rafmacalaba/datause-encoder-data huggingface dataset:
household back in Kerala resides.

<u>Table 3: Correlates of Log Annual Remittances (OLS)</u>

||<br>(1)|(2)|(3)|
|---|---|---|---|
|Reported earnings ratio|-<br>-|0.655***<br>(0.23)|0.591***<br>(0.22)|
|Migrant's annual earnings (log)|0.532***<br>(0.18)|0.737***<br>(0.20)|0.755***<br>(0.21)|
|Hhold has other members working abroad (indicator)|-0.625*** <br>(0.18)|-0.586***<br>(0.12)|-0.645***<br>(0.13)|
|Hhold's annual medical expenditure as a share of migrant's income|0.679*<br>(0.41)|0.664<br>(0.46)|0.665<br>(0.45)|
|Household's monthly income in India (log)|0.0501**<br>(0.02)|0.0343**<br>(0.02)|0.026<br>(0.03)|
|Median monthly income of Gulf migrants (log)|-<br>-|-<br>-|0.745**<br>(0.35)|
|Additional controls|No|No|Yes|
|R-squared|0.31|0.40|0.41|
|Observations|108|108|108|

Notes: The dependent variable is the annual remittances reported by the migrant. Additional controls consist o **f** the wife's years of education and indicators for whether a wife is employed, unaware of her husband's expenses, having disagreements over remittance uses, and if the migrant spends on temptation goods. All regression includes a **c** onstant term. Bootstrapped standard errors (with 500 replications) are displayed in parenthesis, clustered at the district level. *** denotes statistical significance at the 1% level, ** at the 5% level and * at the 10% level. _Source:_ Authors’ analysis based on data from QSKF.

Table 3 presents the OLS estimates of equation (12), where column 1 is the basic remittance regression that excludes the reported earnings ratio. Column 2 is the same regression, now augmented with the reported earnings ratio that is statistically significant at the 1 percent level. The overall fit of the regression substantially improves with the addition of the earnings ratio. Remittances sent home are about 6 percent higher when the reported earnings ratio is 10 percent larger. In column 3, we also include the predictors of the earnings ratio explored in table 2 and find that the correlates of the reported earnings ratio are not statistically different from zero at conventional levels, individually and collectively.<sup>20</sup> This suggests that the earnings ratio can

> 20. An F-test of joint significance yields a p-value of 0.47.

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# **Can Grants to Consortia Spur Innovation and Science-Industry Collaboration?**

# **Regression-Discontinuity Evidence from Poland**<sup>**1**</sup>

Miriam Bruhn, _World Bank_ David McKenzie, _World Bank_ Keywords: R&D, innovation, science-industry collaboration, regression discontinuity design JEL codes: O31, O38, H25

> 1 We thank Matias Cattaneo for helpful feedback on regression discontinuity designs; Aidan Coville and Ana Cusolito for helpful peer review comments; Aleksandra Klupa for research assistance; Maciej Drozd, Thomas Haven, Natasha Kapil and Siddharth Sharma for guidance and support from the World Bank side; and Emilia Nasilowska and the team at NCBiR for their insights on the Innotech program.
To understand how a micro equity contract might work in practice, we worked with KCG Equity to carry out a proof-of-concept pilot in Sri Lanka. KCG Equity engaged a lawyer to design an actual contract that could be issued under Sri Lankan law, and was itself able to enter into partnership agreements with selected enterprises. The process began in 2012. We chose to work in Hambantota district. This is a relatively underdeveloped district in the south of Sri Lanka, which the government planned to transform into a prominent hub. The first phase of a new port opened in 2010, and Sri Lanka’s second international airport opened in March 2013. At the time, this area therefore seemed a promising area for growth-oriented microenterprises.

# **3.1 Selecting microenterprises**

Given the scale of the pilot, we ruled out a business plan competition or competitive application approach. Together with KCG Equity, we met with both a local microfinance organization and the Hambantota Chamber of Commerce, and decided to partner with the chamber. The chamber has around 300 direct members (SME owners), a few large corporate members, and links with different trader associations. They had two programs that offered a promising pool of growthoriented entrepreneurs who might be interested in equity investment. The first was a youth business program, in which they made loans of up to Rs. 200,000 (average size Rs. 75,000) to young business owners, for up to two loan cycles, with the goal of graduating them to other financing. The second was an entrepreneurship award program, where the chamber identified promising businesses in the district and gave several awards each year.

Our hope was that working through the chamber would help induce honest reporting of revenues and repayment. Entrepreneurs were asked to sign a contract in front of a witness from the chamber, and were told that their reputation with the chamber would be affected by any non-payment. The signing of the agreement took place at the chamber itself before the chamber’s lawyer who also undertook the legal registration work. Second, they were told that the ability of KCG Equity to make future investments in other businesses in the community would depend on their performance and repayments. Third, it was emphasized that keeping several years of revenue-sharing records would provide the necessary information to make a persuasive case to banks or other financial institutions for subsequent financing needs.

