rafmacalaba/datause-encoder-data huggingface dataset:
ECOWAS‘S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Table 2.10  All flights from one week in November 2007 for ECOWAS**

||Destin<br>Benin|ation<br>Burkina Faso|Cape Verde|**_Côte D'Ivoire_**|Gambia|**_Ghana_**|Guinea|Guinea-Bissau<br>Liberia|Mali|Niger|**_Nigeria_**|**_Senegal_**|Sierra Leone|Togo|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Benin||5||_10_||_5_|||1|1|**_4_**|_3_||4|
|Burkina Faso|4|||_8_||_3_|||9|1||_6_||4|
|Cape Verde||||||_1_||||||_1_|||
|**_Côte D'Ivoire_**|_10_|_11_||||_22_|_9_|_3_|_8_|_4_|**_8_**|_14_|_1_|_14_|
|Gambia|||||||4|4|||**_2_**|_12_|4||
|**_Ghana_**|_1_|_3_|_1_|_23_||||_13_|||**_41_**||_9_|_8_|
|Guinea||||_8_|4|||2|1||**_2_**|_7_|||
|Guinea-Bissau|||1|||||||||_9_|||
|Liberia||||_3_|5|_9_|2||||**_3_**||3||
|Mali|1|7||_4_||||||3||_18_|||
|Niger|1|1||_4_|||||4|||_2_|||
|**_Nigeria_**|**_3_**|||**_8_**|**_2_**|**_46_**|**_2_**|**_2_**||||**_5_**|**_4_**||
|**_Senegal_**|_3_|_6_|_7_|_14_|_11_||_7_|_9_<br>_1_|_16_|_2_|**_5_**||_4_|_2_|
|in<br>Sierra Leone||||_1_|5|_7_||6|||**_3_**|_4_|||
|Orig<br>Togo|5|4||_15_||_4_|||||**_1_**|_2_|||

Source: Derived from Bofinger 2009.

ECOWAS‘s air transport is considered to be among the most liberalized in Africa. In both WAEMU and BAG, the Yamoussoukro decision has reached a high level of implementation—and, in some cases, full implementation. ECOWAS has achieved the most progress in instituting free pricing, lifting capacity and frequency restraints, and allowing airlines to fly so-called fifth-freedom routes (table 2.12)—far ahead of eastern and southern Africa. A concerted policy effort lies behind that achievement, especially in BAG. By the same token, these countries have suffered some of the steepest declines in capacity and traffic. The source of the declines, or near collapse, may be the overcapacity brought about by Air Afrique. However, liberalization is now helping established African carriers, such as Ethiopian Airlines, provide essential connectivity.

The structure of the regional air transport market has changed significantly since 2001, with an overall reduction in market power (table 2.13). As of 2007, the regional air transport market comprised three major carriers—Air Senegal, Virgin Nigeria, and Bellview Airlines—that together account for 44 percent of the regional market. This is a complete turnaround from 2001, when the three major carriers were Air Afrique, Ghana Airways, and SN Brussels, which together accounted for 54 percent of the market. Overall, there has been a slight reduction in market concentration, with the Herfindahl index falling from around 14 to just under 10 percent.

38
to be 16 percent higher, points to the potential for improving management capabilities among other firms and sectors. By comparison, in Eastern Croatia, exporters are not significantly better managed than the rest of the firms.

The paper beyond this point is organized as follows: Section 2 compares firms in Eastern Croatia with Zagreb and other regions of Croatia on key parameters surveyed, including firm capabilities other than management (e.g. skills, innovation, technology adoption), outcomes on firm performance (e.g. labor productivity), and the constraints that firms face across different regions of Croatia. Section 3 introduces the methodology for computing management scores and compares management capabilities in Eastern Croatia with other regions of the country, and across countries. Section 4 presents some descriptive results on firm characteristics that are associated with management capabilities in Eastern Croatia relative to other regions in Croatia. Section 5 presents a simple regression framework for assessing the association between management practices and firm performance. Section 6 explores the drivers of good management practices. Section 7 concludes with policy recommendations.

