rafmacalaba/datause-encoder-data huggingface dataset:
Figure 6: Industrial Composition & Income (A.1) GGDC10: Industrial Composition & Income (A.2) WDI: Industrial Composition and Income <!-- Start of picture text --> Construction<br>Agriculture<br>Agriculture<br>Wholesale/Retail/Hosp.<br>Manufacturing Industry excl. Manuf.<br>US ICT Share (2000)<br>Finance/Ins./RE US ICT Share (2000) Y(i)/Y: 1-st quartile<br>Y(i)/Y: 1-st quartile Y(i)/Y: 4-th quartile<br>Minidng/Quarrying Y(i)/Y: 4-th quartile Manufacturing<br>Utilities<br>Government/Services<br>Services<br>Transp./Storage/Comm.<br>0 10 20 30 0 20 40 60 80<br>Fraction of GDP (%) Fraction of GDP (%)<br><!-- End of picture text --> _Notes:_ Panel A shows the industrial composition for the top and bottom income quartile in 2000 based on he GGDC10 Database. Industry shares are averaged over 2000 and 2010 and industries are orderd by their ICT Share in the US in 2000. Panel B shows an analogous graph based on WDI sector data.

added shares of countries in the lowest (first) and highest (fourth) quartile of the per-capita income distribution within our sample.

Most notably, agriculture, wholesale, retail and hospitality constitute the largest sectors in low income countries. Even in the US, these sectors do not use ICT intensely. In contrast, services and manufacturing constitute the largest sectors in high income countries, and the ICT intensity of these sectors is substantially higher. This suggests that perhaps one reason for lower ICT stocks in low income countries is lower demand for ICT in production.

To set ideas, consider the following simple model. Economies are indexed _h_ = 1 _, ..., H_ . There are _n_ industries indexed _j_ = 1 _, ..., n_ . Output in industry _j_ in country _h_ is given by where _Ah,j_ is normalized such that the output price of industry _j_ in country _h_ is 1. This specification assumes that ICT and NICT capital intensities are common across countries within the same industry. The rationale behind this assumption is that we wish to fix the intensity with which a given industry uses ICT and ask to what extent the industrial composition alone affects ICT abundance in the aggregate. The first order conditions with respect to ICT and NICT capital in sector _j_ 21
hyperbolic sine transformation to the migration numbers so that we can approximately interpret the estimated coefficients as percentage changes. Although our preferred specification is the one with three-way fixed effects, we find an overall positive and significant effect of signing a BLA on migration flows regardless of the specification considered. As reported in column (3) of table 2, signing an additional BLA increases migration by about 77 percent (0.569 log point) from the origin country to the destination country within a decade of signing the BLA. However, one might be concerned that corridors with small numbers of migrants might be driving the results, as a marginal change in the number of migrants in these corridors over time will translate into a higher percentage change, while overall being meaningless for the overall migration pattern. To address this concern, we restrict our analysis to what we call “regular corridors,” which we capture as corridors with at least 10 migrants over the period of interest (1960-2020). The results are reported in columns (3) to (6) with the same sets of fixed effects as described above. Our preferred specification (column 6) confirms the positive effect of BLA on migration flows. Indeed, entering into a new BLA increases migration in regular corridors from the origin country to the destination country by 27.5 percent (0.243 log point) within the decade of signing the BLA.

Table 2: Migration response to BLAs (Benchmark)

|||Full sample||Regular|corrdiors (|_Mij ≥_10)|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Bilateral labor agreement (BLA)|0.593<sup>_∗∗∗_</sup>|0.570<sup>_∗∗∗_</sup>|0.569<sup>_∗∗∗_</sup>|0.398<sup>_∗∗∗_</sup>|0.306<sup>_∗∗∗_</sup>|0.243<sup>_∗∗∗_</sup>|
||(0.106)|(0.103)|(0.090)|(0.059)|(0.055)|(0.051)|
|Observations|273,042|273,042|273,042|33,306|33,257|33,222|
|Origin country x decade FE|No|Yes|Yes|No|Yes|Yes|
|Destination country x decade FE|No|No|Yes|No|No|Yes|
|Origin x destination countries FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Decade FE|Yes|||Yes|||
|Migrants per decade||154,063,957|||144,293,52|7|
|Number of BLAs per decade||146|||111||

