rafmacalaba/datause-encoder-data huggingface dataset:
TABLE 16 _41_ a' AVERAGE RATE OF GROWTH OF TFP BY INDUSTRY D1=0 FOR 1976-80. 01=1 FOR 1981-85 DO=PR FOR PRIVATE FIRMS AND DO=PU FOR PUBLIC FIRMS

|OBS|IND|DO|D1|NTFP|
|---|---|---|---|---|
|1|311|PR|0|0.02288|
|2|313|PR|0|-0.02090|
|3|314|PR|0|0.04260|
|4|321|PR|0|0.00356|
|5|322|PR|0|0.02840|
|6|323|PR|0|-0.10820|
|7|324|PR|0|-0.08640|
|8|331|PR|0|-0.08392|
|9|332|PR|0|-0.06724|
|10|341|PR|0|-0.01630|
|11|342|PR|0|-0.04844|
|12|351|PR|0|-0.03058|
|13|352|PR|0|-0.03314|
|14<br>|354|PR|0|-0.03904|
|15|355|PR|0|-0.07030|
|16|356|PR|0|-0.09304|
|17<br>|361<br>|PR|0|-0.07780|
|18<br>|362<br>|PR|0<br>|-0.05114|
|19<br>|369<br>|PR|0<br>|-0.02756|
|20|371|PR|0|-0.02888|
|21|372|PR|0|0.00060|
|22<br>|381<br>|PR|0|-0.06276|
|23|382|PR|0|-0.04948|
|24|383|PR|0|-0.05200|
|25|384|PR|0|-0.05876|
|26|385|PR|0|-0.06666|
|27|311|PR|1|-0.00962|
|28|313|PR|1|-0.07838|
|29|314|PR|1|0.06996|
|30|321|PR|t|0.01660|
|31|322|PR|1|0.02760|
|32|323|PR|1|0.02320|
|33|324|PR|1|0.06286|
|34|331|PR|1|0.00112|
|35|332|PR|l|0.08280|
|36|341|PR|I|-0.02928|
|37|342|PR|1|-0.01424|
|38|351|PR|1|0.02062|
|39|352|PR|1|0.07750|
|40|354|PR|1|-0.00094|
|41|355|PR|I|OC3008|
|42<br>|356<br>|PR<br>|1<br>|0.03148<br>|
|43|361|PR|1|0.06754|
|44|362|PR|1|0.03882|
|45|369|PR|1|0.02274|
|46|371|PR|1|0.09780|
|47<br>|372|PR|1|0.04498|
|48|381|PR|1|0.05006|
|49|382|PR|1|0.06286|
|50|383|PR|1|005594|
|51|<br>384|<br>PR|<br>1|.<br>0.05384|
_HOTTER PLANET, HOTTER FACTORIES_ _21_ adapting to changes in temperature for example by changing working hours since they often have greater flexibility in the duration and timing of their active work hours. Compared to large firms, smaller firms face less stringent safety and working standard requirements, particularly for working conditions in hot climates. In contrast, large firms could face stringent requirements on working conditions, which implies that they require higher capital investments in climate adaptations including installing indoor climate controls, provision of air-conditioned transportation, and other facilities, all of which contribute to raising the costs of production and reducing productivity per worker. Although we observe a clearly distinct impact between small and large firms, the distinction is blurred with medium-sized firms, due to the mix of industries in which they are present.

