rafmacalaba/datause-encoder-data huggingface dataset:
for physical inspection by Albanian customs. We propose as the first-stage equation for our IV framework the first-differenced equation: where ∆���_����������� is the first-difference in an indicator variable equal to 1 if more than 50% of import shipments of a firm-HS 6-digit product-origin country in a year is allocated to the red channel and zero otherwise and � is an i.i.d. error term. Our use of the median allocation ���� to the red channel also follows Volpe Martincus _et al_ . (2014), who offer three justifications for the practice. Perhaps the strongest of these is that which we used above for the time measure, that median statistic, unlike the mean, does not depend on the number of transactions. Given the later evidence that we provide of a causal effect of time in customs on the number of shipments this is an important property to require of the instrument in the first-stage regression.

The second-stage equation is Eq. (1) in first-differences: where ∆<sup>�</sup> ����� is estimated from the first stage (in a 2-Stage Least Squares framework (2SLS)) and where for simplicity we retain the same nomenclature for the fixed effects and the error term as in Eq. (1) (rather than generating first-differenced fixed effects and an error term).<sup>36</sup> This equation produces a causal estimate of the impact of customs clearance time on the intensive margin of imports,  2. Combining this estimate with  1 from Eq. (2) gives us a joint estimate of the effect of changes in the (median) allocation to the red channel on the intensive margin of imports. In order to understand the quantitative implications of the policy change we will apply these estimates to the estimated change in median red channel allocations – a decline of 21 percent (-0.21) - reported in Table 2.

# **5. Effects of Customs Clearance Time on Import Value**

# **5.1 Main Results**

Table 5 shows our baseline OLS estimates of Eq. (3) and our preferred 2SLS IV estimates of Eqs. (2)-(3) with standard errors robust to heteroskedasticity. Columns (1)-(7) focus on clearance

> 36 Our use of 2SLS rather than GMM for the IV strategy is driven by computational reasons, i.e., the presence of a very large set of fixed effects in our equations requires us to use the _reghdfe_ Stata command drawing on Guimaraes and Portugal (2010) for which the only possible IV estimates to obtain are 2SLS estimates.

22
POLICY RESEARCH' WORKING PAPER 3024

# Abstract

> Lederman and Maloney trace the evolution of research and development (R&D) expenditures along the development process using a new global panel data set.

> They show that R&D effort measured as a share of GDP

> rises with development at an increasing rate. The authors

> examine how four groups of countries from Latin America, Asia, advanced manufacturing exporters, and

> advanced natural resource-abundant countries fare

> relative to the predicted development trajectory. Latin America generally underperforms as do some countries

> **in** Asia and Europe, but their striking finding **is** that some-Finland, Israel, the Republic of Korea, and

> Taiwan (China)-have radically deviated from the predicted trajectory and displayed impressive R&D

> takeoffs. The authors ask whether these countries overinvest in R&D but find that the high estimates of the

> social rates of return probably justify this effort. Moreover, the returns to R&D decline with per capita

> GDP. The authors attempt to explain why rich countries

> invest more in R&D than poor countries. They conclude that financial depth, protection of intellectual property rights, government capacity to mobilize resources, and the quality of research institutions are the main reasons

> why R&D efforts rise with the level of development.

> This paper-a product of the Regional Studies Program, Office of the Chief Economist, Latin America and the Caribbean Region-is part of a larger effort in the region to understand the role of innovation in development. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Patricia Soto, room

> 18-018, telephone 202-473-7892, fax 202-522-7528, email address psoto@worldbank.org. Policy Research Working

> Papers are also posted on theWeb at http://econ.worldbank.org. Theauthorsmaybecontactedatdlederman@worldbank.org or wmaloney@worldbank.org. April 2003. (38 pages)

> _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 toget 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_

