rafmacalaba/datause-encoder-data huggingface dataset:
# **Table A3 OLS Estimations based on country specific variables**

|**EDUCATION INDEX 2**|**predicted**<br>**(lnOP1990)**|**Δpredicted**<br>**(lnOP1990-2000)**<br>**1985 arrival**<br>**1990-2000**|**(1/10) Δpredicted**<br>**(lnOP1990-2000)**|**predicted**<br>**(lnOP1980)**|**Δpredicted**<br>**(lnOP1980-1990)**<br>**1980-1990**|**(1/10) Δpredicted**<br>**(lnOP1980-1990)**<br>**1975 ar**|**predicted**<br>**(lnOP1990)**<br>**rival**|**Δpredicted**<br>**(lnOP1990-2000)**<br>**1990-2000**|**(1/10) Δpredicted**<br>**(lnOP1990-2000)**|
|---|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|
|**Log of GDP per capita**|**0.0249*****<br>**[0.00496]**|**-0.00882*****<br>**[0.00194]**||0.0151<br>[0.00974]|-0.0064<br>[0.00427]||0.00577<br>[0.00571]|**-0.00333***<br>**[0.00185]**||
|**Openness Index**|-0.00003<br>[0.000155]|0.00004<br>[5.42e-05]|0.00000<br>[4.02e-06]|-0.00006<br>[0.000256]|-0.00001<br>[0.000116]|-0.00001<br>[5.85e-06]|-0.00007<br>[0.000175]|0.00008<br>[6.14e-05]|0.00001<br>[6.23e-06]|
|**Log of Distance to the US**|**0.0303*****<br>**[0.0111]**|**-0.00408*****<br>**[0.00144]**|**0.000468****<br>**[0.000206]**|0.0144<br>[0.0118]|-0.00305<br>[0.00438]|0.000449<br>[0.000394]|**0.0185***<br>**[0.0105]**|**-0.00376***<br>**[0.00210]**|0.0000983<br>[0.000251]|
|**English**|0.0132<br>[0.0105]|**-0.00479****<br>**[0.00224]**||0.0233<br>[0.0144]|-0.00149<br>[0.00618]||0.00911<br>[0.0107]|-0.0026<br>[0.00213]||
|**Military conflict**|**-0.0239****<br>**[0.0111]**|0.00371<br>[0.00261]|-0.0000189<br>[0.000333]|0.000991<br>[0.0248]|-0.00359<br>[0.0119]|-0.000136<br>[0.000450]|-0.00811<br>[0.0135]|-0.00097<br>[0.00308]|-0.000082<br>[0.000389]|
|**Communism index**|-0.00533<br>[0.0164]|**0.00925****<br>**[0.00457]**|**0.00124****<br>**[0.000618]**|0.00645<br>[0.0260]|-0.00408<br>[0.0121]|-0.000495<br>[0.000491]|-0.0112<br>[0.0169]|0.00146<br>[0.00351]|0.000246<br>[0.000427]|
|**Predicted Log of OP in 1990**|||**-0.0216*****<br>**[0.00439]**||||||**-0.0197****<br>**[0.00913]**|
|**Predicted Log of OP in 1980**||||||**-0.0383*****<br>**[0.00478]**||||
|**Constant**|2.204***<br>[0.110]|0.144***<br>[0.0227]|0.0572***<br>[0.0105]|2.389***<br>[0.132]|0.138***<br>[0.0487]|0.103***<br>[0.0121]|2.502***<br>[0.102]|0.0754**<br>[0.0288]|0.0541**<br>[0.0236]|
|**Observations**|71|71|71|72|72|72|72|72|72|
|**R-squared**|0.617|0.621|0.579|0.216|0.089|0.669|0.167|0.109|0.248|

***** p<0.01, ** p<0.05, * p<0.1 Robust standard errors in brackets**

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resources). In reality, these data indicate that SMEs in Chile are already investing in innovation and that if their capabilities are upgraded and a more comprehensive approach to innovation is undertaken, the potential for increasing productivity may be significant.

In Chile, when firms that are innovative in terms of output have been asked about their collaboration activity, the majority of them resulted to be actively engaged with universities (see graph A.3.5).

