rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 6403

# **Abstract**

This paper examines the role of the Indian diaspora in the outsourcing of work to India. The data are taken from oDesk, the world’s largest online platform for outsourced contracts. Despite oDesk minimizing many of the frictions that diaspora connections have traditionally overcome, diaspora connections still matter on oDesk, with ethnic Indians substantially more likely to choose a worker in India. This higher placement is the result of a greater likelihood of choosing India for the initial contract, due in large part to taste-based preferences, and substantial path dependence in location choices. The paper further examines wage and performance outcomes of outsourcing as a function of ethnic connections.

This paper is a product of the Economic Policy and Debt Department, Poverty Reduction and Economic Management Network. 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:// econ.worldbank.org. The authors may be contacted at Eghani@worldbank.org, Wkerr@hbs.edu, and Christopher.Stanton@ business.utah.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
Having already demonstrated that plot size is correlated with a farmer's propensity to over/under-estimate plot size, we explore these relationships econometrically by estimating the following triple difference regression: following triple difference regression: 0 1 2 3 p 4 5 p 6 p 7 p + θ (1) pph pp whereRR = αα + αα stands for continuous (bidirectional) measurement error as well as percentage over- or TT+ αα XX<sup>∗</sup> + αα RR + αα TTXX<sup>∗</sup> + αα TTRR + αα RR XX<sup>∗</sup> + αα TTRR XX<sup>∗</sup> + εε under-estimation in the post-harvest, post-treatment survey, and pph stands for an indicator variable RR for those households/plots exposed to the informational treatment, stands for (log-transformed) TT GPS plot size measure, p is pre-treatment measurement error in self-reported plot size,XX<sup>∗17</sup> is a vector of controls, additional observable characteristics of plots and households collected before pp RR θθ the informational treatment, and is a mean zero error term. In the interest of exploring heterogenous and asymmetric responses we interact the treatment dummy with true plot size and εε pre-treatment measurement error. To facilitate interpretation, we center both the plot size and measurement error variables. The most important parameters in equation (1) are 1, 4, 5, and 7. 1 quantifies the impact of the informational treatment at the average plot size and αα αα αα measurement error in our sample, when the centered and p variables take value zero. 4 αα αα captures heterogeneous information impacts associated with plot size, holding constant pre-XX<sup>∗</sup> RR αα treatment measurement error. Similarly, 5 captures heterogenous information impacts based on a farmer's prior misperception (measurement error), holding constant plot size. Finally, 7 reflects αα the interactive effect of plot size and pre-treatment measurement error. Before we discuss αα estimation results, we note that farmers’ response and adjustment to new information may not represent “learning” if farmers just change reporting behavior without updating their beliefs. This seems unlikely; thus, we interpret these changes as learning.

Table 7 reports results using continuous and bidirectional indicator of measurement error in self-reported plot size, both in the pre-treatment and post-treatment surveys. The dependent variable in Table 7 is given as log-transformed differences between self-reported and GPS values in the post-treatment round. Table 8 reports censored estimation results. The dependent variable in the first three columns in Table 8 is the post-treatment overestimation percentage, while the dependent variable in the last three columns is the underestimation percentage. Our preferred

> 17 Because the post planting survey was only administered for treatment group plots, we use the (no treatment) postharvest measurement error for the control group plots.

20
Strauss and Thomas 1995). More specifically, there are measurement errors in both income and consumption. For example, measurement errors in consumption are likely to be correlated with measurement errors in income, which would tend to induce an attenuation factor that biases coefficients towards zero (Deaton, 1997; Ravallion and Chaudhuri, 1997). In this case, even household panel does not help; we need to instrument income.

The instruments for income ( _Y_ ) and its seasonal shares ( _y_ ) can be the production constraints such as rainfall and a productive environment such as irrigation potential. The labor market clearing wages are also assumed to influence only income and not consumption directly. These variables are represented by the vector ( _M_ ). Here we rely on the assumption of perfect substitutability model of income and consumption with an active labor market to justify the instrumental variable (IV) method (Singh, Squire, and Strauss, 1986).

