rafmacalaba/datause-encoder-data huggingface dataset:
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be seen, the results are very similar in terms of sign, significance and even coefficients for almost all variables considered and in both urban and rural areas. There is only one variable (ageXmarried) that is consistently different for the two models. We should conclude that results on age, marriage, education and household characteristics described for Table 2 are robust to location (urban and rural), fixed and random effects specifications and to the source of data.

# **Table 3 – LFS Vs. HBS Data**

||(1)<br>Urban LFS<br>2007|(3)<br>Urban HBS<br>2007|(2)<br>Rural LFS<br>2007|(4)<br>Rural HBS<br>2007|
|---|---|---|---|---|
|Age|0.332***|0.466***|0.203***|0.200***|
||(0.00715)|(0.0253)|(0.00528)|(0.0401)|
|age squared|-0.00461***|-0.00661***|-0.00248***|-0.00240***|
||(9.48e-05)|(0.000383)|(6.06e-05)|(0.000659)|
|Married|-3.152***|-2.766***|-1.438***|-1.261**|
||(0.135)|(0.441)|(0.0821)|(0.538)|
|ageXmarried|0.0238***|0.0137|0.0283***|-0.00330|
||(0.00336)|(0.0120)|(0.00198)|(0.0186)|
|head of household|0.903***|0.803***|1.530***|1.077**|
||(0.0471)|(0.203)|(0.0484)|(0.534)|
|primary education|-0.0330|-0.215**|-0.537***|-0.110|
||(0.0414)|(0.108)|(0.0660)|(0.145)|
|secondary education|-2.778***|-2.273***|-2.489***|-3.091***|
||(0.103)|(0.253)|(0.363)|(0.904)|
|ageXsecondary education|0.0914***|0.0838***|0.0828***|0.107***|
||(0.00309)|(0.00816)|(0.0137)|(0.0367)|
|tertiary education|1.931***|1.279***|2.768***|1.350*|
||(0.0624)|(0.170)|(0.311)|(0.796)|
|vocational education|2.258***|2.046***|||
||(0.0484)|(0.204)|||
|household head has general education|-0.340***|-0.250***|-0.137**|-0.329|
||(0.0372)|(0.0959)|(0.0689)|(0.223)|
|spouse is active|3.653***|2.313***|3.042***|1.717***|
||(0.0433)|(0.120)|(0.0312)|(0.136)|
|household has children under six y.o.|-0.162***|-0.315***|-0.0131|-0.287**|
||(0.0350)|(0.0980)|(0.0290)|(0.141)|
|household has children in age 7-17 out of school|||||
|<br>and work|0.300***|0.400***|0.00143|0.521***|
||(0.0443)|(0.137)|(0.0284)|(0.148)|
|household has more than 1/3 of other females|||||
|inactive|-0.138***|-0.343***|-0.477***|-1.408***|
||(0.0361)|(0.112)|(0.0443)|(0.264)|
|number of over 60 y.o. inactive|0.0556**|0.155**|0.0490*|-0.199|
||(0.0239)|(0.0675)|(0.0257)|(0.125)|
|Constant|<br>-6.502***|<br>-7.547***|<br>-5.156***|<br>-3.618***|
||(0.130)|(0.405)|(0.105)|(0.594)|
|Observations|63,266|4,625|39,716|1,407|
|Adjusted R-squared|0.393|0.306|0.325|0.220|
|Loglikelihood|-18875|-2037|-17850|-746.0|

16
_Sources_ : Original estimations based on migration data from Özden et al. 2011 and UN DESA 2020; and GDP data from the Penn World Tables (PWT) 10.0 (Feenstra, Inklaar, and Timmer 2015).

_Note_ : The figure traces the destination composition of emigrants for low-income (panel a) and middle-income (panel b) countries as they have developed. The horizontal axis represents the increase in incomes and the vertical axis represents the increase in migration stock relative to the baseline year. The baseline year, for each country, is defined as the year in which per capita GDP in constant 2017 US dollars in purchasing power parity terms (2017 PPP$) was at its lowest level since 1960. Each lightly colored lines represents the trajectory for a particular country to low-return (neighboring or low-income countries) or high-return (high-income countries) destinations. The solid lines show the locally linear fit between increases in incomes and changes in emigration rates to low-return or high-return destinations compared to the baseline year. Low-income countries were among the bottom third circa 1960 in terms of per capita GDP (2017 PPP$), and middle-income countries were among the middle third. Estimates exclude six outliers in terms of changes in emigration rates: three low-income countries/economies (Bosnia and Herzegovina, Grenada, and West Bank and Gaza) and three middle-income countries (Antigua and Barbuda, Dominica, and Saint Kitts and Nevis). CI = confidence interval.