# **5.2. COLOMBIA**

As shown in Table A.3.2, in Annex 3, 79 percent of the Colombian firms<sup>20</sup> can be defined as innovative in the model. Only 7 percent of these firms have R&D investments, which is an unusually small percentage. A further 53 percent said they had introduced a new product, while 47 percent of firms had introduced a new process. These figures are a little lower than corresponding figures from Chile where 58 percent have introduced new products and 53 percent new processes. One caution comparing these figures should be noted, i.e. Colombian figures are not restricted by any timeframe whereas the Chilean firms respond to innovations within a time period of three years.

The firm characteristics show that the clear majority of Colombian firms are small or micro (see graph A.3.6). Two thirds of all Colombian firms have less than 50 employees. Further, 37 percent are geographically located in or around the capital Bogotá and 10 percent have their own R&D department.

When focusing on the collaboration activity it is clear that this is very low when it comes to Colombian firms. Only around one or two percent of the firms have collaboration activities concerning innovation. The number of firms that employ PhDs or persons with a masters degree is low. Three percent of the Colombian firms employ a PhD while 7 percent employ a person with a masters degree. Twenty-six percent of the firms have employees holding a specialist degree (see graph A.3.7).

# **6. RESULTS**

# **6.1. CHILE Innovation inputs.**

As a first step to investigate innovation determinants, we analyze the innovation input, modeling the determinants of innovation expenses in general and of R&D in particular (table A.4.1). In our specification, we control for sector specificities. The results show that mainly export activity, which proxies for the level of openness of the firms, and knowledge and training of the management, which is an interpretation of the concept of human capital of the firm, affect innovation positively and in a significant way in both specifications. Firm size does not seem to be an important factor in determining innovation expenditures in general, and R&D in particular for Chilean firms. It does show, however, a positive sign. This result is peculiar to Chile; size seems to be relevant instead in case of Colombian firms, coherently with literature. A positive sign for the lack of skilled personnel is quite unexpected but it confirms the spurious characteristic of the

> 20 Data from the second Survey of Development Activities and Technological Innovation of Colombian manufacturing firms (2003 and 2004).

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|Switzerland|2011||||||7.6|2.8|OECD (2015)|
|---|---|---|---|---|---|---|---|---|---|
|Thailand|2010|12.6||||3.4|5.7|11.3|Tangtipongkul<br>(2015)|
|Timor-Leste|2007|3.9|||||||Santos (2014)|
|Tunisia|2011|7.0|||||||Rizk (2016)|
|Turkey|2008|11.8|||||||Tansel and Daoud<br>(2014)|
|Uganda|2008|11.9|||||||Peet et al (2015)|
|Ukraine|2002|4.5|||||||Gorodnichenko<br>and Klara (2005)|
|United States|2011|||20.5|14||11.9|11.9|OECD (2015)|
|Uruguay|1990|9.1|||||||Kaboski (2003)|
|Venezuela,<br>RB|2008|6.4|||||||Gonzales and<br>Oyelere (2009)|
|Vietnam|2014|5.7|||||||<br>Doan et al (2016)|
|Yemen, Rep.|1985||10|41|56|2|26|24|Psacharopoulos<br>(1994)|
|Zambia|1995|10.7|||19.2|||5.7|Banerjee and<br>Duflo (2005)|
|Zimbabwe|1994|5.6|16.6|48.5|5.1|11.2|47.6|-4.6|Banerjee and<br>Duflo (2005)|

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variables to concatenate and following changes in sex across the panel led to mismatching (or misreporting) of under .5 percent. Taken together, we have 24 complete panels of 5 periods spanning a total of 28 quarters where transitions could occur across a seven year period which includes a time of recession (1987-88), recovery (1989-91), and then stagnation (1992-1993).

Though the model deals with decisions to be self-employed generally, we further narrow the population by only considering self-employed workers in the "informal" sector. We use the term 'informal' here to refer to those unprotected by labor law, more specifically, owners of firms under 16 employees who do not have social security or medical benefits. In fact, under 1% of these firm have more than 5 employees so the definition corresponds closely to that commonly used in the development literature. Formal salaried workers are defined as those in firms of over 16 workers who enjoy labor protections. To eliminate the "self-employed" in consulting firms or other high end activities, we analyze male workers with a high school education or less between the ages of 16-65.

