rafmacalaba/datause-encoder-data huggingface dataset:
-6-

9. The critical values for the three different tests at the 99%, 95% and 90% significance levels are presented below. The critical values for the DF and ADF test statistics were obtained through Monte Carlo simulations, based on 100 observations and with 10,000 replications, under the assumption that Aet is iden-ically and normally distributed.

# Critical Values of Unit Root Tests

|Tests|Levels of Significan<br>90%<br>95%|ce<br>99%|
|---|---|---|
|CRDW|C.322<br>0.386|0.511|
|DF<br>|3.03<br>3.37|4.07|
|ADF|2.84<br>3.17|3.77|

Source: Engle and Granger (1987).

10. In the case of non-autocorrelation in the residuals, Aet, the ADF test is misspecified and less powerful than the DF test since it estimated parameters that are truly zero. In the case of autocorrelation, the DF is misspecified and less powerful than the ADF test. The CRDW test performs better overall in both the non-autocorreiatre and autocorrelated cases according to the power calculations of Engle ancl Cranger (1987). However, its critical values are sensitive to the particular larameters within the null hypothesis as well as to the sample size. In order to avoid misleading rcsults from these tests all three tests are applied.
* The taxonomy of business ,evealed that education was highly correlated with - formality a state of full ccrporate citizenship, bearing full responsibility for obligations to the state and benefiting from access to bank financing and formal channels of technical assistance. This suggests that investment in education (at least up to the secondary level) and elimination of anti-entrepreneurial biases in the curriculum may encourage expansion of the formal SME sector.

* However, as current technical assistance is reaching primarily the educated, there is a need to look for innovative intermediate-term approaches to technical assistance oriented towards the least privileged, educated and influential entrepreneurs. Models pioneered by several NGOs for microenterprise assistance, using credit conditioned on technical assistance and training to entrepreneurs, might prove useful in extending the reach of support programs to micro- and small enterprises.

* Finally, technical and financial assistance are bound to be required (at least during a transition period) for a diverse, dynamic SME sector to emerge and grow. The experience of APDF suggests that entrepreneurs with medium-to-large projects exist and would likely materialize in force in response to an improved business environment - possibly with some (cost-effective) assistance to link up with financiers and partners. the vast potential of the SME sector (including migrants from a belt-tightening public sector) will, in particular, require assistance to get started. Promising models which involve a combination of busines **s** ervices, equity participation, and loan financing are being experimented with by NGOs, cooperative associations, and for-profit promoters. These models have in common a triangular arrangement in which the would-be (or expanding) entrepreneur obtains term financing from a bank, partly on the basis of an agreement with a provider of technical assistance who also takes a stake in the venture (in the form of either equity or guarantees). Variations of this model appear weli worth developing to support the emergence and growth of both SMEs and productive microenterprises.

**Legal** System There is a need for streamlined, expedient mechanisms for conflict resolution (including effective recourse against government decisions). Courts and court officials require incentives and safeguards against corruptibility. Private businesse **s** upplying the government should have neutral courses of appeal should problems arise during the course of the contract. Other measures are needed to reduce transaction costs and generate business confidence.

Other Support Services Existing policies have turned productive energies inward towards domestic markets. Expansion of export horizons for manufactures (beyond the francophone world) may now require approaches with limited public institutional inputs and a high level of private sector participation, such as partial finaneing of market exploration expeditions.

48
find that while cash transfers to mothers did not lead to any significant difference, transfers to fathers improved young children’s health, increased livestock and cash crop investment, and led to improvements in housing.

We use a panel of 1,214 married households that we track over three rounds of data collection. Using an Analysis of Covariance (ANCOVA) model, we find that whom you target determines the type of impact achieved and the channels through which this impact is realized. From our results, we find that women’s access to extension services increases when only the male or female spouse is targeted, though the size of the increase is larger when the female spouse is targeted directly. We see no similar result for men, which could be because they already have higher access levels. Targeting both spouses also has no effect on access to extension services.

Similarly, we find that the gender of the targeted individual is important for the take-up of new technology. We find a significant increase in the take-up of new technology by women across all agricultural domains when only the male spouse is targeted. We also find a positive and significant increase in men’s overall technology take-up, and specifically for livestock, when the male spouse is targeted. However, when the female spouse is targeted directly, there is no effect on her technology take-up and instead, we find less adoption of technology by men in farming methods. Targeting both spouses does little for women but does have a complementary effect on men’s takeup of new technology in farming methods.