The fact that emigration from low-income origin countries to high-income destination countries increases very slowly with development — both relative to middle-income countries as well as in absolute magnitudes — suggests very high costs of emigrating to these destinations. Much larger income gains are required for many people in those countries to be able to afford migration to high-income destinations.

# **Increased global integration**

Economic development in recent decades has been associated with greater integration with the global economy. Such integration could take the form of increased trade (figure 4) and/or increased capital through foreign direct investment (FDI). Increase in trade and capital flows, or globalization more broadly, affects incentives to migrate in complex ways, which could potentially drive the observed migration patterns.<sup>15</sup>

> 15 At the same time, migration also influences the nature of trade and capital flows, as demonstrated by  Javorcik et al. (2011), among others.

11
WPS4179

**Identifying Supply-Side Constraints to Export Performance in Ecuador: An Exercise with Investment Climate Survey Data**

Paulo Correa (ECSPF) Mariam Dayoub (LCSPF) Manuela Francisco (AFTP4)

## **_Abstract_**

_We apply a Heckman selection model to the 2003-Investment Climate Survey (ICS) to investigate supply-side constraints to export performance at the firm level in Ecuador. To correct for the non-random truncation problem, we use the Heckman selection model to estimate the probability of exporting (export propensity) and the share of total sales that are exported (export intensity) by Ecuadorian firms. A baseline model with 12 independent variables divided into three categories – idiosyncratic characteristics, technology, and business environment – is developed. Three other models are developed with the addition of variables related to trade integration, business environment, and infrastructure. Results corroborate with the hypothesis implicit in the Heckman model, which considers both decisions made by a firm – whether to export, and how much of its sales to export – to be interdependent. In the Ecuadorian case, three important results for the firm’s export performance are found: technology matters; infrastructure does not; and trade orientation is significant, with specialized firms tending to have smaller export intensity when having the countries of the Andean Community as their main trade partners, the opposite happening if the U.S. is their main trade partner. We find a robust and stable relationship for export propensity and intensity with size, import of inputs, labor regulations, in-house R&D, quality certification, web-use, and foreign ownership. Also, capacity utilization and trade with the U.S. positively affect export intensity, while trade within the Andean Community has the opposite effect in our outcome variable. No significant relationship was found for the infrastructure variables._

**Key Words** : Ecuador, exports, innovation, foreign networks, business environment, firm level.

# World Bank Policy Research Working Paper 4179, March 2007

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1
# **Appendices**

**Appendix 1: Exports by Region (as Percentage of GDP), 1970-2007**

<!-- Start of picture text --> World* East Asia & Europe & Euro area High income Latin Middle East South Asia Sub‐Saharan<br>Pacific Central Asia (incl OECD)* America & & North Africa<br>Caribbean Africa<br>1970 1990 2007<br>48.6<br>40.9<br>35.9<br>33.8 34.4<br>28.3 27.1 27.2 26.4<br>24.4 23.9 23.4<br>21.8<br>19.0 19.8 18.9 16.9 18.1 20.4<br>13.6 14.3<br>7.7 9.8 8.7<br>5.3<br><!-- End of picture text --> Source: Own illustration. Data: World Bank, World Development Indicators, National Accounts (1970-2000). *2006 figure.

33
received by each trader, _γi_ , is determined endogeneously.

Since trader 2 and trader 3 are located in the same market town, the outcome of their price competition is similar to a Bertrand outcome. The introduction of a third trader who is able to verify quality, drives down the price of low quality good to the marginal value of low quality, given by _p_<sup>_∗_</sup> . The price offered by trader 2 is then too low for it to attract any high quality goods. In equilibrium, the three traders receive different proportions of low and high quality goods. The proof is in the Appendix. With heterogeneous quality, there are two opposing forces on the prices offered by traders 1 and 2. On the one hand, entry by trader 3 puts upward pressure on the price offered by traders 1 and 2, the competition effect. On the other hand, sorting of farmers based on their quality puts a downward pressure on price offered by traders 1 and 2, the composition effect. It will ultimately be an empirical question to determine which of the two effects dominates.

# **III. Data**

The data used in this paper comes from four different sources. First, information on monthly prices and volume of crops sold in the main _mandis_ of the state from April 2000 to September 2005 is available from the MP State Agricultural Marketing Board. Second, annual district level production, yield and net area under cultivation of crops from 1998-2004 is taken from the Commissioner of Land Records, MP.<sup>27</sup> Third, dates of installation and location of all internet kiosks and hubs is obtained from the company’s business records. Monthly procurement of soybean by ITC Limited at each of its hubs is also obtained from the company and lastly, annual administrative and demographic information at the district level is taken from the census of India 2001.