_Notes_ : The table presents the estimate of _γ_ from OLS estimate of equation (2). It shows the impact of a BLA on migration. The outcome variable is the inverse hyperbolic sine of migration. Columns (4) to (6) restrict the analysis to ‘regular’ corridors which have at least 10 migrants across all time period. Standard errors in parentheses are clustered at the origin-destination level.<sup>_∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗_</sup> _p <_ 0 _._ 01,<sup>_∗∗∗_</sup> _p <_ 0 _._ 001.

These estimates are likely lower bound of the total impact on migration due to our data limitations. Since our data captures the stock of migration between two countries at ten-year intervals, it likely misses or underestimates short-term migration, particularly those that happen between the periods of measurement. This may be particularly salient for BLAs that facilitate 13
behavior. Finally, the Colombian government changed the MRG formula in June 2007, increasing the maximum allowed shortfall and thereby loosening the MRG. This policy experiment allows me to measure the change in behavior associated with the change in the underperformance penalty, arguably holding constant other possible explanations for correlated trading.

The evidence suggests that a tighter MRG results in more trading in the direction of peers and a smaller cross-section dispersion of returns between pension funds. Moreover, the ranking among managers in terms of performance seems to play a role in portfolio balancing decisions. With a tighter MRG, underperforming managers are more likely than their competitors to trade in the direction of their peers. This is done by buying stocks in which the manager has smaller weights in her portfolio relative to her peers, as opposed to selling stocks with larger weights relative to her peers. Since these pension funds were in an accumulation stage during the sample period, managers were presumably able to reduce their participation in stocks to which they were overexposed simply by maintaining a fixed number of shares.

The rest of the document is organized as follows: In section 2 I review the leading literature on strategic behavior by fund managers. The empirical evidence is presented in section 3, where I conduct two empirical exercises that describe the overall trading behavior of Colombian PFAs. Finally, in section 4, I present the conclusions and discuss future work.

# **2. Related Literature**

This paper is related to several strands of the literature. The empirical literature on strategic behavior of money managers has focused on the trading strategies of asset managers competing for leadership to gain status, higher compensation or increased future flows of funds. The game is similar to a typical tournament, where winners get a large prize and the losers end up with much less. In such tournaments, managers optimally increase their risk taking to maximize the probability of reaching a top position at some target date (usually at year end). Using U.S. data, Chevalier and Ellison (1997) document strong gambling incentives among top-performing mutual funds. Examining strategic behavior in the context of fund families, Kempf and Ruenzi (2008) document that mutual fund managers belonging to families with a small number of funds behave differently from managers belonging to large families. They argue that this result is driven by strategic interactions that might be more pronounced in small fund families. For UK funds, Jans and Otten (2008) present evidence of strategic behavior, finding that fund managers recognize the impact of their own decisions on the actions of their peers, rather than treating competing 4
Returning to the estimation results in Table 5, the probability of owning a household business increases with the number of working age individuals in the households. As described earlier, most of these businesses are small, informal family operations, heavily relying on family labor. Thus, the availability of family labor is bound to influence the decision to own a business. It should also be noted that although the coefficient of the number of male adults is greater than the one for the number of female adults, the latter effect is still positive and statistically significant, weakening the assumption that the prevailing social norms in Albania impose constraints on the involvement of female labor in non-farm self-employment activities. However, as hypothesized, the female-headed households are found to be less likely to engage in non-farm self-employment. More specifically, a female headed household is, on average, 9 percent less likely to own a business.