Targeting both spouses initially creates a positive interaction effect on agricultural productivity. This could be driven by the positive effect on technology use in farming methods for the men when both spouses are targeted. This impact is no longer significant by the endline, although it does remain positive. When only one spouse is targeted, we find no significant effect on agricultural productivity.

For livestock outcomes, targeting both spouses is better than just targeting one. Targeting both spouses has a positive effect on the stock value of small ruminants and poultry. Just targeting the female spouse has no similar effect, and actually has a negative effect on the stock value of poultry.

The impact of targeting the female spouse occurs outside of agriculture. Income from femalemanaged businesses increases when the female spouse is targeted by WIF. This suggests that 4
_The Cultural Trade Index_ Page | **7** of **14**

|Table 3| **Royal**|**ties and Lic**|**ense Fees**|**Trade**,2011 **| **|USD at cur|rentprices|and exchang|e rates in millions|
|---|---|---|---|---|---|---|---|
|**Royalties an**<br>International tr|**d license fee**<br>ade in related|**s**<sup>a</sup><br>services, (|annual, 2011)|**Total Tr**<br>**Creativ**|**ade in A**<br>**e Goods**<sup>b</sup>|**ll**<br>(2011)|**Total Trade in All**<br>**Goods and Services**<br>(BPM6)<sup>c</sup>(2011)|
|**Country**|Exports<br>(E)|Imports<br>(I)|Total E + I|E|I|E + I||
|**Thailand**|$177|$3 119|**$3 296**|$6 496|$4 457|**$10 953**|**$260 692 **|
|**Indonesia**|$79|$1 778|**$1 857**|$6 085|$1 463|**$7 548**|**$212 997**|
|**Malaysia**|$149|$1 638|**$1 781 **|$2 851|$1804|**$4 655**|**$254007 **|

aIncludes Franchises, Similar Rights, Other Royalties, and License Fees. b Includes Art Crafts, Audio Visuals, Design, New Media, Performing Arts, Publishing, and Visual Arts. So ‘goods’ here probably implies goods and services given the composition of the items included.<sup>c</sup> BPM6 = Balance of Payments and International Investment Position Manual Sixth Edition.

Source: UNCTADstat Table 3.1 <u>|</u> **Royalties** **<u>and License Fees</u>** <u>, 2011| Total Trade Analysis in Millions (USD)</u>

|**Country**|**Thailand**|**Indonesia**|**Malaysia**|
|---|---|---|---|
|Royalties and license fees /Trade in all<br>Creative Goods|3 296÷10 953 =<br>~0.300922122|1 857÷7 548 =<br>~0.24602544|1 781÷4 655=<br>~0.38259936|
|Percentage ofRoyalties and license fees<br>in Total TradeinCreative Goods|~ 30.0922122%|~ 24.602544%|~ 38.259936%|
|Royalties and license fees /Total Trade in<br>allGoods and Services|3 296÷260 692<br>= ~0.012643273|1 857÷212 997 =<br>~0.0087184|1 781÷254 007=<br>~0.00701162|
|**Percentage of** **Royalties and license fees**<br>**in Total Trade**|~**1.2643273%**|**~ 0.87184%**|**~ 0.701162%**|
|Trade in all Creative Goods/Trade in<br>allGoods and Services|10 953÷260 692 =<br>~0.042015099|7 548÷212 997 =<br>~0.03543712|4 655÷254 007 =<br>~0.018326267|
|Percentage of Creative Goods in Total Trade|~ 4.2015099%|~3.543712%|~ 1.8326267%|

Table 3.2 | **Example of Index Computation** , Royalties and License Fees Trade, 2011 <u>Figures in millions (USD) Total Trade Base Country: Indonesia</u>

|**Country**|**Thailand**|**Indonesia**|**Malaysia**|**Rank**(Base<br>Result in St|d on<br>ep 3)|
|---|---|---|---|---|---|
|**Step 1**: Royalties and license fees||||||
|Trade/Total Trade between the 3|3 296÷727 296|1 857÷727 296|1 781÷727 296|**Thailand**|**#1**|
|countries (Thailand, Indonesia<br>and Malaysia, i.e.|= 0.004531855|= 0.0025532933|= 0.0024487966|**Indonesia**|**#2**|
|$260 692 + $212 997+||||||
|$254007)=$727296||||**Malaysia**|**#3**|
|**Step 2**: Base Country Chosen:|260 692÷|212 997÷|254 007÷|||
|Indonesia∴Country’s Total|212 997 =|212 997 =|212 997|||
|Trade/Indonesia’s Total Trade|1.2239233416|1|=1.1925379231|||
|**Step 3**:Outcome in Step 1|0.004531855÷|0.0025532933÷|0.0024487966 ÷|||
|divided by outcome in|1.2239233416**=**|1**=**|1.1925379231**=**|||
|Step2|**0.0037027278**|**0.0025532933**|**0.0020534329**|||
indicate how a regulator can target oversight efforts across SCPs, our findings are especially relevant to countries seeking to promote SCPs.