Produced by the Research Advisory Staff
research.<sup>2</sup> Modules on stepping out of one’s comfort zone and exposing women to success stories aim to build soft skills. Critically, the program does not include access to credit or any transfer of cash or capital. Women self-select into participation after attending a detailed orientation session. Self-selection mitigates concerns about ineffective program targeting, allowing us to focus on the importance of skills for economic inclusion among women motivated to learn new skills.<sup>3</sup> We collect three types of data to build a holistic understanding of the role skills play in economic inclusion among the women in our sample. First, we collect detailed in-person survey data on households and businesses at baseline before the intervention begins, midline shortly after women graduate from the program, and endline 12–18 months after graduation.<sup>4</sup> Our three rounds of in-person data collection elicit information about all businesses a woman runs, allowing us to observe how women’s business portfolios change over time. Second, we collect high-frequency SMS data on revenues over the entire study period to understand whether the program enables women to better cope with negative shocks. Finally, we collect data on the social and business networks of women in our sample. Doing so allows us to quantify network-based spillovers.

The program strengthens hard and soft skills. We observe improved business tracking, price management, and more hours spent working in the main business. Effects on select business practices persist in the medium-run (18-24 months). Entrepreneurial soft skills also improve, with treated women being 38% more likely to set goals than women in the control group and exhibiting improvements in grit at endline.

Improvements in skills lead to gains in women’s economic inclusion. At

> 2These program components are similar in spirit to the ILO’s Start and Improve Your Business program as in deMel, McKenzie, and Woodruff (2014) and the personal initiative training in Campos et al. (2017).

> 3We study the differential impacts of a variation of this program that delivers coaching at women’s homes or businesses in Lang and Seither (2022). The full design is pre-registered as AEARCTR-0003214Z (Lang, Magruder, and Seither, 2022).

4These differences in timing for the endline survey were caused by COVID-19 restrictions that prevented travel within Uganda.

4
their enquiry was twofold: the perverse flow of capital from poor to rich countries; and the allocation puzzle of Gourinchas and Jeanne (2006), namely, that within the group of developing countries, net capital inflows tend to find their way to the slower-growing countries. Against this background, PRS found a positive correlation between average current account balances and average growth rates for developing countries after controlling for the standard growth determinants during 1970-2000. Moreover, they find that this correlation was driven more by savings than investment: when savings-to-GDP is included in the growth regression, the coefficient on the current account balance dropped to zero but remained virtually unchanged when the investment rate was included (recall the accounting identity national savings =current account surplus plus investment), suggesting the dominant role of savings.

The explanation PRS favor about why savings are positively correlated with growth is that financial systems tend to be under-developed in developing countries. So not all the savings are intermediated into investment and part of it gets parked abroad via current account surpluses, leading to an accumulation of foreign exchange reserves. Alternatively, foreign exchange reserves get built up either because countries want to avoid overvaluation or because they want a cushion against the volatility of capital flows.

The preceding results fly in the face of neoclassical economics, according to which capital should flow from rich to poor countries in order to equalize per capita incomes. One reason this may not happen is that expected marginal returns may not be higher in poorer countries even though they have lower capital-to-labor ratios either because of low total factor productivity or high risk or both. But even in a situation where marginal returns are higher in poorer countries, the welfare benefits of financial integration may be limited. Gourinchas and Jeanne (2006) examined financial integration in a calibrated neoclassical model and found that the welfare gains in switching from financial autarky to full capital mobility equal a paltry 1 percent increase in domestic consumption for the typical non-OECD country. This should come as a dampener to economists who may have advocated capital account openness as a way of speeding up income convergence.

The truly compelling point in Gourinchas-Jeanne is that financial integration will have a significant impact on welfare only if it contrives to substantially bridge the gap between the _levels_ of total factor productivity in poor and rich countries—which goes substantially beyond simply equalizing _growth rates_ or even the marginal returns to capital. For example, Gourinchas-Jeanne find that if financial integration eliminated 25 percent of the productivity gap with the US, the welfare gains for the prototypical developing country would be 50 times larger than that in the standard neoclassical model.

