rafmacalaba/datause-encoder-data huggingface dataset:
- Guerguil, M., and Mandon, P., and Tapsoba, R., (2016). Flexible Fiscal Rules and Countercyclical Fiscal Policy _. IMF Working Paper WP/16/8_ , Washington DC: International Monetary Fund.

- Ilzetzki, E., & Végh, C. A. (2008). Procyclical fiscal policy in developing countries: Truth or fiction? _National Bureau of Economic Research_ , No. 14191.

- Ilzetzki, E. (2011). Rent-seeking distortions and fiscal procyclicality. _Journal of Development Economics, 96(1), 30-46_ .

- IMF. (2012). Macroeconomic Policy Frameworks for Resource-Rich Developing Countries _. IMF report_ , Washington DC: International Monetary Fund.

- IMF. (2014). Fiscal Multipliers : Size, Determinants, and Use in Macroeconomic Projections. _IMF Report_ , Washington DC: International Monetary Fund.

- Konuki, T., and M. Villafuerte. (2016). Cyclical Behavior of Fiscal Policy among Sub-Saharan Countries. _IMF report_ , Washington DC: African Department, International Monetary Fund.

- Levinson, A. (1998). Balanced Budgets and Business Cycles: Evidence from the States. _National Tax Journal, 51_ 715-732.

- Matsusaka, J. G. (2014). Disentangling the Direct and Indirect Effects of the Initiative Process. _Public Choice, 160(3-4),_ 345-366.

- Mendoza, E. G., and Oviedo, P. M. (2006). Fiscal policy and macroeconomic uncertainty in developing countries: The tale of the tormented insurer. _National Bureau of Economic Research_ , No 12586.

- Pesaran, P. H., and R. Smith. (1995). Estimating Long-Run Relationships from Dynamic Heterogeneous Panels. _Journal of Econometrics, 68(1),_ 79-113.

- Ravn, M. O., and H. Uhlig. (2002). On Adjusting the Hodrick-Prescott Filter for the Frequency of Observations. _The Review of Economics and Statistics, 84(2),_ 371-380.

- Suescún, R. (2007). The size and effectiveness of automatic fiscal stabilizers in Latin America. _Policy Research Working Paper_ , No 424, Washington DC: The World Bank.

- Talvi, E., Vegh, C. A. (2005). Tax Base Variability and Procyclical Fiscal Policy. _Journal of Development Economics, 78(1),_ 156-190.

- Tapsoba, R. (2012). Do National Numerical Fiscal Rules Really Shape Fiscal Behaviours in Developing Countries? A Treatment Effect Evaluation. _Economic Modelling, 29(4),_ 1356-1369.

- Vegh, C. and Vuletin, G. (2015). How is Tax Policy Conducted over the Business Cycle? _American Economic Journal: Economic Policy_ . 7(3), 327-370.

- WEO. (2009). World Economic Outlook, April 2009 _. IMF Report_ , Washington DC: International Monetary Fund.

26
# 6 Networks and trade at the ...rm level

While their simplicity is attractive, the aggregated measures we employed in the previous section su¤er from important limitations for examining the e¤ect of migrant networks on export participation and intensity. In fact, a key insight of the model presented in section 2 is that the e¤ects on export participation of trade costs and demand factors depend on ...rm attributes. We are unable to examine the role of ...rm heterogeneity in determining export participation if we measure the extensive margin as the number of exporters. Another concern about this measure stems from the existence of a minimum statistical threshold for the collection of data on internal trade, which implies that the number of exporters is measured with error.

On the other hand, the use of average shipments per exporter for measuring the e¤ects of migrant networks on export intensity is also problematic. In the context of a Melitztype framework, Lawless (2010) shows that the e¤ect of variable trade costs on average sales per exporter is ambiguous. This is because a fall in variable costs increases the sales of every ...rm serving a country, but may also lead to entry of lower-productivity ...rms (and thus lower-sales ...rms) in that market. A similar reasoning applies to the e¤ects of ...rm-destination networks, ac(j). In this section, we therefore exploit the ...rm-destination detail of the data to estimate the e¤ect of migrant networks on export participation and intensity at the ...rm-level.

## 6.1 Networks and export participation

We begin by focusing on the e¤ects of migrant networks on export participation. Based on (7), we estimate a speci...cation of the form: where: E jc = 1 if ...rm j exports to country c and P is a probability distribution function; 'j is a proxy for ...rm e¢ ciency; and �jc is the ...rm-destination error term. The countrylevel regressors have the meaning de...ned above.

