rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 9 Turnover Statistics on Fund Type Fixed Effects**

This table presents the results of the regression of the turnover statistics at the PFA-time-fund-type level on PFA, time, and fund type fixed effects. The table only displays the overall mean and the zero-mean fixed effects for each fund type. The Grinblatt et al. (1995) and the Ferson and Khang (2002) turnover measures are calculated using weights with the contemporaneous price. Standard errors are presented in parentheses. T-tests are two-tailed. One asterisk indicates statistical significance at the ten-percent level and two asterisks indicate statistical significance at the five-percent level. Panel A considers the entire sample period (July 1996 to December 2005) and Panel B only considers the multi-fund period (September 2002 to December 2005), starting six months after it was implemented to avoid distortions. Numbers represent percentages (results are multiplied by 100).

**Panel A. Turnover Statistics on Fund-Type Fixed Effects (1996-2005)**

||Grinblatt et al.|Ferson and Khang|
|---|---|---|
||(1)|(2)|
|Overall Mean|10.92**<br>(0.37)|10.36**<br>(0.37)|
|Fund A|0.64*|0.68*|
||(0.36)|(0.37)|
|Fund B|-0.73**|-0.87**|
||(0.24)|(0.24)|
|Fund C|-5.52**|-5.75**|
||(0.44)|(0.44)|
|Fund D|0.56|0.60|
||(0.43)|(0.43)|
|Fund E|5.05**|5.33**|
||(0.62)|(0.62)|

**Panel B. Turnover Statistics on Fund-Type Fixed Effects (2003-2005)**

||Grinblatt et al.|Ferson and Khang|
|---|---|---|
||(1)|(2)|
|Overall Mean|7.20**<br>(0.17)|6.47**<br>(0.17)|
|Fund A|1.86**|1.91**|
||(0.21)|(0.21)|
|Fund B|-0.64**|-0.80**|
||(0.11)|(0.11)|
|Fund C|-2.00**|-2.17**|
||(0.12)|(0.12)|
|Fund D|-0.10|-0.08|
||(0.21)|(0.22)|
|Fund E|0.89**|1.14**|
||(0.28)|(0.28)|
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<mark>Malawi National Statistical Office (NSO) (Government of Malawi). Malawi - High-Frequency Phone Survey 2020-2024 (HFPS-COVID-19 2020-2024). Ref: MWI_2020-2024_HFPS_v18_M. Downloaded from https://microdata.worldbank.org/index.php/catalog/3766 on January 2, 2024;</mark> Manski, C., 2004. Measuring expectations. _Econometrica,_ 72(5), pp. 1329-1376 .

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Migali, S. & Scipioni, M., 2019. Who's About to Leave? A Global Survey of Aspirations and Intentions to Migrate. _International Migration._ Mussa, E. C., 2020. Youth Aspirations, Perceptions of Farming, and Migration Decisions in Rural Sub-Saharan Africa: Further Empirical Evidence From Ethiopia. _ZEF-Discussion Papers on Development Policy No. 296._ <mark>National Bureau of Statistics. Nigeria COVID-19 National Longitudinal Phone Survey (COVID19 NLPS) 2020-2021, Phase 1. Dataset downloaded from</mark> <u>https://microdata.worldbank.org/index.php/catalog/3712</u> <mark>on January 2, 2024</mark> Nguyen, T., 2008. Information, Role Models and Perceived Returns to Education: Experimental Evidence from Madagascar.. _Working paper, MIT, Cambridge, MA._ OECD, 2021. _PISA 2025 Foreign Language Assessment Framework,_ s.l.: OECD Publishing.

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percent at tertiary; and private returns were 30, 9 and 11 percent. In 1989, in urban Argentina the labor force had 9.1 years of schooling and the private rate of return to another year of schooling was 10.3 percent (Psacharopoulos 1994). Social returns were 8.4, 7.1 and 7.6 percent; and private returns were 10.1, 14.2 and 14.9 percent. Returns to schooling for women are slightly higher than for men: in 1985, 9.1 for men and 10.3 percent for women; and in 1989, 10.7 for men and 11.2 percent for women (Psacharopoulos 1994). Returns to schooling are higher for workers in the private sector: 9.6 versus 7.0 percent in 1985; and in 1989, 11.1 versus 8.9 percent. Overall, in the 1980s, the returns to schooling in Argentina are more like an industrial country than the patterns observed in the Latin America region (Kugler and Psacharopoulos 1989).

