rafmacalaba/datause-encoder-data huggingface dataset:
O.A.M.D.G.

50km, mostly using existing transport provision, such as buses.<sup>18</sup> The goods left behind or carried on the flight vary by family and distance, but the usual choice is consistent with our classification into fixed and mobile. Often, a flight consists of various stages and also the mobile assets are sold at later stages. But at the first stage, and in most families’ expectation<sup>19</sup> , the following goods can be considered fixed and mobile respectively, as Table 2 shows.

|**Fixed Assets**|**Mobile Assets**|Table 2: Definition (1) of<br>fixed and mobile assets|
|---|---|---|
|house|manual sewing machine||
|other real estate|stereo equipment|_Source: based on_<br>_Econometría (2003)_|
|fridge|fan||
|TV|mixer||
|boat/canoe|kerosene lamp||
|diesel generator|bicycle||
|horses|motorcycle||
|cows|chicken||
|donkeys|rabbits||
|pigs|ducks||
|goats|||
|sheep|||

The definition of _fixed_ and _mobile_ may differ in different environments, and according to the means of transport. Therefore we will test the sensitivity of our empirical results with another, more basic, definition, as laid out in Table 3.

> 18 See Econometría (2003).

> 19 Compare Econometría (2003).

16
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Van Staden, A. (2011). “Put Reading First: Positive Effects of Direct Instruction and Scaffolding for ESL Learners Struggling with Reading.” _Perspectives in Education_ , 29(4), 10-21.

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WPS 3221

# **What Do We Know about Competition Agencies in Emerging and Transition Countries?**

**Evidence on Workload, Personnel, Priority Sectors, and Training Needs**

**Tomás Serebrisky**<sup>**1**</sup>

## World Bank Policy Research Working Paper 3221, February 2004

_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 World Bank Institute. Email: tserebrisky@worldbank.org. I would like to thank Antonio Estache and Paul Noumba for their comments and suggestions.

1
Chakravarty, Shubha; Lundberg, Mattias K. A.; Danchev, Plamen Nikolov; Zenker, Juliane; Lundberg, Mattias K. A. (2015). The role of training programs for youth employment in Nepal: impact evaluation report on the employment fund. Washington, DC: World Bank.

Church, A. T. (2010). Measurement Issues in Cross-cultural Research. In G. Walford, E. Tucker & M. Viswanathan (Eds.), The Sage Handbook of Measurement (pp. 152175). London: SAGE.

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Goodman, A., H. Joshi, B. Nasim and C. Tyler (2015) _Social and Emotional Skills in Childhood and their Long-term Effects on Adult Life_ , Social Mobility and Child Poverty Commission, Cabinet Office Haushofer, J. and E. Fehr (2014) “On the Psychology of Poverty”, _Science_ , 344:862867 Ibrahim, S. and S. Alkire (2007). "Agency & Empowerment: A proposal for internationally comparable indicators." Oxford Development Studies.

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14
Policy Research Working Paper 8847

# **Abstract**

Women’s economic empowerment is not a new issue; nevertheless, it continues to challenge governments and development assistance agencies. One of the reasons for this hurdle may well be that gender advocates and growth devotees are speaking different languages when there is a huge space for them to collaborate effectively and pursue both agendas simultaneously. This paper outlines a framework for gender-enhanced growth diagnostics that can be used to identify win-win solutions based on policies that target jointly the binding constraints to economic growth and those limiting female economic participation. The welfare gain from such a reform surpasses the gain from a progrowth reform addressing only the binding constraint to growth or that from a pro-gender reform aimed at closing gender gaps. Using Turkey’s country growth diagnostics and data on gender gaps, the paper uses the approach to identify those constraints to women’s economic empowerment that align with national growth priorities and may therefore gain greater traction with policy makers.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region. 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 eianchovichina@worldbank.org or dleipzig@gwu.edu.

_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
Comparing this with the employment structure in the Nepalese districts, reported in Table 8, we see greater dominance of agriculture in both Ilam and Jhapa. Two-thirds of employment in Jhapa and more than 80% in Ilam is in agriculture; moreover, manufacturing sectors account for only around 8% of employment in Jhapa and just over 1% in Ilam, far below the levels in the Indian districts. These data suggest that the economies of the Nepalese districts are much less diversified than their West Bengal counterparts (particularly Darjeeling) and suggest some complementarity in production structures and hence opportunities for trade.

