rafmacalaba/datause-encoder-data huggingface dataset:
growth have failed to come up with robust findings (Rodrik and Rodriguez 2000). This is partly because of the complex interaction between 1<sup>st</sup> and 2<sup>nd</sup> advantages. Thus, some authors have argued that there are instances, especially in very poor economies, where liberalization is in fact detrimental to growth, by inhibiting infant industries and the local accumulation of knowledge (Krugman 1981, Hausmann and Rodrik 2002, Young 1991). Others emphasize that, in the presence of capital market imperfections, liberalization exacerbates income inequality within countries, imposing high costs on less favored regions, social groups or sectors of activity, and with ambiguous effects on average performance (see Banerjee and Newman 2003, Trefler and Zhu 2001).

Once again, micro-based research is going to be more useful than broad-brush crosscountry work in informing the design of policy in particular countries. For example, Aghion and others show that technological capability and institutional conditions determine whether industries in India benefited from or were harmed by liberalization in 1991 (see section 5). Using Chilean plant level data Pavcnik (2002) find that import liberalization led to within plant productivity improvements. Other recent work on firm level data from a number of countries has also drawn attention to the productivity gains from exporting (HallwardDriemeier and others 2002). Detailed studies of the impact of trade reform on poverty have been undertaken by McCulloch, Winters and Cirera (2002).

Geography, as well as trade policy, is an important determinant of access to markets in which to sell output and from which to source intermediate and capital goods. Evidently, much of the success of the northern border region of Mexico is a direct consequence of its proximity to the US, and coastal regions of China do well because they are on the coast. A number of studies have tried to quantify the importance of these geographical 1<sup>st</sup> advantages. Gallup and Sachs (1999) regress national per capita income on four variables; a measure of the endowment of hydro-carbons, a dummy variable for the incidence of malaria, and two 18
Policy Research Working Paper 10804

# **Abstract**

The informal sector contributes significantly to the total output and employment of low-income countries. While women-owned businesses feature strongly in these informal environments, they are generally characterized by low productivity. This paper explores how household business performance may be influenced by owners’ personality traits and their attitudes toward gender roles. Using multi-topic household survey data collected in two regions of Ghana, the results show that among female business owners, being organized is an important determinant of business success, while among male business owners, power motivation and tenacity are important factors. However, increasing traditionalism tends to dampen the effects of these personality traits for both genders. Other factors that are positively correlated with women-owned business performance include business registration, separating expenses for home and business purposes, ownership of a business bank account, use of social media, as well as urban location of the business. For men-owned businesses, the results show that those that are located in traditional markets, have bank accounts, and use literate employees in their operations tend to perform better. The findings imply that policies that aim to boost women-owned business performance need to consider the main barriers, especially attitudes toward gender roles, that may determine how businesses operate in these settings. The results also suggest the importance of soft skills to boost business performance among men- and women-owned businesses.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at aamankwah@worldbank.org. A verified reproducibility package for this paper is available at http:// reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
employment of men in both urban and rural areas, but the effect is more pronounced in rural areas where work is likely to be more strenuous.

**_Family Care Provision and Employment?_** One frequently raised hypothesis concerning the exit of older women from the labor force lies with growing demands for provision of elder care and care of grandchildren. In order to reduce potential bias, the models estimated in Table 2 use numbers of living parents and grandchildren, respectively, rather than presence of parents or grandchildren as _household_ members as covariates. This distinction is important as a significant negative correlation when an elder is present in the household may lead to misleading causal interpretations if arrangements for care-provision and older worker labor supply are jointly determined.<sup>30</sup> The lack of a systematic negative relationship between employment of China’s older urban workers and the number of family members for whom they might provide care, suggests that the explanation for decline in women’s labor force participation lies elsewhere. In Indonesia and Korea as well, the composition of the extended family does not seem to be strongly and systematically associated with labor supply and retirement decisions.

**_Interdependence of Spouse Retirement Decisions_ .** In Table 3 below, we present results from estimates of model (3), which provide insight into the joint retirement decisions of spouses and the role of spouse health status in retirement. Across rural and urban areas of China, Indonesia and Korea, men are more likely to work if their spouses are working as well. In urban China, where the effect of a spouse working is associated with a 19.6 and 20.2 percent higher probability of employment for men and women, respectively, one policy implication of joint retirement decisions is that efforts to encourage retirement at older ages for both men and women will likely have a positive impact on employment of spouses (Falkinger et al, 1996). Moreover, in rural China and both urban and rural areas of Indonesia, we find that a woman’s employment is more strongly correlated with spouse work status than men’s. In part, this reflects the youth of wives relative to their husbands and the likelihood that women will choose to retire at roughly the same time as their older husbands when there are (effectively) no gender differences in mandatory retirement and pension eligibility ages. In Korea, we observe a positive correlation

> 30Research using census data, which is unable to control for endogeneity of household composition, provides weak evidence that eldercare and childcare may explain a small share of women’s exits from the labor force (MaurerFazio et al, 2011). Giles et al (2006b) find that urban women are more likely to work if they have a college-age adult child, which may reflect a labor supply response the sharp increase in post-secondary tuitions after 1996.

