rafmacalaba/datause-encoder-data huggingface dataset:
||**IMPO**|**RT-RELATED**<br>**BARRERS**||
|---|---|---|---|
|**BaTris**|**Possible**Corrective Measures|**Progress with**Current**Measures**|Recommended Actions at Regional Levdl|
|Ad Valorem<br>Transit Charges|- Rationalization of licensing system (issue of<br>new licenses, requalification of new<br>beneficiaries).<br>- Setting of rates for transit operations (HAD,<br>_honoraires d'agries en douane)_according to<br>actual cost of services rendered instead of on an<br>ad valorem basis.|Tunisia:<br>- Formulation of regulations to govern<br>forwarding agents.<br>- Intensive computerization of customs<br>clearance formalities.|IM - Standardization of intra-Maghreb<br>tariffs.<br>IM - Computerization of customs clearance<br>formalities.|
|**Ad Valoran**<br>**PortCharge**|- Introduction of competitive tariff structure.<br>- Modification of the base for these charges,<br>using the metric ton or m<sup>3 </sup>instead of the value<br>of the merchandise.|Under way in Tunisia.|EM - Essential harmonization throughout<br>the Maghreb.|

TM = Intra-Maghreb EM = Europe/Maghreb
|Table 4: Pover<br>(Food sh|ty Rates in<br>are of pove<br>|February<br>rty basket,<br>|1996 and<br>Susenas<br>|Changes<sup>1</sup><br>unit<sup>prices</sup><br>|<sup>996-99</sup><br><sup>)</sup><br>i|h|
|---|---|---|---|---|---|---|
|Province|Poverty<br>Feb|Incidence<br>ruary 199|(%)<br>6|Percentag<br>Feb 199<br>|e pont c<br>6 - Feb<br>|ange<br>1999<br>|
||Urban|Rural|Total|Urban<br>|Rural|Total<br>|
|Jakarta|0.78|-|0.78|2.04|-|2.04|
|North Sulawesi|6.83|23.69|19.22|4.87|3.14|3.25|
|Riau|2.53|7.20|5.59|6.00|2.42|3.63<br>|
|Papua|6.94|64.97|50.04|-0.87|7.22|4.85<br>|
|Bali|4.98|10.63|8.67|5.69|4.98|4.95<br>|
|West Nusa Tenggara|20.60|39.78|36.16|9.57|4.93<br>|5.62<br>|
|Aceh<br>.|3.25|8.32|7.27|2.18|7.09|5.63<br>|
|West Sumatera|2.40|4.01|3.60|6.39|5.73<br>|5.87<br>|
|North Sumatera|3.68|11.51|8.27|7.13|7.40|7.00<br>|
|Central Kalimantan|2.27|4.39|3.91|2.74|9.03|7.24<br>|
|South Sulawesi|5.84|18.74|15.07|11.57|6.20|7.56<br>|
|West Kalimantan|5.72|25.65|21.32|0.45|12.39|9.44|
|East Nusa Tenggara|21.70|56.48|51.59|6.97|9.63|9.59|
|Bengkulu|4.62|12.22|10.25|5.79|12.33|10.19|
|Yogyakarta|13.10|20.11|16.64|9.02|16.67|10.31|
|Central Java|12.28|25.18|21.03|11.44|12.58|11.75|
|South Kalimantan|4.95|9.99|8.46|3.04|16.39|12.18|
|Southeast Sulawesi|11.61|27.29|23.73|2.13|17.15|12.87|
|Central Sulawesi|5.74|16.97|14.49|10.98|15.72|14.03|
|Jambi|7.32|8.27|8.01|8.09|16.98|14.18|
|East Java|9.40|23.57|18.99|10.10|17.30|14.32|
|Maluku|10.47|41.51|33.81|8.17|18.39|14.59|
|West Java|8.41|14.59|11.93|<br>12.41|17.28|14.67|
|South Sumatera|2.00|11.70|8.75|12.47|16.23|15.05|
|East Kalimantan|0.75|12.36|6.52|7.99|22.71|15.15|
|Lampung|7.50|19.01|17.18|<br>12.40|21.56|19.62|
|Indonesia|7.22|20.54|15.74|<br>9.12|13.55|11.39|