5
||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Expect to lose job<br>or be<br>unemployed=1<br>(conditional on<br>had a job on the<br>day of the survey<br>interview)|Expect<br>income to<br>reduce at<br>least 30<br>percent=1|Unable to<br>meet basic<br>needs=1|Cutting size<br>of meals=1|Received any<br>financial<br>benefit from<br>work or<br>government<br>=1|Expect to lose health<br>insurance coverage=1<br>(conditional on had<br>health insurance on<br>the day of the survey<br>interview)|
|Middle East and North Africa|0.110***|0.065*|0.174***|0.045|-0.131***|-0.059***|
||(0.039)|(0.034)|(0.036)|(0.028)|(0.027)|(0.016)|
|North America|0.196***|0.106**|0.017|0.010|-0.007|-0.081***|
||(0.059)|(0.043)|(0.044)|(0.038)|(0.038)|(0.020)|
|Sub Saharan Africa|-0.096**|0.060|-0.008|0.047|-0.086*|-0.055**|
||(0.042)|(0.056)|(0.050)|(0.047)|(0.049)|(0.025)|
|Observations|4,454|7,427|7,427|7,427|7,427|5,952|
|Pseudo-R<sup>2</sup>|0.1301|0.0576|0.1919|0.1432|0.0549|0.0953|

Note: *** 1%, ** 5%, and * 10% level of significance.

15
<!-- Start of picture text --> Bottom 40%, Top 40% and Total Gini: State average<br><3<br>= o<br>=<br>= 2<br>=i a:<br>s5<br>i<br>oO<br>||<br>3<br>=<br>wy<br>t<br>1960 1970 1980 1990 2000 2010<br>year<br>—+— Total Gin) —— Bottom Ginkh m— Top Gini<br><!-- End of picture text -->
firm size. Following Davis and Henrekson (1997), KRZ (2002) suggest an alternative calculation of employee-weighted firm size. Recalling that our data come in bins containing a range of employment totals, the employee weighted firm size is given by: given bin, _N_ sec _emp_ is the total number of employees in the sector, and _N_ sec _estab_ is the number of establishments in the sector. This alternative measure of average firm size places more weight on larger firms, and hence dampens the impact of a large number of very small firms. The second column of Table 3 shows the employee-weighted average firm size. Indeed, this produces a significantly larger average firm size. For Mexico as a whole, the average firm size is now just over 1,100 employees, which again is close to the average in the median European country reported by KRZ (2002). Across states within Mexico, there is considerable variation in firm size by either measure. For example, the employee weighted average firm size ranges from 275 in the state of Zacatecas to more than 5,000 in the Federal District.

As a third measure of firm size, we take the percentage of total employment found in firms with 10 or fewer employees. This focuses attention on the very small establishments. The third column of Table 3 shows that over half of the employment in Mexico occurs in firms with 10 or fewer employees. Again, there is significant variation across states, with as little as one-third of employment in small firms in the Federal District and just more than two-thirds of employment in the state of Chiapas. As would be expected, the employee-weighted firm size measure and the percentage of 21
An Assessment Of Reform Options For The Public Service Pension Fund In Uganda

**Figure 22: In a pure DC reform with immediate switching replacement rates* for stock of retirees for civil servants are lower than in the baseline scenario****

<!-- Start of picture text --> 100.0%<br>80.0%<br>60.0%<br>40.0%<br>20.0%<br>0.0%<br>2007 2012 2017 2022 2027 2032 2037 2042 2047 2052 2057 2062 2067 2072<br>Year<br>ref I.1A base<br>Replacement rate<br><!-- End of picture text -->

**Notes** : * Average pension as percentage of average group wage; ** Comparison of replacement rates* for stock of retirees for civil servants – baseline with Reform I.1A. **Source** : WB calculations based on PROST.