Table 4 reports the estimates yielded by a linear probability model (LPM). The estimated coe¢ cients give the marginal e¤ects of each regressor on the probability of a ...rm exporting to a given market. In line with the model, the estimates in column (1) reveal that, conditional on the attributes of the export destination, more productive ...rms are more likely to sell there. The coe¢ cients associated with the customary gravity variables also present the expected sign: ...rms are more likely to export to larger and geographically closer nations, that are non-landlocked, and have the euro as the o¢ cial currency.

10
The problem is exacerbated by additional government policies. In most states, mortgages are either “without recourse,” meaning that a defaulting homeowner is not responsible for paying the difference between the value of the home and the principal amount of the mortgage, or else the law makes the process of enforcing the obligation so burdensome that lenders take no action. Thus, homeowners with negative equity are more likely to walk away from the mortgage given this regulatory protection, contributing to the problems of banks.

# **Fair Market Accounting Standards—Not a Cause of the Crisis**

Some critics have alleged that “fair value accounting” standards that often (but, crucially, not always) compel financial institutions to value assets on the books at market values (“mark to market” accounting) have exacerbated the financial crisis. These critics argue that mark to market accounting has forced write downs of assets during the crisis, depleted bank capital and forced sales of assets at distressed sale prices. These prices were ostensibly considerably less than their proper value in a normally functioning market, i.e, less than the present value of the cash flows of the asset.<sup>36</sup> They argue that there is then a downward spiral or contagion as many banks are forced to unload assets at distressed prices, further driving down prices.

Downward spirals in prices, however, arise for many reasons. Given the large number of defaults in the underlying mortgage pools that back the value of the mortgage backed securities and the resulting reduction in the cash flow of these securities, an alternate plausible explanation for the reduced prices of the mortgage backed securities is that they reflect a more accurate assessment of the present value of the cash flow of the securities. In support of this latter view **,** Laux and Leux (2010) maintain that the allegations that fair market accounting contributed to the crisis are not supported by evidence. They find “little evidence that banks reported fair values suffered from excessive write-downs or undervaluation in 2008, which in turn, could have contributed to downward spirals and contagion. If anything, the evidence points in the opposite direction. Fair values played only a limited role for bank’s income statements and regulatory capital ratios except for a few banks with large trading positions. For these banks, investors would have worried about exposures to sub-prime mortgages and made their own judgments 36 See, for example, Institute of International Finance (2008).
**Table 6: Impacts on Owner Time Use**

||Owner's<br>hours|Time<br>concentration|Growth-focused<br>Activities|Time on external<br>and future activities|Delegation|Percent of time<br>sales & marketing|Percent of time<br> accounting & finance|
|---|---|---|---|---|---|---|---|
|**Panel A: Impacts in First Follo**|**w-up Survey**|**(One-year afte**|**r intervention star**|**ted)**||||
|Assigned to Insourcing|-1.414|0.012|0.223|4.074*|0.058|3.172**|-0.474|
||(2.977)|(0.019)|(0.154)|(2.453)|(0.092)|(1.435)|(0.931)|
|Assigned to Outsourcing|-1.061|0.006|0.142|3.622|0.044|3.403**|-1.113|
||(3.175)|(0.019)|(0.160)|(2.575)|(0.084)|(1.400)|(0.948)|
|Assigned to Training|-0.659|-0.006|0.047|1.526|-0.017|1.100|-1.630*|
||(3.398)|(0.018)|(0.157)|(2.615)|(0.092)|(1.370)|(0.923)|
|Assigned to Consulting|-2.487|0.007|0.045|-0.342|-0.061|2.917**|0.880|
||(3.177)|(0.020)|(0.160)|(2.567)|(0.090)|(1.420)|(1.007)|
|Mean of Control Group|46.5|0.407|2.222|30.5|3.397|19.7|14.4|
|Sample Size|670|627|627|663|663|626|626|
|P-value: all treatments zero|0.951|0.864|0.592|0.230|0.674|0.059|0.079|
|P-value: all treatments equal<br>**Panel B: Impacts in Second Fo**<br>|0.941<br>**llow-up Sur**|0.753<br>**vey (Two-years**|0.583<br>**after intervention**|0.226<br>**started)**|0.509|0.377|0.055|
|Assigned to Insourcing|3.634|-0.004|0.054|1.033|0.082|-0.490|-0.915|
||(2.979)|(0.020)|(0.152)|(2.272)|(0.085)|(1.592)|(0.940)|
|Assigned to Outsourcing|1.473|-0.016|0.034|2.957|0.092|0.045|-0.717|
||(2.850)|(0.018)|(0.148)|(2.113)|(0.088)|(1.602)|(0.945)|
|Assigned to Training|0.365|-0.004|-0.065|3.612|0.054|-3.041**|-0.170|
||(2.978)|(0.020)|(0.158)|(2.269)|(0.089)|(1.510)|(0.966)|
|Assigned to Consulting|4.148|-0.010|-0.018|1.149|-0.061|-1.452|0.410|
||(3.000)|(0.018)|(0.145)|(2.163)|(0.084)|(1.532)|(0.999)|
|Mean of Control Group|44.9|0.386|2.091|28.4|3.336|22.9|14.0|
|Sample Size|678|632|632|632|632|632|632|
|P-value: all treatments zero|0.522|0.912|0.952|0.470|0.391|0.156|0.605|
|P-value: all treatments equal|0.528|0.893|0.875|0.593|0.289|0.138|0.482|