21
# **Table 6:  Agreement Ratio for Changes in WGI Estimates of Governance, 1998-2007**

|||**A**|**LL CHANG**|**ES**|**Agree / (Agree +**|
|---|---|---|---|---|---|
||**Sample**|**Agree**|**No Change**|**Disagree**|**Disagree)**|
|**Voice and Accountability**|199|2.4|0.9|1.1|0.68|
|**Political Stability/Absence Violence**|189|2.6|0.6|1.0|0.72|
|**Government Effectiveness**|194|2.5|0.8|1.1|0.69|
|**Regulatory Quality**|194|3.0|0.8|1.3|0.70|
|**Rule of Law**|194|3.2|1.9|1.7|0.65|
|**Control of Corruption**|194|2.5|1.5|1.2|0.68|
|**Average**|**194**|**2.7**|**1.1**|**1.2**|**0.69**|
||**SI**|**GNIFIC**|**ANT CHAN**|**GES (90**|**%)**<br>**Agree / (Agree +**|
|||**Agree**|**No Change**|**Disagree**|**Disagree)**|
||**Sample**|||||
|**Voice and Accountability**|21|3.9|0.5|0.5|0.88|
|**Political Stability/Absence Violence**|25|3.9|0.2|0.5|0.88|
|**Government Effectiveness**|17|3.8|0.5|0.5|0.89|
|**Regulatory Quality**|11|5.6|0.3|0.0|1.00|
|**Rule of Law**|14|5.4|1.2|0.4|0.93|
|**Control of Corruption**|12|4.0|1.1|0.7|0.86|
|**Average**|**17**|**4.4**|**0.6**|**0.4**|**0.91**|

36
2 are included in Waters' collection,l/ we also searched most major journals pertaining to the field of transportation. Among these are Transportation Resetrch Series A and B, Transportation, Transportation Quarterly, Transportation Journal, and Logistics and Transportation Review (a list of journals scanned is included below as Appendix 1). As noted, a few other sources were used, e.g., government reports. The search for these entries was much less extensive.

A total of 70 entries (journal articles, reports and books) were reviewed and summarized in a report appended to this paper. In view of the vast literature related to transport demand, some omissions are inevitable. Nonetheless, the articles reviewed provide a basis for identifying 'typical' price elasticities of demand for transportation.

Some suimary statistics about the review are reported in Table 1. The articles and studies are drawn from several countries, cover many different modes and market situations, and employ various statistical methods and data bases. The individual articles are suimarized in the annotated bibliography attached to this survey as Annex A.

# ITT. Concepts of Elasticities

The basic concept of an elasticity and its application to demand are well known. An elasticity is the percentage change in one variable in response to a percent change in another. In the case of demand, the own-price elasticity of demand is the percentage change in quantity demanded in response to a one percent change in price. The own-price elasticity of demand is expected to be negative, i.e., a price increase decreases the quantity demanded. Demand is said to be 'price-elastic" if the absolute value of the own-price elasticity is greater than unity, i.e., a price change elicits a more than proportionate change in the quantity demanded. A 'price-inelastic' demand has a less than 1 Waters' bibliography covers 40 economic journals. It includes the Journal of Transport Economics and Policy, International Journal of Transport Economics as well as some journals related to regional and urban economics.
using the system of Ks described in footnote 31. For example these standards valued normal deliveries and appendectomies at 50K, and the fee for a single third -class K was set at USD2.50 in 1990 in the private sector.<sup>38</sup> HDB valued the K at USD1.14, i.e. less than half of what the physician would get in the private sector per K. MOH salaries of civil service doctors were divided by USD1.14 to derive the base number of Ks they "owed" the hospital, and they would get income supplements for any additional Ks they delivered at HDB.