We conducted awareness programs for groups of candidate firms identified and invited by the chamber, and explained the details of the micro-equity contract. Interest in this financial product 16
due to accelerated growth cannot be considered “cost savings” because additional domestic spending had to take place to decrease the amount of additional foreign aid. To get a true measure of “the MDG dividend of growth”—the cost savings due to accelerated growth—Figure 4 contrasts the total (current and capital) spending by the government under a MDG scenario that starts from the baseline (i.e., no removal of growth bottlenecks) and a MDG scenario that incorporates accelerated growth (i.e., removal of growth bottlenecks). The figure shows that while total public spending in the accelerated growth MDG scenario is always higher (due to additional investment in infrastructure), the HD expenditure is consistently lower. HD costs depend on the interaction of two factors. On the one hand, faster productivity growth in the private sector leads to higher wages, which then drive up the costs of reaching the MDGs. On the other hand, growth-enhancing investments in infrastructure reduce the costs of attaining the MDGs through positive spillovers and demand-side effects. Overall, the second set of factors––the growth effect–––outweighs the first and, starting from the accelerated growth scenario, the costs of reaching the MDGs are lower.

**Table 7 Ghana: Structure of public finance (% of nominal GDP)**

||**2004**|Accelerated<br>growth|MDG achievement<br>with f<br>oreign grants<br>**2015**|
|---|---|---|---|
|**Total current revenues**|**29.92**|**30.17**|**28.69**|
|Direct taxes|7.19|8.00|7.82|
|Indirect taxes|10.32|10.53|10.43|
|Import taxes|3.60|3.67|3.62|
|Export taxes|1.17|0.95|0.65|
|Current transfers|7.<br>63|7.<br>03|6.<br>17|
|**Total Current Expenditures**|**19.14**|**20.10**|**23.51**|
|Consumption: MDG-related|6.47|7.02|10.83|
|Consumption: other|5.70|6.66|6.79|
|Transfers and interest payments|6.<br>96|6.<br>41|5.<br>89|
|**Public Savings**|**10 8 **<br>**.7**|**10 7 **<br>**.0**|**5.**<br>**17**|
|**Public Investment**|**12 8 **<br>**.3**|**13 2 **<br>**.0**|**18 0 **<br>**.8**|
|**Overall Balance**|**-1.60**|**-2.95**|**-**<br>**13.62**|
|Domestic borrowing|0.33|2.40|2.22|
|Foreign borrowing|1.27|0.56|0.47|
|MDG-r<br>reigngrants<br>elated fo|0.00|0.00|10.93|

Rather than assuming that the increase in services required to achieve the MDGs is entirely brought about through an increase in the _volume_ of public spending, it is also possible to contemplate increases in the _efficiency_ of public spending, thereby reducing the need for additional financing. This possibility suggests itself both in view of the large size of the public spending that would otherwise be required to achieve the MDGs and in light of sectoral evidence on the apparently low efficiency performance in education, health, and water/sanitation in Ghana. For simplicity, it is assumed that the efficiency improvements are similar in all areas of public spending, although it is likely the case that 16
Figure 5: Scatter plot of average _SIIf_ against _SIIb_ for all manufacturing sectors, 2005 <!-- Start of picture text --> IND<br>PRT<br>FRA<br>TWN<br>USA<br>GRC<br>BGR BRA CYP ITA<br>JPN<br>MEX<br>GBR<br>HUN<br>MLTTUR DNKESP NLD<br>LVAKOR AUS BEL<br>DEU<br>AUT<br>CHN IRL<br>POL<br>SVN<br>CAN SWE<br>LTUROM EST<br>ZOW<br>CZE<br>SVK FIN<br>IDN<br>RUS<br>LUX<br>0 .05 .1 .15<br>Business Services Input Intensity<br>.04<br>.03<br>.02<br>.01<br>Financial Services Input Intensity<br>0<br><!-- End of picture text --> Note: The services input intensity measures are based on the WIOD data in 2005.

33
We next quantify at the state-industry level how changes in the size and industrial organization of the organized sector relate to changes in the employment share of the unorganized sector for the state-industry. Our outcome measures consider both changes in the fraction of the state-industry’s employment in the unorganized sector and raw changes in the unorganized sector’s size. Our metrics on industrial organization are indicator variables for where in the establishment size distribution the employment growth occurs and the degree of initial entrepreneurship. We control for state and industry fixed effects.