The probability of owning a business also increases with the age of the household head and with education, though both at a decreasing rate. Households with businesses are less likely to be involved in agriculture, as reflected in the land ownership variables. Furthermore, social capital, as measured respectively by the number of close friends of the household head _,_ is shown to bear a positive and statistically significant relationship with non-farm self-employment decision. Not surprisingly, business ownership is more likely in urban areas, and in communities with better services and infrastructure. Finally, while being located in the coastal region vis-à-vis Tirana is positively associated with the probability of engaging in an own business, the opposite is true if residing in the mountain region.

# **6. Conclusions**

The reliance on the small business sector as one of the engines of growth might be an important strategy for countries such as Albania, which suffer from high unemployment and strive to attain significant reductions in poverty. To the extent that inadequate levels of start-up capital and technical know-how constitute obstacles to the development of microenterprises, returns from international migration possesses the potential to eliminate these constraints through overseas savings, skill development and the establishment of transnational social networks. Although the stakes are high, the empirical literature on the subject is scant, and more so in the Albanian case. Using data from a nationally representative household survey carried out in 2005, this paper analyzes the impact of past migration of current household members on business

- 22 -
# **4.2 Data Description**

We use quarterly data extending from the second quarter of 1960 to the third quarter of 2005. As already discussed, we consider financial assets from the U.S. and Japan. The data was obtained from Global Financial Database, the financial statements from the Ministry of Finance in Japan, and the U.S. Flow of Funds Accounts calculated by the U.S. Federal Reserve. The following asset classes are considered in the analysis: stocks, short-term government bonds, long-term government bonds, and private firms’ profits (ROE). U.S. stock returns are calculated as returns on the S&P 500 index, and Japanese stock returns are given by the returns on the Tokyo Stock Exchange Topix All Shares Index. The U.S. and Japanese returns on short-term interest rates are the quarterly returns implied by the Fed Funds rate and the Japanese Discount rate, respectively. The return on long-term government bonds is calculated as the return on 10year constant maturity U.S. government bonds and as the return on 7-year Japanese government bonds. Government bonds of longer maturity were not available for Japan. ROE is constructed as the total operational profits divided by capital (net worth).

Our model is written in real terms. Therefore, our benchmark asset is the _ex-post_ real return on short-term government bonds. More specifically, the benchmark asset is the real return on a U.S. short-term bond for a U.S. investor and a Japanese short-term bond for a Japanese investor. Real returns are constructed as the difference between the log return on an asset and the log of CPI-inflation. In our theoretical framework, investors analyze excess returns over the benchmark asset. Thus, all excess returns are calculated as the log difference between the real return on a specific asset and the real return on the appropriate benchmark asset, both denominated in the same currency. We use the log change of the real exchange rate to convert returns to a common currency. We define the log real exchange rate as the sum of log nominal exchange rate and log 22
banks in terms of providing other types of financial services and the entry fitness test variables. The column Inter-Industry investigates the impact of competition from other financial services industries by including variables on the size of capital markets and insurance industries. The fourth column combines two sets of indicators, the combined effects of banking structure and contestability. It excludes inter industry competition variables, as those are not statistically significant on their own. Finally, the last column Institutions investigates in addition to the banking structure and contestability variables, the role of property rights. In all regressions, we adjust the standard errors of the regression model using the White method to control for any remaining heteroskedasticity in the error terms.

We run our regressions using weighted least squares as the H-statistics are generated variables from the first step regressions with standard errors and we therefore face heteroskedasticity in the dependent variable. We use as weights the inverse of the estimated variance of a particular country's generated H-statistic, thus giving more value to those H- statistics that are estimated with less error. We also conducted the cross-country regressions using simple least squares as well as for each of the four individual H-statistics and we found very similar results (not reported).

We find little evidence that variables describing the banking system structure can help explain its measured competitiveness, or at least in the way typically posed. We find that bank concentration is not negatively correlated with the H-statistic, as may be expected, but rather we find a positive and statistically significant relationship, that is, more concentrated banking systems face a greater degree of competition. Similarly, the density of banks variable is not significantly positively related to the competition indicator, and, although not statistically significant, has even a negative sign, that is, the fewer banks relative to population, the more 19
Resulting area estimates indicate a potential 69% increase in the vulnerable zone with more than 3 m inundation depth and 14% increase in the vulnerable zone with more than 1 m inundation depth with climate change (See Table 2).