- 55 -
<!-- Start of picture text -->
1.5<br>Ss |<br>3<br>2<br>We) |I<br>a<br>z |<br>nO I<br>fc |<br>e) |<br>a I<br>gz 5 | —e— Control<br>5 —— Treated<br>a) |<br>N|<br>5<br>oO<br>Zz oO === i<br>is |<br>7) |<br>c|<br>$|<br>S|<br>5|<br>-5 -4 -3 -2 -1 0 1 2 3 4 5<br>Birth Year since Coffee Mill Opening<br><!-- End of picture text -->
Information on company affiliation with the<sup>top 30 chaebol is provided by the Korean Fair Trade</sup> Commission on an annual basis. We use<sup>group-affiliation data from the 1994-1997 lists of business</sup> groups. This is because the 1998 list contained<sup>a number of ownership changes, which would have</sup> biased our results. Several debt-ridden business<sup>groups were forced to sell bankrupt firms in 1997</sup> and 1998, which were in some cases subsumed<sup>into larger business groups and/or taken over by</sup> foreigners. A firm's openness to foreign investment<sup>is thus related to its membership at the time of</sup> divestiture. The list of chaebol is fairly<sup>robust from 1994 to 1997, with the same chaebol occupying</sup> the top 25 spots throughout the period. There<sup>was some variation among the bottom five chaebol,</sup> with three chaebol leaving the list by 1996 or 1997.<sup>In the interest of comprehensiveness, we follow</sup> all companies affiliated with the 33 chaebol<sup>that appear on the Korean Fair Trade Commission lists</sup> in 1994-1997.

# **5. The Pattern of FDI**

# **5.1. Mergers and Acquisitions**

The theoretical framework suggests that if<sup>technology transfer or access to local distribution net-</sup> works were important, then the industries<sup>of targets of foreign investment and the industries of</sup> foreign acquiring firms and domestic acquiring<sup>firms should be different. Figures 2 and 3 show the</sup> percentage of foreign and domestic transactions<sup>by target industry and parent industry, respec-</sup> tively. In general foreign investors target<sup>firms in similar industries to Korean firms. The simple</sup> correlation among 2-digit SIC industries is 0.84<sup>and the rank correlation is 0.88. The industries of</sup> the acquiring firms are also very similar, with<sup>a simple correlation of 0.91 and a rank correlation of</sup> 0.82. This suggests that to the extent<sup>that international synergies exist, they are broadly similar</sup> to domestic synergies. Still, it is worth<sup>noting that some industries tend to attract somewhat more</sup> 10
# **Figure 3. Deviation of steady-state employment ratios from sample average**

# **A. Aggregate and non-agriculture**

<!-- Start of picture text --> Percent of working-age population<br>60 Other EMDEs Sub-Saharan Africa<br>40<br>20<br>0<br>-20<br>-40<br>-60<br>-60 -40 -20 0 20 40 60<br>Steady-state nonagricultural<br>employment ratio (Percent)<br><!-- End of picture text -->

# **B. Aggregate and agriculture**

<!-- Start of picture text --> Percent of working-age population<br>60 Other EMDEs Sub-Saharan Africa<br>40<br>20<br>0<br>-20<br>-40<br>-60<br>-60 -40 -20 0 20 40 60<br>Steady-state agricultural employment<br>ratio (Percent)<br><!-- End of picture text --> Source: Authors’ estimates.

Note: Deviation of steady-state employment ratios from sample average for aggregate employment (A, B) and nonagricultural employment (A) and agricultural employment (B). Derived from estimated fixed effects from Table 1, divided by estimated coefficient on the lagged employment ratio. The correlations are shown as OLS regression estimates in Table 3.

# **Figure 4. Deviation of women’s steady-state employment ratios from sample average**

# **A. All women**

<!-- Start of picture text --> Percentage point of female working-age<br>population<br>60<br>40<br>20<br>0<br>-20<br>-40<br>-60<br>5 7 9 11<br>Log GDP per capita<br><!-- End of picture text -->

# **B. Women in non-agriculture**

Percentage point of female working-age population <!-- Start of picture text --> 40<br>20<br>0<br>-20<br>-40<br>5 7 9 11<br>Log GDP per capita<br><!-- End of picture text -->

## Source: Authors’ estimates.

Note: Logarithm of per capita GDP (in 2015 PPP U.S. dollars) and deviation of steady-state employment ratios from sample average for all women (A) or women in non-agricultural employment (B). Derived from estimated fixed effects from Tables 1 and 4, divided by estimated coefficient on the lagged employment ratio. The sample includes 102 EMDEs for 2000-2019.

