rafmacalaba/datause-encoder-data huggingface dataset:
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE The privatization of AES Sonel has led to growing power connections, reductions in unserved energy demand, and increasing investment, particularly in generation infrastructure. Since 2001 AES Sonel has connected some 160,000 customers and invested more than $300 million in generation capacity and rehabilitation of the network. Unserved energy decreased from 2 percent in 2003 to 0.5 percent in 2008. Privatization has also leveraged $360 million in private funds for a five-year investment program focused on rehabilitation of existing hydropower stations and transmission networks. Also, since privatization, the generation capacity of AES Sonel has been expanded. AES Sonel built two new plants powered by heavy fuel oil: Dibamba, an 88 MW emergency power plant near Douala, and Limbe with 85 MW. In addition, in 2006 AES Sonel collected about 94 percent of its bills, above the average for resource-rich countries (at 70 percent, table 10).

The investments in generation infrastructure have put Cameroon’s per capita generation capacity above the level of peer countries. The recent additions to generation assets, in the form of the thermal power plants at Limbe and Dibamba, have helped boost per capita capacity. Cameroon has 58 MW of installed capacity per million people, compared with just 43 MW per million for the resource rich countries as a whole.

**Table 10. Cameroon’s power indicators benchmarked**

|Indicator|Unit|Cameroon|Middle-income<br>countries|Resource-rich<br>countries|
|---|---|---|---|---|
|National access to electricity|% of population|48|50|46|
|Urban access to electricity|% of population|88|85|79|
|Rural access to electricity|% of population|14|33|28|
|Installed generation capacity|MW|1,105|36,971|4,105|
|Installed generation capacity per million|MW per million people|58|799|43|
|Firms that findpower a constraint for business|% firms|67*|31|56|
|Firms with own generator|% firms|51|18|63|
|Outages,number,annually|number per year|128|71|174|
|Outages, value lost, annually|% sales|5|2|7|
|Collection rate, reported by utility|% billing|94|91|70|
|Cost recovery ratio, historical|%|60|85|97|
|Revenue per unit|US cents per kWh|12|13|13|
|System losses|% generation|35|20|52|
|Total hidden costs|% revenue|135|0|168|
|||Cameroon|Countries with<br>predominantly<br>hydro generation|Other<br>developing<br>regions|
|Residential tariff (at 75 kWh)|US cents per KWh|0.10|10.27||
|Commercial tariff at 900 kWh|US centsper KWh||11.73|5–10|
|Industrial tariff(at 50,000 kWh)|US centsper KWh|0.03|11.39||

Source: Eberhard and others 2009. Note: Cameroon data are for 2005 unless indicated otherwise. Constraint to business activity based on the manufacturing sector. Industrial tariffs represent prices paid for ALUCAM, the large aluminum smelter.

31
# TABLE OF CONTENTS

|I.|Introduction|1|
|---|---|---|
|II.|Why Do Growth Rates Differ?|2|
||11.1 Government Policy<br>The Linear Growth Model<br>The Effects of Taxation<br>The Effects of Sectoral Distortions<br>The Role of Government Expenditures|3<br>4<br>8<br>9<br>11|
||11.2 Trends in the Rate of Growth<br>II.3 International Trade<br>II.4 Poverty Traps|12<br>15<br>18|
|III.|Empirical Evidence|20|
||The Shadow Real Rate of Return|"1|
|IV.|The Role of International<br>Capital Markets|23|
||More Corplex Forms of Production and Accumulation<br>Nontradable Goods<br>The Role of Uncertainty<br>The Taxation of Foreign Investment|24<br>25<br>26<br>2P|
|V.|Savings<br>and Growth|29|
|IV.|Conclusion|35|
|Refer|ences|37|
|Tables||44|
|Figur|es|47|