The dependent variable is a 0, 1 index that captures whether the worker moved during a particular quarter. If he should move back to salaried employment and then move again to selfemployment, this is counted as a second quit. The macro wage and benefit variables employed in the regression analysis, logw1 and logb,, are the median for the entire sample (spanning five panels) for each quarter. The probability of being hired, p,,is the number ofthe self-employed who transition to the salaried sector, as a fraction of those looking for a salaried job. Vhile we know the total number of self-employed, we do not observe the share searching in each period which theory predicts should vary with macro-shocks. We assume that this fraction is proportional to, or at least highly correlated with, the standard measure of search, the unemployment rate. The probability p, is therefore proxied by **Pt,** the number of the self-employed moving into salaried work divided by the number of unemployed. Even though we thereby avoid the need for time series data on the search intensity, we still require information about the sample average of this variable in order to rescale the estimated coefficient appropriately when conducting the macroeconomic simulations in Section 4. To this end, we use the survey response from the National Micro-Enterprise Survey (ENAMIN) which in 1992 re-interviewed roughly 11,000 of those in the 1991:4 ENEU who 13
**_Core Indicators for Determinants and Performance of Electricity Sector Reform in Developing Countries_**

# **5.5**

**VII. Macro-Level Indicators**

|**Focus area**|**Indicator**|**Data Source (e.g.**<br>**UK)**|**Source/reference**<br>**(example)**|
|---|---|---|---|
|_? GDP_|? GDP per capita|OECD|Bacon and Besant-<br>Jones (2002)|
|_? Energy intensity_|? Electricity use per unit of GDP<br>? Energy use per unit of GDP|IEA Statistics:<br>World Energy<br>Statistics|See above|
|_? Economic_<br>_liberalisation_|**? Has liberalisation been implemented in**<br>**other infrastructure industries? (no/yes – e.g.**<br>**natural gas, telecom, transport, railway)**||See above|
|_? Country risk_|**? Country risk index**||Bacon and Besant-<br>Jones (2002)|
|_? Institutional_<br>_endowment_|**? Institutional and political strength – e.g.**<br>**Judiciary independence, economic**<br>**management, corruption indices**||Bergara et al. (1997)<br>Kaufmann et al.<br>(1999a, 1999b)|
|_? Foreign aid and_<br>_economic dependence_|? Foreign aid share of GDP (ratio)<br>**? Degree of economic freedom (index)**||Bacon and Besant-<br>Jones (2002)<br>Zhang (2002)|
|_? Human capital_|? literacy rate (%)<br>? Post-secondary education (%)||e.g. World Bank<br>competitiveness<br>indicators<br>Domah et al. (2002)|

# **5.6**

# **VIII. Economic impacts**

|**Focus area**|**Indicator**|**Data Source (e.g.**<br>**UK)**|**Source/reference**<br>**(example)**|
|---|---|---|---|
|_? Relative prices_|? Ratio of industrial to residential prices|Regulator’s<br>publications|Steiner (2001)<br>Hattori (2003)|
|_? Subsidies_|? Are there cross-subsidies between<br>residential, industrial and service users? (no/yes)<br>? Are there public subsidies? (no, yes–amount)|Department of Trade<br>and Industry (DTI)||
|_? Investments in the_<br>_sector_|**? Private and Government investments in G,**<br>**T, D (amounts)**<br>**? Government investments in G, T, D (as %**<br>**of total public budget & investments)**|||
|_? Efficiency gains_|? Estimated efficiency gains (as % of GDP)||Chisari et al. (1997)<br>Bacon and Besant-<br>Jones (2002)|

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**Figure 10: RWA over Total Assets (%) – top 20% banks and bottom 80%**

**Panel A: Top 20% banks**

**Panel B: Bottom 80% banks**

<!-- Start of picture text --> 89.8 88.0 90.5<br>84.5<br>81.4 80.1 76.0 78.1 77.6 80.679.8 79.1 78.0<br>70.9 67.6 68.8 70.9 70.0<br>62.9 63.6 59.2 65.1 62.559.5 58.0 63.2 63.759.0 65.7 61.765.4<br>53.7 55.5<br>50.4<br>48.5<br>39.3<br>EU CE & BC WB SC CA Russia Turkey EE RoW EU CE & BC WB SC CA Russia Turkey EE RoW<br>2007 2017 2007 2017<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30<br>RWA over Total Assets (%) RWA over Total Assets (%)<br>20<br>10<br>0<br><!-- End of picture text --> Source: Archived data from Bureau van Dijk’s Bankscope and BankFocus.