In other words, the income equations can be written as: A test of perfect consumption smoothing is carried out using the HIES of 2000 and 2005. The focus of analysis is to estimate a consumption model to demonstrate the extent of the net effect of income seasonality on consumption.<sup>20</sup> Even with IV method based on cross-section data we will encounter another problem. That is, when we relate seasonal consumption to seasonality of income and nonincome factors even after controlling for joint determination of income and consumption, we invariably introduce a common season effect such as seasonal shock that affects both

> 20 Calculation of seasonal share of income from HIES data is done as follows.  Household income was reported for the entire year and not by season in HIES. However, household’s annual income from crop cultivation was disaggregated by season, because, barring few exceptions, crop type varies by season, and knowing the crops that a household cultivate we tracked its crop income down to a season. So, it’s the share of seasonal crop income we used to proxy for income seasonality. This is a reasonable assumption, given that crop income fluctuation is the root cause of income seasonality in Bangladesh.

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# **1. Introduction**

Recent studies show that SME development is closely linked with growth. For example, Beck _et al._ (2005a) find a robust, positive relationship between the relative size of the SME sector and economic growth, even when controlling for other growth determinants. According to Ayyagari _et al._ (2007), in high-income countries formal SMEs contribute to 50 percent of GDP on average. Furthermore, in many economies the majority of jobs are provided by SMEs. In OECD countries, for example, SMEs with less than 250 employees employ two-thirds of the formal work force (Beck _et al._ , 2008b; Dietrich, 2010). Using country-level data, Ayyagari _et al._ estimate that, on average, SMEs account for close to 60 percent of employment in the manufacturing sector. According to SME Performance Review (EC, 2009), between 2002 and 2008, the number of jobs in SMEs increased at an average annual rate of 1.9 percent while the number of jobs in large enterprises increased by only 0.8 percent. In absolute numbers, 9.4 million jobs were created in the SME sector in EU-27 between 2002 and 2008. Also, it is often argued that SMEs are more innovative than larger firms. In developed countries, SMEs commonly follow “niche strategies,” using high product quality, flexibility, and responsiveness to customer needs as a means of competing with large-scale mass producers (see, for example, Hallberg, 2000, and Snodgrass and Biggs, 1996).

As the world economies are recovering from the financial crisis of 2008-9, many economies urgently need to create employment opportunities for their citizens. In this respect, creation and growth of SMEs is an important item on the policy agenda due to evidence that points to significant contributions by SMEs to employment. In addition, regulatory measures are necessary to ease access to formal financial services by SMEs. Historically, SMEs have been more likely than larger firms to be denied new loans during a financial crisis. For example, Hallberg (2000) argues that the events of the 1990s in Latin America and East Asia confirm this proposition. More recently, in the aftermath of the global financial crisis, SME Performance Review (EC, 2009) reports anecdotal evidence pointing to insufficient market demand as the prime obstacle faced by SMEs, followed by difficulties in accessing finance.

Given the importance of SMEs in supporting sustainable, diversified, long-term economic growth, they have, indeed, attracted renewed attention in the wake of the 2008-9 financial crisis.

2
_The reduced reliance on untreated surface water and reduced prevalence of open defecation is due primarily to an increased availability of intermediate water and sanitation options._ In the case of water, there has been a rapid expansion in the use of stand posts and, in particular, wells and boreholes (even if only a fraction of these can be considered to supply safe drinking water). In the case of sanitation, there has been steady growth in access to both traditional and improved latrines. Indeed, as of 2006, the coverage of improved latrines at 69 percent is seven times greater than the average LIC in Africa (table 8). Due in part to relatively low rates of urbanization, there has been little if any increase in the coverage of higher end solutions, such as piped water and flush toilets; Uganda has achieved its noteworthy improvements in water and sanitation access by pragmatically focusing on intermediate lower cost solutions.