The returns to schooling in Buenos Aires increased from 10 percent in 1986 to 12.5 percent in 1989 (Pessino 1995). Then they dropped to 9 percent in 1990 and increased again to 10 percent by 1993. Pessino (1995) concludes that returns were higher and increasing during the period of hyperinflation. However, when inflation was brought under control in 1990 returns decreased significantly.

Using a dynamic cohort analysis for Buenos Aires for the period 1980-1999, Margot (2001) shows that workers with secondary incomplete experience rather stable returns, which are on average 12 percent although decreasing in recent years, reaching 10 percent in 1999. Workers with secondary complete experience slightly higher returns – at 13 percent on average for the whole period – but very stable over time and reaching 11 percent in 1999. Workers with 3
existing phone, plus the cost of the phone call itself. The demand curve is completed using price elasticities estimated in 1994, a relatively dated estimate given the changes experienced by the sector in the last years.

**Table 5: Subsidies allocated per company 1995-1999**

|**FIRM**|**SUBSIDY ALL**|**OCATED**|**PHONES AL**|**LOCATED**|
|---|---|---|---|---|
||**Thousand Ch$**|**%**|**Number**|**%**|
|CTC|2,642,407|28.1|1,880|31.8|
|CTR|1,481,186|15.8|1,843|31.2|
|GENEVA|190,000|2.0|153|2.6|
|GVT|3,421,843|36.4|1,737|29.4|
|MEGACOM|1,655.633|17.6|303|5.1|
|**TOTAL**|**9,391,070**|**100.0**|**5,916**|**100.0**|

**Source: Memoria 1999, Fondo de Desarrollo para las Telecomunicaciones, Memoria 1999, Subtel.**

The government allocates resources to the TDF through the annual public-sector budget.' Then Subtel sets the priorities and selects the highest-ranking projects until available funds are exhausted. Each selected project is then put out to public tender and awarded to the bidder seeking the smallest subsidy. The technology chosen by the successful bidder does not have to match that used in computing the maximum subsidy. Usually, competition between bidders determines the actual subsidy, and the maximum subsidy only serves as a cap in cases when there is only one bidder. For this reason, the authorities have been especially concerned to ensure that several companies bid for each project. In the first year only two companies entered bids: CTC and CTR. But in subsequent years a further three companies started bidding. Table 5 shows the participation

> 9 The regulated tariff is the same for all public rural phones and is indexed to the cost of living. Currently 100 pesos for 84 seconds.

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export quantity, in percentage of the actual quantity: Under the assumption that penalty and detection rates are higher in the importing countries, Equation (9) suggests the discrepancy increases with the costs of complying with the NTMs of the importing countries: Intuitively, more restrictive NTM requirements of the importing country, which are parsimoniously represented by the larger AVE, increase the trade costs for both the exporting ...rms (pre-shipment inspection, testing and certi...cation requirements, etc.) and the importing ...rms (import licensing requirement, cargo inspection, etc.). Therefore, both the importers and the exporters have incentives to under-report their quantity on their customs declaration forms in order to reduce such costs. However, the penalty and detection rates in the importing countries are normally higher, due to more accurate customs procedures driven by revenue collections and regulatory concerns, unlike the exporting customs that generally have fewer reasons to precisely assess shipments against the export declarations. This results in larger under-reporting by the exporters than by the importers, which implies larger trade discrepancies between the o¢ cial statistics of the importing and exporting countries.