# **Section 2: Firm Capabilities Survey**

In this section, we utilize the firm level survey to understand the differences between firms across regions in Croatia.

# **_2.1 Firm Performance, Technology Adoption and Obstacles – Comparing firms across regions_**

We compare firms based on three broad categories across Zagreb, Eastern Croatia and other regions in Croatia, excluding Zagreb and Eastern Croatia. The three categories are classified as follows: (1) firm performance – we use sales/employee (labor productivity) and gross value added (GVA) per employee as the two indicators; (2) innovation and technology adoption. For innovation, we assess whether a firm has registered itself for a new trademark, patent, utility model or industrial design and for the latter, we review the use of technology for quality and control management and introduction of new products and processes; and (3) the different types of obstacles the firms face during their operations.

_Figure 3: Firm performance in Slavonia is lower than that in Zagreb and other regions_ <!-- Start of picture text --> Productivity<br>Croatian Firms, 2017<br>Labour Productivity GVA per Employee<br>Other Regions Slavonia Zagreb Other Regions Slavonia Zagreb<br>5,000<br>150<br>4,000<br>3,000 100<br>2,000 Real GVA per Employee<br>50<br>Real Labour Productivity (Sales/Employee)<br>1,000<br>0 0<br><!-- End of picture text --> Comparing real labor productivity (sales per employee) and real GVA per employee of firms across Zagreb, Eastern Croatia and other regions of Croatia, we note that both the indicators of performance are highest in Zagreb, and lowest in Slavonia, especially in terms of GVA per employee (Figure 3). Labor productivity in Eastern Croatia is half that of Zagreb and other regions of Croatia; while, in the case of gross value-added it is one-third of that observed in Zagreb and one-half relative to other regions of Croatia.

8
Deming, D. J. (2017). The growing importance of social skills in the labor market. _The Quarterly Journal of Economics_ , 132(4):1593–1640.

Dhar, D., Jain, T., and Jayachandran, S. (2022). Reshaping adolescents’ gender attitudes: Evidence from a school-based experiment in india. _American economic review_ , 112(3):899– 927.

Di Maio, M. and Fiala, N. (2020). Be Wary of Those Who Ask: A Randomized Experiment on the Size and Determinants of the Enumerator Effect. _World Bank Economic Review_ , 34(3):654–669.

Dinarte, L. and Egana-delSol, P. (2019). Preventing violence in the most violent contexts: Behavioral and neurophysiological evidence. _World Bank Policy Research working paper_ , (8862).

Dinarte Diaz, L. I., Egana-delSol, P., and Martinez A, C. (2022). Socioemotional skills development in highly violent contexts.

Duckworth, A. and Quinn, P. (2009). Development and validation of the short grit scale (grit–s). _Journal of personality assessment_ , 91(2):166–174.

Duckworth, A. L. and Kern, M. L. (2011). A meta-analysis of the convergent validity of self-control measures. _Journal of Research in Personality_ , 45(3):259–268.

Duckworth, A. L. and Yeager, D. S. (2015). Measurement matters: Assessing personal qualities other than cognitive ability for educational purposes. _Educational Researcher_ , 44(4):237– 251.

Eagly, A. H. and Wood, W. (2012). Social role theory. _Handbook of theories of social psychology_ , 2:458–476.

Edmonds, E., Feigenberg, B., and Leight, J. (2021). Advancing the agency of adolescent girls. _Review of Economics and Statistics_ , pages 1–46.

Exley, C. L. and Kessler, J. B. (2022). The gender gap in self-promotion. _The Quarterly Journal of Economics_ , 137(3):1345–1381.

Feingold, A. (1994). Gender differences in personality: a meta-analysis. _Psychological Bulletin_ , 116(3):429.

Frese, M., Fay, D., Hilburger, T., Leng, K., and Tag, A. (1997). The concept of personal initiative: Operationalization, reliability and validity in two german samples. _Journal of occupational and organizational psychology_ , 70(2):139–161.

Ganimian, A., Barrera-Osorio, F., Biehl, M. L., and Cortelezzi, M. A.<sup> ́</sup> (2020). Hard cash and soft skills: Experimental evidence on combining scholarships and mentoring in argentina. _Journal of Research on Educational Effectiveness_ , 13(2):380–400.

