rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Figure 7. Change in Household Consumption to Income Ratio<br><!-- End of picture text --> <!-- Start of picture text --> 10.0%<br>7.4%<br>8.0%<br>6.0%<br>4.0%<br>2.0%<br>0.0%<br>-2.0%<br>-4.0%<br>-3.2%<br>-6.0%<br>-8.0%<br>-10.0%<br>-9.2%<br>-12.0%<br>Bangladesh, Peru, 2004-2010 Thailand, 2000-<br>2000-2010 2009<br><!-- End of picture text --> Source: Peru's ENAHO 2004 -2010, Thailand's SES 2000 -2009, and Bangladesh's HIES 2000 - 2010.

Figure 8. Contribution to the decline in Poverty <!-- Start of picture text --> A. National Moderate Poverty Lines B. USD 1.25 Poverty Line in PPP<br>140 250<br>120 200<br>100 19<br>150<br>80 13 163<br>107 100 17<br>53 110<br>60 61<br>50 80<br>40 36 42<br>0 -10 -1215<br>20 40 -43<br>2 21<br>12 -50<br>0<br>-12 -62<br>-24 -24<br>-20 -100<br>-5<br>-40 -150<br>Bangladesh, Peru, 2004 to Thailand, 2000 to Bangladesh, Peru, 2004 to Thailand, 2000<br>2000 to 2010 2010 2009 2000 to 2010 2010 to 2009<br>Consumption-income ratio Adult population<br>Consumption-income ratio Adult population<br>Income Unexplained Income Unexplained<br>Source: Shapely value estimates based on Peru's ENAHO 2004 -2010, Thailand's SES 2000 -2009, and Bangladesh's HIES 2000 -2010.<br><!-- End of picture text --> 25
<!-- Start of picture text --> Upward mobile Downward mobile Aspiring middle class in both Secure in both periods<br>©©©©<br>22) a 2)<br>SE SE SE SE<br>° oj .—_- 0 °<br>PEsf FES& SfSSSf w& MEsf KKSP© FS8 KS KSsf KM& SfSMSf MMSF KMSw Pp © MSSS Yo<br>Population shares are based on synthetic panel estimates for household heads aged between 25 and 60 in the first round of each interval.<br>The poverty line used is RM 920, the vulnerability line RM 1590.<br><!-- End of picture text -->
**Table 3** : **Regressions of Social Welfare Growth on Average Income Growth:  Min 5-Year Spells Disaggregated By Income Level**

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|---|---|---|---|---|---|---|---|---|
|**Social Welfare Function**|**Bottom 10%**|**Bottom 20%**|**Bottom 40%**|**Bottom 90%**|**Atkinson**<br>**Index (1)**|**Atkinson**<br>**Index (2)**|**Atkinson**<br>**Index (3)**|**Sen Index**|
|||**Pan**|**el A: High Inc**|**ome Countrie**|<br><br>**s**|<br>|<br>||
|Avg. growth|1.253***|1.030***|0.950***|0.947***|0.977***|1.021***|1.102***|0.953***|
||(0.255)|(0.156)|(0.106)|(0.0469)|(0.0504)|(0.105)|(0.163)|(0.0635)|
|Number of Observations|75|75|75|75|75|75|75|75|
|R-squared|0.441|0.600|0.755|0.947|0.939|0.776|0.615|0.904|
|Variance decomposition|0.352|0.583|0.795|1.000|0.961|0.761|0.558|0.948|
|P-val. wald test,slope=1|0.329|0.850|0.644|0.268|0.659|0.846|0.536|0.468|
|||**Pane**|**l B: Middle In**|**come Countri**|**es**||||
|Avg. growth|1.164***|1.091***|1.033***|0.997***|1.013***|1.055***|1.097***|1.011***|
||(0.0955)|(0.0719)|(0.0544)|(0.0264)|(0.0279)|(0.0544)|(0.0737)|(0.0319)|
|Number of Observations|168|168|168|168|168|168|168|168|
|R-squared|0.509|0.693|0.819|0.958|0.936|0.728|0.583|0.939|
|Share of variance due to growth|0.438|0.636|0.793|0.960|0.924|0.690|0.531|0.929|
|P-val. wald test,slope=1|0.0911|0.211|0.551|0.922|0.630|0.317|0.193|0.728|
|Avg. growth|0.899***|0.951***<br>**Pan**|0.967***<br>**el C: Low Inc**|0.975***<br>**ome Countrie**|0.978***<br>**s**|0.947***|0.923***|0.975***|
||(0.291)|(0.216)|(0.145)|(0.0535)|(0.0742)|(0.159)|(0.223)|(0.0722)|
|Number of Observations|42|42|42|42|42|42|42|42|
|R-squared|0.269|0.409|0.569|0.836|0.820|0.569|0.418|0.801|
|Share of variance due to growth|0.299|0.430|0.588|0.858|0.839|0.600|0.453|0.821|
|P-val. wald test,slope=1|0.732|0.823|0.821|0.650|0.769|0.744|0.732|0.733|