Table 3—: Estimation of Log Sales per Worker: By Firm Size

||Small|Medium|Large|
|---|---|---|---|
||(_<_20)|(20-99)|(100+)|
|WBT Deviation_×_Zone<sup>_Q_2</sup><br>(23_._7<sup>0</sup>_C,_24_._9<sup>0</sup>_C_]|-0.032|-0.200<sup>_∗∗∗_</sup>|-0.557<sup>_∗∗∗_</sup>|
||(0.042)|(0.046)|(0.063)|
|WBT Deviation_×_Zone<sup>_Q_3</sup><br>(24_._9<sup>0</sup>_C,_25_._7<sup>0</sup>_C_]|-0.121|-0.090|-0.526<sup>_∗∗∗_</sup>|
||(0.074)|(0.083)|(0.118)|
|WBT Deviation_×_Zone<sup>_Q_4</sup><br>(25_._7<sup>0</sup>_C,max_)|-0.411<sup>_∗∗∗_</sup><br>(0.107)|0.175<br>(0.131)|-0.741<sup>_∗∗∗_</sup><br>(0.163)|
|Sector dummy|Yes|Yes|Yes|
|Country FE|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|
|Country x year FE|Yes|Yes|Yes|
|Population (25km radius)|Yes|Yes|Yes|
|Road infrastructure (25km radius)|Yes|Yes|Yes|
|Pollution (25km radius)|Yes|Yes|Yes|
|_Observations_|39,515|30,193|16,759|
|R<sup>2</sup>|0.386|0.387|0.392|

Standard errors are in parentheses. All models control for firm age, ownership, export status, firm size (employment), proportion of permanent workers, proportion of skilled workers, 55 dummy variables indicating a narrow industry classification, and dummies indicating World Bank regions. All WBT categories are included separately in the model, in addition to the interaction terms. The reference category for climate zone is the bottom quartile in the distribution of WBT which is less than or equal to 23.7<sup>0</sup> _C_ . The minimum WBT in our sample is 14.5<sup>0</sup> C. FE = fixed effect; km = kilometers; OLS = ordinary least squares; WBT = wet-bulb temperature. _∗∗∗ ∗∗ ∗ p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1
- Woo, Chungwon et al. (2014). “Impact of Green Innovation on Labor Productivity and its Determinants: an Analysis of the Korean Manufacturing Industry”. _Business Strategy and the Environment_ 23, 567–576.

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52
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 7. Benchmarking of Cameroon’s air transport indicators**

|Country|Cameroon|Central<br>African<br>Rep.|Gabon|Chad|Nigeria|Congo,<br>Rep. of|
|---|---|---|---|---|---|---|
|Total|975,865|44,503|769,912|197,682|13,116,015|913,478|
|Domestic seats (seats per year)|105,742|N/A|374,400|N/A|9,304,568|443,634|
|Seats for international travel within Africa (seats per year)|472,089|20,661|272,792|109,074|1,373,745|351,882|
|Seats for intercontinental travel (seats per year)|398,034|23,842|122,720|88,608|2,437,702|117,962|
|Seats available per capita|0.05|0.01|0.59|0.018|0.089|0.24|
|Herfindahl-Hirschmann Index—air transport market (%)|10|50|39|36|11|31|
|Percent of seat km in newer aircraft|92|100|98|100|71|73|
|Percent of seat km in medium or smaller aircraft|32|24|28|94|28|51|
|Percent of carriers passing IATA/IOSA Audit|0|0|0|0|28.6|0|
|FAA/IASA Audit Status (as of February 2011)|No audit|No audit|No audit|No audit|Passed|No audit|

_Source:_ Bofinger 2009. All data as of 2007 based on estimations and computations of scheduled advertised seats, as published by the Diio SRS Analyzer. This captures 98 percent of worldwide traffic, but a higher percentage of African traffic is not captured by the data.

_Note:_ FAA = U.S. Federal Aviation Administration; IASA = International Aviation Safety Assessment; IATA = International Air Transport Association; IOSA = IATA International Safety Audit.

— = Data not available.

After years of decline, Cameroon’s air traffic and connectivity are recovering from the air transport market collapse. Between 2001 and 2007 the number of total seats declined from 1,784,023 to 975,865. In 2009 the total number of seats grew to 1,259,276 seats, an expansion of 30 percent over 2007. In particular, the number of international seats grew by about 50 percent between 2007 and 2009, from 472,089 to 698,360 (figure 11a).

International connectivity is recovering. Between 2001 and 2007 international city pairs served dropped from 25 to 17. Preliminary numbers show that in 2009 city pairs were 20, showing a slightly recover in the number of international city pairs (figure 11b).

