rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Figure 6. Effective residential tariff for 100 kWh (cents/kWh)<br>35.0<br>30.0<br>25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>-<br>Chad Mali Cape Verde Senegal Uganda Burkina Faso Congo, Rep. Kenya Rwanda Niger Benin Côte d'Ivoire Namibia Cameroon Ghana Botswana Lesotho Mozambique Tanzania Zimbabwe Ethiopia Malawi Congo South Africa Nigeria Madagascar Zambia<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database On average, residential electricity in Africa is among the most expensive in the world. For the average consumer, residential tariffs are over 12 cents/kWh in about 60 percent of the sample countries and over 20 cents/kWh in close to 25 percent of the sample (Burkina Faso, Cape Verde, Uganda, Madagascar, Mali, and Chad) (figure 6). To compare it against other world regions, Africa’s residential electricity price averages between 50 and 150 percent higher than the 8 cents/kWh in Latin-America, Eastern Europe, and East Asia, and up to 400 percent higher than average residential tariffs in South Asia.

What is the pattern of average effective residential tariffs across levels of consumption? Tariffs that increase with consumption effectively impose a penalty on higher consumption and vice versa. About half of the sample countries have increasing average effective residential tariffs (for example, Cape Verde, Chad, Côte d’Ivoire, Ethiopia, Ghana, Kenya, Madagascar, Mozambique, Nigeria, Tanzania, and Uganda [figure 7a and b]). One-third of countries have decreasing average effective residential tariffs, not only because they have explicitly adopted DBT (for example, Senegal) but also because the size of their first blocks is large meaning that the weight of the fixed charge is spread across a larger tranche of consumption. Countries with decreasing residential tariffs include Cameroon, Malawi, Zambia, Senegal, Burkina Faso, Cameroon, the Democratic Republic of Congo, Ghana, and Niger (figure 7c and d).

16
9 cannot, however, be explained entirely by ALMPs. Other determinants that might have some predictive power will be discussed in the next section.

Hungary has a quite high proportion of expenditure on public employment services and administration, roughly comparable with the "high spenders" on this item in the OECD group, while the Czech Republic has an average value for this category of active policy. What is especially interesting, though, is the fact that the Czech Republic spends only negligible amounts on labour market training and the vast bulk of its funds designated for active measures on subsidised employment and work experience for school leavers. Hungary seems to have a more balanced approach with expenditure on labour market training being an item of growing importance. One should add that although Hungarian employment offices also have to provide further training for youngsters who have only eight years of elementary schooling these expenditures are not included in Table 1. The most important Czech and Hungarian ALMPs are described in annex 1.

The ratio of participant inflows (as a percentage of the labour force) over expenditure (as a percentage of GDP) can be used as a rough measure of the average quality of labour market training. A high value implies that on average many participants have been put through training and/or the average value added of such courses has been low. Among the countries in the OECD group where this measure can be calculated not many have a lower value than Hungary. Only Sweden has with 4.6 in 1992 a clearly lower value, while countries like Germany and the United Kingdom have values that are low relative to many other OECD countries but comparable to the 6.3 and 5.91 Hungary has in 1992 and 1993. As Germany has relatively lengthy training courses with on average substantial value added this can only mean that Hungarian labour market training is of relatively high quality. The question whether this labour market training has increased re-employment probabilities of the unemployed cannot be addressed with this type of analysis, it will be pursued in the next section. The Czech values of this measure vary between 10 and 34, pointing to larger throughput and/or lower human capital enhancing content than in the Hungarian case.