In addition, the rate of detection, s; may depend on a few factors. First, for the importing country, products that have higher tari¤s may be subjected to more careful inspection in 10
- 21 -

**<u>Table 7. Simple average DID estimator</u>**

||Before<br>Treatment|Control|After<br>Treatment|Control|Double<br>Coef.|difference<br>Std.Err.||
|---|---|---|---|---|---|---|---|
|Production (kg)||||||||
|Maize|1,385|1,049|2,208|1,511|361|209|*|
|Cotton|1,905|1,215||||||
|Cassava|850|1,025|325|500||||
|Tobacco|885|468||||||
|Vegetable|700|499|447|1,038|-793|1,085||
|Soybean|285|384|397|571|-76|192||
|Groundnuts|366|452|296|271|111|40|***|

*, ** and *** indicate the statistical significance at the 10, 5 and 1 percent, respectively.

To answer the question of why OPRC has a positive impact on agriculture production, Equation (4) is estimated using the same set of independent variables as above. This also addresses a likely question of whether this is because of OPRC or road maintenance works in general regardless of contractual arrangements. The results are broadly unchanged: The selected instruments are valid, and the impact of _WORK_ is positive and significant ( **Table 8** ). This is the main reason for the observed OPRC impact. Not surprisingly, the statistical significance of _WORK_ is much higher than that of _OPRC_ . Holding everything else constant, crop production was increased where road improvement works were actually implemented regardless of contractual arrangements. This is obvious: Without actual works, people’s connectivity would not be improved. The marginal effect is estimated at 0.95. Notably, however, this does not mean that OPRC has no effect on connectivity. It has an important indirect impact (see further discussion below).

Technically, the measured impact can be associated with all kinds of works implemented, such as rehabilitation, gravel roads, emergency works, installation of culverts and road signs. Given the timeline of the project implementation and follow-up survey, it is likely to be the impact of initial rehabilitation works under OPRC. But other possibilities cannot be excluded.<sup>7</sup>

> 7 To answer this question, more detailed and frequent data would be needed on both project implementation and farmers’ behavior (i.e., household surveys).
**6 |** P a g e

Macedonia exports per capita of US$1,495 was 83 percent above BiHs (US$818) and that of Croatia 199 percent. Albania’s exports per capita stood at 39 percent of BiH exports While no reliable foreign trade data are available for the 1990s, the shift in the direction of BiH exports towards its two preferential partners—the EU and CEFTA 2006—has been beyond doubt. These two trading blocs take now around 90 percent of BiH total exports. Yet, the share of the EU, including its new member states from Central Europe, which grew from 53 percent in 2003 to 57 percent (Table 2), remains somewhat below the levels experienced by other European transition economies throughout the 1990s ranging between 60‐65 percent.<sup>3</sup>

**Table 2: Dynamics and direction of BiH exports in 2003‐07: share of major partners and LSG rates of exports growth (in percent)**

||2003|2007|Index 2007,<br>2003=100|LSG rate 2003-<br>07|
|---|---|---|---|---|
|EU-27|53.4|57.3|107|37.8|
|SEE-6|30.3|33.2|110|35.9|
|ROW|16.3|9.6|59|23.7|
|TOTAL|100.0|100.0|100|35.5|

Source: UN COMTRADE database.

BiH export performance does not come across as very impressive when cast against that of Central European EU‐8 members. The reference to EU‐8 as comparators seems to be justified for three intertwined reasons: these countries were on average roughly at a similar level of economic development when the transition to market‐economies began in the early 1990s; and the SAA, not unlike the European Association Agreements for EU‐8, has started the EU accession process with huge implications for foreign trade policy and FDI inflows. The latter reveals itself in its impact on export performance operating through opening to competition from imports and. The caveat is twofold: in contrast to the EU‐8, BiH went through devastating war, and in contrast to BiH, EU‐8 adopted a radical approach to economic reforms.

The differences in domestic circumstances of transition of EU‐8 and BiH are nakedly visible in their export performance. Except for the post‐war recovery of exports phase, the EU‐8 outperformed BiH and other SEE‐economies in terms of volumes of exports of goods and services in other two phases with the difference in growth in their favor of 1.4 percentage points and 0.8 percentage points of average LS growth rates, respectively. Hence, the gap between the EU‐8 and SEE including BiH has

> 3 This share refers to trade with the EU‐15, not EU‐27. Hence, the discrepancy is even larger than implied by these numbers.
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International Monetary Fund (IMF) in 2007 in the Global Financial Stability Risk Map (Dattels and others 2010).