The idea was twofold: to encourage staff to come to work, and to encourage them to work more and earn "bonuses". For example, in 1990, nurses hired from the private sector received salaries of LL120,000 (USD414.00) per month, and nurses who were civil service staff received an income supplement of LL70,000 (USD241.00) per month to compensate for the difference. A similar incentive pattern was followed, and updated over time for administrative staff, technicians, drivers, housekeepers and guards at HDB. This compensation policy was the ALSM's most significant investment in HDB, and allowed the hospital to hire an average of 50% of its staff from the private sector, and to expand service delivery and service mix. Table 5.2a shows the proportion of ALSM expenditures going toward the wage bill for the period 1991-1997.39

**Table 5.2a. ALSM Human Resource Expenditures as a Proportion of Total Expenditures**

|**_Year_**|**_Salaries & Bonise _**|**_`s_**<br>**_TdAIL*SM *)&daris-_**|**_%,of Total_**|
|---|---|---|---|
|_1991_|205,076,500|350,815,030|_58%_|
|_1992_|402,432,500|712,284,489|56%|
|_1993_|634,120,000|1,029,437,181|62%|
|_1994_|896,305,500|1,225,381,642|73%|
|_1995_|1,060,616,000|1,537,154,132|69%|
|_1996_|1,078,964,000|1,488,575,505|72%|
|_1997_|1,535,747,000|2,179,684,701|70%|

Source: Author's construction using HDB financial statements. Figures are in Lebanese Lira (LL), unadjusted for inflation.

As a result of the gradual improvement in the number and quality of its staff, HDB saw a decline in the average length of stay (ALOS), an increase in the number of patients admitted, an increase in average birth rates, and an increase in the number of lab tests carried out.

38 Today the third-class K is valued at LL8,000 (USD5.30).

> 39 Because of the surplus of doctors in Lebanon, this incentive system worked less well for doctors at HDB than it did for paramedical, administrative and support staff.

26
# Table 6

# Benefit-Cost Comparison for the<sup>Three Treatments Delivered In the</sup> New Jersey Ul Demonstration

||JSA-|onlv|JSAttra|inina|JSA/bo|nus|
|---|---|---|---|---|---|---|
|Benefts and costs|Society|Govt.|Society|Govt.|Society|Govt.|
|Increased output|$736|$0|$417|$0|$715|$0|
|Claimants' taxes|0|156|0|81|0|161|
|Ul payments|0|87|0|81|0|170|
|Demonstration costs|-169|-169|-491|-491|-174|-299|
|Offset cost of existing services|14|14|115|115|24|24<br>|
|Sum of benefis and costs|$581|$88|$41|-$214|$565|$56|

Source: Comson and others<sup>(1989: Tables Vii: 3, VIl: 4, and VII: 5).</sup>
|Rest Distribution*Bottom25<br>Electricity Rest||||0.759<br>(0.958)|-9.053*<br>(5.250)||
|---|---|---|---|---|---|---|
|Rest Electricity*Top25|||||<br>-12.00<br>(7.732)||
|Rest Electricity*Bottom25|||||<br>2.858<br>(3.929)||
|Transport Rest||||||-2.273**<br>(0.896)|
|Rest Transport*Top25||||||<br>-2.140<br>(2.157)|
|Rest Transport*Bottom25||||||<br>0.596<br>(1.274)|
|Constant|0.493***|0.397***|0.387***|0.419***|0.416***|<br>0.459***|
||(0.0671)|(0.0342)|(0.0368)|(0.0641)|(0.0399)|(0.0436)|
|Observations|40,208|40,208|40,208|40,208|40,208|40,208|
|R-squared|0.034|0.033|0.033|0.033|0.035|0.035|
|<br>Number of id|25,425|25,425|25,425|25,425|25,425|25,425|
|Robust standard errors in parentheses<br>*** p<0.01, ** p<0.05, * p<0.1|||||||

# Table 3b: Controlling for Characteristics

|DepVar: TFP Index|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|PolicyRestrictiveness in Services FDI|-1.119**|-1.378**|-1.393**|-1.308**|
||(0.552)|(0.610)|(0.585)|(0.522)|
|Restrictiveness*Top25%|-0.964|-0.889|-0.932|-1.092|
||(0.761)|(0.787)|(0.789)|(0.763)|
|Restrictiveness*Bottom 25%|1.157*|1.102|1.109|1.154*|
||(0.659)|(0.750)|(0.748)|(0.660)|
|Bottom 25% Dummy|-0.167**|-0.166*|-0.166*|-0.166**|
||(0.0745)|(0.0833)|(0.0832)|(0.0748)|
|Top25% Dummy|-0.0119|-0.0202|-0.0159|6.24e-05|
||(0.0737)|(0.0751)|(0.0756)|(0.0739)|