Table 3 and 4 give Polish labour market policy expenditure for the years 1990 to 1993, while Table 5 presents the yearly participant inflows for various active measures. The figures for ALMPs expenditure in Table 3 are the official figures published by the Polish Ministry of Labour. They are strongly inflated since apprenticeship training, undertaken in most coLintries by the regular educational system, is in Poland counted as an ALMP measure. Having taken out the
|(1)<br>(2)|(3)|
|---|---|
|;<br>6.29<br>9.08<br>Some<br>Tracking for Math Classes<br>(12.08)<br>(1018)|1?.96*<br>(6.89)|
|.<br>-14.49<br>Public School<br>(9.59)|-11.51<br>(6.31)|
|..<br>-6,.95<br>Hiring Teachers Autonorny<br>6.99)|“5.435<br>7.39)|
|.<br>.<br>4760°"<br>Located<br>in the Metropolitan Stratum<br>(17.53)|26.24<br>(16.88)|
|.<br>42.63"<br>Located<br>in the Urban Stratum<br>(16.34)|29.63°*<br>(14.22)|
|.<br>451*<br>StudentTeacher Ratio<br>(2.49)|3.96*<br>(2.21)|
|Co-ed School|-5.65<br>(1.61)|
|Student<br>R<br>d<br>|<br>ttant<br>forSchool<br>Admissi<br>udent<br>Record<br>Irnportant<br>for<br>School<br>Admission|10.02<br>Gan)|
|Female|-15.15"*<br>(4.72)|
|Hours of Instruction in Math|i<br>(1.30)|
|;<br>;<br>Mother's Education: None (relative ta Upper Sec.)|-705<br>@.17)|
|\<br>.<br>.<br>.<br>Mother's Education: Primary (relative to Upper Sec.)|-11.01<br>vat|
|1<br>a<br>Mother's Education: Lower Sec. (relative to Upper Sec.)|-10.06**<br>(4.28)|
|.<br>a<br>.<br>Mother's Education: Post Sec. (relative to Upper Sec.)|-12.60**<br>6.17)|
|.<br>O- 10 books at hore (relative to 10-100)|-43,55°*<br>6.39)|
|.<br>100+ books at home (relative ta 10-100)|36.16***<br>3.71)|
|Canstant<br>566.1***<br>455.067"<br>onstan<br>(10.35)<br>(36.85)|452.57***<br>(34.51)|
|NMurmber of Observations<br>a524<br>a404|2795|
|Average R-oquare<br>O.0012<br>0.0695|O.1b2|
|Mote: Standard errors are reported in parentheses and significance at the 1%, 5%, 10%<br>denoted “***, and *, respectively. Estimates were calculatetd using 5 plausible values <br>estimates of achievernent and Balanced Repreated Replication with Fays(0.5) adjustme<br>to estimate the standard errors.<br>Sourcedata:PISA2003Korea,GeneralSchoolStrata,Grade10Students|levels is<br> for<br>nt were used|
# **7. Conclusions**

For Africa’s awaited growth resurgence to occur, a broad range of factors—political, institutional, and economic—must be improved. The World Bank’s landmark Africa Competitiveness Reports (2004 and 2007) focus on problems that inhibit economic growth. It is agreed that improving Africa’s infrastructure is a crucial step toward penetrating international markets and meeting the goals of continuous growth and poverty reduction.

Infrastructure quality has a pervasive influence on all areas of an economy. Low-quality infrastructure and limited transport and trade services increase logistical and transaction costs, rendering otherwise competitive products uncompetitive, as well as limiting rural production and people’s access to markets—with adverse effects on economic activity and poverty. A large number of empirical studies illustrate the impact of infrastructure on economic performance. All suggest that Africa’s infrastructure gap is an important growth bottleneck, with a negative impact on productivity and the overall competitiveness of the region. Using the methodology of Escribano and Guasch (2005, 2008) and Escribano et al. (2008), several studies have found empirical evidence—in cases such as Brazil, Chile, Costa Rica, Mexico, Turkey, and several Southeast Asian countries—that improvements in investment climate conditions in general, and in infrastructure quality in particular, may lead to important gains in productivity and in other economic performance measures: employment, real wages, exporting activities, and inflows of foreign direct investment (FDI).

## _Approach and methods_

This paper provided a systematic, empirical assessment of the impact of infrastructure quality on the TFP of African manufacturing firms. We applied micro-econometric techniques to investment climate surveys of 26 African countries to gauge the impact of infrastructure quality on TFP.