Finally, we contribute to the literature related to differences in value added and returns by institution and program characteristics. Overall, this literature finds great variation in net returns to higher education by field and study major.<sup>17</sup> Other studies also find that specific institution and program characteristics affect higher education returns, including selectivity,<sup>18</sup> funding availability,<sup>19</sup> public or private administration,<sup>20</sup> and for-profit institutions.<sup>21</sup> Our paper makes three additional contributions. First, it provides evidence of associations between program value added to outcomes and multiple program and institution characteristics and practices.<sup>22</sup> While some of these quality determinants have been analyzed in the literature, others, such as institutional specialization and high-quality accreditation, have not. Second, our analysis of the connections between program value added and local labor market conditions is novel. Lastly, evidence from the US is based on data from one state, whereas we rely on data from an entire country.

# **3. Institutional Context: Short-Cycle Programs in Colombia**

The higher education system in Colombia offers bachelor degrees (typically lasting 5 years) and short-cycle programs (SCPs). SCPs award either technical (2 years) or technological (3 years) degrees. In 2019, SCPs in Colombia captured 32 percent of total higher education enrollment

> 17 See Altonji and Zimmerman (2017), Altonji et al. (2016), Altonji et al. (2012), Andrews et al. (2017), Bahr (2016), Falch et al. (2022), Hastings et al. (2013), Hastings et al. (2016), and Kirkeboen et al. (2016).

> 18 See Barrera-Osorio and Bayona-Rodriguez (2019), Dale and Krueger (2014), and Hoekstra (2009).

> 19 See Cohodes and Goodman (2014).

> 20 See Teixeira et al. (2013), Hoxby and Bulman (2015), and Bound et al. (2010).

> 21 See Cellini and Turner (2019) and Cellini and Chaudhary (2014).

> 22 We follow an approach similar to the student-level estimations in Dinarte et al. (2021) for Brazil and Ecuador. In that paper, the authors also include program-level estimations with a rich set of program and institution practice and characteristics that they collected from a survey of five developing countries. In contrast, the current paper uses administrative data only.

8
- Fischer, G., Nachtergaele, F. O., Prieler, S., Teixeira, E., T ́oth, G., Van Velthuizen, H., Verelst, L. and Wiberg, D. (2012). Global agro-ecological zones (gaez v3. 0) - model documentation. Technical Report. FAO, Rome.

- Freire, S., MacManus, K., Pesaresi, M., Doxsey-Whitfield, E. and Mills, J. (2016). _Development of new open and free multi-temporal global population grids at 250 m resolution_ , Geospatial Data in a Changing World; Association of Geographic Information Laboratories in Europe (AGILE) edn. AGILE.

- Fritz, S., See, L., McCallum, I., You, L., Bun, A., Moltchanova, E., Havlik, P. and Co. (2015). Mapping global cropland and field size. _Global change biology_ , **21,** number 5, 1980–1992.

- Fujii, T. (2010). Micro-level estimation of child undernutrition indicators in cambodia. _World Bank Economic Review_ , **24,** number 3, 520–553.

- Fujii, T. and van der Weide, R. (2020). Is predicted data a viable alternative to real data? _World Bank Economic Review_ .

- Gibson, J., Le, T. and Kim, B. (2017). Prices, engel curves, and time-space deflation: Impacts on poverty and inequality in vietnam. _World Bank Economic Review_ , **31,** 504–530.

- Goldberger, A. S. (1962). Best linear unbiased prediction in the generalized linear regression model. _Journal of the American Statistical Association_ , **57,** 369–375.

- Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S.V., Goetz, S.J., Loveland, T.R. and Kommareddy, A. (2013). High-resolution global maps of 21st-century forest cover change. _Science_ , **342,** 850–853.