**Table 8: Employment structure in Nepalese districts (share of total), 2008**

|**Ilam**||**Jhapa**||
|---|---|---|---|
|Sector|Share|Sector|Share|
|Agriculture and related service activities|80.6%|Agriculture and related service activities|65.5%|
|Construction|5.8%|Retail trade|8.2%|
|Retail trade|2.9%|Construction|4.9%|
|Education|2.0%|Land transport|3.0%|
|Land transport|1.5%|Manufacture of textiles|2.8%|
|Health and social work|1.2%|Manufacture of food products and beverages|2.0%|
|Public administration and defense|1.1%|Education|2.0%|
|Forestry, logging and related service activities|1.1%|Hotels and restaurants|1.9%|
|Manufacture of wood and cork|0.5%|Manufacture of wearing apparel|1.9%|
|Post and telecommunications|0.5%|Forestry, logging and related service activities|1.5%|
|Manufacture of wearing apparel|0.4%|Health and social work|1.3%|
|Mining of coal and lignite; extraction of peat|0.4%|Manufacture of furniture|1.0%|
|Other mining and quarrying|0.4%|Private households with employed persons|0.8%|
|Wholesale trade and commission trade|0.4%|Other business activities|0.7%|
|Hotels and restaurants|0.4%|Public administration and defense|0.6%|
|Manufacture of furniture|0.1%|Sale, maintenance and repair of motor vehicles|0.3%|
|Manufacture of textiles|0.1%|Manufacture of wood and cork|0.3%|
|Manufacture of food products and beverages|0.1%|Activities of membership organizations|0.2%|
|||Wholesale trade and commission trade|0.2%|
|||Manufacture of non-metallic mineral products|0.1%|
|||Manufacture of basic metals|0.1%|
|||Activities auxiliary to financial intermediation|0.1%|
|Total|100%|Total|100%|

_Source: Nepal labor force survey 2008_ We assess the extent to which the Nepalese and Indian districts may benefit from integration by comparing more formally their employment structures. To do so we use a simple cosine index of similarity between Nepal and Indian districts’ employment. That is essentially defined as the ratio of the covariance between the two variables and the product of each variable’s variance: where y is employment (in districts _j_ and _s_ ), �� is the average employment in the relevant district; _i_ is the tradable sector and _N_ is the number of tradable sectors. The index varies between -1 (perfect complementarity) and 1 (perfect similarity).

Given the importance of agriculture, especially among the tradable, it is unsurprising that the values of the index are close to 1 for all pair-wise combinations (Table 9). Ideally we would need more 15
Table 4: Regression results including agricultural subsidies

|Dep. var. ln(postings+1)|(1)|(2)|(3)|
|---|---|---|---|
|Output tariff exposure|-0.005|-0.009|-0.006|
||(0.015)|(0.014)|(0.015)|
|Imported input tariff exposure|-0.020***|-0.020***|-0.020***|
||(0.007)|(0.007)|(0.007)|
|Export tariff exposure|-0.126***|-0.131***|-0.120***|
||(0.035)|(0.036)|(0.036)|
|Total ag subsidy||0.001<br>(0.001)|0.001*<br>(0.001)|
|Ag subsidy * Export tariff exposure|||-0.000<br>(0.000)|
|Observations|14,640|14,640|14,640|
|Adjusted R-squared|0.977|0.977|0.977|
|<br>FE|CZ YM|CZ YM|CZ YM|
|Cluster|CZ YM|CZ YM|CZ YM|

Notes: This table displays the results from the regressions of the log of monthly job postings in each commuting zone on the associated trade barrier exposure measures, with the addition of controls for agricultural subsidies. Only observations with non-missing values of agricultural subsidies are included, hence the small differences between column (1) and the baseline results. All specifications include CZ and month-year (YM) fixed effects, and standard errors (in parentheses) are two-way clustered at the CZ and month-year level. Tariff exposure measures should be interpreted in units of $1,000 dollars per worker and agricultural subsidies in units of $1,000,000. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

28
# **Table 11 The Return on Assets with Lagged Fee Income and Non-deposit Funding Shares**