Notes: This table reports the results from a regression of growth in the indicated social welfare function (in each column) on growth in average incomes, in the sample of non-overlapping spells lasting at least five years. The three panels correspond to sets of countries at different income levels, following the World Bank’s classification for 2012. Heteroskedasticity-consistent standard errors reported in parentheses. * (**) (***) indicate significant differences from zero at the 10 (5) (1) percent level. The last row in each panel reports the p-value associated with the test of the null hypothesis that the estimated slope coefficient is equal to one.

43
- decreases with γ, the speed with which existing and potential customers respond to the price differential between the dominant utility and the fringe—clearly the more responsive customers are to the price differential the smaller will be the amount by which the utility will need to underprice in order to attract a given number of customers to its network;

- decreases with φ, the rate at which the utility’s adjustment costs increase with the speed with which it expands its network—the utility will have less of an incentive to price aggressively below the fringe in order to rapidly expand its network if the adjustment costs that it incurs rise rapidly with the speed of expansion;

Through simple differentiation we obtain from (19) If N0 is small, then P<sup> ̇</sup> (t) > 0, i.e. the utility follows a path of increasing These results could have signi fi cant implications for the design of regulatory policy in developing countries where the utility networks are underdeveloped and only a small portion of the population has access to basic infrastructural services. To the extent that the utility’s intertemporal pro fi t- maximization plan calls for a low initial price, there is no need to subject it to a detailed regulatory scrutiny, at least with respect to pricing. This is comforting because the pursuit of pricing and other regulations to elicit optimal industrial performance in many developing countries is hindered, especially during the early stages of the reform process, by the lack of proper accounting systems and by the dearth of information on marginal costs, demand elasticities, and other pertinent attributes of demand and cost relationships. Under the traditional command-and-control regulatory model, any prices calculated without such information are apt to be inconsistent with economic efficiency and be damaging to economic welfare. The information available to the fi rms themselves is also highly imperfect in many developing countries. Still, it seems likely that the fi rms will have better 16
**<u>Table 7.2 Impact on enrollment and child development outcomes using the DID methods by playgroup enrollment at baseline</u>**