# **3.2 Identification of critical impact elements exposed to inundation risk:**

In order to identify critical impact elements exposed to inundation risk, geographic Information System (GIS) software has been used to overlay the best available, spatially‐ disaggregated data on current assets and activities in the coastal zone of Bangladesh, with the inundation zones projected for 2050 ‐ with and without climate change.

Impact elements considered in this analysis include: housing ‐ by building material type, education institutions, growth centers, mills/ factories (large scale), national highways, regional highways, feeder roads‐type A, feeder roads‐type B, bridges, power plants, power transmission lines, deep tubewells, mosques, temples, historical places and tourist destinations. Relevant exposure surface dataset for land area, population, poverty map, and agriculture extent were also used. Exposure surface data were collected from various public sources, such as Bangladesh Railways, Bangladesh Water Development Board, Local Government Engineering Department (LGED), Center for Environmental and Geographic Information Services (CEGIS), Public Works Department, Roads and Highways Department, Water Resources Planning Organization and the World Bank. Estimates of exposure for each indicator were calculated by overlaying the inundation zone with the appropriate exposure surface dataset.

For the exposure grid surfaces, three GIS models were built for calculating the exposed value. Exposure indicators, such as land surface, agriculture extent, road infrastructure and railways were measured in square kilometers or kilometers. The values of the pixels in population surfaces represent number of people, therefore the exposure is calculated by multiplying the exposure surface<sup>25</sup> with the inundation zone and then summing up by multiplying grid count and value. Exposure estimates of other impact elements were counts.

> 25 Population density of the lowest administrative unit, _Thana_ , has been used in the computation .
44

## **Appendix A: The Survey Instrument**

# **<mark>In-Service Teacher Training Survey Instrument (ITTSI)</mark>**

## **Introduction**

- 1 What is the name of the in-service teacher training program under discussion?

- 2 What is your full name?

- 3 What is your role in this program?

## **<mark>Overarching aspects</mark>**

- 4 By the end of this training what is it that you expect teachers to be able to do differently?

|5|How m|any years has this program been running?|
|---|---|---|
|6|At wha<br>1.<br>2.<br>3.<br>4.|t scale is this program implemented? (_Please select only one answer)_<br>National<br>Multiple states or regions<br>One state or region<br>Less than one state or region|
|7|What<br>_(Selec_<br>1.<br>2.<br>3.<br>4.|kind of organization designed this teacher training program?<br>_t all that apply)_<br>Government<br>Non-governmental organization<br>Private company or social enterprise<br>Researchers|
|8|What<br>_(Selec_<br>1.<br>2.<br>3.<br>4.|kind of organization is implementing this teacher training program?<br>_t all that apply)_<br>Government<br>Non-governmental organization<br>Private company or social enterprise<br>Researchers|
<!-- Start of picture text --> Box 4 continued:<br><!-- End of picture text --> <!-- Start of picture text --> Reforms announced and implemented in Senegal<br><!-- End of picture text --> Overall, looking across the 15 case study countries, by 2015 the gap between announcement and delivery of reforms led to Power Sector Reform Index scores that were between 10 and 50 points below where they would have been had all announced reforms been fully implemented. The smallest gaps were found in Ukraine, Peru and Uganda, while the largest gaps were found in Tajikistan and Tanzania.