Hart, C. M., Ritchie, T. D., Hepper, E. G., and Gebauer, J. E. (2015). The balanced inventory of desirable responding short form (BIDR-16). _Sage Open_ , 5(4):2158244015621113.

25
that women respond to weaker labor markets by spending more time in school, while young men either continue seeking work or become discouraged and drop out of the labor force.

# **Differences among young urban and rural residents**

For both urban and rural youth, employment and labor force participation rates are interchangeable labor market indicators. For both, home status and joblessness are also very highly correlated. However, there are notable differences between the geographically differentiated youth.

- Unemployment and employment are more closely tied states in rural than in urban markets.  Unemployment is also more closely tied to higher enrollment rates and lower labor force participation rates in rural markets.  In contrast, urban youth unemployment rates appear relatively uncorrelated with every other measure of labor market success save joblessness for which unemployment is an element. This suggests that variation in youth urban unemployment rates across developing countries is due to idiosyncratic factors that do not affect other measures of urban youth time use.

- Unemployment rates are more highly positively correlated with enrollment rates and employment rates are more highly negatively correlated with enrollment rates in rural markets.  Urban unemployment rates are virtually uncorrelated with enrollment rates.  The correlations suggest that employment is a more important opportunity cost of schooling for rural youth.

- Youth enrollment rates are more highly negatively correlated with home status and jobless rates in urban than in rural markets.  This surprising finding suggests that it is in urban markets that opportunity costs related to the value of home time or of other non market time use are most important in limiting schooling.

The general conclusion is that no single labor market indicator will adequately summarize the state of the youth labor market in a country, but that not all six measures are needed. Because the employment rate and labor force participation rates measure similar things, only one is needed. The employment rate is easier to measure in that it does not require assessing whether the jobless are seeking work. The enrollment rate has significant independent information and is also relatively easily measured. Of the remaining indicators, joblessness is the easiest to measure and it also incorporates much of the information in the home status rate. One might wish to use the unemployment rate rather than the jobless rate because the unemployment rate is most commonly used in international comparisons of labor market strength. Its disadvantage is that it is based on an assessment of active job search among the jobless that requires household surveys and so it can be costly to obtain for budget constrained governments.

8
Moreover, the methodology allows us to be explicit about the precision – or imprecision – of our estimates of governance in each country. This imprecision is not a consequence of our reliance on subjective or perceptions data on governance – rather imprecision is an issue that should be squarely addressed in all efforts to measure the quality of governance, recognizing the inherent complexity and imprecision associated with such a task.

The aggregation procedure first rescales the individual indicators from each underlying source in order to make them comparable across data sources. It then constructs a weighted average of each of these rescaled data sources to arrive at an aggregate indicator of governance. The weights assigned to each data source are in turn based on the estimates of the precision of each source that are produced by the unobserved components model. In brief, the identifying assumption in the unobserved components model is that any observed correlation between two measures of corruption, for example, is due to their common, but unobserved, signal of corruption. From this assumption it follows that data sources that are more correlated with each other provide more reliable information about corruption, and so receive greater weight. In past work, we have discussed in detail the merits of this approach--see particularly Kaufmann, Kraay and Mastruzzi (2006a, Section 3). We have also documented that, since the underlying data sources on average are quite correlated with each other, the choice of weights used to construct the aggregate indicator does not substantially affect the estimates of governance that we report (Kaufmann, Kraay and Mastruzzi 2006a, 2007a).<sup>4</sup> Here we briefly report some summary information on the weights for the 2008 indicators. Table 3 reports the weights assigned to each data source in each of the six governance indicators in 2008.<sup>5</sup> This table reports the weights that would be used in the case of a hypothetical country appearing in all of the available underlying data sources

> 4 It is also worth noting that a far more consequential weighting decision is whether to include a data source or not.  In the WGI we have for the most part opted to include as many data sources as possible, then allowing the data and aggregation procedure to select the weights.  In contrast, household-survey based data sources receive zero weight in the Transparency International Corruption Perceptions Index (TI-CPI) because the constructors of that measure have chosen to exclude all such data sources.