A total of 1704 web kiosks and 45 hubs were established in 23 (out of a total of 45) districts of MP during the time period from October 2000 to January 2005. The installation date of an internet kiosk is defined as the day the computer equipment was installed in the village by ITC. On the other hand, the hub installation date is defined as the day the first direct sale was made by a farmer at the hub. In this way, I measure effective installation of hubs. Table 2 provides the dates at which the first kiosk and the

> 27Net area cultivated refers to area under crops grown only during the monsoon season of MP.

14
3 Table S. 1: Alternative Resting Energy Expenditure (REE) and Activity Level (AL) estimates

|Panel A: REE from National Fami|ly Health Surve|y (NFHS)||
|---|---|---|---|
|Ever mar. female 15-49|Female 15-49|Male 15-54|Child 0-2|
|1998 (NFHS-2)<br>1139|.|.|486|
|1998 Rural<br>1124|.|.|484|
|1998 Urban<br>1182|.|.|492|
|2005 (NFHS-3)<br>1153|1161|1452|461|
|2005 Rural<br>1133|1143|1430|453|
|2005 Urban<br>1199|1199|1491|487|
|2015 (NFHS-4)<br>1193|1194|1496|479|
|2015 Rural<br>1173|1177|1476|472|
|2015 Urban<br>1235|1229|1532|496|
|Panel B: Weight (kilograms) from Nation<br>|al Family Healt|h Survey (NF|HS)|
|1998 (NFHS-2)<br>46.5|.|.|8.8|
|1998 Rural<br>45.0|.|.|8.7|
|1998 Urban<br>50.9|.|.|8.9|
|2005 (NFHS-3)<br>48.0|47.3|55.1|8.3|
|2005 Rural<br>46.0|45.6|53.3|8.2|
|2005 Urban<br>52.6|51.1|58.4|8.8|
|2015 (NFHS-4)<br>51.9|50.5|58.7|8.6|
|2015 Rural<br>49.8|48.7|57.0|8.5|
|2015 Urban<br>56.1|54.1|61.7|8.9|
|Panel C: AL from Time Use Su<br>Male 15+|rvey (TUS) 199<br>Female 15+|8-1999<br>Male 6-14|Female|
|1998-99 mean<br>1.80|1.82|1.43|1.45|
|1998-1999 SD<br>0.45|0.34|0.24|0.24|
|1998-99 Rural mean<br>1.87|1.89|1.45|1.48|
|1998-99 Urban mean<br>1.62|1.66|1.39|1.39|

_Source_ : Authors’ analysis based on data from 1998-1999 India Time-Use Survey (TUS) and National Family Health Survey (NFHS) rounds 2, 3 and 4. _Note_ : Panel A uses the (Indian Council of Medical Research, 2009) REE equations, which are based on the (FAO/WHO/UNU Expert Consultation, 2001) equations with a 5% downward adjustment for children. Panel B reports weight in kilograms. Panel C uses the activity factors found in Ainsworth et al. (2000). All statistics use sample weights.
<!-- Start of picture text --> HKG: Labor productivity and Backward participation in GVCs<br>a. HKG: across sectors and years b. HKG: within sectors, across years<br>lo} ie 2<br>N<br>y<br>x ee 2<br>=ySy —eseFT peer. >> . ea 6 44 ,<br>ey ? a] is 4<br>S is} if 13 saa tit<br>Qa 2 “ea 4 oa<br>loo , “ye 4 ~<br>é<br>»<br>»<br>i) 0 2 4 6 8<br>' Foreign value added embodied in gross exports (log)<br>cs 0 Fe 10 45 — 3-food,bev.&tobacco — 4-textiles<br>Foreign value added embodiedH FFin gross exports (log) — 6-woodSehemidle — 7-paper10-ubberiplastice<br>all countries fitted line (all countries) —— 11-non-metallic 13-machinery minerals —— 12-basic&fabr. 14-elec.&opticalmetalequip.<br>* HKG — — fitted line (HKG) 15-transport 16-furniture&other<br><!-- End of picture text --> <!-- Start of picture text --> KOR: Labor productivity and Backward participation in GVCs<br>a. KOR: across sectors and years b. KOR: within sectors, across years<br>° i) .<br>ry ‘<br>Y ‘ yy L, t t<br>we ee 2s aft J: ad a<br>&yD aes ioei eae —> ‘ :az fhef of *: YLP.5 9 :<br>= * oS yy y<br><!-- End of picture text --> <!-- Start of picture text --> a 8 A hf OP aa .<br>© an 7 Bi0 + /<br>y “J a4<br>op . df<br>aed<br>Y<br>=} 2 4 6 8 10 12<br>' Foreign value added embodied in gross exports (log)<br>5 0 5 10 45 — 3-food,bev.&tobacco — 4-textiles<br>Foreign: value added embodiedaw Ain gross exports (log) — 6-wood9-chemicals — 7-paper10-rubbetiplastics<br>all countries fitted line (all countries) —— 11-non-metallic 13-machinery minerals —— 14-elec.&optical 12-basic&fabr. metalequip.<br>* KOR — — fitted line (KOR) 15-transport 16-furniture&other<br><!-- End of picture text -->
**Figure 7: Priority initiatives for households and leaders (%)**

