government’s institutional reforms that include the inception of a sector policy, the establishment of a regulatory body (the National Communications Institute of Mozambique, or INCM), the creation of a universal service fund, and the progressive liberalization of the telecommunications market, including the ending of exclusivity for the incumbent Telecomunicações de Moçambique.

# **Challenges**

Despite improvements in the mobile market, in 2008 Mozambique’s penetration was the third-lowest in southern Africa (table 17). The expected launch of a third mobile operator (three companies were shortlisted in July 2010 following a tender) should help to extend coverage, lower prices, and increase penetration. Remaining coverage gaps could be met through the universal service fund.

In the case of mobile telephony, much of the population—up to 87 percent—could be reached on a commercially viable basis, according to AICD estimates (figure 17). This result is based on the assumption that 4 percent of local income in each area could be captured as revenue for voice telephony

> 3 At the end of 2008, the network of the incumbent mobile operator covered 83 percent of the population and 60 percent of the national territory (see Mcel 2009; _Relatório Anual_ 2008).

37
In the second column, the GDP ( _Yit_ and _Yjt_ ) and infrastructure ( _Kit_ and _Kjt_ ) variables are considered as endogenous. The results show that these variables are correlated with the country pair-specific effects. Indeed, the Hausman test, which compares HT with GLS, validates the hypothesis that the instrumentation improves the model (the hypothesis on the exogeneity of GDP and infrastructure variables is rejected).

Coefficients all have the expected sign and are mostly significant at the 1% level. In line with the theoretical hypotheses (see above), import volumes of i from j increase with GDP, and the coefficients are close to unity. The elasticity of bilateral trade to distance is significantly negative and equals -1.5., while the coefficients for remoteness variables are positive (and significant at the 1% level). This is because the more remote a pair of countries is from the rest of the world, the more they will tend to trade with each other. Countries with a common border trade 2.7 times<sup>17</sup> more than expected by the gravity equation. All these results are similar to the gravity results found in the literature (Limao and Venables, 2001; Brun et al., 2005; Carrère 2006; Soloaga and Winters, 2001).

The coefficients of both infrastructure variables (importer and exporter) have the expected positive sign and are significant at the 1% level, i.e. the volume of trade increases with the level of infrastructure. Moreover, they have a sizeable effect on trade volumes. Moving from the median to the top 25<sup>th</sup> percentile in the distribution of infrastructure raises import and export volumes by 11.5% and 8.5% respectively. This is equivalent to being 556 km closer to other countries for imports and 426 km for exports.<sup>18</sup> The elasticity of trade to internal infrastructure is lower than reported in Limao and Venables (2001). This is not surprising since we control for bilateral-specific effects and correct for the endogeneity of infrastructure variables. Note, however, that our results are consistent with those of Brun et al. (2005) and Carrère (2006), who applied the same infrastructure indicator, but across a larger sample. They also mirror the findings of Raballand (2003) who applied a similar measure on a smaller sample.<sup>19</sup>

> 17 2.7=e0.984.

> 18 Estimates from column 2 which are evaluated at median distance of 8037, so 1.115=(2.098/1.403)^(0.269), 1.085=(2.098/1.403)^(0.204), 556.34=8037-8037* (1.115^( -1/1.507)) and 425.55=8037-8037*(1.085^( -1/1.507)). The infrastructure quartiles are computed on the average variable for each country in the sample for the 1992-2004 period. See Table 3 below.

> 19 We did not find any specific impact of the infrastructure level of individual countries on bilateral trade for landlocked countries in general and Central Asian countries in particular. This is not surprising as the specificity of landlocked country trade is the dependence on overland distance and

19
- Shapira, G. (2013): “How subjective beliefs about HIV infection affect life-cycle fertility : Evidence from rural Malawi,” _Journal of Human Resources (forthcoming)_ .

- Srinivas, M. (1962): _Caste in Modern India: And Other Essays_ , Asia Publishing House, Bombay.

- Stillman, M., J. J. Frost, S. Singh, A. M. Moore, and S. Kalyanwala (2014): “Abortion in India: A Literature Review,” New York: Guttmacher Institute.

- Stopnitzky, Y. (2017): “No toilet no bride? Intrahousehold bargaining in male-skewed marriage markets in India,” _Journal of Development Economics_ , 127, 269–282.

- Trivers, R. L. and D. E. Willard (1973): “Natural selection of parental ability to vary the sex ratio of offspring,” _Science_ , 179, 90–92.

- Visaria, L. (2005): “Female Deficit in India: Role of Prevention of Sex Selective Abortion Act,” _mimeo_ .

- Yang, W.-S., H. H. Knöbel, and C.-J. C. Chen (1996): “Gender differences in postneonatal infant mortality in Taiwan,” _Social Science and Medicine_ , 43, 1461–1465.

34
women may choose to forego higher earnings for a better package of non‐monetary benefits. Section b) i., indeed, demonstrates some evidence in support of this hypothesis.

As discussed earlier, such preference for a better non‐monetary package is likely to be driven by the fact that in Vietnam, as in most societies, social norms place the burden of care and household responsibilities in the purview of women. Notably, although the _need_ to balance market and household roles may only emerge in adulthood, it is worth noting that the Young Lives data suggest that already at the age of 12 girls seem to also aspire to occupations with better non‐monetary characteristics, in addition to occupations with higher wages. Table 2 shows that girls aspire to occupations where the non‐monetary characteristics we analyze in this paper are more common ‐ formal contract (Columns 1‐4), paid leave (Columns 5‐8), health insurance (9‐12), social insurance (13‐16). Table 3 shows preference for occupations with shorter weekly hours. The gender gap seems to narrow at higher levels of ability for most characteristics – the coefficient on the interaction term is significant in Columns 3, 9 and 12 of Table 2.

23