> 5 A full version of this table reporting the weights for all years in Excel format is available for downloading on the Resources tab of  www.govindicators.org

13
<mark>De Soto, Hernando. 2000.</mark> _<mark>The Mystery of Capital: Why Capitalism Triumphs In the West and Fails Everywhere Else</mark>_ <mark>. 288pp. New York, NY: Basic Books.</mark> Do, Quy-Toan, Sriya Iyer and Shareen Joshi. 2013. The economics of consanguineous marriages. _The Review of Economics and Statistics_ , 95(3), 904-918.

Dorsten, Linda Eberst, Lawrence Hotchkiss and Terri M. King. 1999. The effect of inbreeding on early childhood mortality: twelve generations of an Amish settlement. _Demography_ , 36(2), 263-271.

Farah, Abdul-Aziz and Samuel H Preston. 1982. Child mortality differentials in Sudan. _Population and Development Review_ , 8(2), 365-383.

Finlay, Keith and Leandro Magnusson. 2009. “Implementing weak-instrument robust tests for a general class of instrumental-variables models.” _Stata Journal_ , 9(3): 398-421.

Givens, Benjamin P. and Charles Hirschman. 1994. Modernization and consanguineous marriage in Iran. _Journal of Marriage and Family,_ 56, 820-834.

Goody, Jack. 1973. Bride wealth and Dowry in Africa and Eurasia” in Jack Goody and Stanley Tambiah (eds.), Bridewealth and Dowry, Cambridge: Cambridge University Press.

Griffin, Keith, Azizur R. Khan, and Amy Ickowitz. 2000. _Poverty and Distribution of Land._ United Nations Development Programme.

Hamamy, Hanan. 2012. Consanguineous marriages. Preconception consultation in primary health care settings. _Journal of Community Genetics_ , 3(3): 185-192.

Hambleton, Ronald K., H. Swaminathan and H. Jane Rogers. 1991. _Fundamentals of Item Response Theory_ . 184pp, London: Sage Publications.

Hasnain, S. and S. K. Hashmi. 2009. Consanguinity among the risk factors for underweight in children under five: a study from rural Sindh. _Journal of Ayub Medical College_ , 21(3), 111-116.

Holy, Ladislav. 1989. _Kinship, honour and solidarity: Cousin marriage in the Middle East._ 143pp. Manchester: Manchester University Press.

Hussain, Rafat. 1999. Community perceptions of reasons for preference for consanguineous marriages in Pakistan. _Journal of Biosocial Science_ , 31, 449-461.

Hussain, Rafat. and Alan H. Bittles. 1998. The prevalence and demographic characteristics of consanguineous marriages in Pakistan. _Journal of Biosocial Science_ , 30(2), 261-275.

Jacoby, H. G., and G. Mansuri. 2010. “Watta Satta: Exchange Marriage and Women’s Welfare in Rural Pakistan,” _American Economic Review_ , 100(4), 1804–1825.

<mark>Kaiser, Jocelyn. 2016. When DNA and culture clash.</mark> _<mark>Science</mark>_ <mark>. 354, 1217-1221.</mark> 18
Since house rental cost is likely to be the largest expense when children decide to leave parental home, we would expect coresidency rates to be higher in locations where rental rates are higher (Nicoletti and Francesconi (2006)). We take advantage of the recently available built up density data as a source of variation in house rent. Note that built-up density is a measure of supply of housing in a location, with a lower house rent where built-up density (supply) is higher. This implies that we expect a negative relation between built up density and coresideny rates on a priori grounds. The source of built-up data is the Global Human Settlement Layer (GHSL) (Pesaresia et al. (2015)). The built-up data are at 300 meters by 300 meters grids. We super-impose the digital maps from the censuses on the pixel-level data to estimate the total built-up area at the district level.