Notes: Regressions control for stratification batch fixed effects. Robust standard errors in parentheses. *, **, *** denote significance at the 10, 5, and 1 percent levels. **Owner's hours** are hours worked in the firm by the owner in past week; **Time concentration** is number of different functional areas out of 13 categories in which owner allocates 5% or more of time (lower scores means more concentrated); **Growth-focused activities** is number of growth-focused functional areas that owner spends 5% or more of time on; **Time on external and future activities** is percent of time on external and future-focused activities; **Delegation** is index measure of five questions on delegation **. Percent sales and marketing** , and **percent accounting and finance** are percent of owner time devoted to these areas of the firm.

41
- 4 -

discuss, the accessibility to jobs may be more limited to women in the labor market or job assignment framework. Second, in the transportation or mobility context, women may also have disadvantage over men: Female workers are often more likely to rely on public transportation (Bento et al., 2005) or walking (Mondschein and Taylor, 2017). In addition, public transit may not be safe for women and girls (e.g., Ceccato 2017).

7. The firms’ locational choice is perhaps another side of the job accessibility issue: While employees decide where they live and how they commute to work, employers or firms also choose where they are located, possibly depending on transport connectivity. According to Sweet (2014), firms are more likely to relocate if they face large-scale traffic congestion at the regional level in the Philadelphia Metropolitan Area. In general, firms prefer to locate themselves where access to transport infrastructure is good (e.g., Cieślik and Ryan, 2004; Boudier-Bensebaa, 2005; Milner et al., 2006).

8. Finally, there are an increasing number of studies that aim to develop various indices measuring job accessibility. In recent years, various GIS-based high-resolution data have become available even at the city level, which allow to estimate all kinds of connectivity and accessibility in different ways (e.g., Cheng and Bertolini, 2013; Wang and Chen, 2015; Saghapour et al. 2016). The current paper also uses some spatial data to compute transport connectivity to the central business district (CBD) and job locations.

9. The current paper aims at examining the impact of public transit connectivity on access to jobs, particularly focusing on wages earned by those who commute to work. Given the above discussion in the literature, there are two issues that constitute this result chain: First, among others, accessibility to public transit may affect people’s decision on whether or not to commute to work. Second, once people are employed and decide to commute to work, the level of wages is determined. Commuting is generally costly. Thus, holding everything else constant, commuters’ wages must be higher than those that non-commuters earn.
To summarize the main empirical findings, the evidence suggests that a more strict MRG prior to June 2007 is associated with more trading in the direction of peers, and in particular more buying of stocks in which managers were underexposed. Meanwhile, underperforming managers traded more heavily towards the peer portfolio prior to June 2007, by buying stocks in which they were underexposed, as opposed to selling stocks in which they were overexposed. This asymmetric behavior between buys and sells could be explained by the fact that these funds were growing within the sample period.

# **3.4. Alternative Explanations**

The specification strategy above assumes that the policy change is exogenous to the domestic stocks’ return process. In the estimation I control for stock-specific attributes such as past returns and trading volume. However, one cannot control for all stock characteristics that might have changed after July 2007 and that might have induced the funds to adjust their trading behavior. For example, PFAs might have received more good signals about the fundamentals of stocks in which they were underexposed prior to July 2007 than after, inducing them to buy more of those stocks before the policy change than after. The shortcoming of this argument is that if a PFA is underexposed in a particular stock relative to the peer portfolio, by construction there must be at least one PFA overexposed in the same stock. As favorable new information arrives about a stock, both underexposed and overexposed PFAs should increase their holdings. Hence, one would need some sort of argument for why PFAs with underexposure were the only ones receiving good signals.

