rafmacalaba/datause-encoder-data huggingface dataset:
countries with annual information from 1970 to 2007.<sup>11</sup> We first test whether sudden stops are determined by the indicators that capture sound macroeconomic policy frameworks, healthy financial sectors, outward orientation of the economy and external shocks. Then, we examine whether the impact varies according to the source of sudden stops. Finally, we assess whether some particular features of the domestic economy (exchange rate regime in place, liability dollarization and openness) amplify the impact of external shocks on the likelihood of sudden stops.

# **4.1 Set-up**

<mark>Our dependent variable is the binary variable</mark> _<mark>SS</mark>_ <mark>that can only have two possible outcomes: it can take the value of 1 when there is a sudden stop (net, inflow-driven, outflow-driven or mixed) and 0, otherwise.</mark> _<mark>X</mark>_ <mark>is a vector of explanatory variables that influence the outcome</mark> _<mark>SS</mark>_ <mark>.</mark> Hence, the model takes the form: <mark>where the left hand side of the equation represents the probability</mark> _<mark>P</mark>_ <mark>of a sudden stop occurring given</mark> _<mark>X</mark>_ <mark>, and  is the</mark> _<mark>Probit</mark>_ <mark>function. Our goal is to estimate the vector  of parameters by maximum likelihood. We specify the</mark> _<mark>Probit</mark>_ <mark>model as a latent variable model and</mark> assume that there exists a random variable _SS_<sup>*</sup> such that: where  represents the error term, and _SS_<sup>*</sup> indicates whether this latent variable is non-zero: <mark>We denote our dependent variables as (net) sudden stops (</mark> _<mark>SS</mark>_ <mark>), inflow-driven sudden stops (</mark> _<mark>iSS</mark>_ <mark>), outflow-driven sudden stops (</mark> _<mark>oSS</mark>_ <mark>), and the mixed cases (</mark> _<mark>mSS</mark>_ <mark>). The definition of these binary variables is summarized in Section 3.1 while the variables that comprise the</mark> _<mark>X</mark>_ <mark>matrix are described in Section 3.2. Note that all explanatory variables are lagged one period to avoid reverse causality issues.</mark>

# **4.2 Baseline Regression**

> 11 We collected annual data for 185 countries on capital flows and the determinants of sudden stops from 1970 to 2007. We excluded from our sample countries with less than 9 consecutive observations of capital flows and small countries (i.e. countries with population less than 1 million in 2007 and gross national income per capita lower than 2,000 US dollars). Those restrictions along with other data availability problems in the set of forcing variables rendered an effective sample of 82 countries.

13
ownership information, encompassing over 30 million “links” between companies and their shareholders. For each target/affiliate/subsidiary company we know the amount of foreign investment in company stock, together with the country of origin of the investor.

We have 7,000+ unique firms over the period 2005 to 2012, amounting to 35,000+ observations. FDI represents 3 to 4 percent of these firms receiving foreign investment, which is a typical average number in Europe (much higher in Latin American countries). The econometric analysis is based on the work by Fons-Rosen, Kalemli-Ozcan, Sorensen, Volosovych, and VillegasSanchez (2014).

Two well-known findings in the literature are that multinational subsidiaries generally outperform domestic firms and the most prevalent form of multinational entry is through acquisition, rather than greenfield investment. These facts suggest that the superior performance of companies receiving FDI could be due to multinationals selecting domestic firms which a priori were better performing. It is not straightforward then to gauge how much of the correlation between ownership and productivity is due to selection and how much due to active improvements caused by, say, transfers of superior technologies and organizational practices to foreign subsidiaries. We try to address both sides of this issue. The focus on the manufacturing sector allows us to compare results with those in the existing literature.

Both labor productivity and total factor productivity are computed. Labor productivity is defined as value added to labor ratio, log (VA/L�,�). We construct TFP as the residual from a CobbDouglas production function with capital and labor: log (TFP�,�) = log (Y�,� − M�,�) − α� log (L�,�) − α� log (K�,�), where the coefficients are estimated by the method of Woolridge (2009) that improved upon Olley and Pakes (1996) and Levinsohn and Petrin (2003), as explained in detail in Fons-Rosen et al. (2014). Y is output (operating revenue or sales), M is materials, K is capital (fixed tangible assets) and L is labor.