**Table 8. Benchmarking water and sanitation indicators in Uganda, 1995-2006**

||Unit|Low-income<br>countries<br>**Mid-2000s**|**1995**|**Uganda**<br>**2001**<br>**2006**|Middle-income<br>countries<br>**Mid-2000s**|
|---|---|---|---|---|---|
|Access to piped water|% pop|9.3|3.0|2.2<br>3.2|61.1|
|Access to stand posts|% pop|17.1|4.6|7.6<br>9.8|22.1|
|Access to wells/boreholes|% pop|39.3|64.2|75.5<br>73.5|4.8|
|Access to surface water|% pop|34.2|28.0|14.9<br>13.4|10.9|
|Access to flush toilets|% pop|4.7|1.4|1.5<br>1.1|47.7|
|Access to improved latrines|% pop|18.3|64.9|67.0<br>69.0|33.7|
|Access to traditional latrines|% pop|38.5|13.2|13.4<br>17.5|6.9|
|Open defecation|% pop|38.3|20.3|17.8<br>12.0|11.0|
|||**Mid-2000s**|**1996**|**2001**<br>**2009**|**Mid-2000s**|
|Domestic water consumption|liter/capita/day|51|41.0|39.1<br>41.3|196|
|Revenue collection|% of total billings|94|47.9|51.9<br>99.0|99|
|Distribution losses|% of production|35|64.5|47.5<br>35.8|29|
|Cost recovery|% total costs|89|153|133<br>92|86|
|Operating cost recovery|% operating costs|125|209|240<br>138|121|
|Labor productivity|Connections per<br>employee|176|45.1|91.4<br>148.9|203|
|Hidden costs|% of total revenues|163|176|149<br>43|140|
|||**Uganda**||Scarce water<br>resources|Other developing<br>regions|
|U.S. cents per m<sup>3</sup>|**2001 **||**2006**|||
|Residential tariff|47||66|60|3–60|
|Nonresidential tariff<br>_Source:_Demographic and Health|80<br>Survey and AICD water and|sanitation utilities|104<br>database(w|121<br>ww.infrastructureafrica.org/ai|<br>cd/tools/data<br>).|

_Source:_ Demographic and Health Survey and AICD water and sanitation utilities database (www.infrastructureafrica.org/aicd/tools/data). Access figures from Demographic and Health Surveys (1995, 2001, and 2006).

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7 In 1991 the Soviet Union disintegrated, Russia was facing a political crisis, and discussions on debt succession among former Soviet republics were in full swing since the prior year.<sup>13</sup> At the end of that year Russia held only US$2.25 billion in net reserves against roughly 30 times as much debt.<sup>14</sup> In November 1991, Vneshekonombank declared insolvency, which largely affected debts incurred against the London Club of creditors.<sup>15</sup> In December 1991, Russia started debt restructuring talks with private creditors and postponing commercial debt repayments through a series of three-month deferrals that lasted until 1995, when it commenced goodwill payments into an escrow account pending final agreement with the London Club.<sup>16</sup> This behavior is evidence of Russia’s willingness to engage constructively with creditors at the time – _the integrationist stance_ . At this point, though there was no final agreement, the agreement in principle, consistent payments into escrow, and access to market in 1996 provide sufficient bases to consider the default resolved.

The third default is fresh in the memories of many.<sup>17</sup> In August 1998, Russia defaulted on short- and medium-term domestic-currency debts (commonly known as OFZs and GKOs) and announced a 3-month moratorium of foreign commercial debt.<sup>18</sup> It stopped servicing the London and Paris Clubs obligations and principal on the third MinFin (domestic USD-denominated bond) 13 Tikhomirov, note 8 _supra_ .

> 14 Boughton, note 9 _supra_ , at 288.

> 15 Signyanina-Woodruff, note 6 _supra_ , at 537. Vneshekonombank was the foreign trade bank of the USSR and thus one of the limited financial gateways traversing the Iron Curtain. The London Club is a group of banks which during the XX c. was very active in lending to sovereigns via syndicated loans.

> 16 _See generally_ Tikhomirov (2001), note 8 _supra_ ; Signyanina-Woodruff (2003), note 6 _supra_ ; Boughton (2012), note 10 _supra_ . _See also_ The Financial Times (August 4, 1993) “Russia Secures Limited Debt Restructuring;” Reuters News (June 24, 1994) “Russian bank debt rollover seen but no talks soon;” The Financial Times (November 17, 1995) “Pact with Western Banks to Reschedule $32.5Bn;” The Wall Street Journal (November 13, 1996) “Russia’s Debt Deal with London Club is Delayed” (chronicling the saga of failed restructuring attempts over these years).

