rafmacalaba/datause-encoder-data huggingface dataset:
|# (age, over 55, female)*|[0.07]*<br>-0.44||[0.05]***<br>-0.2|[0.13]<br>0.15||[0.04]***<br>0.15|
|---|---|---|---|---|---|---|
||[0.14]**||[0.10]*|[0.21]||[0.08]|
|# (age, over 55, male)*|0.45||0.35|0.28||0.31|
||[0.15]**||[0.11]**|[0.26]||[0.09]***|
|Sindh|0.69||1.24|1.58||2.07|
||[0.08]**||[0.11]*|[0.30]*||[0.16]***|
|NWFP|0.63||0.42|0.47||2.53|
||[0.08]***||[0.05]***|[0.15]*||[0.22]***|
|Balochistan|0.37||0.68|0.82||2.5|
||[0.07]***||[0.08]**|[0.27]||[0.25]***|
|school distance|1.46||1.42|1.15||1.14|
||[0.79]||[0.62]|[1.13]||[0.44]|
|presence of girl school|0.29||0.45|0.08||0.04|
||[0.06]***||[0.08]***|[0.05]***||[0.01]***|
|presence of boy school|0.11||0.13|0.84||0.65|
||[0.06]***||[0.06]***|[0.64]||[0.20]|
|median wage for men*|0||0|0||0|
||[0.00]||[0.00]|[0.00]||[0.00]|
|% household having piped water|1.56||1.09|0.45||0.8|
||[0.46]||[0.21]|[0.20]||[0.13]|
|% households have pumped|||||||
|water|1.81||0.96|1.38||1.3|
||[0.56]||[0.20]|[0.65]||[0.23]|
|time fixed effect, 01/02|0.93||0.91|0.71||0.91|
||[0.10]||[0.08]|[0.14]||[0.07]|
|time fixed effect, 05/06|0.87||0.91|0.71||0.9|
||[0.10]||[0.08]|[0.14]||[0.06]|
|N|80|38|||7852||

Note: 1. * continuous variables, where coefficients are presented. Otherwise, relative risk ratio (RRR) is reported. 2. capita is adjusted with adult equivalence.

33
- 5 -

|**Table 2. Infrastructure procurement**|**costs in East Caribbea**|**n states**|
|---|---|---|
|Labor|Materials|Equipment|
|Argentina<br>1|1|1|
|East Caribbean<sup>1</sup><br>1.79|2.55|3.75|
|Source: World Ba|nk, 2008a.||
|Note: 1/ Dominica, St. Vincent,|Grenada, and St. Lucia.||

**Figure 1. Unit costs of road construction maintenance in Sub-Saharan Africa**

<!-- Start of picture text --> 500<br>Less than 50 km<br>More than 50 km<br>402<br>400<br>353<br>300 291 300<br>200<br>100<br>0<br>Construction Rehabilitation<br>Source: Africon, 2008.<br>Thousands of U.S. dollars per lane km<br><!-- End of picture text --> From the competition point of view, small contracts can lower the entry barrier. This has three effects. First, the competition among bidders will increase if the size of contracts is reduced. Small and medium-sized enterprises (SMEs) often constitute the vast majority of enterprises in the economy. But their capacity to undertake public contracts may be technically and financially limited. Therefore, downscaling the contract size could encourage SMEs to enter the market. Second, fringe entrants are particularly important to break hidden collusive arrangements among incumbent bidders. Without new entry, the market tends to be collusive among incumbent players (e.g., Porter and Zona, 1993). Finally, fringe bidders often submit aggressive bids (Maskin and Riley, 2003). Empirically, entrants are in fact found to be more aggressive, especially at the lower end of the bid distribution, in the Oklahoma State’s road auction market (De Silva and others, 2002; De Silva, Dunne, and Kosmopoulou, 2003).

However, the endogenous auction theory also suggests that too small public contracts can be an entry barrier, because preparing financial and technical proposals is a time-consuming task for potential contractors. Regardless of the size of contracts, this is a sunk cost for them. Therefore, the bidder participation can be reduced if the entry cost is relatively high
65 weights. All averages, standard deviations, and counts we compute using HIES data are weighted using these weights.