||FE<br>Midline<br>Bat|: Never-En<br>*Actual<br>ch3<br>|rolled at baseli<br>Endline*Ac|ne<br>tual Batch3<br>|F<br>Midline*Ac|E:Ever-Enr<br>tual Batch3<br>|olled at baseli<br>Endline*A|ne<br>ctual Batch3|
|---|---|---|---|---|---|---|---|---|
||Coeff|Robust<br>SE|Coeff|Robust<br>SE|Coeff|Robust<br>SE|Coeff|Robust SE|
|**Enrollment in ECED Programs**|||||||||
|Was child enrolled in project playgroups in survey year (1=Yes)|0.173***|(0.016)|0.304***|(0.020)|(dropped)||(dropped)||
|Was child enrolled in non-project playgroups in survey year (1=Yes)|-0.052***|(0.016)|-0.081***|(0.019)|(dropped)||(dropped)||
|Was child enrolled in Islamic Kindergarten in survey year (1=Yes)|0.017*|(0.010)|0.040***|(0.015)|0.027|(0.019)|0.012|(0.023)|
|Was child enrolled in Kindergarten in survey year (1=Yes)|-0.037|(0.026)|-0.143***|(0.030)|-0.274***|(0.081)|-0.276***|(0.069)|
|Was child enrolled in any ECED programs in survey year (1=Yes)|0.097***|(0.030)|0.068**|(0.029)|(dropped)||(dropped)||
|Was child enrolled in Primary school in survey year (1=Yes)|-0.003|(0.006)|-0.002|(0.006)|0.001|(0.018)|0.000|(0.002)|
|Cumulated number of months of project playgroup|1.600***|(0.156)|3.606***|(0.264)|4.133***|(0.615)|5.753***|(0.885)|
|Cumulated number of months of non-project playgroup|-0.488***|(0.151)|-0.779***|(0.244)|-1.800***|(0.638)|-2.037***|(0.751)|
|Cumulated number of months of Islamic Kindergarten|0.228*|(0.119)|0.543**|(0.252)|0.266|(0.194)|0.250|(0.320)|
|Cumulated number of months of Kindergarten|-0.467*|(0.272)|-1.614***|(0.465)|-2.605***|(0.826)|-4.592***|(1.288)|
|Cumulated number of months of any ECED programs|0.873***|(0.301)|1.757***|(0.475)|-0.006|(0.177)|-0.626|(0.854)|
|Cumulatednumberof months of Primary school|-0.048|(0.069)|0.213|(0.370)|0.057|(0.182)|1.258|(0.860)|
|**Standardized Child Development Outcomes**|||||||||
|EDI: Physical Health & Well-Being|-0.057|(0.088)|0.149*|(0.088)|0.254|(0.252)|-0.033|(0.224)|
|EDI: Social Competence|0.222***|(0.076)|0.054|(0.076)|0.045|(0.191)|-0.227|(0.189)|
|EDI: Emotional Maturity|-0.006|(0.081)|0.182**|(0.078)|0.145|(0.214)|0.071|(0.199)|
|EDI: Language & Cognitive Development|0.115**|(0.047)|0.045|(0.042)|-0.101|(0.132)|-0.042|(0.104)|
|EDI: Communication & General Knowledge|0.036|(0.046)|0.015|(0.076)|0.064|(0.097)|0.007|(0.176)|
|<br>SDQ: Emotional Symptoms|0.019|(0.073)|-0.109|(0.080)|-0.004|(0.199)|-0.153|(0.204)|
|SDQ: Conduct Problems|-0.191**|(0.077)|-0.092|(0.078)|0.220|(0.202)|0.178|(0.182)|
|SDQ: Hyperactivity & Inattention|0.098|(0.080)|0.011|(0.083)|0.216|(0.207)|0.353|(0.215)|
|<br>SDQ: Peer Problems|-0.120|(0.090)|-0.179**|(0.088)|-0.087|(0.214)|0.227|(0.212)|
|SDQ: Pro-social Behavior|-0.061|(0.084)|0.026|(0.078)|0.191|(0.245)|0.309|(0.201)|
|SDQ:Total Difficulties|-0.083|(0.076)|-0.144*|(0.079)|0.127|(0.183)|0.181|(0.185)|

Notes: *** p<0.01, ** p<0.05, * p<0.1; Children and their household characteristics in Table 2 are controlled. Robust standard errors are clustered at the village level.

40
literature. Countries first diversify then they start specializing at around PPP GDP per capita of 25,000 (constant 1985 US dollars).!° Re-specialization in export services occurs at a higher level of GDP per capita compared with merchandise exports. However, the data is classified into only 13 sectors which is more aggregated than the other datasets used.