<!-- Start of picture text --> Socio Economic<br>62<br>73 Security<br>Governance<br>26<br>18<br>12 9<br>Citizens Leaders<br><!-- End of picture text --> We will now focus on preferences within each category and compare preferences of households with those of local authorities to assess whether preferences of households and authorities align.

# **Governance**

For households, the management of their own affairs is the most important governance initiative. This holds across all three regions. In both rural and urban areas around onethird of all households prioritize this initiative. In Timbuktu, households attribute the same importance to the management of their own affairs as to greater representation of the northern population in national institutions. We find this to be consistent when disaggregating by ethnicity.<sup>25</sup> The second most important measure varies slightly as can been seen in table 7.

**Table 7: Two most important governance initiatives for households**

||**Most important governance**<br>**initiative**|**Second most important governance**<br>**initiative**|
|---|---|---|
|**Gao**|Greater responsibility in managing<br>our own affairs (35%)|Establishment of communities with<br>elected bodies through universal suffrage<br>(21%)|
|**Kidal**|Greater responsibility in managing<br>ourownaffairs (35%)|Strengthening the rule of law and<br>accountability (23%)|
|**Timbuktu**|Greater responsibility in managing<br>ourownaffairs (32%)|Greater representation of the northern<br>population in national institutions (32%)|
|**Urban**|Greater responsibility in managing<br>ourownaffairs (33%)|Greater representation of the northern<br>population in national institutions (26%)|

> 25 We are only considering Songhai and Tamashek who represent 47% and 37% respectively of the survey population.

16
- 4 -

**Table 1. Social spending by the enterprise sector** in **Russia, in 1992-94 in**<sup>**real and nominal terms.**</sup>

||1992|1993|1994|
|---|---|---|---|
|Social spending, total, trln rb, current prices|0.739|5.36|21.12|
|- health|n.a.|0.99|2.79|
|- education and culture|n.a.|0.58|1.78<br>|
|- housing, only maintenence|n.a.|1.96|6.48|
|- investments|n.a.|n.a.|5.42|
|- other|n.a.|1.83|4.65|
|Social spending, total, as % of GDP|3.89|3.13|3.46|
|Social spending, total, real, 1992= 100|100|72.89|71.27|
|**MEMO:**||||
|GDP, trln. rbl|19<br>|171.5<br>|611<br>|
|ICPI|14.54|9.95|4.03|

**Source: Goskomstat, own estimates**
**Table 11: long-term impacts of the 4P program on empowerment - exposure to IPV, NPDV, and NDV - ITT**