References Tables Figures Most of this paper was developed while I was visiting the World Bank in the uwmmer of 1990. 1 am thankful to the World Bank for providing me with a stimulating research environment and to David Renelt, Plyabha Kongsamut, and Maria Cristina Almero for research assistance. I benefitted from frequent conversations with Bill Easterly and from the comments of Robert Barro, Marianne Baxter, Jack Glen, Robert King, Ross Levine, Masao Ogaki, Charles Plosser, David Renelt, and Guido Tabellini. I am also grateful for the suggestions of Darticipants in the Carnegie-Rochester Conference, in the European Science goundation Conference on Economic Growth, and in seminars at the World Bank, Wharton School, New University of Lisbon, University of Lausanne, and at the Enrico Mattei Foundation in Milan.
financial development would be higher. At the same time, it remains an open question as to why those mechanisms do not seem to work in Africa. Perhaps a period of sustained economic growth will ignite those mechanisms, but the African puzzle continues in a different form.<sup>19</sup>

# **III.3 Additional Tests and Robustness Checks**

We now perform additional tests and robustness checks. In particular, we analyze in further depth the role of population density, which shows up more strongly in Africa than elsewhere.

Moreover, we consider indicators of stock market development to test whether a gap similar to the one found for banking development indicators arises in the context of stock markets. Although stock markets still remain small and illiquid, African countries seem to have committed to the equity sector in promoting financial development and several stock exchanges have emerged in Africa in the recent past.

**1. Population density:** We try to better understand why sparsely populated African countries have low levels of banking development by, first, examining the ‘shape’ of the relationship between density and financial development based on our regression models and, second, including additional variables related to population density in the regressions.

Figure 3 shows the relationship between population density and liquid liabilities/GDP for the typical African and non-African developing country based on Table 5. Specifically, the vertical axis plots predicted values from Model 2 in Table 5. For all variables other than population density, we use the _mean_ value for the subsample (either Africa or other developing countries) multiplied by the appropriate coefficient for that subsample. For population density, we run through the range of possible values and multiply by the appropriate coefficient for each subsample to generate the

> 19 Another possibility is that variables that are important for African financial development are omitted from our models. For example, Easterly and Levine (1997) demonstrate that ethnic fractionalization explains a large share of factors that are linked to (slow) growth within Africa. We experimented with two types of variables: fractionalization and armed conflict. We use the fractionalization data as in Alesina _et al._ (2003); and we use the UCDP/PRIO dataset to calculate the average number of armed conflicts per year for each country. Neither variable is significant in our banking development regressions, and their inclusion does not alter the main qualitative results of our models.

24
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The choice of explanatory variables X is guided by the analytical arguments discussed earlier. In particular a number of variables capturing corruption and regulatory burden are included. Specifically we look at variables on prevalence of corruption and bribes, tax regulation and administration, business registration and licensing, labor rigidities, and constraints to acquiring and transferring immovable property.

Since the hypothesis is that enterprises’ preference for informal finance is a spillover effect of a burdensome regulatory environment, we expect the signs of most of our variables to be positive, indicating a positive association between measures of regulation and exclusive use of informal finance. The exception will be the expected signs on our variables which measure transparency (taxreport_sales and taxreport_labor). Here we hypothesize a negative sign, indicating that the closer aligned actual and reported levels of income and labor figures are, the more likely enterprises will choose bank credit over informal finance. The rationale is straightforward – the less these enterprises have to hide from regulatory authorities, the more likely they are to credibly signal to formal financial intermediaries with accurate financial reporting.

We now consider the maximum likelihood estimates of the binomial logit model of enterprise sectoral choice. Tables 5-7 show the cross-sectional results on the factors influencing the choice of informal finance, testing the impact of various dimensions of the regulatory environment for businesses on enterprises’ financing patterns.

Table 5 presents the results of the regressions using variables measuring corruption and paying bribes. Controls for enterprise size and legal status are included.<sup>6</sup> A number of the variables are positive and significant. In particular, the variables measuring the degree to which bribes are solicited during meetings or inspections (Br-taxmtg and Brtaxinsp), and the cost (in bribes) of securing government contracts (Br-convalue) are significant and positive. Our composite variable for overall bribes (Binary Bribe Index)

> 6 Other controls (ownership, age, assets) proved to be consistently insignificant, and were dropped.

10
with maximum retreat occurring during the 1989/1992-2001 period. Bajracharya and Mool (2010) show that several glaciers in Dudh Koshi basin in Nepal retreated in both 1976-2000 and 2000-2007, while a few glaciers show stability. Temporal variability is observed in the Gangotri Glacier, a well-monitored Indian glacier, which showed no retreat during 2006-2010 (Kargel et al., 2011) despite a high retreat rate in earlier decades (Table 2). Reported retreat rate of many glaciers across the Himalaya also illustrate this variability (Table 2).