- Henderson, C. (1953). Estimation of variance and covariance components. _Biometrics_ , **9,** 226–253.

- Henderson, J. V., Storeygard, A. and Weil, D. N. (2012). Measuring economic growth from outer space. _American Economic Review_ , **102,** number 2, 994–1028.

- Hertel, T. W., Burke, M. B. and Lobell, D. B. (2010). The poverty implications of climate-induced crop yield changes by 2030. _Global Environmental Change_ , **20,** number 4, 577–585.

- Hijmans, R. J., Cameron, S., Parra, J., Jones, P. G., Jarvis, A. and Richardson, K. (2005). _WorldClim_ , version 1.3 edn. University of California, Berkeley.

55
ing in this sector. By 2002, all countries expanded significantly the share of employment in agriculture with four countries with well over 50% of the workers in this sector while the share in industry declined in all countries. On the contrary, other sectors do not show consistent trends. Employment in transport and communication, trade and catering and other services have all mixed performances during the growth period. On average, employment in industry declined by 4 percentage points while employment in agriculture increased by 6 percentage points between 1996 and 2002.

**Table 2.3 Employment Structure by economic Sector During the Growth Phase (1996-2002)**

||**Industry and**<br>**Construction **|**Agriculture**|**Transport &**<br>**Communication **|**Trade &**<br>**Catering**|**Other Services**|**Total**|
|---|---|---|---|---|---|---|
||||**1996**<br>||||
|Armenia|23|41|3|8|26|100|
|Azerbaijan|15|31|6|16|32|100|
|Georgia|10|52|6|9|23|100|
|Moldova|15|42|4|16|22|100|
|Kyrgizstan|15|46|5|9|25|100|
|Tajikistan|14|58|3|4|20|100|
|CIS-7*|15|45|5|10|25|100|
|Othe CIS|30|18|8|14|30|100|
||||**2002**||||
|Armenia|16|45|4|9|27|100|
|Azerbaijan|12|40|5|16|27|100|
|Georgia|10|52|4|10|24|100|
|Moldova|14|50|4|10|22|100|
|Kyrgizstan|10|53|4|11|22|100|
|Tajikistan|8|66|2|4|20|100|
|CIS-7*|12|51|4|10|24|100|
|Othe CIS|27|20|8|15|31|100|
|||**1996-20**|**02 (% changes)**||||
|Armenia|-6|4|0|1|1|0|
|Azerbaijan|-4|9|-1|1|-5|0|
|Georgia|0|0|-2|1|1|0|
|Moldova|-1|7|0|-6|-1|0|
|Kyrgizstan|-4|7|-1|2|-3|0|
|Tajikistan|-6|8|-1|-1|0|0|
|CIS-7*|-4|6|-1|0|-1|0|
|Othe CIS|-3|1|0|1|1|0|

Source: CIS-Stat (1998 and 2004). (*) Uzbekistan excluded (data not available for 2002) Part of this relative employment shift toward agriculture is explained by the privatization process, restructuring and changes in productivity. In agriculture, privatization has generated a large number of small household farms and the economic recession has pushed people to seek refuge in subsistence agriculture. In industry and manufacturing initially delayed reforms and the heritage of labor hoarding kept workers formally on the job. By the time growth started, there were plenty of productivity gains to be made with the existing workforce and the need to accelerate restructuring has in fact pushed more workers out of industrial production. Some of these workers are evidently still taking up farming as the only available alternative in the labor market. Thus industrial growth in recent years (if and where it occurred) has been achieved by increasing productivity and not by increased employment. Productivity and wages have in fact closely followed output developments and explain the low employment-output elasticity as illustrated in previous sections.

**Fourth** , relative wages have not followed the relative performance of industry and agriculture. In Armenia, Georgia and Kyrgyzstan, nominal wages in agriculture between 1997 and 2002 have been in between one-fourth and one-half of the wages in industrial manufacturing and the gap between agricultural and industrial wages has been constantly growing in nominal terms during the

16
three dependent variables separately. Because data on all three are missing for a sizable portion of the country-years in the overall dataset (13, 52, and 73 percent missing, respectively), the dependent variable is the greatest constraint on the size of the samples used in these regressions.1<sup>8</sup> _Bzvanate Associattons._ If the hypothesis is correct, then countries on GSP should be more protectionist (with higher _Dutzes, Tariff,_ and _Closure)_ than countries dropped from GSP. In Figure 1, which displays averages across all country-years in the sample, on and off GSP, we see this is indeed true. The average ex-GSP recipient, compared to the average beneficiary, has less than half the nominal tariff and duties, and imports nearly 9 percent more of its GDP per year. This association is evident for the continuous measure of GSP benefits as well. For example, the correlation between _GSP Fraction,,t_ and _Tarnff,,t_ is 0.15 (p < 0.001, n = 572). Of course, these simple summaries are by no means definitive, since they fall to control for alternative explanations of trade policy, but they nonetheless indicate the association of developing country protectionism with GSP.