The dependant variable is _Return on assets_ defined as profit before tax as a percentage of total assets of a bank. _Assets_ is the natural logarithm of total assets in US $. e _quity_ is the capital to asset ratio, which is defined as equity as a percentage of total assets. We used one period lag of equity in the regressions. _Assets growth_ is the inflation-adjusted growth rate of bank assets. _Overhead_ is defined as overheads as a percentage of total assets. _Fee income_ is the share of noninterest income in total operating income. _Non-deposit funding_ is the share of non-deposit shortterm funding in total deposits & short-term funding. _Fee income sq_ and _non-deposit funding sq_ are the square of fee income and non-deposit funding, respectively. We take one year lag of fee income, non-deposit funding and their squares in our regressions. _Inflation_ is the inflation rate based on consumer prices. _GDP growth_ is the inflation adjusted growth rate of GDP per capita of the country. _GDP per capita_ is in thousands of 2000 constant US$. We obtain the bank level variables from Bankscope and macroeconomic variables from World Development Indicators (WDI) of World Bank. We estimate all regressions using country and time fixed effects and clustering at bank level. Standard errors are in parentheses. *, ** and *** denote significance at 10%, <u>5% and 1%, respectively.</u>

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Assets|0.000|0.001*|0.000|0.001|
||(0.000)|(0.001)|(0.000)|(0.000)|
|Equity|0.080***|0.112***|0.084***|0.079***|
||(0.017)|(0.027)|(0.021)|(0.020)|
|Assets growth|0.010***|0.011***|0.009**|0.008**|
||(0.004)|(0.004)|(0.004)|(0.004)|
|Overhead|-0.157*|-0.023|-0.113|-0.122|
||(0.088)|(0.061)|(0.092)|(0.092)|
|Fee income (one-year lag)|0.015**||0.007|-0.025**|
||(0.008)||(0.006)|(0.011)|
|Non-deposit funding (one-year lag)||0.001|0.009|0.008|
|||(0.007)|(0.006)|(0.011)|
|Fee income sq (one-year lag)||||0.037**<br>(0.015)|
|Non-deposit funding sq (one-year lag)||||-0.004<br>(0.015)|
|Inflation|0.155***|0.128***|0.146***|0.142***|
||(0.042)|(0.042)|(0.040)|(0.040)|
|GDP growth|0.121***|0.098***|0.114***|0.113***|
||(0.035)|(0.034)|(0.033)|(0.033)|
|GDP per capita|-0.000|-0.001|-0.001|-0.000|
||(0.001)|(0.001)|(0.001)|(0.001)|
|N|5410|5372|5304|5304|
|R-sq|0.274|0.296|0.284|0.288|
|Country fixed effects|Y|Y|Y|Y|
|Time fixed effects|Y|Y|Y|Y|
|Clusteringlevel|Bank|Bank|Bank|Bank|

49
Section 4. Section 5 presents the robustness checks, and the final section concludes and presents policy implications.

# **2. Hypotheses development**

What drives female financial inclusion is a question that is attracting growing interest. The literature underlines several theoretical reasons explaining the persistent gender gap in access to financial services. First, financial exclusion of women might be explained by statistical discrimination, in the sense that their lower degree of education and involvement in the formal labor market is an obstacle for women to access formal financial services. Thus, Aterido et al. (2013) demonstrate that lower wage, educational attainment and involvement in formal employment contribute to explaining why, prima facie, female households tend to be less likely to have access to finance in Sub-Saharan Africa. In the same vein, Grohmann et al. (2018) present evidence that financial literacy has a significant influence on women’s financial inclusion even after controlling for country-level, institutional and other individual factors.

Second, the lower involvement of women in the formal financial market might reflect taste-based discrimination embodied by traditional gender role distribution in society that leads to misrepresentations about the out-group, i.e., women. Behavioral differences might be important, leading to taste rather than statistical discrimination in credit markets (Beck et al., 2018). Demirgüç-Kunt et al. (2013) show that adverse social norms contribute to explaining part of the variation in the use of financial services between men and women. In the same vein, male heads of households in Kenya are more likely to receive formal credit (Johnson, 2004) On another note, gender marking in language creates an unconscious bias that affects womenled businesses’ access to microfinance according to country-level data from 115 countries between 1995 and 2015 (Drori et al _.,_ 2018).