Table 4 shows that all provinces experienced an increase<sup>in poverty incidence</sup> between February 1996 and February 1999, but the variation<sup>of these increases is</sup> very large. The highest increase in poverty incidence<sup>occurred in Lampung with</sup> almost 20 percentage points increase. This is almost<sup>double of national increase,</sup> 18
the analysis public administration and defense, compulsory social security, education, health, social work, sewage and refuse disposal, sanitation, membership organization services, recreational, cultural, or sporting services.

We use two data sources to cover the most recent period in the transition 19972004: (1) the _WIIW Handbook of Statistics 2005: Central, East, and Southeast Europe_ from the Vienna Institute which covers services sub-sectors at a more aggregate level than NACE 2-digit for Bulgaria, Croatia, the Czech Republic, Hungary, Macedonia, Poland, the Slovak Republic, Slovenia, Romania, Russia, Serbia and Montenegro, and Ukraine; and (2) the _KLEMS database 2007 release_ from the Groningen Growth and Development Center which covers services sub-sectors at the NACE 2-digit level for the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, the Slovak Republic, and Slovenia (see Timmer et al., 2007). We designate the countries covered by the WIIW database as ‘the group of EU-5 countries, South Eastern European countries, Russia and Ukraine’ and those covered by the KLEMS database as ‘EU-8 countries’. When both databases are combined, we use the term ‘ECA countries’ or ‘ECA region’. To obtain a broad picture of recent trends in the services sector in the ECA region, it is important to cover, in addition to EU-5 countries, both South Eastern European countries and Ukraine as well as Baltic countries. Thus, we will rely on both databases in the analysis that follows (i) jointly when analyzing the importance of services in the economy and (ii) separately when analyzing labor productivity. From the WIIW database, we use for each country and sub-sector data on gross value added and employment.<sup>4</sup> From the KLEMS database, we use for each country and sub-sector data on gross value-added, employment, output price deflators, and purchasing power parity (PPP) conversion rates.

Figure 1 shows the share of services in total value added and employment for each country averaged in 1997-2000 and in 2001-2004, combining the WIIW and the KLEMS databases. In the ECA region, services represent on average 44.6% of total value added in 1997-2000 but that share increases substantially to 47.1% in 2001-2004. Services represent on average 31.4% of total employment in 1997-2000, increasing to 32% in 2001-2004. Interestingly, the average shares for ECA countries are not too distant from the corresponding averages in EU-15 countries also shown in Figure 1. This finding

> 4 Appendix Table 2 defines all variables used in the analysis showing country and time coverage for each.

4
<!-- Start of picture text --> 1 2 3 4 5 6 7 8 9<br>Moderate risk |Marginal risk Special. Substandard Very high risk | defauitinImminent<br>Attention defaut<br>soc2 soc 3 SOc 8<br>SOC 9<br>| IPPs ar soc5 SOC 10<br>pFi2_ SOC6<br>soc 4<br>| SSF | DFia__| SOC7 SSF 5<br><!-- End of picture text -->
_Table 1: Progress in market integration of African RECs_

|**REC**|**FTA**|**CU**|**CM**|**EMU**|**SU**|**FM protocol**|
|---|---|---|---|---|---|---|
|ECOWAS|||**×**|**×**|**×**|All 15|
|EAC||||**×**|**×**|3 out of 5|
|COMESA|||**×**|**×**|**×**|Only Burundi|
|ECCAS||**×**|**×**|**×**|**×**|4 out of 11|
|SADC||**×**|**×**|**×**|**×**|7 out of 15|
|AMU|**×**|**×**|**×**|**×**|**×**|3 out of 5|
|IGAD|**×**|**×**|**×**|**×**|**×**|No protocol|
|CENSAD|**×**|**×**|**×**|**×**|**×**|Unclear|

_Source:_ Authors’ elaboration based on UNECA, 2016.