# **2.2. Which firms are foreign-owned in Turkey?**

In this analysis, we apply the standard OECD definition of FDI: “a direct investment enterprise is an incorporated or unincorporated enterprise in which a single foreign investor either owns 10 per cent or more of the ordinary shares or voting power of an enterprise [...]”.<sup>5</sup> Prior to

> 5 See <u>http://unctad.org/en/Pages/DIAE/Foreign-Direct-Investment-(FDI).aspx, accessed March 13, 2016.a</u>

5
offset the cost of distance. In this sense, PTAs appear to matter for investors _no_ t for their political consequences – of signaling credible commitment to investment friendly policies—but instead because of their trade boosting effects, which are compounded by proximity. PTAs allow firms involved in international production networks to more easily move intermediate and finished goods between countries. Yet, the cost-reductions associated with the PTA will be eventually offset by the higher costs associated with greater distance between origin and destination countries. This result is in line with the literature on the role of distance in the effect of PTAs on trade (see, for example, Freeman and Piegnakura 2016).

In Model 9, we interact BIT and PTA with _Contiguity_ . The negative sign of the coefficient of the interactive term _BIT*Contiguity_ suggests that these agreements have a weaker effect on FDI for pairs of countries that share a border. This confirms our previous finding that BITs have greater relevance when investors consider faraway, unfamiliar destinations, or put, differently, that BITs work to reduce the cost of distance. The coefficient for the _PTA*Contiguity_ interaction, however, does not reach acceptable levels of statistical significance in the hurdle model.

Table 5. Physical distance, IEAs, and FDI. Hurdle negative binomial regressions

||(|8)||(9)|
|---|---|---|---|---|
|VARIABLES|Logit|NB|Logit|NB|
|_GDPpcj_|0.0617|-0.00504|-0.0346|-0.0341|
||(0.153)|(0.165)|(0.165)|(0.155)|
|_Populationj_|0.804***|0.603***|0.618***|0.773***|
||(0.115)|(0.115)|(0.115)|(0.116)|
|_Distance ij_|-0.0003***|-0.0003***|-0.0002***|-0.0002***|
||(6.59e-05)|(6.77e-05)|(4.94e-05)|(4.34e-05)|
|_Trade ij_|2.71e-09|7.09e-08**|5.09e-08*|9.59e-09|
||(1.79e-08)|(3.14e-08)|(2.70e-08)|(1.74e-08)|
|_Comlang ij_|2.189***|1.646***|1.727***|2.320***|
||(0.383)|(0.381)|(0.379)|(0.395)|
|_Poll stabilityj_|0.593***|0.434**|0.397*|0.561***|
||(0.209)|(0.215)|(0.216)|(0.217)|
|_Contiguity ij_|||2.887**|2.565*|
||||(1.322)|(1.523)|
|_BIT ij_|-0.269|-0.0616|1.398***|1.390***|
||(0.640)|(0.652)|(0.424)|(0.392)|
|_PTA ij_|2.171***|1.753***|0.511|0.465|
||(0.583)|(0.615)|(0.330)|(0.308)|
|_BIT*Distance ij_|0.0002***|0.00023***|||
||(7.30e-05)|(8.00e-05)|||
|_PTA*Distance ij_|-0.00028***|-0.000215**|||
||0.0002***|0.00023***|||
|_BIT*Contiguity ij_|||-3.239*|-3.316*|
||||(1.719)|(1.918)|
|_PTA*Contiguity ij_|||-2.002|-1.043|
||||(1.378)|(1.577)|
|Constant|-17.38***|-13.46***|-14.06***|-16.85***|
||(2.205)|(2.374)|(2.274)|(2.192)|

17
is especially visible in the advanced-country subsample; also, the evidence of residual dependence weakens. The overall fit of the model improves relative to that in Table 3.