**Reforms announced and implemented in 15 case study countries**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2015 Delivered Reform Score 2015 Promised Reform score<br><!-- End of picture text --> Source: Elaboration based on PPI 1990‐2015 database, RISE and Rethinking Power Sector Reform Data 54
|Table4 Dynamic effects of<br>Dependent: Change in grad|Bangladesh flood onscho<br>es from 1998 to 2004|oling progressi|on||
|---|---|---|---|---|
|Flood variable:|Depth Days|Repair cost|Outhome Depth|Days Repair cost Outhome|
|Flood<br>|-0.1586 0.0015<br>|-0.00006<br>|-0.0141 -0.3359<br>|-0.0271 -0.0005 0.0072<br>|
||(1.790) (0.310)|(0.910)|(2.490) (3.820)|(3.450) (3.260) (0.260)|
|Flood * asset|1.50E-06 3.71E-08|-2.50E-10|-1.01E-07||
||(2.940) (1.870)|(0.680)|(0.620)||
|Flood * max educ|||0.0263|0.0025 0.00004 0.0003|
||||(2.560)|(4.070) (3.040) (0.180)|
|Flood * land|||0.0005|6.56E-06 -2.57E-07 -0.0002|
||||(1.690)|(0.620) (1.310) (1.310)|
|Flood * household size|||0.0211|0.0014 0.00002 -0.0032|
||||(1.130)|(1.870) (1.260) (0.950)|
|Flood * livestock|||-1.98E-06<br>|2.77E-07 2.14E-08 -1.23E-06<br>|
||||(0.660)|(0.830) (2.930) (0.940)|
|Flood * female|0.0892 0.0072|0.00007|0.0128 0.0406|0.0084 0.0001 0.0032|
||(1.300) (1.480)|(2.290)|(1.240) (0.640)|(1.890) (2.250) (0.400)|
|Union FE|yes yes|yes|yes yes|yes yes yes|
|Number of observations|489 489|489|489 458|458 458 458|
|Number of unions<br>|21 21<br>|21<br>|21 21<br>|21 21 21<br>|
|R squared (within)|0.0564 0.0508|0.0462|0.0595 0.0947|0.1080 0.0827 0.0801|

Numbers in parentheses are absolute t values using robust standard errors with union clusters. Age and female dummies are included in all specifications.

26
# **1. Introduction**

Production firms can export directly by themselves or rely on intermediary trading firms to export for them (indirect exports).<sup>1</sup> Intermediary trading firms play an important role in international trade. In the U.S., wholesale and retail firms account for about 11 percent and 24 percent of exports and imports, respectively (Bernard et al., 2010). In the early 1980s, 300 Japanese trading firms ( _sogo shosha_ ) handled 80 percent of Japanese trade (Rossman 1984). According to the data from China Customs, indirect exports in China accounted for 38 percent of the shipments and 21 percent of the value in the year of 2005. Despite the extensive studies at the firm level in the recent trade literature, the role of trading firms has not been fully explored. This paper investigates the tax evasion motive behind indirect exports in China, stemming from the partial export value-added tax (VAT) rebate policy.

In countries that adopt a destination-based VAT, including China, the VAT should be collected on domestic transactions, but not on exports. The VAT on the value added at the final stage before exporting should be exempted, and the previously paid input VAT should be fully refunded in principle so that exporters can regain their initial competitiveness. Unlike the full rebate policy in the EU and elsewhere, China has a partial rebate system with rebate rates less than or at most equal to collection rates. The unrebated part of the VAT becomes effectively an export tax,<sup>2</sup> which is calculated based on the export prices for direct exporters but domestic purchasing prices for trading firms (indirect exporters).

To evade such an export tax, exporters have an incentive to underreport either their export prices or domestic purchasing prices.<sup>3</sup> If a production firm exports its products

> 1 Our focus is on the case where a production firm sells its products to an intermediary trading firm, with the trading firm taking charge of the tax rebate. Alternately, the intermediary’s main role may be to help a production firm to find buyers, while the production firm is still in charge of the tax rebate itself. We analyze the former case because we do not observe the latter case directly.

> 2 Feldstein and Krugman (1990) show that a destination-based VAT system should provide exporters full rebates and an incomplete rebate is equivalent to an export tax.

> 3 The evasion behaviors discussed in this paper are different from another popular type of VAT evasion by firms within a VAT-adopting country in which they over-report their input VAT using fake invoices to obtain more input VAT credits and hence reduce their VAT liabilities.

2