Most recently, Scherler et al. (2011) analyzed 286 mountain glaciers from the Hindu Kush, Karokaram, western Indian Himalaya, Tibetan Plateau, West Kunlun Shan, and southern central Himalaya (Nepal, Bhutan, Sikkim, Uttarakhand, and Himanchal) through satellite images from 2000-2008. They found 58% of sampled glaciers in the westerlies-influenced Karokaram region either stable or slowly advancing, while more than 65% of glaciers in the monsooninfluenced regions are retreating with several heavily debris-covered glaciers with low slope at the terminus being stable. Spatially, they found a higher concentration of retreating glaciers (79%) in the western Indian Himalaya and in the northern central Himalaya and West Kunlun Shan (86%) where debris-free glaciers are dominant. In comparison, they found 65% and 73% of sampled glaciers retreating at relatively slower rates respectively in Nepal and Bhutan Himalayas and Hindu Kush where debris-cover is common. They also note that a high rate of glacier retreat is widely observed in the Tibetan Plateau which has the largest concentration of glaciers in China. Ding et al. (2006) analyzed satellite data and found that more than 80% of analyzed glaciers in western China have retreated, losing 4.5% of their combined areal coverage over the past 50 years. While glaciers in the central and northwestern Tibetan Plateau appear relatively stable, glaciers in the mountains surrounding the plateau show extensive areal loss (Ding et al., 2006). Likewise, Yao et al. (2007) analyzed 612 glaciers in the Tibetan Plateau and found that an overwhelming number of glaciers are retreating with retreat rates increasing from 90% for 19801990 to 95% for 1990-2005. Recently, Yong et al. (2010) studied glacial change in China’s Mt. Qomolangma National Nature Preserve (the Preserve) for 1976-2006 covering four major river basins: Pengqu (Arun), Poiqu (Bhote Koshi – Sunkoshi), Gyirong Zangbo (Trishuli), and Yarlung Zangbo (Brahmaputra). Most of the glaciers in the study area have retreated with rates varying between -9.10±5.87 and -14.64±5.87 ma<sup>-1</sup> with most retreat occurring at elevations of 4700-6400 m (Nie et al., 2010). They attribute observed retreat to increasing temperature and decreasing precipitation in the study period.

6
# **5.  Wage Rigidity and Non-Wage Labour Cost Adjustment**

Are firms subject to wage rigidity more likely to use the alternative margins of adjusting labour costs? In the previous section we have found that firms are more likely to use other channels of labour costs adjustment besides reducing base wages if unions are present in wage setting. In parallel, there is an ample literature now (Dickens et al., 2007, Holden and Wulfsberg, 2008 and Babecký et al., 2009, the latter using this dataset) suggesting a prominent role of unions in the determination of downward (nominal or real) wage rigidity. Hence, it is natural to ask in our framework if firms subject to some form of wage rigidity are more likely to use any of these other margins of adjustment.

Our survey allows the construction of three different measures of wage rigidity. We asked directly the managers of firms if they ever cut or froze wages during the previous five years. Following the identifying assumption in some of the micro literature of downward nominal wage rigidity (see for instance Nickell and Quintini, 2003), we regard firms that froze wages at any point during this interval as showing evidence of nominal wage rigidity. Most likely this reflects downward nominal wage rigidity, since an analysis of more than 360 yearly wage change distributions for individuals who stayed in the same job in a large number of countries suggests upward nominal wage rigidity, as suggested by 'menu costs', is not an important element of wage setting (Dickens et al., 2007). However, our data does not allow disentangling symmetric from asymmetric nominal wage rigidity, so we cannot rule out that some of these wage freezes reflect pure menu costs. Nonetheless, they constitute a symptom of rigid wage structures. An important element to take into account is that this measure refers to the previous five years. Since the survey was conducted between the end of 2007 and the beginning of 2008, in most cases the firms are responding about the incidence of wage freezes in an upswing, or period of relatively favourable conditions. Hence, we are most likely underestimating the incidence of downward nominal wage rigidity. In this case, to the extent that the latent association between downward nominal wage rigidity and the use of alternative margins of labour cost adjustment is positive, our estimates would be a lower bound of the true impact.