> 17 The Financial Times (Aug. 24, 1998) “Stakes high in talks to restructure debt;” The Wall Street Journal (Dec. 31, 1998) “Moscow is Declared in Default on Debt.”

> 18 Signyanina-Woodruff, note 6 _supra_ , at 537. Note that even the default on domestic debts could be considered a default on private external creditors, since about a third of GKOs was held by nonresidents. Boughton, note 9 _supra_ , at 322 fn. 100.
P-values are in brackets. *, **, *** represent statistical significance at the 10%, 5%, and 1% level respectively. Standards errors allow for clustering at the country level. All models are estimated via two methods: ordinary least squares (OLS) model and robust regression model. In dropping outlier OLS model, top 5% samples on each dependent variable are dropped except % of Women Borrowers. Though we do not present the coefficients to conserve space, all models include all of the following controls; annual % of inflation rate (GDP deflator), annual % of GDP growth, Rule of law , Bank overhead costs / Total assets, Ease of doing business index, Bank Credit / GDP, regional dummies (Europe and Central Asia, Latin America and Asia, Sub-Saharan Africa, South Asia and East Asia and Pacific, omitting Middle East and North Africa), lending methodology dummies (individual and solidarity lending, omitting village banking) and log of MFI's total asset values. Combined group dummy is 1 if one of the three status dummies (commercial bank, credit union / coop or rural bank) equals 1. NGO dummy is omitted in these models.

- <u>Panel C: Regulatory and supervisory capacity for microfinance (0 4)</u>

||V|<br>ersion 1(Ful sampl|<br>Depvar: Financial S<br>es)|elf-Sufficiency<br>Version 2|<br> (DroppingRural b|<br>ank samples)|
|---|---|---|---|---|---|---|
||OLS|Robust Reg|OLS Dropping<br>top 5% of dep<br>var|OLS|Robust Reg|OLS Dropping<br>top 5% of dep<br>var|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Regulatory and supervisory capacity for microfinance (0-4)|0.0103|0.0024|0.0005|0.0114|0.006|-0.0007|
||[0.684]|[0.867]|[0.982]|[0.657]|[0.687]|[0.978]|
|NBFI dummy * Regulatory and supervisory capacity for microfinance (0-4)|-0.0296|-0.0218|-0.0226|-0.0348*|-0.0305|-0.0244|
||[0.153]|[0.290]|[0.259]|[0.093]|[0.159]|[0.225]|
|Combined group dummy * Regulatory and supervisory capacity for microfinance (0-4)|-0.0705**|-0.0574**|-0.0539**|-0.0781***|-0.0666**|-0.0514*|
||[0.022]|[0.011]|[0.032]|[0.010]|[0.019]|[0.050]|
|NBFI dummy|0.0624|0.0649|0.0602|0.0647|0.0722|0.0514|
||[0.288]|[0.189]|[0.282]|[0.293]|[0.163]|[0.377]|
|Combined group dummy|0.111|0.1035**|0.0950*|0.0894|0.0827|0.0476|
||[0.109]|[0.049]|[0.078]|[0.271]|[0.210]|[0.483]|
|Observations|636|636|611|579|579|556|
|Number of countries|39|39|39|39|39|39|
|R-squared|0.222|0.229|0.258|0.228|0.237|0.259|
|Adjusted R-squared|0.198|0.206|0.235|0.202|0.211|0.232|
|_Microscope index+ interaction term (P-value in brackets)_|||||||
|NBFI dummy|-0.0194|-0.0194|-0.0221|-0.0234|-0.0245|-0.0251|
||[0.359]|[0.266]|[0.261]|[0.284]|[0.182]|[0.221]|
|Combined group dummy|-0.0602**|-0.055***|-0.0534**|-0.0667**|-0.0605**|-0.0521|
||[0.014]|[0.005]|[0.014]|[0.050]|[0.022]|[0.118]|

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following a normal distribution with a mean of zero and variance of _δi_ . Although the true WTP level cannot be observed directly, respondents state their lower and upper bound of WTP for the sustainability of the cluster. The study estimated the mean WTP using maximum likelihood methods based on the “intreg” procedure of STATA with a robust error estimation.