21
**Figure 7. Credit-to-GDP ratio and HP trend (quarterly data)**

<!-- Start of picture text --> Credit to GDP HP trend (lambda = 400000)<br>% HP trend (lambda = 1600)<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>19971998 1999 20002001 20022003 20042005 20062007 20082009 2010<br><!-- End of picture text --> Notes: lambda = 400000: BCBS proposal; lambda = 1600: typical value of smoothing parameter in business cycle analysis. Source: authors’ calculation.

**Figure 9. Credit-to-GDP ratio gap (quarterly data)**

<!-- Start of picture text --> HP trend (lambda = 400000) HP trend (lambda = 1600)<br>p.p.<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> Source: authors’ calculation.

**Figure 11. Structure of loans for nonfinancial sector**

<!-- Start of picture text --> Households Corporates<br>%<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Source: NBP.<br><!-- End of picture text -->

**Figure 8. Housing loans-to-GDP ratio (quarterly data)**

<!-- Start of picture text --> Housing loans to GDP HP trend (lambda = 400000)<br>% HP trend (lambda = 1600)<br>25<br>20<br>15<br>10<br>5<br>0<br>-5 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> Source: authors’ calculation.

**Figure 10. Housing loans-to-GDP ratio gap (quarterly data)**

<!-- Start of picture text --> HP trend (lambda = 400000) HP trend (lambda = 1600)<br>p.p.<br>5<br>4<br>3<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>-4<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> Source: authors’ calculation.

**Figure 12. Currency structure of loans for nonfinancial sector**

<!-- Start of picture text --> FX loans Zloty loans<br>%<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Source: NBP.<br><!-- End of picture text --> 31
Information was collected on importer and customer relationships by interviewing importers. Interviews were conducted with retailers in markets and stores. Detailed illustrations and examples are provided in the appendix. Table 1 summarizes the number and composition of the pool of interviewees.<sup>5</sup>

**Table 1. Breakdown of Interviews with Brokers, Importers, and Retailers, Port of Douala, Cameroon**

|Category|Number<br>of<br>interviews|Comments|
|---|---|---|
|Brokers|12 (5 informal and 7<br>formal–CAD)|Formal and informal brokers were<br>interviewed equally. Most informal<br>brokers adhered to professional practices<br>and had knowledge of activity. It was<br>difficult to locate occasional_bala-bala_<br>brokers.<sup>a</sup>|
|Importers|Total: 21<br>SH clothing, 4<br>SH cars, 7<br>Furniture<br>and<br>appliances, 7<br>Computer hardware, 3|The smallest-scale importers were more<br>difficult to identify (those who do more<br>bulking and occasional imports); there<br>was an equal proportion of formal to<br>informal.<br>Most<br>interviews<br>were<br>scheduled in advance.|
|Retailers|Total: 7<br>SH clothing, 3<br>SH cars, 1<br>Furniture<br>and<br>appliances, 1<br>Computer hardware,2|Some interviews were scheduled in<br>advance; others were spontaneous visits<br>in marketplaces and shops.|

_Source:_ World Bank _Note:_ All interviews were carried out in Douala. CAD = commissionnaires agréés en douane (certified customs brokers); SH = secondhand.

a. A _bala-bala_ is a nonprofessional broker.

The questionnaire for brokers was designed to capture broker typology in terms of clearance practices and the selection of and matching with customers, public administration relations (particularly customs), and types of service and contractual arrangements with importers. A final

> 5 Interviewees were selected through human networks. Because it was relatively difficult to find individuals willing to talk openly about their business, interviewees were recommended by neighbors, relatives, or business relations.

8
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