Note:  - Yes, **×** - No, FTA = Free Trade Area, CU = Custom Union, CM = Common Market, FM = Free Movement of persons, EMU = Economic and Monetary Union, SU = Supranational Union.

# **2.1 Key Trends in Regional Trade across RECs**

The share of intra-African exports to total exports has doubled from less than 10% in 1990 to more than 20% in 2019. However, despite the strong positive trend in intra-African trade since 2008, the African continent remains the least integrated region in terms of trade flows (Figure 3). Africa’s low intraregional trade coexists with the region’s extremely high dependence on exports to the rest of the world. The continuously lower level of exports diversification explains this lower regional trade and higher dependence on exports to the ROW. Export dependence, defined as the share of regional exports to the RoW to total regional exports, has slowly decreased from around 90% in 1990 to less than 80% in 2019. This high export dependence also reflects the continent’s historical links with the outside world and lack of export diversification. Nonetheless, the lower level of intra-African trade could be underestimated given the omnipresence of informal trade. It could also be overestimated because of the multiple and overlapping memberships of many countries. Overall, there is significant variation in trade and other indicators of regional integration across the major RECs.

Regional integration, as measured by intra-regional exports as a share of total exports. which is the focus of this study, is deeper in EAC and SADC, followed by CENSAD, ECOWAS, and COMESA (Figure 4). The differences in the levels of intraregional export performances within the African RECs reflect many factors including the size of economies in each REC, varying levels of integration in infrastructure and policies, institutional quality to implement the agreements and the provisions in each REC, the difference in the level of the industrialization process, and the degrees of infrastructure integration and the cost of trading across borders in each REC. A closer 7
# **1   Introduction**

The advent of the Industrial Revolution was a defining moment in the economic history of the world. It ignited a period of Great Divergence in which the industrializing nations broke away from the rest (Clark, 2014). For the most part, this divergence that started centuries ago still defines the fault lines between the rich and the poor economies. It is not so surprising then that industrial expansion is a key component of many growth policies.<sup>1</sup> At the same time, deindustrialization remains a robust feature of economic development (Clark, 1957; Kuznets, 1957; Herrendorf _et al._ , 2014), especially when considering the sector's employment share instead of its output contribution (Rodrik, 2016). Moreover, there is emerging evidence that the onset of deindustrialization has been _premature_ in recent decades (Rodrik, 2016; Felipe _et al._ , 2019). The peak industrial share in the developing economies is lower relative to the early industrializers and materializes at a lower income per capita.

A growing literature is trying to uncover the underlying factors that drive deindustrialization. More crucially, it seeks to quantify the growth implications of deindustrialization and identify policies that can arrest the industry's slide. However, the country-level studies only cover a handful of economies. This analysis requires quality long-term sectoral data, which are not available for many countries. Small and low-income economies are particularly under-represented. Using learnings from other cases to devise policies is of limited use if factors driving deindustrialization vary across countries.

This paper addresses this gap by conducting a country-level analysis of six Central American economies in the last two decades. I piece together data from several sources and harmonize them at the sector level. The raw data require cleaning, and I make several adjustments to ensure quality before using them in analysis. Though value-added shares are indeed a part of the examination, the study concentrates on the dynamics of sectoral employment shares. Two facts guide this choice. First, as already mentioned, deindustrializing trends are more pronounced in employment (Rodrik, 2016). Second, unlike its employment counterpart, the industrial value-added share is only a weak predictor of attaining economic prosperity (Felipe _et al._ , 2019).

> 1 Examples include _Make in India_ and _Made in China 2025_ . However, these large-scale efforts transcend the boundaries of the developing world. The _American Jobs Plan_ seeks to reinvigorate industrial production. The plan deems the sector the _Arsenal of American Prosperity_ that can ‘ _fuel an economic recovery for working families_ (White House, 2021)’.