We extract wage information from the 2019 HIES, which lists household members who “were paid employees during last four weeks/last calendar month”. We compute each district’s average wages as the (weighted) average of compensation in the main occupation across the district’s workers (excluding workers with zero compensation), with compensation defined as the sum of “wages/salaries” and “tips, commissions, overtime pay etc” and measured in LKR. We compute average wages separately for estate and non-estate (mobile) workers. Estate workers are workers who live and work in Sri Lanka’s tea plantations and for which changing jobs is nearly impossible in the short run, as their housing is tied to the tea estate they work in (Chandrabose, 2015). Average wages are multiplied by 12 so that they are expressed in annual terms.

We use information from the 2016 HIES to count the number of mobile workers, estate workers, and farmers in each district (making using of the statistical weights to obtain total counts). We define a “worker” as a person with a main occupation (in agriculture, manufacturing, or services) that paid her above-zero employment compensation in the previous month. We count the total number of such workers in each district and the fraction among these who are estate workers. We then subtract the number of estate workers from the total number of workers to obtain the number of mobile (i.e. non “tea-estate”) workers. To compute the number of farmers, we first count the number of households in each district with positive landholdings. We only count land owned by the household (as opposed to leased, rented, etc.) with no housing built on top of it. We then estimate the number of farmers per district by multiplying the number of farming households by the average number of economically active agents per household. To estimate the average number of economically active agents per household, we divide the number of workers by the number of working households in the district, where a working household is defined as a household with at least one worker. The implicit assumption is that working households and farmer households are similar in terms of household size, structure, and labor force participation. Finally, we scale up the resulting numbers of workers and farmers by Sri Lanka’s population growth between 2016 and 2019 (2.8%) to compute numbers for 2019, our baseline year for model estimation (see Section VI).

For parameter estimation in Section VI.A.1, we also use 2019 HIES data on household size (number of members, regardless of age), income, and expenditure patterns, in particular the fraction of income spent on food. Values and quantities of crops purchased by each household are available, allowing us to compute the prices faced by the household by dividing values by quantities. We also use information on “income by chance or _ad hoc_ gains”, including lottery gains and disaster/relief payments, for our IV estimation in Section VI.A.1.
research studies suggest that greater discrimination against women persists in unorganized sector of labor markets in India, the analysis on differences in gender wage gaps in organized and unorganized sectors of labor market becomes difficult due to lack of availability of data.

# 2.2.3 Pakistan

Pakistan’s women’s participation in the labor force is among the lowest in the world. The WEF’s Global Gender Gap Report (2017) ranks Pakistan at 143 of 144 countries surveyed, just ahead of the Republic of Yemen. Pakistan has more men than women working in any occupation and the level of women’s participation in the labor force is low at 25 percent (as opposed to a male participation rate of 75 percent) in 2015. The cultural barriers and are prevalent, emphasizing women’s primary role as homemakers and caregivers, coupled with high levels of early marriage, discourage women from entering the workforce, particularly as employed women shoulder the same burden of household-related unpaid work as do their non-employed counterparts.<sup>16</sup> Though female labor market participation has increased from 13.7 percent in the 1990s to 21.1 percent in 2007 and further to 25 percent in 2015 in Pakistan, it is still very low as compared to other South Asian countries. Nearly 82 percent of the employed women are in agriculture/ forestry/ hunting & fishing industry division followed by manufacturing (13.9 percent). Within manufacturing, the textile and garment sector is the most significant employer for women in Pakistan. About 55 percent of women employment in the manufacturing sector is in the garment industry, while the corresponding figure for men is 19 percent.<sup>17</sup> The largest proportion of women workers are employed in manufacture of wearing apparel (42 percent) followed by agricultural, forestry and fishery (40 percent) and manufacturing of textiles (30 percent). As per UN estimates,<sup>18</sup> the highest percentage share of women working in agriculture sector was in animal production at 47 percent, followed by mixed farming at 23 percent and growing of non-perennial crops at 18 percent. Thus, there is a disproportionately high concentration of women still employed in agriculture and other informal activities. Some services sectors with women employees are retail trade and health services (in capacities of being doctors and more as midwives and lady health visitors).