**Figure 11. Evolution of seats and city pairs in Cameroon**

<!-- Start of picture text --> a. Seats b. City pairs<br>2,000,000 60<br>1,800,000<br>50<br>1,600,000<br>1,400,000<br>40<br>1,200,000<br>1,000,000 30<br>800,000<br>600,000 20<br>400,000<br>10<br>200,000<br>- 0<br>2001 2004 2007 2009 (Est) 2001 2004 2007 2009 (Est)<br>Total International Intercontinental Domestic Total International Intercontinental Domestic<br>City Pairs<br>Number of seats<br><!-- End of picture text --> _Source_ : Bofinger 2009. Derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data _Note_ : As reported to international reservation systems.

21
# _Table 6 Regression Results for Alternative Specifications_

||(1)|(2)<sup>*1</sup>|(3)|(4)|
|---|---|---|---|---|
|Foreign dummy|.38***|.59***|.38***|-.029|
||(.035)|(.045)|(.038)|(.097)|
|State dummy|.28***|.45***|.28***|-.19***|
||(.023)|(.028)|(.022)|(.053)|
|Private dummy|.071***|.21***|.069***|-.093*|
||(.021)|(.026)|(.021)|(.049)|
|Foreign*education||||.047***<br>(.0097)|
|State*education||||.051***<br>(.0050)|
|Private*education||||.023***<br>(.0055)|
|Female|-.11***|-.17***|-.19***|-.12***|
||(.015)|(.017)|(.029)|(.014)|
|Married|.052***<br>|.039*<br>|.027<br>|.055***<br>|
||(.018)|(.021)|(.019)|(.017)|
|Minority|-.061**|-.079**|-.12***|-.057*|
||(.030)|(.034)|(.034)|(.030)|
|Education|.053***|.056***|.062***|.030***|
||(.0024)|(.0028)|(.0043)|(.0032)|
|Potential experience|.027***|.059***|.025***|.026***|
||(.0025)|(.0032)|(.0026)|(.0024)|
|Experience<sup>2</sup>|-.0005***|-.0012***|-.00047***|-.00044**|
||(.00006)|(.000072|(.000055)|(.000054|
|Urban|.090***|.10***|.18***|.076***|
||(.016)|(.019)|(.034)|(.016)|
|Selection correction|||.26***||
|term|||(.098)||
|Industry dummies|X|X|X|X|
|Regional dummies|X|X|X|X|
|Number of obs.|6623|6623|6623|6623|
|R<sup>2</sup>|.35|.41|.35|.36|

Notes: The dependent variable is the log of hourly wage except in Model (2). *1 The dependent variable is the log of weekly wage. The informal wage sector is the omitted ownership category. *, **, *** indicate that the coefficients are significant at the 10, 5, and 1 percent level, respectively. The robust standard errors are between parentheses.

33
_Services PTAs_ 2007; Yotov et al., 2016).<sup>17</sup> We use two types of country-pair fixed effects (FEs) in this paper: _symmetric_ pair FEs when investigating the effect of policies with no distinction on the direction of trade (or VA) flows; _asymmetric_ pair FEs when separating the effect depending on the direction of the flow (e.g. high-income to low-income country and _vice-versa_ ).

In addition, the country-year fixed effects, _μ_ **it** and _δ_ **jt** , control for any time-varying country characteristics that could affect its services trade, or sourcing of VA, such as incomes, business cycles, national policies, etc. These fixed effects also account for the multilateral resistance terms of the gravity equation, which capture the fact that changes in bilateral trade costs due to entering a PTA are not independent from changes in trade costs with third countries (Anderson and Van Wincoop, 2003).