Three important themes emerge from this empirical analysis:

1. _In both manufacturing and services, the most important factor for reducing the unorganized sector share is growing the overall size of the organized sector_ – Our primary estimations separate the overall growth of the organized sector from its establishment size distribution and entrepreneurship rates. Growth in the sector size, versus the particulars of where in the establishment size distribution growth occurs, is central to reducing the share of stateindustry workers in the unorganized sector. Demonstrating the strong persistence of the unorganized economy, however, this overall organized sector growth has limited effect on the absolute levels of unorganized sector employment. Its effect comes through increase in the overall state-industry size, and thereby reducing the unorganized sector’s share.

2. _Within manufacturing, the most consistent force for reducing the unorganized sector share is growth in employment in large establishments in the organized sector. High rates of initial entrepreneurship in the organized sector are also important since 2000_ – Our manufacturing data allow us to observe three time periods: 1989-1994, 1994-2000, and 2000-2005. Across these three periods, state-industries with their strongest relative employment growth in large establishments with more than 100 workers have the greatest declines in unorganized sector shares. Moreover, these state-industries experience absolute reductions in unorganized sector employment levels. In 2000-2005, high initial entrepreneurship rates in the organized sector also closely links with declines in the unorganized sector’s employment share and level. This role is exclusive to entrepreneurs and is not evident in small manufacturing establishments generally.

3. _By contrast, employment growth in small establishments is the essential factor for services, and this role of small establishments is less linked to entry_ – Ghani (2010b) describes the unique role of the services sector in India’s development, in part allowing India to overcome its underdevelopment in manufacturing. Given the importance of services to current South Asian growth, we quantify its unorganized sector transition for 2001-2006 in an estimating framework similar to that of manufacturing. Throughout our services study, the development of small establishments is the most important factor for reducing the share of employment in the unorganized sector.

4
As with all policy and programmatic interventions, the moral and commonly aspirational arguments for justice interventions must be enhanced by collection of data representing how different intervention designs enhance or undermine the social groups’ equality before and after access to justice. Sucha a granular measure of the equality of social groups under specific justice policy settings could enhance measurement of this component of process legitimacy. Further, the legitimacy of justice interventions can be enhanced by better quantifying and comparing (and communicating) their economic and societal benefits, particularly where this can be done in such a way to demonstrate disproportionate impact for excluded social groups. Justice data collection and analysis, which employs broader societal data, as well as data generated from existing justice sector analytical methods, is a prerequisite for a ‘business case for justice’. An enhanced CBA APP, as described above, serves these objectives by improving the accuracy of justice sector resource allocation for maximum economic and societal outcomes while targeting society’s most vulnerable and excluded social groups.

Traditional CBAs commonly seek to weigh anticipated policy or intervention benefits against a policy reform or an intervention’s cost to determine overall societal benefit. CBAs demand resources and time due to the many challenges of quantifying anticipated costs and benefits, idiosyncratic to the nature of policies and interventions in the diverse complexity of contexts in which they occur (Prescott, 2010). Drawing on multiple sources of societal and justice process data in a systematic way, allows governments and stakeholders more exhaustive identification of interventions’ cost-benefits. It also enables systematic intervention comparison across a range of metrics beyond monetary benefits (such as crimes avoided and level of safety improvement) and to identify which interventions have greatest benefit for the most vulnerable social groups. Future utilization of an enhanced CBA APP, which includes the tested heterogenous impact component and future machine learning capacity, will enable significant contextual factors identified in previous CBAs to be tested in future intervention CBAs.

The enhanced CBA APP confronts the human and material resource problem of repeated individual CBAs across different justice processes. Broader societal data (e.g. geographic, economic, demographic, climate, security) as well as data specific to civil, criminal, and informal justice sector capacity can be retained and employed by the APP across (where appropriate) justice processes. Identified intervention costs are disaggregated by cost bearer (e.g. criminal justice system, local government, business, and society) and 30
# **2.2 Foreign aid**

We first construct a project-level database of aid disbursements from the World Bank through its two principal institutions, the International Development Association (IDA) and the International Bank for Reconstruction and Development (IBRD). From the World Bank Project Database (so-called API data), we obtain information on approval date, commitment amount, sector and instrument type for each project. We combine this dataset with monthly project-level information on disbursements from Kersting and Kilby (2016).<sup>7</sup> Next, we draw on this database to construct our main aid variable, _Aidit_ , which aggregates disbursements from the World Bank across all projects in a given country _i_ in a given quarter _t_ . By construction, this variable does not include aid from other sources such as humanitarian assistance and development aid from individual countries. It also excludes debt relief. The main reason to focus on aid from the World Bank is that we observe the timing of disbursements within the year, which is important for empirical identification because the main effect shows up at quarterly frequency. Data on other sources of aid, including the leading aggregate measure of development aid Official Development Assistance (ODA), is typically available only at the annual frequency. Further, the fact that Word Bank disbursement data is available at the level of individual projects allows us to implement an instrumental variables strategy (see below).