**Table 5:  Women Labor Force Participation by Economic Activity in Pakistan (in 2015)**

|**S.No.**|**Sectors/Economic activity) (2015)**|**No. of**<br>**Men**|**No. of**<br>**Women**|**Share of women**<br>**in sectoral**<br>**employment**<br>**(%)**|**Share of the**<br>**sector in total**<br>**women**<br>**employment (%)**|
|---|---|---|---|---|---|
|1|Crop and animal production, hunting and<br>related service activities|13691|9077|39.9|82.4|
|2|Manufacture of wearing apparel|1327|975|42.4|8.8|
|3|Manufacture of textiles|1251|525|29.6|4.8|

> 16 Khan, Frida (2017).

> 17 N. Jamal (2015).

> 18 UN Women (2016).

17
skill abundance, suggesting a horizontal motivation. However, the effect was reversed for FDI in developing countries, although the relationship was not statistically significant.<sup>3</sup> As Blonigen and Wang’s (2004) criticism would suggest, a drawback of the earlier studies is that, except for Gao (2003), they did not consider data for specific developing countries or NICs, such as Singapore. Furthermore, many multinationals base themselves in Singapore and invest in neighboring developing countries, and hence there is a need to take into account the market size of the entire ASEAN region. In principle, application of the KK model to Singapore is both novel and appropriate, because during the period of analysis the country made a transition from attracting labor-intensive foreign investment to acquiring skill-intensive foreign investment. Furthermore, as MNEs use Singapore as regional headquarters to invest in neighboring countries, the use of ASEAN GDP to account for host market size is appropriate for inbound foreign investment. As this research incorporates both these investment objectives, a panel study of two-way foreign investment into and out of Singapore taking into consideration the impact of the regional market size should be of considerable interest. In addition, we also incorporate the role of the GOS in maintaining political stability, strict anti-corruption laws as well as the development of infrastructure such as roads, rails, energy, telecommunications and ports.

# **3.  Empirical Framework and Data**

Here we specify the econometric approach that represents the KK model and address a number of questions. We then describe our dataset, which was developed specifically for this analysis and seems unique in its comprehensive coverage of a panel of two-way FDI

> 3 See also Yeaple (2003).

10
**<u>Table A.2 Baseline Balance (Unweighted)</u>**

||**Control**<br>**(Mean)**|**Arm 1**<br>**(Mean)**|**Arm 2**<br>**(Mean)**|**Arm 3**<br>**(Mean)**||**Nor**|**malized Mea**|**n Differen**|**ces**||
|---|---|---|---|---|---|---|---|---|---|---|
|Number of gram panchayats (GPs)<br>Number of households|80<br>1,872|60<br>1,406|60<br>1,394|60<br>1,395|**Control-**<br>**Arm 1**|**Control-**<br>**Arm 2**|**Control-**<br>**Arm 3**|**Arm 1-**<br>**Arm 2**|**Arm 1-**<br>**Arm 3**|**Arm 2-**<br>**Arm 3**|
|**A. Household characteristics**|||||||||||
|**Number of members in the household**|||||||||||
|All|5.167|5.144|5.215|5.111|.0082|-.0173|.0204|-.0261|.0123|.039|
|Children less than 5 years|.6245|.5597|.6722|.643|.0605|-.0425|-.0169|-.1036|-.0785|.0262|
|Children between 6–14 years|1.096|1.09|1.042|1.025|.0043|.0356|.0478|.0316|.0438|.0118|
|**Caste/social category of the household**|||||||||||
|General|.297|.2738|.3199|.2903|.0363|-.0351|.0104|-.0714|-.0259|.0455|
|Scheduled caste/scheduled tribe (SC/ST)|.5438|.542|.5043|.5749|.0026|.056|-.0443|.0533|-.0469|-.1004|
|**Religion of the head of household**|||||||||||
|Hindu|.2473|.2724|.2453|.2695|-.0404|.0033|-.0359|.0437|.0046|-.0391|
|Sikh|.6918|.6728|.7052|.6817|.0288|-.0206|.0153|-.0494|-.0134|.036|
|**Economic category**|||||||||||
|Below poverty line (BPL)|.5422|.5334|.5796|.5369|.0124|-.0533|.0075|-.0658|-.0049|.0608|
|Above poverty line (APL)|.2997|.2553|.2188|.2595|.0701|.131|.0634|.0608|-.0067|-.0675|
|**Landholding by category**|||||||||||
|Small and marginal farmer|.2051|.2041|.2532|.2222|.0018|-.081|-.0295|-.0828|-.0312|.0515|
|No land|.7366|.7269|.6851|.7226|.0156|.0805|.0224|.0649|.0068|-.0581|
|**Asset ownership**|||||||||||
|Own house|.9658|.9737|.9699|.9749|-.0325|-.0163|-.0379|.0163|-.0055|-.0217|
|Radio|.03579|.03485|.04591|.03584|.0036|-.0362|-.0002|-.0397|-.0038|.036|
|Color television|.8734|.8364|.8766|.8774|.0743|-.0069|-.0086|-.0812|-.0829|-.0017|
|Gas stove|.8344|.7952|.8128|.8459|.0715|.0401|-.0221|-.0314|-.0936|-.0623|
|Refrigerator|.797|.7703|.8042|.8022|.0459|-.0127|-.0091|-.0586|-.055|.0036|