<!-- Start of picture text --> classified into only<br><!-- End of picture text --> <!-- Start of picture text --> sectors which is<br><!-- End of picture text --> <!-- Start of picture text --> aggregated<br><!-- End of picture text --> <!-- Start of picture text --> the other datasets used.<br><!-- End of picture text --> Figure 3: Lowess Curve for the Gini index for services exports <!-- Start of picture text --> Services Exports<br>+ ;<br>©, ||<br>|<br>|<br>|<br>nN2||<br>|<br>|<br>|<br>|<br>Za |<br>Chee<br>|<br>|<br>|<br>|<br>R. |<br>|<br>|<br>©]<br>S<br>010000 20000 30000 40000 50000<br>GDP per capita, PPP (constant 1985)<br><!-- End of picture text -->

Source: Authors’ calculations <!-- Start of picture text --> Within Re-<br><!-- End of picture text -->

- 4.2 Breaking-Down Sources of Variation for Pooled, Between and Within Relationships Using Employment, Value-Added and Exports

<!-- Start of picture text --> lationships Using Employment,<br><!-- End of picture text --> <!-- Start of picture text --> Exports<br><!-- End of picture text --> In this section, we try to understand what is driving the U-shaped pattern of the lowess curve and whether it is due to differences between-country or within-country. Figure 4 shows non-parametric lowess curves for the pooled, between and within-relationship between employment and GDP per capita.

<!-- Start of picture text --> tween employment and GDP per capita.<br><!-- End of picture text -->

> '6 The lowess curve has a U-shaped curve for all economies. Looking at resource-rich and resource-poor countries separately, it seems that the downward section of the U shaped is driven by diversification in resource-poor countries and the upward section is driven by concentration in resource-rich countries. Results are available upon request.

<!-- Start of picture text --> countries and the upward<br><!-- End of picture text --> <!-- Start of picture text --> section is driven by concentration in<br><!-- End of picture text --> <!-- Start of picture text --> countries.<br><!-- End of picture text --> <!-- Start of picture text --> available upon request.<br><!-- End of picture text --> 14
<u>Table 3. R&D intensity (USD) by firm type</u>

||**Ban**<br>**Average**<br>**all firms**|**gladesh**<br>**Average**<br>**firms**<br>**performing**<br>**R&D**|**I**<br>**Average**<br>**all**<br>**firms**|**ndia**<br>**Average**<br>**firms**<br>**performing**<br>**R&D**|**N**<br>**Average**<br>**all**<br>**firms**|**epal**<br>**Average**<br>**firms**<br>**performing**<br>**R&D**|**Pak**<br>**Average**<br>**all**<br>**firms**|**istan**<sup>**b**</sup><br>**Average**<br>**firms**<br>**performing**<br>**R&D**|
|---|---|---|---|---|---|---|---|---|
|Small (<20)|2.66|44.20|27.51|131.37|0.14|6.68|0.62|82.17|
|Medium (20-99)|7.98|59.57|19.14|62.42|3.44|50.55|82.28|2,706|
|Large (>100)|8.01|30.70|24.33|55.93|2.25|39.08|31.56|429.20|
|Non-exporter|5.33|44.22|21.02|72.27|1.40|41.91|26.24|944.76|
|Exporter|8.59|38.60|30.86|71.60|1.19|15.20|125.68|2,226.26|
|national|6.12|42.09|22.99|72.77|1.14|31.61|43.52|1,371.92|
|foreign (25%)|7.43|40.88|4.96|10.75|13.93|62.68|||
|age<5|7.54|96.16|48.19|212.04|||||
|age 5-9|3.03|30.05|30.31|99.80|0.23|6.98|4.77|162.14|
|age 10-14|2.41|16.74|16.67|55.43|0.05|2.59|17.00|2,057.40|
|age 15-19|5.09|35.76|16.68|53.25|0.66|12.33|142.98|4,384.87|
|age 20+|9.14|53.66|20.10|57.79|3.79|71.00|48.07|933.99|

Source: Author’s elaboration from Enterprise Survey (2014)<sup>b</sup> For Pakistan only 23 firms with information available

# 5. Innovation Patterns

We start the analysis of innovation patterns by distinguishing innovations into two types: (i) technological innovations, which involve product and process inventions, and (ii) non-technological innovations, which involve organizational and marketing inventions.

## 5.1 Overall Pattern

Countries in the South Asia region can be divided into two groups: leaders, i.e., Bangladesh and India, and laggards, i.e., Nepal and Pakistan, according to technological innovations (Table 4). On the one hand, we find Bangladesh and India, as strong innovators, with innovation rates around roughly 80 percent, respectively, showing a performance that surpasses the average of the ECA and Africa regions. On the other hand, we find Pakistan and Nepal, with innovation rates of 14.8 percent and 21.2 percent, respectively, displaying a performance below the average of the ECA and Africa regions.