2
Table 7: Tests of significance for the effects of reform policy variables on sector performance

|**Impact variable**||||**R**|**eform P**|**olicy Test**|**ed**||||**Study**|
|---|---|---|---|---|---|---|---|---|---|---|---|
||**U**<br>**P**|**R**|**C**|**T**|**UP**|**UR**|**UC**|**RP**|**RC**|**PC**<br>**Index**|**number**|
|Connectionsper worker|Y:↑|||Y:↑|||||||1|
||Y:↑|||N|||||||12|
|Energysoldper worker|Y:↑|||Y:↑|||||||1|
||N<br>N|N|||||||||2|
||Y:↑|||N|||||||12|
||N<br>Y:↑ o|Y:↑|N||||||||24|
|Distribution losses|Y:↓|||Y:↓|||||||1|
||Y:↓|||N|||||||12|
||Y:↓o|N|N||||||||2|
||Y:↓o|N|||||||||3|
||||||||N||||8|
|||||||||||N|17|
||N<br>N|N|N||||||||20|
||N<br>N|N|N|||||N|||21|
||N<br>N|N|N|||Y:↓||Y:↓|||16|
|||||||||||Y: ↓|23|
|Electricity generationper worker|||||||Y:↑||||8|
||N|N|N||||||||25|
||N|Y:↑|Y:↑|||||Y:↑|N|N|26|
|Plant load factor|||||||Y:↑||||8|
||Y:↑<br>N|N|N||||||||20|
|Reserve margin|||||||Y: ↑|||Y: ↑|10|
|Collection rate|Y:↑|||Y:↑|||||||12|
|Number of employees|Y:↓|||Y:↓|||||||1|
||Y: ↓|||Y: ↓|||||||12|

34
<!-- Start of picture text --> Kernel density estimate Kernel density estimate<br>.5 1 1.5 2 -1 -.5 0 .5<br>b_iv c_iv<br>kernel = epanechnikov, bandwidth = 0.0183 kernel = epanechnikov, bandwidth = 0.0183<br>3 3<br>2 2<br>Density Density<br>1 1<br>0 0<br><!-- End of picture text --> <!-- Start of picture text --> Kernel density estimate Kernel density estimate<br>-.02 0 .02 .04 .06 .08 -.3 -.2 -.1 0 .1<br>b_iv_ls c_iv_ls<br>kernel = epanechnikov, bandwidth = 0.0010 kernel = epanechnikov, bandwidth = 0.0069<br>80 8<br>60 6<br>40 4<br>Density Density<br>20 2<br>0 0<br><!-- End of picture text --> <!-- Start of picture text --> Kernel density estimate Kernel density estimate<br>-.5 -.4 -.3 -.2 -.1 -.2 -.1 0 .1<br>b_ls c_ls<br>kernel = epanechnikov, bandwidth = 0.0061 kernel = epanechnikov, bandwidth = 0.0060<br>10 10<br>8 8<br>Density 64 Density 64<br>2 2<br>0 0<br><!-- End of picture text --> 43
<!-- Start of picture text --> Burkina Faso<br>“\<br>2 ry ay<br>‘' }<br>H t‘<br>= f ‘<br>’ \<br>‘ \<br>Nn ,' ‘‘<br>é \<br>If ‘x<br>bon ><br>°o<br>2 o 2 4 6<br>Lag of consurnption per cupits<br>[----- Nondean season = Laan aeascr ]<br><!-- End of picture text --> <!-- Start of picture text --> Niger<br>, ¥<br>‘‘ ’‘1<br>o ui ‘<br>‘ ‘<br>i‘ ti<br>3. 4 ‘<br>a<br>( ‘<br>¢ .<br>TM ’‘ ‘<br>/<br>’/<br>i ¢<br>e -<br>71 i] 1 2 3 4<br>Log of commuenptionper capeta<br>~~> Non-lean season ———_ Laan season<br><!-- End of picture text --> <!-- Start of picture text --> Senegal<br>=<br>v'<br>‘<br>‘<br>2 ‘ s<br>)‘<br>‘‘<br>)‘<br>'<br>: ‘‘‘‘ '<br>‘\<br>‘‘<br>Nn ’f<br>ul<br>G<br>-<br>o Be<br>38 a 2 4<br>Lag of consumption per capita<br>[----- Noreen sesso Lean aeaace ]<br><!-- End of picture text -->
_DICCb_<sup>_m,whereIRS_</sup> _s_<sup>_aandDICC_</sup> _b_<sup>_mbind,andthecompensationschemeischaracterizedby:_</sup> Equality (32) identifies the total compensation that a buyer has to offer to an altruistic seller in the contract. Equality (31) gives the base payment that the seller receives during date _t_ and equality (30) gives the size of the bonus that the buyer promises to pay at the end of the period to induce the seller to not change the land-use.

