rafmacalaba/datause-encoder-data huggingface dataset:
differ between urban and rural households (or landed and landless households), suggesting that gender preferences may play a role in shaping fostering decisions beyond considerations related to the production system.

This paper is related to several strands of the literature. First, it relates to the literature documenting son preference in different areas of the world. Evidence of son-preferring fertility behavior is found especially for women in Asian countries (Chowdhury and Bairagi, 1990; Clark, 2000; Dreze and Murthi, 2001; Filmer, Friedman, and Schady, 2009). For the United States, in addition to the well-documented preference for sex-balance (Ben-Porath and Welch, 1976; Angrist and Evans, 1998), recent research has documented unusually high sex ratios among Asian mothers: this pattern is found to be related to the increasing availability of technologies to determine the sex of the fetus (Dahl and Moretti, 2008; Abrevaya, 2009). Similar evidence has been found among Asian immigrants in Canada (Almond, Edlund, and Milligan, 2009). In this paper, I quantitatively investigate if fertility decisions are gender-biased in a context where the sex ratio at birth is in the normal range.

Second, it speaks to the literature examining the effects of son preference in generating gender disparities in health and educational outcomes. In the context of India, the diffusion of prenatal sex determination technologies has not only been found to be associated with increasing sex ratios, but also with preferential _prenatal_ treatment for boys (Bharadwaj and Lakdawala, 2013). The ‘try until you have a son’ fertility rule may also ‘passively’ lead to a lower amount of resources being allocated to female children within the household. This may happen either as a consequence of shorter breastfeeding for girls because parents with daughters want to ‘try again’ for a son (Jayachandran and Kuziemko, 2011), or because girls tend to be born in larger households (Jensen, 2003). Recently, Barcellos, Carvalho, and Lleras-Muney (2014) find that, accounting for the effect of son-preferring stopping rules on family size, boys still receive more parental investment (including childcare time, breastfeeding, vaccinations, and vitamin supplementations) than girls in India. While this literature has mainly focused on young (or not yet born) children, this work represents one of the first attempts to understand the effects of the preference for male children on adult women’s health and well-being.

Third, it relates to the economic and sociological literature on the effects of a child’s gender on marital stability, divorce, and parental involvement (see Lundberg, 2005, for a review). Dahl and Moretti (2008) analyze Census and other data sources for the United States and find that women with a first-born daughter are less likely to marry, more likely to be divorced, and that –after 5
Policy ReseaRch WoRking PaPeR 4611

# **Abstract**

Using individual level employment data from Bangladesh, this paper presents empirical evidence on the relative importance of farm and urban linkages for rural nonfarm employment. The econometric results indicate that high return wage work and self-employment in nonfarm activities cluster around major urban centers. The negative effects of isolation on high return wage work and on self-employment are magnified in locations with higher agricultural potential. The low return nonfarm activities respond primarily to local demand displaying no significant spatial variation. The empirical results highlight the need for improved connectivity of regions with higher agricultural potential to urban centers for nonfarm development in Bangladesh.

This paper—a product of the Sustainable Rural and Urban Development Team, Development Research Group—is part of a larger effort in the department to understand the impact infrastructure on the pattern of nonfarm employment in developing countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at fshilpi@worldbank.org.

_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
# **3.2 Extensive margin**

Table 8 summarizes the rates of entry and exit of firms into the census of exporters. There is significant churning of exporters in the data – anywhere between 16 to 37 percent of firms exit, and another 19 to 37 percent enter each year. While there seems to be significant destruction and creation of exporters in the data, the net effect of the number of firms seems to be marginal. Moreover, entry and exit rates are lower than in countries at a comparable level of development (Figure 2 1 ).