Beyond the microeconomic statistical- and taste-based discriminatory drivers discussed above, other empirical studies have pointed out the role of institutions and legal barriers in explaining the differences in access to financial services between men and women. A study conducted by Balasubramanian et al. (2019) shows that women’s land ownership status has an effect on their financial inclusion in 148 countries. This finding has been subsequently confirmed by Adegbite and Machethe (2020). They demonstrate that the gender gap in formal financial services in Nigeria is mostly impacted by sociocultural, institutional, legal and regulatory factors. Using a panel of 752 microfinance banks in Nigeria during the period 2011 to 2014, Ogunleye (2017) finds evidence that differential treatment under the law or customs may prevent women from entering into contracts under their own name, including the opening 5
**Table 3. Upward mobility decelerated during the crisis**

Transition matrix for intra-generational mobility in Moldova, 2008 to 2011 (percent of individuals)

||||**Des**<br>|**tination(2011)**<br>|||
|---|---|---|---|---|---|---|
|||**<$2.5**|**$2.5-$5**|**$5-$10**|**$10+**|**Total**|
||**<$2.5**|**12.5**|3.8|0.0|0.0|_16.3_|
|**Origin**|**$2.5-$5**|0.2|**35.4 **|8.1|0.0|_43.8_|
|**(2008)**|**$5-$10**|0.0|0.4|**29.6**|2.2|_32.3_|
||**$10+**|0.0|0.0|0.1|**7.5**|_7.6_|
||**_Total_**|_12.8_|_39.7_|_37.9_|_9.7_|_100.0_|

Source: Calculations based on HBS data. Note: Results presented are for the lower-bound mobility estimates using the synthetic panel methodology by Dang et al (2011).

**Overall, upward mobility in Moldova was more pronounced in the first half of the 2000s, and during the crisis more people were “stayers”** . Figure 15 summarizes the transitions matrices presented before and confirms significant movements upward, little downward economic mobility and a growing share of “stayers” particularly closer to the crisis years.

**Figure 15. The share of “stayers” increased during the crisis period**

Share of the population with upward and downward mobility, and stayers <!-- Start of picture text --> 100%<br>90%<br>80%<br>70% 57.8<br>73.1<br>60%<br>85.0<br>50%<br>40%<br>30%<br>20% 42.2<br>26.3<br>10%<br>14.2<br>0%<br>Period 1: 1999-2004 Period 2: 2006-2011 Post crisis: 2008-2011<br>Upward mobility Downward mobility Stayers<br><!-- End of picture text --> 20
**<u>Table 3: Determinants of output and input intensity per ha for owner-cum-sharecroppers (household fixed effects)</u>**

||**Value of**<br>**output per**<br>**hectare (log)**|**Family labor**<br>**per ha (log)**|**Total labor**<br>**per ha (log)**|**Pair of oxen**<br>**days per ha**<br>**(log)**|**Quantity of fe**<br>**– Random**<br>**Coef.**|**rtilizer per ha**<br>**effect tobit**<br>**Marginal**<br>**effect**<br>**(dlny/dx)**|
|---|---|---|---|---|---|---|
|Ownership dummy|0.071**|0.171***|0.163***|0.014|59.804***|0.380***|
||(2.48)|(6.55)|(6.25)|(0.49)|(6.17)|(6.16)|
|Number of years possessed|-0.002|-0.002*|-0.003**|-0.001|-0.752*|-0.005*|
||(1.32)|(1.89)|(2.12)|(0.81)|(1.86)|(1.86)|
|Good soil quality|0.107***|0.034|0.030|-0.010|-20.215**|-0.129**|
||(3.66)|(1.30)|(1.14)|(0.34)|(2.15)|(2.14)|
|Medium soil quality|0.091***|0.024|0.019|0.015|-12.818|-0.082|
||(3.32)|(0.98)|(0.76)|(0.59)|(1.45)|(1.45)|
|Flat land|0.064|0.049|0.026|0.046|-8.880|-0.056|
||(1.22)|(1.03)|(0.56)|(0.91)|(0.52)|(0.52)|
|Gently sloped land|0.085|0.035|0.021|0.058|-18.830|-0.120|
||(1.61)|(0.73)|(0.44)|(1.15)|(1.09)|(1.09)|
|Irrigated land|-0.055|-0.040|-0.056|-0.046|-28.103|-0.179|
||(0.83)|(0.66)|(0.93)|(0.72)|(1.09)|(1.09)|
|Constant|7.143***|4.663***|4.736***|3.480***|-81.877***||
||(124.51)|(89.34)|(90.85)|(63.18)|(4.20)||
|Observations|7569|7569|7569|7569|7569||
|Number of household|591|591|591|591|591||
|R-squared|0.25|0.12|0.13|0.15|||

**Note.** Absolute value of t statistics in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%. The reported marginal effects for the fertilizer regression are provided in the form of _dlny/dx_ such that we can interpret the coefficient of the ownership dummy similar to that of the labor intensity regressions as the percentage deviations of input intensities on owner-cultivated plots relative to sharecropped plots. Time-varying district ( _woreda_ ) dummies are included, but not reported.

22