25
Unadjusted and adjusted changes in risk associated with pre-natal and post-natal risk factors for infant and early childhood stunting (24-60 months of age) <!-- Start of picture text --> Unadjusted Adjusted<br>Low birth weight (<2,500g)| > 132 14<br>Mother has short stature (height < 145cm) ee —<br>Mother has low body mass index (BMI < 18.5) FA nd Ait<br>Skilled birth delivery} & —— ——<br>Household is food secure} & Ogs —-<br>HH consumed protein rich foods in past week 3 og8 ><br>HH consumed vitamin A rich foods in past week} 2 - 7<br>Underwent neonatal screening] , on a<br>Age appropriate vaccine status x — a<br>In Vitamin A supplementation program | 2 + +<br>Participated in OPT plus} > a +e<br>Improved drinking water | ., — —+—<br>Improved sanitation (toilet is water sealed) m — —<br>>75% of HHs in municipality have improved sanitation = — —<br>Improved cooking fuel & no garbage burn S oe a<br>Functioning refrigeratorHH memberin household] smokes 3TM —_07 + —O77 +<br>Female +> ><br>Single birth 2 ot<br>Urban ><br>Total HH size (N)} 5 194<br>Total children under five in HH (N)]6 *<br>Total HH income per month (1 = 1000 PHP) (log) S ogs<br>In poorest quintile of total HH income per capita ry i<br>Mother has high school or tertiary education ° oge<br>Mother is married re<br>Mom wasa teenager at time of birth —-<br>Mother hasa job or business +<br>0.5 1.0 1.5 2.0 0.5 1.0 1.5 2.0<br>Relative Risk (95% Cl)<br><!-- End of picture text -->

Source: Author's calculations based on 2015 National Nutrition Survey (NNS). Notes: Only the relative risk estimates for significant risk factors are labeled. Age, age squared and age cubed (in months) is also used as a control but is not shown in the chart. The unadjusted regression model does not control for socio-economic factors.
- 16 -

Table 3 (Continued) A Comparative Analysis of Caribbean and Other Developing Countries Aggregate Freight Rates for Exports to the United States

||1987 Exports to|U.S. (S_mill)_|Nomina|l Freight|Factor 1/|
|---|---|---|---|---|---|
|Exporter|f.a.s.|<br>c.i.f.|19_|1986|1987|
|Kenya|79.3|<br>85.2|7.8|6.0|7.4|
|Mozambique|139.6|<br>153.9|8.8|9.6|10.2|
|Rep.ofSouth Africa|1,345.5|<br>1,3990.0|5.2|4.7|4.0|
|All Other Countries|538.4|<br>570.1|7.2|6.6|5.9|
|Near East Asia|10,810.6|<br>11,602.0|5.3|8.9|7.3|
|Iraq|494.8|<br>525.7|3.6|7.4|6.2|
|Iran|1,667.5|<br>1,751.5|5.2|7.5|5.0|
|Israel|2,639.3|<br>2,724.4|3.7|3.6|**_3.2_**|
|Kuwait|521.5|<br>568.6|7.7|15.1|9.0|
|Saudi Arabia|4,433.1|<br>4,886.5|6.3|12.3|10.2|
|United Arab Emirates|664.1|<br>723.5|7.5|9.7|8.9|
|All Other Countries|390.3|<br>421.8|8.7|10.8|8.1|
|South Asia|3,757.5|<br>4,090.3|9.0|9.0|8.9|
|India|2,528.6|<br>2,725.4|8.0|7.9|7.8|
|Pakistan|404.5|<br>438.2|9.2|8.6|8.3|
|Nepal|31.7|<br>37.6|19.9|18.0|18.6|
|Bangladesh|369.9|<br>419.2|15.8|14.9|13.3|
|Sri Lanka|417.1|<br>463.8|11.0|11.5|11.2|
|All Other Countries|5.7|<br>6.1|6.0|5.9|7.0|
|Other Asia|69,014.3|<br>73,517.2|7.2|6.8|6.5|
|Thailand|2,220,3|<br>2,387.1|8.0|7.2|7.5|
|Malaysia|2,921.0|<br>3,053.1|4.3|4.7|4.5|
|Singapore|6,201.0|<br>6,395.0|3.6|3.4|3.1|
|Indonesia|3,393.9|<br>3,719.0|8.0|11.0|9.6|
|Philippines|2,263.6|<br>2,481.1|8.8|9.0|9.6|
|Republic of Korea|16,986.9|<br>17,991.0|7.0|6.0|5.9|
|Hong Kong|9,854.1|<br>10,490.2|7.1|6.6|6.5|
|Taiwan|24,621.8|<br>26,406.5|8.5|7.4|7.2|
|All Other Countries|551.7|<br>593.9|8.1|7.9|7.6|
|ALL EXPORTERS 5/|405,900.6|424,082.3|4.7|4.6|4.5|

1/ Nominal freight factors are measured by the ratio of total transport and Insurance costs to the free-alongside-ship value of imports. This ratio is then multiplied by one hundred to derive the nominal freight factor.