Another possible explanation for the results is that PFAs altered their trading strategies due to managerial changes around the time of the policy change. For example, trading strategies might result from changes in management within the firms or shifts in preferences among the top investment officials. A closer look at PFAs’ CEO replacement indicates that, while there were some changes in management over the sample period, there is no evidence of an industry wide event before or after the MRG adjustment.<sup>11</sup> In terms of preference shocks, interactions between PFAs dummies and the MRG dummy should account for individual PFA changes before and after the policy experiment. However, an industry wide taste shock occurring in mid 2007 would be indistinguishable from the policy experiment. While such event is unlikely, it cannot be ruled out under the current empirical specification.

> 11According to ASOFONDOS, four of the six PFAs had only one CEO replacement each during the sample period, occurring on the following dates: October 2006, February 2008, October 2008 and May 2010. The other two PFAs changed their CEO four times each between January 2004 and December 2010.

14
accepted). The use of written procedures only may also limit access to the judicial system.

Table 4 shows how long enforcement related to disputes takes. The first column shows the time lag between filing of a complaint and a summons to court. The second column shows the time taken for adjudication and the third column shows the time taken for enforcement after adjudication. Korea and the Phillipines seem to do particularly well in enforcement of judicial decisions for check collection. In the UK and in the US for example, it takes 14 days after adjudication for enforcement. Of note is the fact that the Project also highlighted that establishing mandatory time limits for the various steps in adjudication, seemed to have little effect on delay or perceptions of efficiency once procedural complexity is accounted for.

# **Table 4. Duration: East Asian Countries**

||**Eviction**<br>Duration until<br>completion of<br>service of<br>process|<br> <br>Duration of<br>trial|Duration of<br>enforcement<br>(from<br>notification to<br>actual|enforcement)<br>Total Duration|**C**<br>Duration of<br>trial and<br>Duration of<br>enforcement|**heck Collection**<br>Duration until<br>completion of<br>service of<br>process|<br>Duration of<br>trial|Duration of<br>enforcement<br>(from<br>notification to|actual<br>enforcement)<br>Total Duration|Duration of<br>trial and<br>Duration of|enforcement|
|---|---|---|---|---|---|---|---|---|---|---|---|
|China|15|105|60|180|165|15|120|45|180|165||
|Korea|30|180|93|303|273|20|40|15|75|55||
|Malaysia|60|90|120|270|210|15|15|60|90|75||
|Thailand|30|510|90|630|600|30|90|90|210|180||
|Vietnam|35|55|60|150|115|35|35|50|120|85||
|Indonesia|30|165|30|225|195|30|165|30|225|195||
|Philippines|42|97|25|164|122|42|97|25|164|122||

_Source:World Bank 2001 and Djankov et al. (2003)_ Enforcement of a decision in the check collection case takes significantly longer in French but not in other civil law countries relative to common law countries on average. The reasons for this are not clear at this stage. This does not mean however that countries whose legal systems are based on the French civil law system will be constrained in the future by the nature of their legal systems from achieving better results.