Our main sample consists of the 22 countries that, on average over the sample period 19902010, receive annual disbursements from the World Bank equivalent to at least 2% of GDP.<sup>8</sup> As shown in Table 1, annual aid disbursements from the World Bank are almost 3% of GDP on average whereas development aid from all sources exceeds 10% of GDP on average. Foreign aid is thus a major source of income within this sample. The distribution of our main explanatory variable, the ratio of quarterly aid disbursements from the World Bank to annual GDP, is displayed in Figure A2 in the Online Appendix: aid disbursements exceed 1% of GDP in around 25% of the quarters and exceed 2% of GDP in around 5% of the quarters.

Aid disbursements are potentially endogenous to contemporaneous economic shocks and, building on Kraay (2012, 2014), we therefore construct an instrument that exploits the time lag between commitments and disbursements of aid.<sup>9</sup> After a World Bank project is approved, dis-

> 7While the World Bank Project Database contains information on disbursements, it does not allow for bulk download. Kersting and Kilby (2016) retrieve the disbursement information from the database using an automated script.

> 8In extensions, we also study a broader set of countries with annual disbursements above 1% of GDP.

> 9Existing studies have used other instruments for aid. Werker et al. (2009) use oil price variation to instrument aid provided by OPEC members. Galliani et al. (2017) exploit the crossing of the IDA eligibility threshold to assess the impact of aid on growth.

7
Consistent with the literature examining promotions of local officials using prefecture-level data in China (Landry, Lü and Duan 2017), we fail to find any economically and statistically significant effect of reported performance on promotion likelihood for PSs. Alternatively, we adopt an ordered probit model, as in Li and Zhou (2005), to distinguish lateral moves from demotions. In column 4, we show the marginal effect of average reported GDP growth on the likelihood of promotion. Though the coefficient now is more precisely estimated, the magnitude of this effect, similar to that of the linear probability model, is small. Increasing reported growth by one percentage point leads to an increase in the probability of promotion by only 0.46 percentage point, amounting to 6% of the mean.<sup>46</sup> Since the reported performance does not contribute much to promotion for city secretaries, does true performance matter? This scenario represents the case of the provincial government being able to distinguish data faking and real performance. In columns 2 and 5, we fail to find any significant effect of true GDP growth predicted by the nighttime lights on the likelihood of promotion. This is consistent with the fact that the true performance is hidden from provincial leaders.

Finally, to test whether manipulation matters, we examine whether promotion is related to GDP over-reporting after controlling for the predicted true economic growth. In column 3 of Table 9, we find a statistically significant and large coefficient of over-reporting. A one percentage point increase in growth over-reporting leads to an increase of 1.7 percentage points in the likelihood of promotion, or a 22 percent increase of the mean promotion rate. Since over-reporting is a generated variable, we also estimate the standard error using the bootstrap method with 1,000 replications. The result, reported in brackets in column 3, is similar, and the coefficient of the over-reporting variable stays significant at the 10% level. In column 6, we use the ordered probit model. Again, the magnitude is similar to the linear probability model, and the coefficient is now significant at the 1% level.

To summarize, consistent with the existing literature, our results show a weak association between reported economic growth and promotions for city secretaries. However, for cities with the same true growth, it pays in promotion for the city secretary to manipulate performance.

# **7.2 Why over-reporting less?**

If over-reporting has career payoffs, why do not all PSs over-report and do it all the time? Before proceeding, keep in mind that the over-reporting measure, by construction, has a mean around zero, and the reported performance being lower than the predicted true growth should be understood as relative under-reporting. Nevertheless, we still need to explain why some party secretaries would under-report (in a relative sense). We provide two possible explanations.

> 46 The mean and the standard deviation of promotion in our sample are 0.077 and 0.266, respectively.

27
Previous results provide a different story for U.S. MBA graduates. The CFO survey found that 70% of the executives surveyed assigned a grade of “C” or “D” (on a scale of A-F, with A being the best and F being the worst). This is a far higher level of dissatisfaction with the communication and interpersonal skills of MBA graduates than found in the IFC MBA survey. Neelankavil found that business executives thought that most MBA graduates lacked communication skills and were very unprepared in the area of written communication.

A comparison of results from this survey with other research shows that firms in developing and transition countries were far more satisfied with the quality of MBA communication skills from their local business schools, than were U.S. firms with the communication skills of graduates from U.S. business schools. But it is important to point out that this does not mean that the MBA graduates have better communication skills abroad; rather it simply means that developing country firms are more satisfied with the communication skills of their local graduates, and programs aimed at improving the quality of business skills in these countries should focus less on training in the area of communication and interpersonal skills and more on other priority areas.

15