For each country we estimated, by regression techniques, the impact of infrastructure quality based on 10 different productivity measures and showed that the results were robust once we controlled for other observable fixed effects (red tape, corruption and crime, finance, innovation and labor skills, and so on) obtained from the investment climate surveys (see Escribano and Guasch, 2005, 2008).

We pooled data from the investment climate surveys only for the few African countries for which we did not have sufficient observations for estimation purposes. Otherwise, we performed a country-by-country estimation to reveal firm and industry information by country. After pooling the data from several countries, the econometric results were then suitably disaggregated following the method of Olley and Pakes (1996) (Escribano et al., 2008), which allowed us to make country-specific evaluations of the impact of investment-climate quality on aggregate TFP, average TFP, and allocative efficiency.

We ranked the African countries in the study according to several aggregate indices: per capita income, ease of doing business, firm perceptions of growth bottlenecks, and the recent concept of 65
|**Table 6: Frequency of growth acceleration and deceleration and growth rate at the country level**|
|---|

||**Frequency of**<br>**growth**<br>|**Frequency of**<br>**growth**<br>|**Growth**<br>**rate (1976-**<br>|**Growth rate**<br>**during growth**<br>**acceleration**<br>|**Growth rate**<br>**during growth**<br>**deceleration**<br>|
|---|---|---|---|---|---|
|**Country**|**acceleration **|**deceleration **|**2005)**|**years**|**years**|
|Angola|0.48|0.28|0.70|3.93|-5.48|
|Benin|0.27|0.23|0.59|1.60|-0.99|
|Botswana|0.43|0.00|6.24|6.87||
|Burkina Faso|0.43|0.00|1.21|1.39||
|Burundi|0.20|0.23|-0.47|1.48|-4.45|
|Cameroon|0.23|0.23|0.81|2.21|-5.34|
|Cape Verde|0.42|0.00|3.26|3.57||
|Central African Republic|0.23|0.53|-1.27|0.89|-1.95|
|Chad|0.20|0.20|1.34|7.66|-1.66|
|Comoros|0.24|0.28|-0.14|0.11|-1.89|
|Congo, Dem. Rep.|0.00|0.30|-3.94||-7.60|
|Congo, Rep.|0.20|0.43|0.61|10.05|-2.25|
|Cote d'Ivoire|0.20|0.63|-1.57|1.82|-3.81|
|Equatorial Guinea|0.42|0.00|10.55|20.88||
|<br>Eritrea|0.00|0.00|1.96|||
|Ethiopia|0.25|0.25|0.42|3.71|-3.05|
|Gabon|0.00|0.40|-0.91||-1.52|
|Gambia, The|0.00|0.23|0.29||-0.99|
|Ghana|0.43|0.20|0.60|2.15|-4.33|
|Guinea|0.37|0.00|0.98|1.78||
|Guinea-Bissau|0.23|0.20|-0.70|0.76|-1.87|
|Kenya|0.20|0.40|0.48|1.76|-0.75|
|<br>Lesotho|0.23|0.00|3.27|3.83||
|Madagascar|0.00|0.43|-1.38||-2.12|
|Malawi|0.23|0.37|0.22|1.68|-2.12|
|Mali|0.33|0.23|0.86|2.75|-3.09|
|Mauritania|0.00|0.00|0.10|||
|Mauritius|0.28|0.00|4.22|5.65||
|Mozambique|0.32|0.00|2.08|5.08||
|<br>Namibia|0.32|0.00|0.15|2.14||
|Niger|0.00|0.43|-1.01||-3.55|
|Nigeria|0.53|0.20|0.27|1.99|-4.79|
|Rwanda|0.20|0.20|1.68|2.27|2.12|
|Sao Tome and Principe|0.47|0.00|0.31|0.99||
|Senegal|0.27|0.23|0.36|1.75|-1.38|
|Seychelles|0.53|0.00|2.46|4.01||
|Sierra Leone|0.20|0.47|-0.57|7.95|-2.92|
|South Africa|0.23|0.40|0.12|1.96|-1.72|
|Sudan|0.30|0.00|1.72|3.90||
|<br>Swaziland|<br>0.27|<br>0.00|<br>1.15|<br>4.63||
|Tanzania|0.47|0.00|1.69|3.69||
|Togo|0.20|0.60|-0.60|4.27|-2.61|
|Uganda|0.30|0.00|1.92|3.69||
|<br>Zambia|0.23|0.50|-1.23|2.35|-2.38|
|Zimbabwe<br>Note: blank space means countr<br>Source: Arbache andPage (|0.20<br>y did not experience gr<br>2007b).|0.27<br>owth acceleration a|-1.26<br>nd/or deceleration.|2.61<br>|-5.34|