Services PTAs are not typically stand-alone agreements; rather, services chapters are negotiated as part of agreements covering a range of trade areas. In this paper, we only use information from the ’Services’ chapter of the WB-DTA. It is possible that services provisions are correlated with provisions in other subject areas, which are unobserved here but which may also affect services trade and value added. This could give rise to an omitted variable bias. The WB-DTA 2.0 database encompasses a total of 18 subject areas, and at the level of individual agreements we are able to control for the potential impact of provisions in other subject areas outside of service chapters by including a ‘nonservices depth variable’ that contains the number of provisions in a given PTA outside services. Inclusion of this control variable does not change any of our main results, and its estimated coefficient is typically near zero. We therefore omit it in most tables but

> 17Alternative methods based control functions and instrumental variables have been proposed to account for endogenous trade policy variables (see e.g. Egger et al. (2011), Egger and Shingal (2020), Osnago et al. (2020)). However, in our context we considered the option of a PPML estimator with country-pair fixed effect to be the preferred approach.

22
- 18 -

# **VI. CONCLUSION**

Africa has great potential for agriculture from the agro-ecological point of view. However, the potential has not been fully explored yet. One of the important constraints is transport connectivity. In the case of Ethiopia, access to ports as well as markets is crucial. Many farmers do not have access to a market: The latest Rural Access Index is estimated at 21.6 percent, leaving about 63 million rural people unconnected to the road network (World Bank, 2016). Port connectivity is also crucial because Ethiopia is a landlocked country. Significant transport and trade costs are incurred to the economy.

Spatial data and analyses are becoming increasingly important to identify economic opportunities and reveal possible constraints in a geographic manner. There are only a few studies that analyze the relationship between agricultural production and transport infrastructure with spatial data. Taking advantage of a historical event that rail operations were ceased in Ethiopia in the late 2000s, the paper applied the spatial autocorrelation panel regression to estimate an unbiased impact of port connectivity, which is mainly characterized by rail transportation in the case of Ethiopia, on agricultural production. Methodologically, the paper’s approach has the advantage of addressing the endogeneity of infrastructure placement as well as autocorrelation in spatial data.

It is found that agricultural production increases with port connectivity or decreases with transport costs to the port: The elasticity is estimated at -0.094 to -0.143, depending on model specification. The results are largely consistent with other available evidence. Among conventional agricultural production inputs, labor and land are found to have relatively high elasticities, which represent the fact that the current crop production is based on extensification of inputs, rather than intensification. Fertilizer and irrigation are productive, but their impacts remain limited in Ethiopia.
We have not at this stage covered two important areas of interest to developing countries. First, cross-border trade in business processing services (associated with the ―outsourcing‖ phenomenon) is not covered because it is either largely free from explicit restrictions or the fragmentation of services—facilitated by advances in information technology—has made it possible to trade unregulated parts of services.<sup>8</sup> Second, the Database does not cover policies affecting international movement of less-skilled individuals to deliver, say, construction services because (immigration) policies affecting such movement are notoriously opaque. We hope to address these gaps in future data collection exercises. We recognize that some of our conclusions, such as the overall restrictiveness of services trade policy at the country level and the relative restrictiveness of services sectors, are necessarily influenced by the choice of sectors. We also recognize that the growing scope for digital delivery, for example in certain intermediate professional services, allows service providers to circumvent traditional barriers to trade, thus trade in these areas may be _de facto_ more liberal than our data suggest. Most of our descriptive and econometric findings, however, rely on crosscountry patterns of trade restrictions for given sectors, and therefore are not affected.

Within each sector or subsector, the database covers the most relevant modes of supplying the respective service: establishing commercial presence or FDI (mode 3 in WTO parlance) in every subsector; cross-border supply (mode 1) of financial, transportation and some professional services; and the presence of service supplying individuals (mode 4) in professional services. Table 1 summarizes the combinations of subsectors and modes for which information is available.

> 7  Regarding policies governing cross-border (mode 1) trade in international air passenger transportation services, we draw on the WTO‘s QUASAR database since it represents the most comprehensive source currently available on bilateral air services agreements, covering over 2000 agreements.

> 8  As a result, the market for ―intermediate‖ services is increasingly contestable even though entry into the ―final‖ stage, on which this Database mainly focuses, is still affected by regulatory requirements.  For instance, in legal services, research and documentation can be outsourced whereas representation in courts must be performed by a local firm.  In accounting, bookkeeping can be outsourced whereas conformity with local requirements and ultimate responsibility rests with local professionals/firms.