The total payment and both the base and performance payments are decreasing in the seller’s coefficient of altruism. Recalling the assumptions about the cost of forest conservation, _c_ ( _<u>q</u>_ <u>)</u> = 0 and the carbon benefits from changing the land use, _V_ <u>(</u> _<u>q</u>_ ) = 0, the base payment included in the optimal REDD contract for an altruistic seller also equals zero. That means that regardless of the the seller’s preferences, under the optimal relational contract the seller does not get paid anything upfront or during the time she is under the contract until the end of the period. The contingent payment includes the complete payment to the seller. It includes the cost of providing optimal forest conservation and the opportunity cost of the alternative land use.

In the case of the optimal REDD contract in the presence of an altruistic seller, parties find cooperation (self enforcement) to be the best strategy if they value the future relationship at least as much as the discount factor described in Proposition 7.

_a_<sup>_s_</sup> **Proposition 7.** _Let_ _<u>δ</u> >_ 0 _. Cooperation under the optimal REDD contract is achievable a_<sup>_s_</sup> _a_<sup>_s_</sup> _c_ ( _q_ )+ _<u>u</u>_ _~~−~~ as_ ( _V_ ( _q_ ) _−V_ ( _<u>q</u>_ <u>))</u> _∀ δ ∈_ <u>�</u> _<u>δ</u> ,_ 1� _, where_ _<u>δ</u>_ = _V_ ( _q_ ) _−π_ _~~−~~ as_ ( _V_ ( _q_ ) _−V_ ( _<u>q</u>_ <u>)</u> _−π_ <u>)</u><sup>_._</sup> Proposition 7 reports the range of discount factors that can support a cooperative equilibrium under the optimal REDD contract when an altruistic seller and a self-interested buyer trade. The term in the numerator includes the total payment the buyer has to make to the seller to avoid carbon emissions net of the seller’s altruistic value for the buyer’s benefits. The denominator represents the buyer’s net benefit of the carbon sinks from the contract adjusted by the seller’s altruistic parameter. With an altruistic seller, the discount factor is also inversely related to the parameter of altruism as _∂δa_<sup>_s_</sup> _/∂as <_ 0. The overall result is analogous to the case with an altruistic buyer; the more altruistic the seller is, the wider the range of discount factors that sustains cooperation because the threshold for cooperation is 21
# **Table 1 Summary Statistics**

– This table shows the descriptive statistics for the subsample that answered questions 1 8. We show results differentiated by treatment and control groups in columns 1 and 2, and the difference between groups in column 3.

The variables used are: age, male indicator, student status indicator, country, indicator for the presence of children, married indicator, an indicator for whether the mother of the respondent has an education degree equal or higher than high school, an indicator for whether the father of the respondent has an education degree equal or higher than high school.

||Trea|tment|Co|ntrol|Dif|erence|
|---|---|---|---|---|---|---|
||N|=378|N|=389|N|=767|
||Mean|Std. Error|Mean|Std. Error|Mean|Std. Error|
|Age|23.4418|0.2625|23.5039|0.2705|-0.0621|0.3772|
|Male|0.5423|0.0257|0.5476|0.0253|-0.0052|0.0360|
|Student|0.6931|0.0238|0.7172|0.0229|-0.0241|0.0330|
|Colombia|0.6058|0.0252|0.5861|0.0250|0.0197|0.0355|
|Mexico|0.2884|0.0233|0.2828|0.0229|0.0056|0.0327|
|Chile|0.1058|0.0158|0.1311|0.0171|-0.0253|0.0234|
|Children|0.0794|0.0139|0.0951|0.0149|-0.0158|0.0204|
|Married|0.0688|0.0130|0.0643|0.0124|0.0045|0.0180|
|Mother Education|0.7037|0.0235|0.7018|0.0232|0.0338|0.0252|
|Father Education|0.6667|0.024279|0.6427|0.024328|0.024|0.034378|