||||**Tabl**<br>**Export Entr**<br>|**e 8**<br> **y and Exit**<br>|||
|---|---|---|---|---|---|---|
|**Year**|**Exits**|**Entries**|**Net Entry**|**% Exits**|**% Entries**|**% Net Entries**|
|2003|5,481|5,435|-46|37.3|37.0|-0.3|
|2004|4,368|4,397|29|29.7|29.9|0.2|
|2005|3,798|3,633|-165|26.1|24.9|-1.1|
|2006|3,497|3,628|131|23.8|24.7|0.9|
|2007|2,870|3,250|380|19.0|21.6|2.5|
|2008|2,775|3,057|282|18.1|19.9|1.8|
|2009|2,664|2,982|318|17.0|19.0|2.0|
|2010|2,597|2,846|249|16.3|17.9|1.6|

Source: Authors’ calculations based on data from the Exporter Dynamics Database (World Bank).

**Figure 181 Entry and exit rates in Pakistan and in peer countries**

<!-- Start of picture text --> Exporter Entry Rate Exporter Exit Rate<br>0.7 0.6<br>0.6<br>0.5<br>0.5<br>0.4<br>0.4<br>0.3<br>0.3<br>0.2<br>0.2<br>0.1 0.1<br>0 0<br>2004 2005 2006 2007 2008 2009 2004 2005 2006 2007 2008 2009<br>PAK BGR COL ECU PER ZAF PAK BGR COL ECU PER ZAF<br><!-- End of picture text --> Source: Authors’ calculations based on data from the Exporter Dynamics Database (World Bank).

30
Messenger. The tested campaigns consisted of short video clips that in total amounted to approximately 25 minutes, in line with communication research suggesting that short video clips are needed for effective social media marketing campaigns (Constantinides, 2014). We recruited individuals ages 18-to-24 years living in New Delhi and six other northern Indian cities using a Facebook ad campaign. We collected self-reported and objective online outcomes and measured impacts one week and four months after program exposure. To understand whether implicit messaging was more effective than explicit messaging, the treatment group was randomly exposed to either a humorous fake reality TV drama on gender norms and misconceptions (implicit) or a docuseries about VAW and gender discrimination with clear calls to action (explicit).

Our findings show that both edutainment formats worked, with effects varying for different outcomes. Our objective viewership data shows that take-up rates for the humorous drama were twice as high compared to the more information-focused docuseries. On effectiveness, however, neither format dominated in the one-week follow up survey. While the drama was more effective at raising knowledge and reshaping attitudes related to gender norms and VAW (intent-to-treat effects oscillated between 0.16 and 0.21 standard deviations), the docuseries was more impactful in increasing willingness to share video clips with friends (5 p.p.) and promoting online information-seeking behaviors (10 p.p.).

In the four-month survey, individuals assigned to the docuseries were 91% (7.5 p.p.) more likely to add the frame “End Violence Against Women” to their Facebook profile picture. This effect along with users’ greater willingness to share campaign videos with online friends are important outcomes for social media campaigns designed to update perceptions of social norms within online communities. Moreover, we document spillover effects in the long-term. We found that about one and a half years after the frame was first presented to the respondents, it was used by more than 34,000 people all over the world, an amplification of about 55 times the initial audience size. Our findings are strengthened by evidence of baseline balance across experimental arms, no evidence of attrition bias or placebo effects, and are robust to the addition of controls across practically all outcomes of interest.

In our post-hoc heterogeneous analysis,<sup>2</sup> consistent with theories suggesting that people’s beliefs and behaviors are influenced by their perception of prevalent social norms (e.g., Bicchieri 2005, 2016; Miller and McFarland 1987), we observe smaller effects for individuals that perceived their Facebook friends to have more conservative gender views at baseline.

> 2The study analysis was not pre-registered, including the variables used in our heterogeneous analysis. Thus, we refer to the latter as post-hoc.