Table 9: Additional robustness checks

|||All countries||
|---|---|---|---|
||Spatial error|Two factors|Demeaning|
|_gt−_1|0.260|0.270|0.270|
||(15.727)|(16.204)|(16.806)|
|GDP growth|0.29|0.31|0.30|
||(2.749)|(3.015)|(2.785)|
|∆Real interest rate|-0.66|-0.64|-0.75|
||(-11.011)|(-10.828)|(-11.997)|
|Real credit growth|0.17|0.15|0.14|
||(4.817)|(4.315)|(4.357)|
|_Wgt_|0.390|0.150|0.800|
||(1.199)|(3.983)|(71.969)|
|_Wgt−_1|-0.160|-0.090|-0.230|
||(-2.276)|(-2.233)|(-9.765)|
|Spatial error|0.110|||
||(0.321)|||
|Pesaran CD statistic|1.660|0.105|2.551|
|(p-value)|(0.048)|(0.458)|(0.005)|
|Exponent of CSD|0.505|0.444|0.719|
|_R_<sup>2</sup>|0.589|0.643|0.524|
|||Advanced countries||
|_gt−_1|0.305|0.232|0.305|
||(14.283)|(10.981)|(14.481)|
|GDP growth|0.246|1.223|1.027|
||(1.349)|(7.142)|(5.578)|
|∆Real interest rate|0.005|-0.646|-0.441|
||(0.022)|(-2.684)|(-1.969)|
|Real credit growth|0.005|-0.074|0.003|
||(0.086)|(-1.207)|(0.054)|
|_Wgt_|0.023|0.532|0.832|
||(0.082)|(17.438)|(80.199)|
|_Wgt−_1|-0.313|-0.082|-0.243|
||(-5.701)|(-1.867)|(-8.509)|
|Spatial error|0.701<br>(2.799)|||
|Pesaran CD statistic|12.788|-0.594|-0.261|
|(p-value)|(0.000)|(0.276)|(0.397)|
|Exponent of CSD|0.882|0.661|0.738|
|_R_<sup>2</sup>|0.702|0.760|0.648|

Notes: Bias-corrected QML estimates. The dependent variable is the real stock return computed as the first difference of the log real equity price. GDP growth is the first difference of the log of real GDP, ∆Real interest rate is the first difference of the real interest rate, and Real credit growth is the first difference of the log of the real credit stock. ’Exponent of CSD’ is the exponent of cross-sectional dependence. T-statistics in brackets and P-values in brackets for Pesaran CD statistic. The sample period covers 1995:1-2016:3.

27
Table <u>9: Impacts of hosting refugees on alternative forms of salaried employment</u>

||||Permane<br>|nt employm<br>|ent<br>|||Temp<br>|orary (casu<br>|al) employm<br>|ent<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Em|ployed (=|1)||Hours worke||E|mployed (=|1)||Days worke|d|
|||||||Indiv<br>|iduals<br>||||||
||All|Rural|Urban|All|Rural|Urban|All|Rural|Urban|All|Rural|Urban|
|Refugee Intensity|-0.014|-0.025|0.059|10.624|-13.722|167.399|-0.077*|-0.084*|0.019|1.648|1.814|0.548|
||(0.025)|(0.026)|(0.060)|(30.351)|(18.833)|(137.356)|(0.040)|(0.047)|(0.028)|(2.616)|(3.011)|(3.825)|
|Constant|0.163|0.192|-0.140|48.318|126.555|-598.599|0.137|0.006|0.106|-23.943|-33.625*|14.086|
||(0.152)|(0.153)|(0.352)|(169.613)|(104.267)|(761.697)|(0.240)|(0.278)|(0.191)|(15.921)|(18.450)|(25.160)|
|Round FEs|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Fixed Effects (FEs)|ID|ID|ID|ID|ID|ID|ID|ID|ID|ID|ID|ID|
|Controls|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|R-squared|0.002|0.003|0.010|0.003|0.006|0.010|0.034|0.047|0.010|0.010|0.015|0.010|
|# of EAs|433|290|143|433|290|143|433|290|143|433|290|143|
|N|28714|21017|7697|28710|21013|7697|28599|20923|7676|28585|20915|7670|