- 10% Significance Level

- ** 5% Significance Level

- *** 1% Significance Level

21
# **2.2.2.1 Comparability of School Attendance before COVID-19 and during COVID-19 after School Reopening**

School attendance after school reopening was measured in October 2020, when school participation is expected to be the highest in the school year. School attendance before school closure is defined based on attendance at any time during the academic year 20192020. Therefore, we are fairly confident that school attendance before and after the outbreak of the pandemic is comparable. Nonetheless, because the reference period used to measure school attendance before and during COVID-19 is different, we explore further the comparability of our measure of school attendance between the two periods (before and during COVID-19). For example, during the agricultural period, households might withdraw their children from school to help with household farming. However, in mining the reasons for not attending school given by respondents in the COVID-19 NLPS round 6, no respondent mentioned employment as a reason. Furthermore, respondents might not attend school because they are waiting for admission. Therefore, in robustness analyses, we assume that these respondents attended school when the schools reopened (in October 2020). These results are consistent with those from our main specification.

# **2.2.3 Lockdown Indicators**

On March 19, 2020, a circular from the Federal Ministry of Education ratified the closure of all schools in Nigeria starting from March 23, 2020. At the same time, as described in Section 2.1, the federal government implemented various social distancing and mobility restrictions, and decided for the closure of business operations and regional borders. We define the baseline COVID-19 lockdown measures (labeled as _C19Shock_ in our specifications below) as a binary variable taking value one for the period after the outbreak of the pandemic and the implementation of the above-mentioned restriction measures, zero otherwise.

In addition to these interventions, the federal and state governments implemented additional lockdown measures and strict mobility restrictions in Abuja FCT, Lagos, Akwa Ibom, Borno, Osun, Rivers, Ogun, Kano, Delta, Ekiti, Kano, Kaduna, Kwara, and Taraba 14
Figure 1.1. Airports size and revenue sources. The Spanish case (1997) <!-- Start of picture text --> 70<br>60 ._ S --- ><br>>; 50<br>0 5000 10000 15000 20000 25000<br>Passengers (thousands)<br><!-- End of picture text -->

**Table 1.3 Traffic and revenue distribution. Selected airoorts**<sup>**3**</sup>

_

|Annual aircraft<br>movements<br>(thousands)______|78|**_165_**|391|169|188|
|---|---|---|---|---|---|
|No. of passengers<br>(millions)|6.6|11.9|28.4|12.0|11.1|
|Airside revenues as<br>percentage of total<br>revenues____|70%|**_50%_**|36%|62%|43%<br>___|
|Landside revenues as<br>percentage of total<br>revenues<br>Source: Kapur (19|30%<br>95).|50%<br>_<br>_<br>_<br>_<br>_<br>_<br>_|64%<br>_<br>_<br>_<br>_<br>_<br>_<br>_|38%<br>_<br>_<br>_<br>_<br>_<br>_<br>_|57%<br>_<br>_<br>_<br>_<br>_<br>_|

3Different airport ownership structures are defined and analyzed at section 3. Selected airports are: **Government Department:** Buenos Aires, Santiago, Mexico City, Quito, Libreville, Nairobi, Budapest, Athens, Gothenburg, New Delhi, Hong Kong, Bangkok and Kuala Lumpur. **Public Corporation:** Sydney, Auckland, Singapore, Rio de Janeiro, Amsterdam, Madrid, Vancouver and Montego Bay. **Regional Government:** Washington, Boston, Chicago, Pittsburgh, Atlanta, Dallas, Miami, Orlando, Paris and BaselMulhouse. **Public-Private:** Toronto, Vienna, Rome, Copenhagen, Zurich and Yaounde. **Private:** Heathrow, Gatwick, Stansted, Aberdeen, Edinburgh, Glasgow and Southamnpton.

**_5_**