# **4 Estimation results.**

The section presents four sets of results. First, it presents the baseline results on the impact of CTA adoption on SSCBT’s trade outcomes that are the daily turnover and profit, using the LATE approach. Thereafter, it describes the potential mechanisms using OLS and Logit techniques. Second, it relates change in trade outcomes with its spillover effect by assessing the effect of CTA adoption on household incomes and poverty reduction. Third, it assesses SSCBT’s willingness to pay for the sustainability of the Clusters with an interval regression approach. Fourth and ultimately, it conducts robustness check for competing explanations and assesses the heterogeneity analysis using quasi-homogeneous sub-samples.

## **4.1 Mean difference in trade and household outcomes**

Table 2 presents the mean difference analysis of the impact of CTA adoption in terms of daily profit, daily turnover, daily volume of purchase, monthly spending and monthly income. A straightforward comparison between both household per capita income and per capita expenditure of adopters and non-adopters was considered.

Table 2: SSCBTs socio-economic characteristics by adoption status

|Characteristics|Total sample<br>n=1159|Adopters<br>n=522|Non-Adopters<br>n=637|diference<br>Test|
|---|---|---|---|---|
|Daily Proft (average in CDF)|16881.9|21799.1|12852.4|8946.6***|
|Daily Turnover (average in CDF)|181992.|265110.6|113880.0|151230.6***|
|Daily Volume purchace (average in CDF)|123843.8|167462.8|88099.5|80309.8***|
|Monthly spendings (average in CDF)|414270.1|479050.0|361185.1|121805.1***|
|Monthly per<br>~~c~~apita spendings (average in CDF)|70674.5|78512.5|64224.3|14446.9***|
|Monthly income (average in CDF)|471532.0|602929.7|363855.9|250018.5***|
|Monthly per<br>~~c~~apita income (average in CDF)|87898.4|115477.9|65201.8|52127.2***|
|Headcount ratio (%)|82.55|77.68|86.55||
|Poverty gap ratio (%)|56.74|51.99|60.65||
|Extreme Poverty Headcount ratio (%)|49.69|45.64|53.02||

Source: Our own computation based on data collected ***,**,* Denote significance at 1%, 5% and 10% levels.

While household income indicates the ability of the household to purchase its basic needs, per capita expenditure reflects the effective consumption of households and there13
we generally find that both treatments had smaller impacts on gender norms attitudes and VAW indexes for individuals who reported having friends with more conservative views on gender-related topics. This result is consistent with theories suggesting that people’s beliefs and behaviors are influenced by their perception of prevalent social norms (e.g., Bicchieri 2005, 2016).

## **4.6 Placebo estimates**

Given that our surveys and video interventions were delivered on the same Facebook Messenger platform, social desirability bias could potentially be affecting our estimates. To mitigate this concern, both follow up surveys included a series of placebo questions that should not be affected by our treatments (unless respondents were trying to please the researchers with an expected answer). Appendix Table A31 presents impacts on placebo outcomes, such as thinking that climate change is a threat to humankind, willingness to vote for better fuel-efficient cars, thinking to be working in a paid job in two years’ time, or thinking that corruption is an issue in India. We find no evidence of social desirability bias for the short-term (Panel A) and the medium term (Panel B) samples.

# **5 Discussion**

Social media platforms engaged around 4.1 billion users in 2020, more than half of the world’s population (Digital 2020). By reaching large segments of the population, social media could potentially be used to correct beliefs’ distortions, challenge gender stereotypes, and discuss misconceptions about socially harmful practices at scale and at low cost. Complementing evidence that shows the effectiveness of edutainment at reshaping VAW attitudes and behaviors in community settings, our study shows that social media, with its more private consumption of information, can also be an effective medium. In addition to testing a new delivery mechanism – namely, Facebook Messenger versus community screenings or radio – we are able to objectively measure new behavioral outcomes (i.e., likelihood to click and time spent on pro-gender-equality websites and public displays indicating disapproval of VAW by adding a frame to Facebook profile pictures).

Our study provides empirical evidence that edutainment delivered through social media can be an effective tool for reshaping gender norms and VAW attitudes. Our results show that in the short-term (1 week after the intervention), the drama was effective at increasing knowledge and awareness of gender practices and shifting gender norms towards more progressive stances. Moreover, both implicit and explicit formats increased short-term information 24