||Prevalence of<br>physical<br>intimate<br>partner<br>violence|Prevalence of<br>sexual<br>intimate<br>partner<br>violence|Prevalence of<br>emotional<br>intimate<br>partner<br>violence|Prevalence of<br>economic<br>intimate<br>partner<br>violence|Incidence of<br>physical<br>intimate<br>partner<br>violence|Incidence of<br>sexual<br>intimate<br>partner<br>violence|Incidence of<br>emotional<br>intimate<br>partner<br>violence|Incidence of<br>economic<br>intimate<br>partner<br>violence|
|---|---|---|---|---|---|---|---|---|
|ITT|-0.026|-0.027|-0.001|0.066*|-0.003|-0.013|0.026|0.080**|
||(0.48)|(0.15)|(0.97)|(0.06)|(0.91)|(0.39)|(0.56)|(0.02)|
|Observations|437|437|437|437|437|437|437|437|
|Adj. R2|-0.00|0.00|-0.00|0.01|-0.00|-0.00|-0.00|0.01|
|Control mean|0.136|0.045|0.227|0.141|0.095|0.027|0.186|0.105|
||Prevalence of<br>physical non-<br>partner<br>domestic<br>violence|Prevalence of<br>sexual non-<br>partner<br>domestic<br>violence|Prevalence of<br>emotional<br>non-partner<br>domestic<br>violence|Prevalence of<br>economic<br>non-partner<br>domestic<br>violence|Prevalence of<br>physical non-<br>domestic<br>violence|Prevalence of<br>sexual non-<br>domestic<br>violence|Prevalence of<br>emotional<br>non-domestic<br>violence|Prevalence of<br>economic<br>non-domestic<br>violence|
|ITT|0.034|-0.005|-0.011|-0.008|-0.013|-0.004|-0.031|0.000|
||(0.28)|(0.31)|(0.77)|(0.80)|(0.46)|(0.57)|(0.25)|(0.99)|
|Observations|437|437|437|437|437|437|437|437|
|Adj. R2|0.00|-0.00|-0.00|-0.00|-0.00|-0.00|0.00|-0.00|
|Control mean|0.091|0.005|0.205|0.109|0.041|0.009|0.091|0.009|
|P-values inpar|entheses: *p<0.1|**p<0.05 ***p|<0.01||||||

34
22 governments whose behavior is antitherical to their own values. In particular, vocal, well organized, and often well informed private voluntary organizations are among those most critical of official assistance to kleptocratic regimes in the developing world.

From a purely economic perspective, grors corruption often makes it more difficult for governments to implement agreed upon policies and decreases the overall efficiency of resource allocation. For private investors, arbitrary and capricious implementation of government regulations adds to the uncertainties and headaches of doing business in Sub-Saharan Africa. Indeed, one serious obstacle to private investment and growth in Africa is that everything is open to negotiation with government officials.19 Reforms targeted at economic governance could include: more transparent government budgeting; open and competitive bidding for large public investment projects; accountability of management of state-owned enterprises; and greater consistency in the implementation of regulations governing itivestment. Such conditionality might raise serious questions regarding programs in countries such as Ethiopia and Zaire, where a World Bank annual report boasted of positive net transfers of $211 million and $340 million, respectively, during FY 198589, but is a necessary remedy nonetheless.20 Even in circumstances of gross misgovernance, however, lending agencies may want to sustain modest core programs in order to maintain some continuity and presence. In the long run, it would be perverse and even counterproductive to punish poor people within a country because of the practices of a government 19. This is the opinion of one acute observer of the African scene. See Carol Lancaster, "Reform or Else?", Africa Report, July-August, 1990.

20. World Bank, Annual Report, 1989, p.<sup>112</sup> .
**Table 1. Access Indicators by Region**

||**Banks Deposit Accounts**|**Banks Loan Accounts per**|**Private Credit as**|
|---|---|---|---|
|**Region**|**per 1000 adults**|**1000 adults**|**% of GDP**|
|Sub-Saharan Africa|315.5|87.5|23.7|
|South Asia|653.8|54.2|43.9|
|**Middle East & North**<br>**Africa**|**744.0**|**213.3**|**50.6**|
|Europe & Central Asia|1,395.4|325.2|45.8|
|East Asia & Pacific|1,116.9|343.9|58.4|
|Latin America & Caribbean|1,227.5|366.8|44.7|
|High income: non OECD|1,865.0|851.5|83.4|
|High income: OECD|2,383.7|702.4|130.8|

Source: CGAP and World Bank Financial Access 2010, International Financial Statistics

**MENA countries tend to underperform on access to SME lending compared to financial sector depth and relative to countries with similar GDP levels** , as illustrated in figures 1 and 2. MENA financial systems are bank-dominated (bank assets were 63.6 percent of GDP in 2008), and banks lend to a narrow set of clients, including the government and larger corporates. MENA countries also tend to have relatively low access to deposits compared to GDP per head.

**Figure 1. Bank Loan Accounts per population against Financial Sector Depth and GDP per capita**

<!-- Start of picture text --> 1,000<br>800<br>600<br>Lebanon<br>400 Oman<br>200 Tunisa Jordan<br>Syria Egypt<br>0 Yemen<br>0.0 50.0 100.0 150.0<br>Private Credit as % of GDP<br>Loans accounts per 100,000 Adults<br><!-- End of picture text --> <!-- Start of picture text --> 1,000<br>800<br>600<br>Lebanon<br>400 Oman<br>200 Tunisa<br>Jordan<br>Egy ptSyria<br>0 Yemen<br>0 5,000 10,000 15,000 20,000<br>GDP per Capita<br>Loans accounts per 1,000 Adults<br><!-- End of picture text --> 6