10
Most trading in Nile perch and other fish occurs at the landing beach. Increasingly, however, agents are utilising transporter boats to gather fish from distant beaches or direct from fishing vessels which is then landed at a single beach. This widens their supply base and enables the newly caught fish to be cooled in ice at the earliest opportunity. The majority of traders/processors only deal with Nile perch (LVFRP, 1999a).

# **Figure 7. Suppliers of fish to Lake Victoria fish traders/processors:**

<!-- Start of picture text --> 100%<br>80% Fish factories<br>60% Beach-side processors<br>Wholesalers at markets<br>40%<br>Fishers<br>20%<br>0%<br>Fresh fish Processed fish<br><!-- End of picture text --> _Source: LVFRP (1999a)._ In many cases the conditions in which Nile perch is graded and sold at the beach are rudimentary. At more developed beaches this occurs in a permanent banda (as in Figures 8), although in some it still takes place in the open air. Often fish is placed on the ground and there is no supply of potable running water or cool storage. Further, the banda is not isolated from the surrounding area with fencing and is accessible by domestic animals and wildlife and members of the community not involved in fish marketing.

# _Industrial fish processing_<sup>_22_</sup>

The industrial fish processing sector first started to evolve in Kenya at the start of the 1980s and by 1987 ten factories were operational (Reynolds and Greboval, 1988; Gibbon, 1997). At this time no facilities had been established in Tanzania or Uganda and Kenyan processors sourced fish in all three countries. Initially, factories were situated in Kisumu and Nairobi, but more recently facilities were established nearer to landing beaches in Homa Bay and Migori in an attempt to reduce transport costs and maintain quality. Processing capacity continued to expand in the early 1990s, with the number of facilities peaking at 15 in 1995 (Bokea and Ikaiara, 2000).

A significant proportion of traders/processors experience problems with fish spoilage (LVFRP, 1999a). These problems are related in particular to poor storage conditions and, where trading occurs in the open air, rain (Table 2). Another significant problem faced is transportation, due largely to the poor state of the roads in the region.

> 22 For a more detailed review see Wakwabi _et al_ ., 2003.

23
employment coefficients, i.e., the number of workers needed to produce one billion dong worth of goods,<sup>12</sup> were calculated using the employment and production data from the annual _Enterprise Survey_ data and exports data from the UN Comtrade System at the two-digit VSIC level for the period 2000-2007. As the _Enterprise Survey_ data do not include household enterprises, Vietnam’s goods are assumed to be exported through formal enterprises.<sup>13</sup> The visual comparison between Figures 3.1-3.3 and Figures 4.1-4.3 reveals that the employment effect of exports is disproportionately larger for labor-intensive industries due to the relatively larger employment creation per dollar of goods exported in such industries. In contrast, the employment effect of increases in the value of mining sector exports is relatively modest due to that sector’s low labor-intensity despite the importance of the mining sector in Vietnam’s exports in value terms. Since Vietnam’s exports to the U.S. consist mainly of labor-intensive goods, if exports are evaluated in terms of their labor content, the share of the U.S. in Vietnam’s “industrial” exports for the year 2004 rises to about 28 percent. Specifically for the period 20012004, exports to the U.S. accounted for about 35 percent and 70 percent of _changes_ in Vietnam’s total industrial exports in terms of export values and labor content respectively. In the empirical section, I will use these large changes to identify the impact of exports on wages.

Appendix Table A.3 reports the evolution of Vietnam’s imports by the VSIC sub-categories for the period 2000-2008. Vietnam’s imports rose from $15,637 million in 2000 to $31,969 million in 2004, and, after Vietnam’s accession to the WTO in January 2007, increased at an accelerated pace to $80,714 million in 2008. Since United States goods enjoyed MFN tariff status in

> 12 The implicit assumption of this exercise is that export products are typical of their industries. However, Figures 4.1-4.3 may be understating the labor content of exports if the exports were relatively more labor-intensive than other non-exported products within the same industries. On the other hand, the figures may be overestimating the labor content of exports as exporting firms tend to be more productive and thus use less labor to produce a given value of output than non-exporters.