We use 1975 built up density in a district interacted with time trend in the estimating equation where the dependent variable is a dummy indicating whether a child is coresident with his/her father (with location and year xed eects along with the other control variables used in the DiD design), and generate the estimated probability weights.<sup>2</sup> Built up density interacted with the time trend has substantial explanatory power in the coresidency equation; the estimated coecient is signicant at the 5 percent level, and has a negative sign consistent with the a priori expectations. To check if the IPW correction is eective, we estimate the linear and quadratic mobility models in Table B.6 discussed above using IPW correction for census data. The IPW corrected census estimates are reported in columns 3 (linear CEF) and column 6 (quadratic CEF) of Table B.6 in this appendix. The evidence suggests that the IPW correction makes the census estimates closer to the corresponding IFLS estimates.

We then apply the IPW correction to our estimates of the eects of Inpres schools reported in Table 2 and Table 3. The IPW corrected estimates for the relevant CEFs are reported in the appendix Table B.7.<sup>3</sup> A comparison with our main estimates in Table 2 and Table 3 shows that the inverse probability weighted estimates are not substantially dierent from the unweighted estimates; the null hypothesis of equality cannot be rejected for any of the causal estimates. The main conclusions of the paper thus remain intact after correction of possible 2The location xed eects mop up any direct eect of built up density on children's education through agglomeration channel. The year xed eects account for macroeconomic and international shocks common to all districts.

> 3Recall that the relevant CEF is linear for years of schooling in the case of daughters, but concave in all other cases.

61
- 40 -

See Adayemo, 1985; Aluko et.al., 1972 and 1973; Child, 1977; Liedholm and Chuta, 1976; Olufokunbi, 1981; Schadler, 1968 and Thomi and Yankson, 1985. Their data are reproduced in Haggblade, Hazell and Brown, 1987.

1/ See Haggblade, Hazell and Brown's (1987) analysis of data from Zambia, (Milimo and Fisseha, 1986) and Sierra Leone (Liedholm and Chuta, 1976).

Kilby (1987) has also begun looking at time series data on nonfarm employment in Kenya. However, his analysis is not readily comparable with the others cited here, because his data track only "informal sector" firms, essentially those employing between one and nine workers. While important on employment and equity grounds, trends in informal nonfarm activity may not be representative of the entire rural nonfarm economy given that the "informal sector" accounts for only about 20 percent of nonfarm activity (Ndua and Ng'ethe, 1984).

2/ Vanvali's data are reproduced in Haggblade, Hazell and Brown (1987).

IQ/ Table 1 indicates that between 7 and 30 percent of the African rural labor force engages in nonfarm activity as a secondary source of employment, with agriculture presumably their primary occupation. Farm management data indicate this secondary employment may be even higher. The data cited earlier (Section III.A) indicate that 15 to 65 percent of farmers engage in nonfarm activities at certain seasons of the year. Given that farm employment typically accounts for 85 percent of rural employment, this leads to an estimated 13 (15 x .85) to 42 percent (65 x .85) of the rural labor force with secondary nonfarm employment. We must add to that the flip side of the coin, the primarily nonfarm workers who work seasonally in agriculture, to arrive at the total share of the rural labor force moving seasonally between farm and nonfarm activities. Given that as much as 75 percent of rural nonfarm workers may participate in agriculture (Mbithi and Chege, 1973), this results in an additional 7.5 to 15 percent of the rural laborforce (.75 x 10 to 20 percent primarily engaged in nonfarm activity). Thus the total flow lies between about 15 (7. + 7.5) and 57 percent (42 + 15). Within this interval, we believe that 20 to 40 percent represents the most likely norm.

Considerable evidence indicates that 60 percent of rural consumption involves cash purchases, while the remaining 40 percent derives from own consumption of home-produced food (see Section V.A). Combining this with rural consumption data from Sierra Leone (King and Byerlee, 1977) and Nigeria (Hazell and Roell, 1983) results in the following stylized disaggregation of rural consumption: 40 percent non-marketed, home-produced food; 30 percent purchases of locally produced food; 5 percent imported foods; and 25 percent nonfood goods and services. Given these parameters, the share of locally produced food in total rural cash expenditure stands at 50 percent [30/,30 + 5 + 25)]. Specific computation for Sierra Leone results in a 40 percent estimate. while use of the Gusau, Nigeria data leads to an estimate of 58