Note: **EU** stands for Western Europe, Southern Europe, Northern Europe; **CE & BC** for Central Europe & Baltic Countries; **WB** for Western Balkans; **SC** for South Caucasus; **CA** for Central Asia; **EE** for Eastern Europe. We report means weighted by bank size.

The declines in risk-weighted assets could be driven by improvements in asset quality in the aftermath of the crisis. As risk-weights only weakly reflected the true asset risks of banks prior to the crisis, this decline raises concerns about the accuracy of measures of risk-weighted assets and questions about the improvements in regulatory capital ratios. When we examine the difference between regulatory capital ratios and simple leverage ratios, we find that the gap between the two ratios has been widening over the past decade. Figure 11 shows the gap or the difference between these two ratios. We observe that in most ECA regions the gap between total regulatory capital and capital to assets ratios has increased between 2007 and 2017. The gap is larger for the largest institutions and for banks located in the EU, Central Europe and the Baltic countries, Eastern Europe and for large banks in Central Asia. This suggestive evidence highlights the importance of the computation of risk weights in the assessment of the capital adequacy of banks in ECA countries.

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The 1985-1994 Global Real Estate Cycle. Page 8 Figure 4: Changes in Property Related Loans and Net Japanese<sup>Capital Outflows, 1980-1990</sup> <!-- Start of picture text --> Al'<br>.......... .... AL 1<br>5X .j I...1\<br>4.0<br>3.2 \\ u<br>2.4<br>1.6<br>0<br>-1.6 1983 l990<br><!-- End of picture text --> _AF - Change in nd foreign invesntna AL_ - _Change in nd leading to real ecsta and construedon (Trilion Yen, Matchedkwes and Ranges)_ _Source R Werner (19941_ to Califomia (See _Economist July_ 23, 1994).<sup>T</sup> Trophy' or papcbot'<sup>(hwry lina) investment by</sup> Japanese and other tuveszou had similr impacts in most of the<sup>intcAdonal cides</sup>
our setting of Tanzania.

In this previous study of ELA in Tanzania, approximately 25% of the eligible population participated in ELA clubs (Buehren et al., 2017). Previous research has found no significant selection of females into ELA clubs. Buehren et al. (2017) find that, while ELA participants in Tanzania are less likely to have a child than non-participants, there is no evidence that they differ by education enrollment status, relationship status, engagement in income generating activities, or across several measures of household wealth. Likewise, in Uganda, Bandiera et al. (2020) find little evidence of selection on observables into ELA participation. We compare our sample of ELA members at baseline to the random sample of adolescent females from the same communities from the baseline sample of Buehren et al. (2017) and find no evidence of systematic differences (see Table A1). Therefore, ELA participants seem to be representative of female adolescents in study communities.

# **2.2 Study Design and Interventions**

This paper focuses on a subset of interventions from a larger RCT, which included an additional intervention that aimed to increase access to contraceptives. Figure 1 illustrates the overall design of the RCT. Treatment status was assigned at the ELA club level and at the individual level, depending on the treatment. At the ELA club level, the 149 clubs were randomly allocated to three groups of equal size, stratified by region: two treatment arms and one control arm. The control arm (49 clubs) maintained the status quo, ELA clubs. The two treatments arms are (i) _Supply_ (50 clubs), which provided access to free contraceptives, and (ii) _Boys_ (50 clubs), which layers a soccer intervention for males on top of the free contraceptives. At the individual level, a sub-sample of 865 females across all three study arms were randomly selected, stratified at the club level, to receive an invitation to participate in an individual goal setting activity. The evaluation of the _Supply_ arm, which produces null results due to no take up of the contraceptives (see Table B1), is discussed in detail in Shah, Seager and Rubio (2021). While it will not be discussed further in this current paper, we control for this arm in all analysis.

8
by 0.5 percentage points per annum. Zooming in the quantity of infrastructure, the largest growth benefits are obtained by narrowing the gap in electric power (0.7 percentage points higher per year). In terms of quality, improving road quality provides the largest benefits.

The results for income groups in SSA (Figure 9): while distance to the benchmark (and, hence, the growth effect) is greater for LICs in electric power installed capacity, it is greater for quality of roads among LMCs. On the other hand, UMCs in the region only trail the world median in terms of electric power installed capacity. The gap is narrow and so are the growth benefits (about 0.05 percentage points per annum). Finally, narrowing the infrastructure gap among the worst performers in the region (bottom 10 percent) improves growth per capita by 3 percentage points per year (2 percentage points attributing to closing quantity gaps) while it is only about 0.5 percentage points per annum (0.2 percentage points attributed to closing quality gaps) for the best performers (top 10 percent).