42
position. Outcomes are positive and statistically significant. Thus we have robust findings across different specifications.

# **[Table 5.2 is about here]**

During fieldwork a number of political, social and other events took place that would affect the mood and the feeling of individuals when judging their life-satisfaction. As documented in the brief report<sup>12</sup> on Life in Transition Survey (2006) the timing of the fieldwork coincided with the holy month of Ramadan. This could be a source of exogenous bias in countries with Muslim majorities. In Table 5.3 we show seven countries, where more than 50 percent of the nationally representative sample population is Muslim (figures are given in the second column)<sup>13</sup> . In two other columns we show percent of respondents being reluctant to answer any question and who had difficulties in answering questions respectively. In all countries except in Albania, more than 50 percent of the respondents did not show any reluctance responding to the questions. Also according to interviewer’s subjective evaluation, on average 44.5 percent of the respondents in Muslim majority countries never had difficulties in answering questions compared to 47.1 percent from other countries.

# **[Table 5.3 is about here]**

The fraction of the population reporting satisfied with life varies substantially among these countries, from 27.6 percent in Azerbaijan to 72.6 percent in Uzbekistan. We also look at the correlation between reported life-satisfaction and reluctance and difficulties in answering questions during interview. Correlation coefficient (given within brackets) indicates negative relation in all cases except in Kazakhstan where reluctance and lifesatisfaction are positively correlated. Apparently we do not have any systematic bias generated by the incident of Ramadan since outcomes in Muslim majority countries are similar to those of non-Muslim majority countries.

> 12 <u>http://www.ebrd.com/pubs/econo/litsrepo.pdf</u> 13

> In LiTS, 22.7 percent of the total respondents were Muslim

20
# TRADE POLICIES, INVESTMENT CLIMATE, AND EXPORTS ACROSS COUNTRIES

By Murat Şeker<sup>*</sup> Keywords: _Export performance, trade policy, investment climate, institutions, trade facilitation, cross country analysis._ JEL Classification: _F13, F14, F15, F4, O1._

> * <u>www.enterprisesurveys.org, Enterprise Analysis Unit in Finance and Private Sector Development Vice-Presidency</u> of the World Bank. Contact: 1818 H Street NW Washington DC 20433 MSN F4K-411. mseker@worldbank.org.

1
**Table 7: Participation in local budget events**

|**Indicator**|Someone from my|Knows the date of|Participated in|
|---|---|---|---|
||household tried to<br>influence local<br>budget process in<br>the last 12 months|the last budget<br>hearing in the local<br>government|budget hearing of<br>Aiyl Aimak during<br>the last 12 months|
|Personal characteristics||||
|Age|0.000 **|0.001 ***|0.001 ***|
|Female<br>|-0.010 **|-0.031 ***|-0.027 ***|
|Uzbek<br>|-0.002|-0.032|-0.003|
|Russian<br>|-0.035|0.000|-0.025|
|Other ethnicity|-0.038 **|-0.057 **|-0.060 ***|
|Years of schooling|0.005 ***|0.008 ***|0.007 ***|
|Risk attitude|0.006 ***|0.003 **|0.003 **|
|Labor market status||||
|Inactive|-0.007|-0.032 ***|-0.028 ***|
|Unemployed|0.004|-0.027|-0.012|
|Social capital||||
|Internal migrant|0.010|-0.007|0.004|
|Member of a social group|0.024 ***|0.056 ***|0.023 ***|
|Can get financial help from others|0.016|0.028|0.010|
|Trust in local community|-0.010 **|0.007|-0.001|
|Trust in local government|0.007 *|0.007|0.011 ***|
|Political attitudes||||
|Supports current political system<br>|-0.003|0.000|-0.003|
|No preference for political system|0.012|-0.019|-0.032 ***|
|Is going to vote in local elections|0.040 ***|0.008|0.017 *|
|Household characteristics||||
|Female HH head|-0.010|0.016|0.016 **|
|Children|0.000|0.005 *|0.000|
|Income per person|-0.001|0.002 **|0.000|
|Land size|0.001|0.004 **|0.002 *|
|Livestock|0.000|0.000|0.000|
|Asset index|0.000|0.000 *|0.000|
|Community characteristics<br>1||||
|Population|0.000|0.002 **|0.000|
|Kindergarden is available|-0.009|0.022 **|0.013 *|
|Medical service is available|0.002|-0.029 **|-0.013|
|Frequent energy interruptions|0.004|-0.014|-0.010|
|Oblasts||||
|Issyk-Kul|-0.033 **|-0.026|-0.033 **|
|Naryn|0.015|0.001|0.021 *|
|Batken|-0.023 *|0.005|0.011|
|Osh|-0.039 ***|-0.048 ***|-0.036 ***|
|Talas|-0.037 *|0.027|0.010|
|Chui|-0.009|-0.030|-0.016|
|Pseudo R<sup>2</sup>|0.163|0.214|0.227|
|No of observations|5,042|4,859|5,042|