2
# **Contents**

|Con|tents .............................................................................................................................................................. 1|
|---|---|
|Ack|nowledgements ............................................................................................................................................ 3|
|Abb|reviations ..................................................................................................................................................... 4|
|I.|Introduction ............................................................................................................................................... 5|
|II.|Benefits of a debt management system .................................................................................................. 6|
|III.|Coverage and functions of a debt management system ....................................................................... 8|
|i.|Coverage .................................................................................................................................................. 8|
|ii.|Core functions ........................................................................................................................................ 9|
|iii|.<br>Additional functions ............................................................................................................................ 11|
|IV.|Selection and Implementation of a DMS ............................................................................................. 14|
|i.|In-house built solutions ...................................................................................................................... 15|
|ii.|Off-the-shelf solutions ........................................................................................................................ 18|
|iii|.<br>Suggestions ............................................................................................................................................ 20|
|V.|Survey Results .......................................................................................................................................... 21|
|VI.|Conclusions .............................................................................................................................................. 28|
|App|endix 1: List of countries surveyed and summary of survey responses ........................................... 29|
|App|endix 2: Modules of public DMS and one commercial software ...................................................... 36|
|Ref|erences ......................................................................................................................................................... 40|

1
supply-side effect.The measures are developed using the 2001 population census, and Table 1 provides descriptive statistics.

Our population control captures the size of the local consumer market, which can be especially important for services businesses, and the overall level of surrounding economic activity (e.g., general availability of workers).Ghani et al. (2011c) find higher entry levels partially correlate with greater population, but we do not have a theoretical reason to suspect population influences the gender balance after controlling for other district attributes. Population density again reflects some measure of local market size, but it also goes beyond to consider the competition for local resources (e.g., higher land rents). Ghani et al. (2011c) find population density reduces organized manufacturing entry, as has been shown in other contexts, but that it is not systematically related to entrepreneurship in the unorganized sectors that we study here. With respect to gender ratios for entrepreneurs, the work of Rosenthal and Strange (2011) on sorting in the United States suggests that the female entrepreneurial share may be declining in density.

We model the district’s age structure asthe ratio of working age population to nonworking age population. This ratio is sometimes called the demographic dividend in the Indian context.While some work finds age structure matters for entry rates (e.g., Evans and Leighton 1989, Bönte et al. 2009, Glaeser and Kerr 2009), this fact has not been established for India. The general education of the workforce has been linked to higher entry in India (e.g., Ghani et al. 2011c) and the United States (e.g., Doms et al. 2010, Glaeser et al. 2010), and Amin and Mattoo (2008) further study human capital in the Indian context. It is not clear, however, if general education should influence the gender balance of entrepreneurs. We measure the general education level of a district by the percentage of adults with a graduate (post-secondary) degree. Our results below are robust to alternatively defining a district’s education as the percentage of adults with higher secondary education.

We next consider three traits specific to female advancement that areemphasized by prior studies.<sup>4</sup> The first is the female literacy rate. Given the general link established between education and entrepreneurship, we anticipate a higher literacy rate will correlate with higher relative female entrepreneurship. The second is the total fertility rate measured as a composite of age-specific fertility rates in the district. This does not have clear prediction. The third factor is the sex ratio measured as the number of females per male in the district. We anticipate this to have a positive effect for raising female entry rates relative to male entry rates.

4 Dhaliwal (2000), Mitra (2002), Ghosh and Cheruvalath (2007), Amin (2010), Field et al. (2010), and Pillania et al. (2010) study India in particular. Verheul et al. (2006), Bruhn (2009), Estrin and Mickiewicz (2009), Kobeissi (2010), and Klapper and Parker (2011) provide international evidence.

8
Moreover, technology development proceeded with little feedback from customers or suppliers.

- The Soviet technology system did not cultivate a demand for technology by the enterprise sector; it did not develop the enterprise sector’s ability to <u>search for technology</u> produced elsewhere and adapt it for use in the enterprise; and it certainly did not encourage development of technology **<u>by and in</u>** enterprises.  As a result, the enterprise sector was divorced from both the supply and demand for technology.