We also asked firms if they had a policy that linked wage changes to inflation. Firms that replied yes to this question were further asked if the link with inflation was automatic or discretionary and whether the link was with respect to past or expected inflation. Using information from these questions, we consider two different definitions of wage indexation, which we view as a particular form of real wage rigidity. We consider firms to apply a “strict indexation rule” if they have an automatic link between wages and past or expected inflation, 18
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Source: OECD PISA 2015
# 6.2 FDI spillovers take place through domestic input and output linkages

We revert to the sector-level regressions used in the main results to extend this concept, with sectorlevel labor productivity as the dependent variable. In column (1) of Table 14, the coefficient of the _ln FDI_dom_backward_ is positive and statistically significant, which suggests that FDI spillovers take place through the domestic backward linkages. The results in column (2) also demonstrate a positive correlation between the domestic forward FDI and labor productivity. In column (3), the coefficient of the domestic backward FDI is still positive and statistically significant, yet the significant correlation between the domestic forward FDI and the labor productivity disappears. The backward FDI has a more pronounced effect than the forward one. In other words, FDI into downstream sectors will benefit suppliers in the upstream sectors.

**Table 14: FDI Spillovers Take Place through Domestic Backward and Forward Linkages**

|Dependent variable: ln labor<br>productivity(**_ln Y_**)|(1)|(2)|(3)|
|---|---|---|---|
|_ln FDI_dom_backward_|0.005***||0.005*|
||(2.58)||(1.83)|
|_ln FDI_dom_forward_||0.003*|0|
|||(1.76)|(-0.140)|
|_Constant_|4.026***|4.035***|4.030***|
||(96.47)|(98.18)|(96.54)|
|Country-sector FE|Yes|Yes|Yes|
|Country-year FE|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|
|Number of country-sector|1,706|1,705|1,701|
|Observations|7,731|7,752|7,711|
|R-squared|0.532|0.518|0.532|

**Notes:** The dependent variable is the log of labor productivity. The standard errors are clustered at the country-sector level. Country-year, sector-year and year fixed effects are included. *** p<0.01, ** p<0.05, * p<0.1.

In Table 15, the results in columns (1) and (3) suggest that FDI spillovers exist through foreign backward linkages. The FDI into a sector will improve the productivity of its foreign upstream sector. That is to say, the FDI has cross-border spillovers. Columns (2) and (3) reveal that there is no significant FDI spillovers taking place through foreign forward linkages.
> Based on this model, we will use a simple way of predicting ln<sup>_y_ˆ</sup> _i_ ( _t_  _k_ ) _t_  _k_ by using

> <sup>_x_</sup> ' _i_ ( _t_  _k_ ) _t_  _k_ <sup>ˆ</sup> _t_ . However, we should recognize that this short cut of the model will result in an underestimate of the variance of the distribution of the predicted value of income.<sup>14</sup>

# **_Health and Nutrition Index_**

Secondly, Sahn and Stifel (2002) suggest using a height-for-age z-score (HAZscore) in order to approach well-being. This score can be stated as follows: where _H i_ is height for child _i_ , _H median_ is the median height for a healthy and wellnourished child from the reference population of the same age and gender and  _H_ is the standard deviation from the mean of the reference population. By convention, a child whose HAZ-score falls below -2 is classified as malnourished (stunting). Note that in the health and nutrition literature the HAZ-score is generally considered as a reliable indicator of chronic malnutrition. This score in Haiti is relatively high, with about one child under 5 years old out of four being concerned by stunting or chronic malnutrition.

To go one step further, in order to determine the health and nutritional status of children, we consider a health production function: where<sup>_x_</sup> _it_<sup>is consumption,</sup> _Zit_ is a vector of household and individual characteristics, _Cit_ is a vector of community-level characteristics, and _uit_ is unobserved heterogeneity. To apply this model empirically, we use the HAZ-score for<sup>_h_</sup> _it_<sup>and, in the absence of data</sup> concerning consumption, we will use predicted income or asset index as proxies for<sup>_x_</sup> _it_<sup>.</sup> Note that the continuous index<sup>_h_</sup> _it_<sup>can also be considered as a latent variable, since we</sup> could class the children into two groups: one group whose HAZ-score is below -2 and one whose HAZ-score is above -2, with -2 being the malnutrition poverty threshold.