A first decision is the selection of indexes that are to be constructed. The appropriate selection of indexes depends, among other considerations, on the structural characteristics of the financial system and the authorities’ preferences regarding comprehensiveness and coverage. However, it can be useful to make a conceptual distinction between three different types of indexes.

- _Stress indexes_ : A first set of indexes measures the contemporaneous stress level in the system, usually on the basis of a series of high-frequency financial market data, such as volatility in equity and fixed income markets, sovereign bond spreads, interbank lending spreads, credit default swap spreads of (parent) banks, and foreign exchange (forex) and interest rate swap spreads where available. The stress index can also help policymakers in signaling turning points from “normal” to “crisis” mode (and vice versa).

- _Vulnerability indexes_ : A second set of indexes is designed to pick up on vulnerabilities and imbalances that within the policy horizon (for example, one to two years) could translate into systemic threats. It is helpful to construct an index that tracks the financial cycle based on the behavior of credit and asset prices, particularly property prices, considering that peaks in the financial cycle tend to be closely associated with systemic banking crises (Borio 2012). In a similar vein, it can be useful to construct indexes for the financial position of households and nonfinancial corporates, taking into consideration such indicators as DTI ratios, debt service ratios, and exposure to interest and/or exchange rate risk. A sustained weakening of the financial position of households and corporates can set the stage for a marked deterioration of asset quality further down the road, eventually putting severe stress on the banking system’s liquidity and solvency buffers.

- _Resilience indexes_ : The last set of indexes is meant to measure the resilience of banks and other financial entities in the face of adverse shocks. In the banking sector, this would cover a broad range of prudential indicators measuring capital, asset quality, and earnings indicators, as well as the liquidity outlook.<sup>21</sup> A similar index may be created for non-deposit taking financial entities.

The next step is to select the series of indicators on the basis of which each composite index can be constructed. The selection process usually involves several iterations before a satisfactory specification is found. In deciding on the specification, it is important to keep the following requirements in mind:

- _Variables with an unambiguous relation to financial stability:_ In selecting the indicators, it is important to ensure that each variable has an unambiguous relation to financial stability. The inclusion of variables that do not have such a clear-cut interpretation in

> 21 Such liquidity and capital indexes can be usefully complemented by various kinds of stress tests, which produce outcomes that are specific to individual banks.

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Moments (GMM) estimations. It would thus be interesting to test the significance of financial sector development as a determinant of remittances. Freund and Spatafora (2005) do include the financial development variable in their panel regression of the determinants of remittances and find its positive and significant effect on remittance flows. However, they do not control for any reverse causality between financial sector development and remittance levels as found in Aggarwal _et al_ . (2005).

Finally, a growing number of migrants is said to be partly responsible for the recent increase in remittance flows. Indeed, Freund and Spatafora (2005) find the stock of migrants to be the main determinant of remittances. Nevertheless, one of the crucial issues in the migration literature is that migrants are not a random sample of the underlying native population of home country. While the number of migrants abroad affects the level of remittances, the desire to remit also influences the migration level (Ozden, 2006). The possibility of this endogeneity bias is unfortunately not considered in Freund and Spatafora (2005) and it is important to see if their finding is consistent even after controlling for the endogeneity bias.

In sum, this paper aims to contribute to the existing literature by examining the impact on remittance flows of, among other things, the following three factors: (1) the education level of migrants; (2) financial sector development of recipient countries; and (3) the level of migration. The endogeneity issues regarding the latter two variables will be taken into account in our estimation.

# **3. Data and Econometric Specification**

The data used for our analysis covers 85 countries, for which we have observations for all the variables employed in the estimation. Although it would be ideal to have a panel data set, we will concentrate our analysis on a single point in time, 2000, given that there are no time-series data on the number of migrants abroad, their education level and the passport costs. Aggregate data on remittances come from the IMF’s _Balance of Payment_ 5