> 13 Some household enterprises sold directly to the international market. Out of 4,326 households who reported nonfarm household activities in the VHLSS 2004, 50 households (about 1.2 percent) responded affirmatively to the question asking whether they “have sold goods/services on international market”.

8
holds the dominant position in explaining variation in property rights protection (3.89%). Removing the former Socialist economies (which arguably have more in common than just legal tradition) significantly reduces the explanatory power of the legal variables by 44%, leaving them to explain just 2.16% of the variation in property rights, while the ethnic fractionalization variable explains 4.57% of the variation in property rights.

In a subsequent paper, using Directed Acyclic Graph methodology to address endogeneity issues, Ayyagari, Demirguc-Kunt, and Maksimovic (2012) again confirm that ethnic fractionalization is the dominant institutional predictor of property rights protection across the world.

The working hypothesis in the literature is that strong investor protections promote efficient contracting and increase firm value. Strong creditor rights may however, lead to premature liquidation of viable firms which are undergoing temporary distress (see, for example, Aghion, Hart, and Moore, 1992; Hart, La Porta, Lopez-de Silanes, and Moore, 1997). Vig (2011) examines this possibility in his study of the 2002 Securitization and Reconstruction of Financial Assets and Enforcement of Security Interests Act 2002 in India, This reform significantly straightened and made streamlined the process of liquidation firms which did not fulfill their contractual obligations to creditors. Vig (2011) finds that the act led to a reduction in debt, debt maturity, asset growth and an increase in liquidity hoarding by firms. As expected, these effects are expected to be larger for firms with tangible assets, since these assets are more easily secured. Thus, it is not the case that more investor protection is always better than less: very strong creditor rights can increase the likelihood of inefficient liquidation and cause firms to decrease leverage in response.

# **_4.3 Information Quality and Availability_**

A critical element of corporate governance in any country is the quality of firm-specific information and its availability to regulators and investors. Greater transparency and better quality of firm specific information make for more efficient contracting between management and investors and also make it easier for firms to identify good investment opportunities.

30
|**Ta**|**ble 2. Pair-**<br>_Hi_<br>(Exportin|**Wise Corre**<br>_gh-Skill_Fir<br>g and/or Inn|**lations Bet**<br>ms<br> ovating)|**ween Meas**<br>Educ|**ures of Skill**<br>ation and Sk<br>Workforce|**s Demand,**<br>ills of|**Workforce S**<br>Enterprise<br>Training|**kills and S**<br>Skill|**kill Gaps**<br>Problems a<br>Vacancies|nd Job|
|---|---|---|---|---|---|---|---|---|---|---|
||Exporters<br>AND/OR<br>Innova-<br>tors|Exporters|Innova-<br>tors|%<br>Tertiary-<br>educated<br>workforce|% TVET<br>in<br>workforce|% MPT in<br>workforce|In-house or<br>external<br>training|Education<br>a big<br>problem|# Skill<br>problems|# job<br>vacancies<br>last 2<br>years|
|HighSkill|1.000||||||||||
|Export|0.325**|1.000|||||||||
|<br>Innovate|0.838**|-0.127**|1.000||||||||
|% Tertiary|0.084*|0.127**|0.027|1.000|||||||
|<br>%TVET|0.055*|0.047*|0.031|0.270**|1.000||||||
|%MPT|0.044*|-0.117*|0.096*|0.184**|0.133**|1.000|||||
|Any training|0.161**|0.099*|0.157**|0.105*|0.017|0.028|1.000||||
|Education a big<br>problem|0.014|-0.076*|-0.000|-0.128**|0.095*|-0.081*|-0.111*|1.000|||
|#skill Problems|0.125*|-0.065*|0.164**|-0.092*|0.026|0.029|0.006|0.099*|1.000||
|#Job vacancies|0.091*|0.218**|0.045*|0.134**|0.028|-0.029|0.221**|-0.071*|0.108*|1.000|
|Note: Significan|ce ofpair-w|ise correlati|ons:*sign|ificant at 5%|level **|significant a|t1%level||||

22