> 14 Note that one important drawback of the methodology concerns the calculation of standard errors of the estimates (Tarozzi and Deaton, 2009). Indeed, although the methodology has been forcefully advocated and considerably enhanced by Elbers et al. (2003), it is still criticized, in particular because it relies on assumptions that are virtually untestable. This approach has for instance been used by the World Bank (2006) to compare welfare over time in Haiti. The estimates show a small decline in extreme poverty over time nationally, from 60% in 1986 to 54% in 2001. Estimates based on the US$2-a-day poverty line show trends broadly similar to those for US$1-a-day poverty rates. The US$2-a-day headcount estimates show a decline from 84% to 78%. However, given the large margin of error in the estimates, the change has not been proved to be statistically significant.

8
|||(0.00)||(0.00)|(0.00)|
|---|---|---|---|---|---|
|Standardized literacy|0.126***|0.056***|||0.044***|
||(0.02)|(0.02)|||(0.02)|
|Browser-based tasks|||0.357***|0.282**|0.275***|
||||(0.10)|(0.10)|(0.09)|
|MS Office-based tasks|||0.443***|0.340***|0.332***|
||||(0.08)|(0.08)|(0.08)|
|Basic program tasks|||0.566***|0.463***|0.456***|
||||(0.11)|(0.11)|(0.11)|
|Advanced program tasks|||0.650***|0.521***|0.512***|
||||(0.11)|(0.11)|(0.11)|
|Constant|0.887***|0.169**|0.684***|0.085|0.131|
||(0.06)|(0.07)|(0.06)|(0.10)|(0.09)|
|Observations|6,699|6,699|6,699|6,699|6,699|
|R-squared|0.19|0.23|0.23|0.25|0.25|

_Note_ : Robust standard errors in parentheses (* _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01). The sample includes wage and self-employed workers. _Schooling_ is measured as completed years of education. Reference categories for _Self-employed_ and _Gender_ variables and for computer complexity dummies are those not self-employed, males, and no computer use, respectively. Scores on the reading proficiency assessment and personality and behavior traits have been standardized to a mean of 0 and standard deviation of 1. The model includes country fixed-effects.

32
**Table 2: Relationship between databases on policies and outcomes in services sectors**

|**Subject:**|**Affecting**<br>**foreign providers**|**Affecting**<br>**allproviders**|
|---|---|---|
|**Policy or**<br>**Regulation**|**Services Trade**<br>**Restrictions Database**|OECD Product Market<br>Regulation|
|**Administrative**<br>**or procedural**<br>**requirements/outcomes**|WB/IFC Investing Across<br>Borders|WB Doing Business;<br>OECD Product Market<br>Regulation|

Differences across these datasets in terms of goals, units of measurement and sampling period notwithstanding, we find our services trade policy information to be reasonably consistent with other databases. For instance, there is a tight negative correlation between the country-level STRI score regarding establishment of commercial presence and the Investing Across Border‘s ̳Ease of Establishment‘ index, suggesting that across countries a more liberal services trade policy stance is associated with a greater ease of establishment.<sup>12</sup> There is likewise a high positive correlation between the country-level overall STRI score and the OECD‘s PMR index (both increasing in restrictiveness) (see Annex 2).

# **3. MEASURING SERVICES TRADE POLICY**

It is notoriously difficult to measure policies affecting services trade because of their variety and complexity (see, for example, the overview by Deardorff and Stern, 2008). We consider three approaches. In Section 3.1, we describe our workhorse method, ―the services trade restrictions index‖ or STRI, which has the weakness of being subjective but the virtue of being simple, transparent and robust. The STRI is the basis for the discussions of policy patterns in Sections 4 and 5. We then describe two relatively sophisticated methods: the ordinal ranking of policy regimes in Section 3.2; and the econometric assessment of restrictiveness of individual policies on the basis of their impact on certain outcomes of interest in Section 3.3. We return to these methods in Section 6 where we demonstrate that

> 12 The STRI stands for the Services Trade Restrictions Index, which is discussed in more detail in Section 3.

11