# 4.2 Control Variables

_Trend._ Virtually all developing countries have liberalized their trade policies significantly over the course of the past decade or two. Since 36 countries have been dropped from the US GSP program over time, a positive correlation between GSP withdrawal and liberalization could result from this secular trend alone. To isolate GSP's impact from the secular trend, we include the variable _TRendt,_ i.e., the mean of the (relevant) dependent variable across all countries in the dataset in year t.19 _Condstionality._ Many developing countries in this time period have experienced acute financial crises, and, in most cases, sought assistance from the International Monetary Fund (IMF), whose conditionality terms typically include trade liberalization. Compliance with IMF conditionality is seriously lacking on average (e.g., IMF [1998] and Goldstein [2000]). Rodrik [1994] similarly argues that trade liberalization in the developing world has _not_ been driven by pressure from international financial institutions. Nonetheless, to control for this potential cause of freer trade policies, we include the variable _IMF,,t,_ a dummy which is 1 if an ongoing IMF program is underway in country i in year _t_ (Vreeland [2002]),20 and 0 otherwise. IMF programs were underway in about 38 percent of the country-years in the dataset.

_Market Size._ Countries with larger markets may be more diversified and thus have a smaller proportion of GDP involved in trade. Hence we introduce a control for (the natural

> 18Testifying to the importance of redundancy for this analysis, the three measures are not highly correlated For Dutzes,t and _Tariff,,t,_ r = 0.37 (n = 542); _Dut%es,,t_ and _Closure,,t,_ r = 0.13 (n = 1540); Tanff,,t and _Closure,,t,_ r = 0.42 (n = 864).

> "<sup>9</sup> We thank Jagdish Bhagwati for suggesting the inclusion of this variable.

> 2('Thanks to Jim Vreeland for the most current update of his dataset of IMF program participation.

12
metropolitan settlements (i.e., ‘townships’) (Collinson et al., 2006) in search of higher wages and better services (Choe and Chrite, 2007). New migrants place pressure on local municipalities to provide basic services, including water and sanitation. As shown in Table 16, poorer urban households consume more water per capita than their rural counterparts. For example, the poorest urban quintile consumes eight times more water per capita than rural households at similar levels of expenditure.<sup>24</sup> Thus the continued migration of lower-income households from rural areas to urban centers will dramatically increase the amount of water demanded via established distribution networks .

In this section we present two scenarios reflecting the current structural and demographic changes taking place in SA. The first scenario (scenario III) examines the impact of ruralto-urban migration on urban household water demand and the additional pressures that this places on water resources under current water allocations, i.e. not allowing for changes in current regional and sectoral availability of water (i.e. the _Water-Restricted Urbanization_ scenario). The second scenario (scenario IV) implements scenario III under liberalized regional water markets allowing for market-based transfers of water between irrigation agriculture and non-agriculture within WMA’s ( _Water-Liberalized Urbanization_ ) while maintaining current inter-basin transfers (between WMA’s) unchanged.

Scenario III is implemented in the model by exogenously increasing urban demand through an urbanization mechanism (i.e. rural-urban migration). To capture the rapid pace of rural-to-urban migration in SA, we model an out-migration of half of the remaining rural population living in the lowest three expenditure quintiles (i.e., the rural population shares fall to around 20 percent). We assume that migrants move from rural quintiles to equivalent urban quintiles within their own WMA. For example, migrant workers and their families in the lowest rural quintile move into the lowest urban quintile, thereby increasing the labor endowment of this representative household in the model and hence its share of labor incomes earned within their WMA-specific labor market.

> 24 Part of the difference in rural/urban water use may be attributed to the lack of formal water distribution systems in rural areas, such that rural households reported using less water than urban households in the household survey (i.e., they paid less for water). However, this gap also exists for higher-income rural/urban households, who have better access to formal water distribution networks, thus confirming the higher per capita water demand in urban areas.

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