Source: Life in Kyrgyzstan (LiK) Survey, 2012 Note: 1/ Community data are from 2011 wave Standard errors are corrected for cluster effects at the household level. Significant estimates are indicated by * p<0.1, ** p<0.05, *** p<0.01. ‘Kyrgyz’, ‘Employed’, and ‘Jalalabad oblast’ are the reference groups.

19
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However, there are also reasons to question whether the new policy environment would succeed in putting India on a new path of rapid poverty reduction. The greater openness to external trade came with sufficient productivity growth to assure a higher growth rate of national output.<sup>9</sup> But it appears that new inequality-increasing forces also emerged, and a number of observers have reported evidence of rising consumption inequality since the early 1990s.<sup>10</sup> This may well reflect the antecedent inequalities in other “non-income” dimensions, particularly in human capital, which can mean that the poorest are largely left behind; these inequalities were far greater in India around 1990 than China around 1980.<sup>11</sup> Some observers have also questioned whether the post-reform growth process has fulfilled the expectations of reform advocates that it would increase aggregate demand for <u>unskilled</u> labor and (hence) help reduce poverty. The fastest growing sectors of India’s economy have tended to be more intensive in capital and skilled labor, notably the booming businessservices sector. And the share of employment in agriculture has fallen much less than (say) China. This pattern of growth is hardly what the “comparative-advantage” arguments of reform advocates in the 1980s predicted as the outcome of India becoming a far more open economy.<sup>12</sup> However, it is also important to note that the non-farm sectors that are relatively intensive in unskilled labor—trade, construction and the informal manufacturing sector—fared better in the post-1991 period than earlier (Kotwal et al., 2009).<sup>13</sup> The non-farm sector’s aggregate demand

> 8 On India’s reform agenda since the early 1990s see Ahluwalia (2002) and Panagariya (2008).

> 9 See the interesting discussion in Eswaran and Kotwal (1994, Chapter 7) who argue that domestic productivity growth is key to the outcomes for poor people from trade openness in India. Here the sequencing of reforms was important, and India’s reformers wisely emphasized domestic reforms (such as industrial de-licensing) prior to external reforms (Bhagwatti, 1993).

> 10 Evidence of rising inequality in India since 1991 is reported in Ravallion (2000), Deaton and Drèze (2002) and Sen and Hiamnshu (2004b).

11 See the discussion in Drèze and Sen (1995) on the constraints stemming from India’s poor human development attainments at the outset of its current reform period, and the contrast with China. Also see Chaudhuri and Ravallion (2006) on the distinction between “good” and “bad” inequalities in both China and India, and the discussion of inequality of opportunity in World Bank (2005).

12 Few people in the 1980s would have predicted that technological changes in the 1990s, combined with the required human skills, would entail that India developed a comparative advantage in sectors such as business services.

> 13 The formal (“organized”) manufacturing sector has not seen rising employment after trade liberalization (Sen, 2009), although this is a moot point given that 80% of manufacturing employment is in the informal sector (Kotwal et al., 2009).

4
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