2/ Gaudeloupe and dependencies and Martinique.

3/ The Leeward Islands consist of Antiuga, St. Kitts-Nevis-Anguilla and Montserrat. The Windward Islands are St. Vincent, St. Lucia and Dominica.

4/ Mexico Is excluded from the total since a large share of this country's commerce with the United States passes directly across the border and does not incur International transport costs. 5/ Includes developed, developing and socialist countries.
# **Table 5. Drivers of Digital Payment Usage during COVID-19**

|DEPENDENT VARIABLE: Share adults making or receiving payments, 2021<br>EXPLANATORYVARIABLES:|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Overall Restrictions on Banking Activities (2016)|0.182|0.138|0.269|0.449|-0.359|-0.251|
||(0.429)|(0.439)|(0.448)|(0.468)|(0.870)|(0.900)|
|Made or received a digital payment (% age 15+) (change 2017-2014)|18.592*|16.923|22.583**|14.852|28.716|30.513|
||(10.704)|(10.597)|(11.167)|(9.805)|(18.700)|(18.449)|
|Made or received a digital payment (% age 15+) (2017)|0.882***|0.934***|0.868***|0.947***|0.992***|0.962***|
||(0.043)|(0.038)|(0.045)|(0.038)|(0.071)|(0.077)|
|Banks' assets (% GDP) (2019)|0.018|0.010|0.007|-0.013|0.041|0.047|
||(0.019)|(0.023)|(0.022)|(0.021)|(0.034)|(0.034)|
|GDP per capita growth (mean over 2015-2019)|0.271|0.213|0.304|0.236|-0.873|-1.141|
||(0.455)|(0.485)|(0.438)|(0.440)|(0.841)|(0.772)|
|log(population) (2019)|1.444***|1.281**|1.568***|1.625***|2.461**|2.316**|
||(0.538)|(0.533)|(0.526)|(0.525)|(0.963)|(0.970)|
|Inflation, consumer prices (annual %) (2019)|-0.022*|-0.009|-0.001|-0.021*|-0.400|-0.413|
||(0.012)|(0.014)|(0.013)|(0.011)|(0.434)|(0.452)|
|Mean years of schooling (2019)#COVID growth differential (2020)|0.064*||||||
||(0.035)||||||
|Mean years of schooling (2019)|0.386||||||
||(0.614)||||||
|Access to electricity (% of population) (2019)#COVID growth differential (2020)||0.008*<br>(0.004)|||||
|Access to electricity (% of population) (2019)||0.021<br>|||||
|||(0.088)|||||
|Individuals using the Internet (% of population) (2019)#COVID growth differential (2020)|||0.011**||||
||||(0.005)||||
|Individuals using the Internet (% of population) (2019)|||<br>0.069||||
||||(0.082)||||
|Mobile cellular subscriptions (per 100 people) (2019)#COVID growth differential (2020)||||0.009***<br>(0.003)|||
|Mobile cellular subscriptions (per 100 people) (2019)||||0.036<br>(0.031)|||
|enablers_score#COVID growth differential (2020)|||||0.090*||
||||||(0.045)||
|Enablers data economy (2020)|||||<br>-0.845<br>(0.701)||
|score_safeguards#COVID growth differential (2020)||||||0.069**|
|||||||(0.032)|
|Safeguards data economy (2020)||||||-0.278|
|||||||(0.521)|

37
- 28 -

Fig 17 <!-- Start of picture text --> Wage Bill, 1991.1 - 1992.3<br>By Firm ASset Size (1991.1 prices)<br>2.<br>2<br>1.9<br>1.4<br>1.2<br>1.6<br>1.5<br>1.4<br>1.3<br>0.9<br>0.8<br>0.O __A<br>1991.1 91.2 91.3 91.4 92.1 92.2 1 9921.3<br>0 Asset Size I + Asset Size 11 0 Asset Size III<br><!-- End of picture text -->