_Notes:_ This table provides estimates of the impact of logarithm of refugee intensity on individual hosts’ salaried permanent (the first six columns) and temporary (the last six columns) employment. The first and third three columns report results for a binary measure of permanent and temporary salaried employment, respectively. The second three columns report results for number of hours worked in permanent salaried employment while the fourth three columns report results for number of days worked in temporary salaried employment. Columns labeled “All,” “Rural,” and “Urban” report results for all, rural, and urban individuals, respectively. Individual sample weights from ESS2 applied. All columns control for individual (ID) FEs, round FEs, and 12 time-varying characteristics. The coefficient estimates for these 12 control variables are not reported here, but can be available on request. Robust standard errors clustered at EA level are given in parenthesis. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

47
# **1 Introduction**

Policies designed to develop infrastructure and expand service delivery are key elements of the Government of Indonesia’s (GoI) strategy for the country’s economic and social development. There is broad consensus among national officials and analysts that the expansion and improvement of the country’s transportation network is vital in order to expand internal trade and essential in order to improve the competitiveness of Indonesian manufacturing. Increasing access to services such as education, health, and sanitation have a similar degree of prominence given their importance to improving the lives of people and communities and enabling individuals to reach their full potential.

Indonesia is becoming one of the most decentralized countries in the world in terms of spending, with subnational governments spending 37 percent of total public funds (World Bank, 2007, xv). Local governments are heterogeneous in terms of their wealth, administrative capacity, the quality of political leadership, and the preferences of constituents along with several other factors that are likely to impact local government efforts to improve citizens welfare. Despite the decentralization of financial responsibility and authority to relatively autonomous local level governments, procurement reform in Indonesia is a centrally-driven effort. The central government introduced new regulations, organizations, processes, and capacities to increase the efficiency and effectiveness of the procurement system. Given the vastly different conditions found across sub-national units, it is not surprising that early evidence suggests that the uptake and partial implementation of procurement reform is not translating into uniform improvements in procurement performance across local governments.

Sub-national decision-making is not necessarily furthering Indonesia’s progress toward achieving national objectives. In fact, the decisions of a large portion of local governments with respect to how much to procure and what to procure appear to be at odds with the GoI’s developmental goals. Over the last few years, district governments have reduced the absolute and relative value of their procurement spending. Declining procurement signals a move away from capital investments in infrastructure and the purchasing of goods and services vital to expanding service delivery to spending on either administrative expenses or social protection. Existing disparities in infrastructure quality and services across local governments appear to be widening, as poorer districts are particularly likely to be reducing their procurement 1
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-29-

an aggregate defined over many plants a small change in price will produce a response at some, but not necessarily all, sites, so that there is always an aggregate response to price changes.

The second diLtinctlve feature of the econometrlc studies is that typically the link between capltal and energy **is** not formally specified. They are usually viewed as two factors which may or may not be complements or substltutes. If one fuel is to be substituted for another the prices of the fuels are used as explanatory factors but the capital costs of the alternative fuel technologies are not specLfically introduced. Instead an aggregate cost of capltal is introduced as a separate explanatory factor. This is in sharp distinction to engineerLng studLes where the capital costs of alternative fuel burning tochnologies are crucial in determlning fuel choice. These two aspects of the econometric approach highlight the fact that the technology is not specified, whLie in engineering studies this is the central point in formulating the optimal choice of fuel.

Early econometric studies on the use of energy as a factor input treated it as a single fuel and concentrated on its relation to capital and labor. Berndt and Wood (19753, Fuss 119771, Magnus [19791 using data for a single country found energy and labor to be subatitutes while energy and capital were complements. Fuse also found some substitutability between oil, coal and gas in Canada but none between those fuels and electricity. Halvorsen 119771 found a larger measure of substitutability between all fuels for the _U.S._ Griffin and Gregory 119763 used cross-section data at five year intervals to capture long run effects and found substitutability between energy and capital.