Latvia inherited this technology development model and it has continued operating, more or less intact, today. Unfortunately, this model is distinctly unsuited for building competitive industries and enterprises that can thrive and prosper in the EU and other global markets.<sup>34</sup> Specifically, in successful market economies:

**Figure 26 The Industrial Technology Development System: A Schematic Framework**

<!-- Start of picture text --> INSTITUTIONAL CONTEXT<br>Policy Incentive System, Financial and Funding System, Legal Framework,<br>Organizational Structures<br>OTHER<br>CUSTOMERS INDUSTRY KNOWLEDGE<br>SOURCES<br>Large TNC<br>Research<br>Institutes<br>Large Domestic Research<br>Institutes<br>Export<br>Research<br>SME Institutes<br>Domestic<br>Foreign<br>Start-ups<br>Technology<br>Sources<br>Knowledge Linkage Transfer and Developmen Research t Organizations<br>Institutes<br><!-- End of picture text -->

> 34  The following discussion and diagram of the Industrial Technology Development System are derived from Erik Arnold, Martin Bell, John Bessant and Peter Brimble, _Enhancing Policy and Institutional Support for Industrial Technology Development in Thailand: The Overall Policy Framework and the Development of the Industrial Innovation System_ , World Bank processed, 2000.

41
# Table 2

- PENSION **FUNDS: INVESTMENT LIMITS** PER **ISSUER**

- State securiti **s .** 45% of fund Liabilities of financial institutions The lesser amount of: * **15% of** the fund - **3** x capital x Fj/Ft ** - **2.5 x** Pi/Pt ******** x risk factor x value of fund

- Corporate bonds The lesser amount of: * 20% of issue - **(0.8** x issuer's net capital x Fil/Ft x risk factor) tess investments in **bonds** of issuer's subsidiaries.

- Bonds of leasing companies The lesser amount of: * 20% of issue - **(0.8** x issuer's net capital x Fi/Ft x risk factor) less investments in **bonds** of issuer's subsidiaries.

- Commercial paper - The 20% lesser of issueamount of: * (0.2 x issuer's net capital x Fi/Ft **x** risk factor) less investments in commercial paper of issuer's

- subsidiaries. Corporate equities** The lesser amount of: **-** "Chapter 121 companies * **7%** of fund x concentration factor or **7%** of shares x concentration factor.

- **-** "Non-Chapter 12" companies * **1%** of fund, or **1%** of shares. * Real estate companies * **7%** of fund x concentration factor or 20% of shares x concentration factor.

- Investment in shares, bonds and commercial paper of the same issuer - **72** of fund Quotas of other pension funds * **5%** of fund Quotas of investment funds  - 10%** of fund

Foreign issuers

- **. 0.5%** of fund

- Investment limits are reduced **by** half **if** there exists a relationship between<sup>theissuer and</sup> shareholders, executive officers or directors of the A.F.P., or if the issuer is a shareholder with more than **1%** of the A.F.P.. In the last case, the investment limit for shares is zero.

In the case of Banking Corporations or Credit Institutes, the percentage of shares has<sup>to</sup><sup>**be 2.5%.**</sup>

*** In the case of risk capital investment funds, the limit must not exceed **5%** of the fund.

- ** FI/Ft **=** Investor Fund/Total pension funds. Pf/Pt **=** Issuer's capital/Total capital financial institutions.

Source: Habitat **(1991)**

**13**
<!-- Start of picture text --> O Residual (including exceptional financing) GO Current account deficit<br>@ Net FDI (negative= inflow) (@ Price and exchr a tengchang e s.<br>G Nomina interest rate @ Real GDP growth x»<br>“ue Gonein external debt 2008-2009<br>2006-2007 15<br>15<br>0<br>0<br>“15<br>5<br>- “0<br>45 | -45<br><!-- End of picture text -->

<!-- Start of picture text --> 30 SSA Oil exporting LMICs Ucs Fragile 30 ©2012-2013SSA Oil exporting LMICs UCs Fragile<br>2010-2011<br>15 15<br>15 “15<br>30 oe |)<br>45 45<br>~60 60<br>SSA Ol exporting LMCs ucs Fragile SSA Oil exporting LMGs ucs Fragile<br><!-- End of picture text -->
combined. While prices are elements of the household's budget constraint, they generally do not vary over a community. Moreover, since the data used in this study (discussed below) come from a cross-sectional household survey, community prices do not vary over time. Thus, the price vector which conceptually belongs in a reduced form equation for nutrition, such as equation 3, gets subsumed into the unobserved community effects, s.