# **_C. Does growth accelerations and deceleration matter?_**

Although growth accelerations and decelerations are important features of Africa’s low and volatile long-run growth, do they matter for economic and social outcomes beyond their direct consequences on the rate of growth? If growth accelerations and decelerations have effects that are not neutral, one would expect economic, social, and governance 16
of transactions) and in Machinery/Electrical (67%). This alerts us to the fact that some nontraditional export data do not reflect underlying productive capacity of the Zambian economy. If we took out re-exports from the trade data, Zambia’s reliance on traditional exports would be even stronger. Applying the re-export shares from Table 6 to the export values of Table 5 leads us to conclude that the value share of Metals and Mineral Products in Zambian sourced exports is even higher than suggested by the raw data, i.e. somewhere in the order of 92%.

The reliance of Zambian exports on copper and related products becomes further evident in Table 7, where we report the ten main export products in value terms across three sub-periods. This table suggests that traditional exports if anything have become more dominant in recent years: while the top non-traditional product, sugar, made it into fourth place in 1999-2003 (4% of export value), it has dropped to seventh place in 2008-2011 (2% of export value). In the last subperiod, the top six products were all copper related and together accounted for 79% of Zambian exports.

While the rising share of mining products has given an increasing weight to overseas destination markets for Zambian exports, intra-African exports have actually grown in relative importance with regard to non-traditional exports. This is shown in Figure 1: while Sub-Saharan Africa (SSA) accounted for 60% of the value of non-traditional exports in 1999, it absorbed fully 80% of those exports by 2011. In sectors other than mining, therefore, some reorientation of trade towards regional destinations appears to have taken place. As we in addition show in Table 8, intra-SSA exporters became more diversified, and export values increased more, than exporters that exclusively served non-SSA destination markets. The bulk in the value of trade, however, was accounted for by exporters that served both SSA and non-SSA destinations – presumably dominated by the large mining conglomerates.

_Export concentration_ 5
# **Online Appendix**

Table A1: Attrition

|Dependent variable:|(1)|(2)|Panel<br>(3)|(4)|(5)|
|---|---|---|---|---|---|
|Size|0.000673<br>[0.00568]|-0.00199<br>[0.00633]|0.000206<br>[0.00588]|-0.000120<br>[0.00574]|0.00131<br>[0.00582]|
|Age|0.0242***<br>[0.00792]|0.0317***<br>[0.00862]|0.0265***<br>[0.00807]|0.0240***<br>[0.00792]|0.0232***<br>[0.00799]|
|Export|0.0137|0.0198|0.0112|0.0135|0.0143|
||[0.0166]|[0.0181]|[0.0170]|[0.0166]|[0.0167]|
|Manufacturing|0.148|0.286|0.149|0.151|0.150|
||[0.153]|[0.273]|[0.152]|[0.153]|[0.153]|
|Retail|0.213|0.346|0.213|0.215|0.216|
||[0.154]|[0.274]|[0.154]|[0.154]|[0.154]|
|Other services|0.213|0.341|0.213|0.216|0.215|
||[0.152]|[0.272]|[0.152]|[0.152]|[0.153]|
|Listed company|-0.00170|0.000831|0.00520|-0.000125|-0.0104|
||[0.0637]|[0.0773]|[0.0651]|[0.0637]|[0.0643]|
|LLC|-0.0169|0.00524|-0.00870|-0.0153|-0.0217|
||[0.0611]|[0.0740]|[0.0624]|[0.0611]|[0.0616]|
|Sole proprietorship|-0.0379|-0.00927|-0.0290|-0.0355|-0.0433|
||[0.0611]|[0.0739]|[0.0625]|[0.0611]|[0.0616]|
|Partnership|-0.0158|0.0128|-0.00760|-0.0137|-0.0203|
||[0.0617]|[0.0745]|[0.0631]|[0.0617]|[0.0623]|
|Ltd Partnership|-0.0378|-0.00804|-0.0278|-0.0360|-0.0428|
||[0.0620]|[0.0747]|[0.0634]|[0.0620]|[0.0626]|
|Constrained by informal||-0.00239<br>[0.0154]||||
|Loan availability|||-0.00155<br>[0.0161]|||
|Loan application||||0.0141<br>[0.0140]||
|Investment|||||0.000102<br>[0.0144]|
|Model|Logit|Logit|Logit|Logit|Logit|
|Geographic area FE|yes|yes|yes|yes|yes|
|Observations|5219|4347|5063|5219|5181|
|Pseudo R2|0.0328|0.0367|0.0331|0.0328|0.0327|