31
|Judicial Reform<br>Georgia Harley|
|---|
|BuildingCapacityfor M&E of Social Development Impacts in TourismGaro J. Batmanian|
|BuildingCapacityfor M&E of Social Development Impacts in TourismGaro J. Batmanian|
|BuildingCapacityfor M&E of Social Development Impacts in TourismGaro J. Batmanian|
|RatingFund Phase II<br>Xavier Reille|
|RatingFund Phase II<br>Xavier Reille|
|RatingFund Phase II<br>Xavier Reille|
|RatingFund Phase II<br>Xavier Reille|
|RatingFund Phase II<br>Xavier Reille|
|RatingFund Phase II<br>Xavier Reille|
|China: China-GEF-World Bank Urban Transport PartnershipProgramBinyam Reja|
|China: China-GEF-World Bank Urban Transport PartnershipProgramBinyam Reja|
|China: China-GEF-World Bank Urban Transport PartnershipProgramBinyam Reja|
|China: China-GEF-World Bank Urban Transport PartnershipProgramBinyam Reja|
|China: China-GEF-World Bank Urban Transport PartnershipProgramBinyam Reja|
|China: China-GEF-World Bank Urban Transport PartnershipProgramBinyam Reja|
|China: China-GEF-World Bank Urban Transport PartnershipProgramBinyam Reja|
|China: China-GEF-World Bank Urban Transport PartnershipProgramBinyam Reja|
|China: China-GEF-World Bank Urban Transport PartnershipProgramBinyam Reja|
|China: China-GEF-World Bank Urban Transport PartnershipProgramBinyam Reja|
|China: China-GEF-World Bank Urban Transport PartnershipProgramBinyam Reja|
|China: China-GEF-World Bank Urban Transport PartnershipProgramBinyam Reja|
|GEF-Ningbo Water and Environment Project<br>Meskerem Brhane|
|GEF-Ningbo Water and Environment Project<br>Meskerem Brhane|
|GEF-Ningbo Water and Environment Project<br>Meskerem Brhane|
|GEF-Ningbo Water and Environment Project<br>Meskerem Brhane|
|GEF-Ningbo Water and Environment Project<br>Meskerem Brhane|
|GEF-Ningbo Water and Environment Project<br>Meskerem Brhane|
|GEF-Ningbo Water and Environment Project<br>Meskerem Brhane|
|QualityEducation in the Rural Areas of Moldova<br>Andrea C. Guedes|
|QualityEducation in the Rural Areas of Moldova<br>Andrea C. Guedes|
|QualityEducation in the Rural Areas of Moldova<br>Andrea C. Guedes|
|QualityEducation in the Rural Areas of Moldova<br>Andrea C. Guedes|
|QualityEducation in the Rural Areas of Moldova<br>Andrea C. Guedes|
|QualityEducation in the Rural Areas of Moldova<br>Andrea C. Guedes|
|QualityEducation in the Rural Areas of Moldova<br>Andrea C. Guedes|
|QualityEducation in the Rural Areas of Moldova<br>Andrea C. Guedes|
|QualityEducation in the Rural Areas of Moldova<br>Andrea C. Guedes|
|QualityEducation in the Rural Areas of Moldova<br>Andrea C. Guedes|
|QualityEducation in the Rural Areas of Moldova<br>Andrea C. Guedes|
|QualityEducation in the Rural Areas of Moldova<br>Andrea C. Guedes|
|QualityEducation in the Rural Areas of Moldova<br>Andrea C. Guedes|
|QualityEducation in the Rural Areas of Moldova<br>Andrea C. Guedes|
|QualityEducation in the Rural Areas of Moldova<br>Andrea C. Guedes|
|QualityEducation in the Rural Areas of Moldova<br>Andrea C. Guedes|
|QualityEducation in the Rural Areas of Moldova<br>Andrea C. Guedes|
|QualityEducation in the Rural Areas of Moldova<br>Andrea C. Guedes|
sportive lemur, none of which is found outside of the eco-region (Frontier-Madagascar, 2005). The area is a high priority for the World Wildlife Fund and other conservation organizations; however only 3.2 percent of Madagascar’s spiny forest was protected at the start of this decade (WWF, 2000; Gorenflo et al., 2005). Local inhabitants are poor and livelihoods often rely on shifting maize cultivation characterized by low yields. The shifting cultivation practices have resulted in deforestation and the first part of the study examines how indirect and temporary incentives resulting from trade policy affected household incomes and rates of deforestation. More specifically, the study looks at how an EU program designed to stimulate livestock production in nearby Ile de la Réunion, an overseas department of France, led to a temporary surge in maize exports from Madagascar to Réunion and to an acceleration in the rate of forest clearing.

The second component of the study looks at how contracting for fresh vegetables destined for European markets affected the incomes and farming practices of 10,000 smallholders in the Madagascar highlands. Rates of erosion are high in this area, and finding ways to preserve soil fertility by adding organic matter is a priority. The study looks at how farm contracts, which were motivated by special EU trade provisions, affected smallholder use of organic compost on contracted and non-contracted crops.

# <u>India</u>

The Indian study looks at the ecology of the Sunderbans and the livelihoods of its inhabitants in West Bengal.<sup>16</sup> The Sunderbans is the world’s largest delta covering 6.2 million acres in Bangladesh and India. Over a million acres of mangrove forest cover the Indian portion of the Sunderbans, of which about 40 percent is inundated. The climate in West Bengal is moderate with heavy rains during the monsoons, which typically begin in July and end in October. The water of the Sunderbans is brackish, but with seasonal and tidal variations. Two wildlife sanctuaries and the Tiger Reserve National Park are located in the Sunderbans. In addition to the tiger, the area is home to a number of rare and endangered animals, including the fishing cat, spotted deer, wild boar, Gangetic dolphin, water monitor, estuarine crocodile, river terrapin, Olive Ridley turtle, ground turtle, hawksbill turtle and king

> 16 This section draws on Chopra, Kumar and Kapuria (forthcoming).

11