7
may lead to a potential selection problem because the data in the Industrial Statistical Yearbook is gradually becoming more comprehensive over time. Another potential selection effect would exist if entire sectors disappear during the crisis period. The latter is however not the case in our sample. Since we are interested in the difference in growth between the pre-crisis and crisis period, we need to use a balanced panel. The final data set contains a total of 448 sector-country observations from 19 cr.sis cou,-,ies.

The number of firms within the sectors varies widely over time. In particular we see a large increase in the number of firms within certain sectors at certain points in time. This maybe the result of a re-classification or the inclusion of firms that were previously excluded from the statistics on value added. In both cases, changes in value added are not related to firm performance, and such observations need therefore be excluded from the analysis. We have deleted all sectoral observations **if LUe rLU.Ver** of LIJLS **lWULL** UI, sectoLraULU **OlVi.LL JL-L1 ULWar.** **_LV0U0 VI_ -/0 ,/0 (ubLL'U UL oLralIVeUj** between the pre-crisis and crisis periods. This criterion deletes around 5% of observations across the different sub-periods. We also have deleted observations if the real growth in value added exceeds 100%, which excludes only a small number of cases.

Table 3 presents the summary statistics of some variables that indicate changes in real sector and financial sector activity for both the pre-crisis and during crisis periods. When comparing the summary statistics of **tue** pre-L[I1b1 **d Lrbis cU** periods, we **irnu** Uth folluwigU C w lbi4almuLLisics. During crises periods, on average countries experience lower real GDP growth, lower real growth in sectoral value added (both for sectors that are highly dependent on external finance and sectors that are not), lower real growth of M2, and lower growth of (private sector) credit. Financial crises thus 12
|Table 3.1: Age Distribution for Argentine Youth,2005|
|---|
|Age<br>Percent<br>Cumulative|
|15 years<br>11.84<br>11.84|
|16 years<br>13.25<br>25.1|
|17 years<br>12.47<br>37.57|
|18 years<br>9.80<br>47.37|
|19 years<br>11.37<br>58.75|
|20 years<br>9.33<br>68.08|
|21 years<br>8.39<br>76.47|
|22 years<br>8.24<br>84.71|
|23 years<br>7.84<br>92.55|
|24years<br>7.45<br>100|

_Source_ : Own calculations on ESCVJA (four urban areas) The number of youth in the entire country is currently peaking, and is projected to continue at that level until 2030 (Lam 2006), and this creates economic opportunities if optimally exploited. The current youth bulge creates economic opportunities for Argentina resulting from a potentially favorable ratio of working age population to the dependent population, once the surge has topped and youth move into adulthood and hopefully experience a successful transition into the labor market. As an example to follow, the rise in working population to non-working population in East Asia, following the youth bulge, played an important role in the economic miracle in the region (ibid.). In contrast, lack of opportunities for youth may have unbearable consequences for the country.

With youth being a large proportion of the population, it is immensely important for the country to ensure a smooth transition from childhood to adulthood and from school to work. This paper will not address these issues in particular, but consider what factors are important in determining whether or not youth will experience good or bad outcomes.

# **3.2 Family and Health**

The family is a strong protective factor but can also be an important risk factor. Support and security provided by parents are extremely important in a young person’s development. In contrast, lack of support from parents, or too many responsibilities, for example, because of marriage and having children too early, jeopardizes the well-being of youth.

A large proportion of youth live with only one or no parent in the household. While it should not be considered a risk factor for youths over a certain age to live without their parents, younger cohorts still need support and guidance from an adult. Of the 15 year old youth, just 3.4 and 4.8 percent of female and male youth, respectively, live without any of their parents (Table 3.2). However, only 23.9 and 36.5 percent of females and males, respectively, in this age group live with both their father and mother in the household. Considering slightly older age groups (16─17 years), 7─13 percent live without a parent in the household. In contrast, nearly half of the 24 year old females and a 6