**Figure 9. Growth benefits of narrowing the gap vis-à-vis world median in infrastructure** _(In percentage points per annum)_

<!-- Start of picture text --> 3.5%<br>3.0%<br>2.5%<br>2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>Sub-Saharan SSA LICs SSA LMCs SSA UMCs SSA Bottom SSA Bottom SSA Top 25%SSA Top 10%<br>Africa (SSA) 10% 25%<br>K-Tele K-Power K-Roads Q-Power Q-Roads<br><!-- End of picture text --> _Note: The coefficient estimates are taken from column [3] of Table 6 (Calderón and Servén 2010)._ Figure 10 depicts the second scenario of countries in the region closing the infrastructure gap relative to the best performers in the world —the latter being proxied by the top decile of the distribution of infrastructure stocks and quality. For the region, closing the quantity and quality of the infrastructure gap renders higher growth rate per capita of about 1.8 and 0.8 percentage points per annum, respectively. This amounts to greater growth per capita of 2.6 percentage points per annum. Note that the largest potential growth benefits are coming from closing the gap in electric power generating capacity and the length of the road network.

The growth benefits for the region are closer to the gaps and potential gains of narrowing the gap for LICs and LMCs in the region. Energy appears to be a more binding constraint among LICs whereas roads is the most binding constraint among LMCs —both in terms of quantity and quality. The worst performer in the region (bottom 10 percent) is bound to have a growth rate higher by 3.9 percentage points per year if it were to close its infrastructure gap with the top decile of the world distribution. About 2/3 of these potential growth gains are attributed to closing the gap in quantity. For the best performers in the region, growth per capita might increase by 1.3 percentage points per year in this scenario (with 0.8 percentage points attributed to closing _Infrastructure in Sub-Saharan Africa - 24_
hectares.<sup>8</sup> On these tiny farms, the soil has become progressively exhausted and less productive. This problem has been compounded by the extensive deforestation of the country which, in turn, has led to severe erosion of the fertile topsoil. As yields have declined, Haitian peasants have found themselves locked into a self-destructive cycle in which the cutting of trees for charcoal production, and the farming of land higher up the mountainsides, can stave off short term financial disaster, but only create greater problems for the sector as a whole in the long term. Moreover, the agricultural sector is characterized by scarcity of physical capital including tools, machines, fertilizers, transportation, and infrastructure (Egset 2004). Especially, poor households with low incomes pose a key obstacle to innovations and technological changes in agriculture.

Table 2.9: GDP by Sector (percent), 1996-2002

||1996|1998|2000|2002|
|---|---|---|---|---|
|Agriculture|32.9|30.9|28.5|27.1|
|Industry|15.4|16|16.6|16.3|
|Services|51.7|53.1|54.9|56.5|

_Source:_ WDI, World Bank 2004 Agricultural production for export has undergone a significant decline, partly because farmers have been obliged to shift to growing food crops to avoid starvation and partly because of changes in international markets. At the same time, food production has failed to keep pace with population growth. Today, the country must import more than half of the food that is consumed, creating a further pressure on the balance of payments.

# **Potential Growth Areas**

Three potential areas of growth in the medium term are crafts, selected agricultural products, and tourism. Below each will be addressed in turn. It would be important to have involvement from Haitian expatriates as well as other foreign investors in order to give the economy a push forward.

In the beginning of the 1990s around 400,000 people were working in craft production such as basketry, embroidery and needlework, leather goods, and pottery. Most worked in family enterprises or small workshops, but some were employed in urban factories. The bulk of sales were to the tourist market in the Caribbean, but even so, craft accounted for more than 10 percent of exports in 1990. The recent economic and social collapse has brought the craft sector to its knees. Haiti's artisans need access to credit, and support for marketing and distribution of their products. More attention has to be given to tourism.

Like the decline in crafts, the political turmoil that followed the 1991 military coup and lack of infrastructure made most tourists stay away. If tourism is to bring real benefits to the poor, more needs to be done to promote eco-tourism and other sustainable development initiatives. In 1979, a peak year for Haiti’s tourism sector, over 173,000

> 8 See Verner (2007), Labor Markets in Rural and Urban Haiti.

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