# 3. Data

The Andean nation of Peru has been a nation of contrasts. The country is sharply divided in three different climatic zones - the Amazonian jungle region in the East; the central, largely rural and indigenous highlands spanning the center of the country from North to South; and the Pacific Coast in the West with almost half of the urban population of the whole nation living in- and around the capital Lima. While poverty and malnutrition rates have decreased substantially over the past years in Peru, they still remain at quite alarming levels. In 1997, about half of the population was poor and about every fourth child was malnourished. Key social indicators are presented in table 1.

Differences between living conditions of the population in rural and urban areas indeed remains strildng. Malnutrition levels in rural Peru are more than three times as high as in urban areas; for the severe poverty rate the factor is 2.5. Similarly, access to basic services such as electricity, sanitation and water is highly tilted in favor of towns and cities. In 1997, only every tenth Peruvian in the countryside had access to a flush toilet while eight out of ten Peruvians in cities commanded such service.

**Table 1. Basic Social Indicators, Peru 1994 and 1997**

||_Nati_|_onal_|_Ur_|_ban_|_R_|_ural_|
|---|---|---|---|---|---|---|
||1994|1997|1994|1997|1994|1997|
|Malnutrition rate|30.0|23.8|17.4|12.2|44.7|37.3|
|Literacy rate|87.6|90.2|92.3|94.3|77.4|82.1|
|Poverty rate|_53.5_|49.0|46.1|40.4|67.0|64.7|
|Severe poverty rate|18.8|14.8|12.9|9.3|29.5|24.5|
|Electricity connection|68.8|73.7|93.7|97.4|23.2|30.3|
|Sanitation connections|48.2|58.6|73.4|84.3|2.4|11.6|
|Water, public network|65.0|72.8|84.9|89.0|28.8|43.1|
|_Source:_World Bank (1999,|p.10)||||||

_5_
of types (e.g., small, large, foreign, domestic, and niche banks). They also intend to identify institutional and policy constraints to SME banking. The IFC surveys, by contrast, focus on the banks (1 or 2 per country) thought to be leaders in SME banking in a diverse set of 8 countries. Whereas the results from the World Bank surveys can be reasonably taken as representative of what most banks in the surveyed countries do, the results from the IFC surveys can be thought as representative of “leading cases” of banks operating with SMEs.

Both the World Bank and IFC surveys collect qualitative and quantitative data. However, IFC interviews of bank managers were more exhaustive than those conducted by the World Bank, as the IFC tried to capture in more detail the business processes and other operational details of SME banking. The IFC team spent 2 days with each bank (as opposed to the 1 day or sometimes 1⁄2 day spent by the World Bank team) trying to identify the factors that might lead to their successful involvement with SMEs. The IFC collected information both on site and through the completion of submitted data requests. The IFC questionnaire consists of 32 questions and some additional data sheets, and tries to evaluate comprehensively 5 major areas: segmentation and business models, products, sales and delivery channels, credit risk management, and information technology and management information systems.

Finally, the FRS (Inmark Group) surveys collect data from SMEs, as opposed to banks. These are annual surveys conducted throughout 2002-2006 in a series of Latin American economies: Argentina, Chile, Colombia, Mexico, Peru, Puerto Rico, and Venezuela. Fortunately for our purposes, there is overlap with the countries surveyed by the World Bank, hence allowing us to analyze bank engagement with SMEs from the 15