_Notes:_ logit marginal effects. The estimating sample is composed of the entire set of firms interviewed in the 2013-wave of the WBES. The dependent variable is a dummy taking value of 1 if the firm is included in our panel estimation (i.e., it is interviewed in the following wave), and 0 otherwise. All regressors are timed at the beginning of period. Measures are defined in Table B4. Robust standard errors in brackets. *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

22
TABLE 12: Impacts on social interactions about ECD with targeted leaders

||(1)<br># leaders|(2)<br>Healthpromotor|(3)<br>Pre-sch Teacher|(4)<br>PrimaryTeacher|(5)<br>Other Educator|(6)<br>Local leader|(7)<br>Other com. leader|
|---|---|---|---|---|---|---|---|
|ITT|-0.01|0.02|-0.04|-0.01|-0.01|0.01|0.00|
||(0.05)|(0.02)|(0.02)|(0.02)|(0.01)|(0.01)|(0.01)|
|ITT Leader|-0.18<sup>_∗∗_</sup>|-0.03|-0.03|-0.07<sup>_∗∗∗_</sup>|-0.02<sup>_∗∗_</sup>|-0.02|-0.02<sup>_∗∗_</sup>|
||(0.07)|(0.03)|(0.02)|(0.02)|(0.01)|(0.02)|(0.01)|
|Observations|2073|2073|2073|2073|2073|2073|2073|
|Mean Control|0.89|0.13|0.33|0.24|0.05|0.10|0.03|

_Note:_ The dependent variables are the social interactions between the household and targeted opinion leaders during the previous week. Column 1 is an index accounting for the social interaction between the household and health promoters, preschool-teachers, primary school teachers, other teachers, the local leader or other community leader. The dependent variables in the remaining columns are binary and indicate whether the household had at least one interactions with each type of leader during the previous week. All regressions include controls for the average level of education of the leaders (the stratification variable for village level leader randomization), as well as the level of education of the main caregiver, whether there is a male caregiver in the household, whether the leader household had access to electricity, child age and gender and test-administrator fixed effects, as well as a binary indicator indicating the data was collected in 2016. The standard errors (in parentheses) are clustered at village level.<sup>_∗_</sup> p<0.1,<sup>_∗∗_</sup> p<0.05,<sup>_∗∗∗_</sup> p<0.01 46
# **3.  Increasing returns, expectations, and coordination failure**

We now open up two central features of the model. The first is increasing returns to scale – agglomeration economies – in tradable production, this creating the possibility of multiple equilibria.<sup>18</sup> We show that it is possible that there is a low equilibrium in which the city produces tradables only, and also a high equilibrium in which the city is active in both sectors. The possibility of being trapped in the low-equilibrium arises because of coordination failure.<sup>19</sup> In the simplest case (section 3.1) this is an inter-firm coordination failure; potential producers of tradables do not coordinate to internalize the external economies of scale associated with agglomeration. However, given endogenous choice of the way the city is built – its size and density – the coordination failure goes deeper. Section 3.2 turns to the role of sunk costs and expectations in shaping construction decisions, and shows how (self-fulfilling) expectations may be such that the city is constructed in a way that locks it into the low equilibrium. It is possible that – even if the inter-firm coordination failure between produces of tradables were somehow resolved – the built urban form is incompatible with expanding into tradable goods production.

# **3.1 Increasing returns and multiple equilibria**

Suppose that tradable production is subject to agglomeration economies so that productivity in tradable production is increasing with the size of the sector. The left-hand segment of the labor _D_ demand curve, _w_ ( _L_ : _LT_  0) is unchanged, and the segment with tradable sector active, _wD_ ( _L_ : _LT_  0) , becomes upwards sloping. Fig. 2 illustrates for the case in which productivity is linear in tradable employment between lower and upper bounds, _a_ 0, _am_ , so _a_ ( _LT_ )  min  _a_ 0   _LT_ , _am_  ,   ,0 _a_ 0  _am_ .

As illustrated, returns to scale are strong enough for labor demand to have three intersections with labor supply. Points M and M’ are stable equilibria, while the intermediate intersection is unstable (under a dynamic in which the tradable sector expands or contracts according to whether profits are positive or negative). At the lower point, M, the city is specialized in nontradable production and wages are above the trigger point, _a_ 0 , at which tradable production commences. Entry of a small mass of tradable produces is not profitable; this point is an equilibrium if coordinated entry of a sufficiently large mass of tradable producers (who would reap the benefits of agglomeration economies) is not possible. At the upper equilibrium, M’, the tradable sector is active and is large enough for agglomeration economies to have cut in, raising

> 18 For discussion of agglomeration economies in the context of developing economies see World Bank

> (2009) and for evidence see Chauvin et al. (2016).

> 19 As in, for example Murphy et al. (1989), Henderson and Venables (2009).

14
information problems have been the main factors driving the process of foreign bank entry in developing countries.<sup>11</sup> Another recent strand of that literature emphasizes that the physical and cultural proximity between a parent bank‘s home country and the host country of its affiliate helps to ameliorate information problems and therefore affect its commitment to (and activities within) that host market (Claessens and Van Horen, 2008 a,b; 2009).

Wide variation in the participation levels of foreign banks at the end of the 1990s can be viewed as a reflection of the depth of commitment of foreign banks to those host countries.<sup>12</sup> This leads to the following hypotheses regarding the effect of the depth of foreign banks‘ commitment to a country on the relative severity of the crisis for firms in the ECA region:

**_Hypothesis 4._** _In host countries where foreign banks have a high level of commitment, as reflected in their initial participation level, we expect that reported financial constraints at the time of the crisis would be less severe than in countries with low initial levels of foreign bank participation._

**_Hypothesis 5._** _We expect the increase in the severity of financial constraints at the time of the crisis to be less for firms in countries with high initial levels of foreign bank participation._

It remains unclear, however, whether the current crisis, which started in developed countries, would deepen or lessen foreign banks‘ commitment to these countries for the reasons described above. Therefore, our regressions provide an implicit test of those conjectures.

# **III. Data**

This paper uses data from two enterprise surveys. The first is the Business Environment and Enterprise Performance Surveys (BEEPS), which were conducted in most countries in Eastern

> 11 For a summary of this literature see Cull and Martinez Peria (2010).

> 12 A simple regression shows that the share of banking sector assets held by foreign-owned banks in 1999 is associated with  cultural similarity between home and host country as reflected in a shared legal origin, the prospects for future growth at that time as reflected in low initial GDP per capita, and the quality of the contracting environment as reflected in indexes of institutional development (see World Bank. 2011). All of these features should tip the balance in favor of the support motive rather than the substitution motive for banks with high levels of foreign participation in banking in 1999.

10