Griffin 119771 and Pindyck 119791 have provided definitive accounts of the standard econometric approach to mode)ling the shares of fuels in industry or electrical power goneration. Most of the subsequent work refers to the ideas of these studies so a detailed account is given of the approach used by Griffin. The starting point is the oroduction function: the output (Q) is assumed to be a twlce differentiable function of the services of capital (K), labor (L) and energy inputs, as well as of technical change (t). In the case of electricity
18 Most of these variables were directly derived from the PENDAT file, but outside sources were used in a few instances. The variable called TOPIOMG indicates whether the pension system used a money manager in the Top 10 performance group as identified by Lakonishok et al (1992). The term UNEMPD represents the degree of fiscal stress experienced by the state proxied by the deviation of the unemployment rate in 1990 from the mean of the previous nine years; previous analysts suggest that this type of fiscal stress reduces funding possibilities (Mitchell and Smith 1992b). The variable DEFPOS is also derived from outside sources, and indicates whether a state is permitted to carry over a budget deficit frem one year to the next. Public pension funding may be seen as an off-budget safety-valve, relieving the pressure of having to meet state balanced budget requirements. Hence pensions may be better funded when state budget deficits can be carried through time, while underfunding may prevail more often when state budgets must be balanced, by law, at year's end." Summarizing this discussion in a multivariate framework, the following model is postulated: (l)Y, = a, + a, X, + a2 X2 + a3 X3 + **a,** X4 + a, Z + e, (2) Y2 = b, + b, X, + **_b,_** X2 + **_b3_** X3 + **_b4_ X4 +** b5 Z + **_e2_** where Y, represents a vector of variables reflecting public pension investment performance: Y,represents a vector of funding variables; X, - X,represent vectors of variables reflecting pension Board composition, Board management practices, investment practices, reporting requirements and assumptions; and Z represents a vector of other Olivia S. Mitchell - Public Pension Governance and Performance
# Table of Contents

|Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<br>1|
|---|
|The Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<br>4|
|The Data<br>.<br>. . . . . . . . . . . . . . . . . . .<br>. .<br>. . . . .I<br>. .<br>8|
|The Estimation Results<br>. . . . . . . . . . . . . . . . . . . . . . . . . . 16|
|Concluding Remarks<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . 22|
|References<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25|
|Tables|
|Table 1 Average Years of Primary Education, by Region . . . . . . . . . . 13|
|Table 2 Average Years of Secondary Education,<br>by Region . . . . . . . . . 13|
|Table 3 Average Years of Total Education, by Region . . . . . . . . . . . 13|
|Table 4 Ratio of Primary Education Attainment to|
|Secondary Education Attainment . . . . . . . . . . . . . . . . . . . . . . 14|
|Table<br>5Alternative Specifications<br>of the Aggregate|
|Production Function with Region-specific<br>Effects of<br>education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17|
|Table 6 Alternative<br>Specifications<br>of Aggregate|
|Production<br>Function<br>without<br>Region-specific|
|Effects<br>of Education<br>. . . . . . . . . . . . . . . . . . . . .18|
|Table<br>7 Tests<br>of Hypotheses<br>. . . . . . . . . . . . . . . . .19|
|Table 8 Percentage Change in Real GDP in Response to an<br>Additional Year of Education per person of Working Age<br>in 1985 . . . . . . . . . . . . .<br>. . . . . . . . . . . . .20|

# Figures

|Figure 1 Effect of one Additional Year of Primary|
|---|
|Education per person on Real GDP (%) . . . . . . . . . . . . . . .23|
|Figure 2 Effect of one Additional Year of Secondary|
|Education per person on Real GDP (%) . . . . . . . . . . . . . . .23|
|Figure 3 Effect of one Additional Year of Total|
|Education per person on Real GDP (%) . . . . . . . . . . . . . . . . . . . 24|
|ADpendices|
|Appendix 1 List of Countries in the Sample, by Region . . . . . . . . . . 27|
|Appendix 2 Comparison of Estimates of Educational|
|Capital Stocks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28|
|Appendix 3 Estimation Results under|
|Constant Returns to Scale . . . . . . . . . . . . . . . . . . . . . . . . 32|
|Appendix-Table 1 Alternative Estimates of Mean Years<br>of Schooling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30|
|Appendix-Table 2 Corrleation between the Estimates|
|of Total Years of Schooling . . . . . . . . . . . . . . . . . . . . . . . 31|
|Appendix-Table 3 Alternative Specifications of the|
|Aggregate Production Function with Region-specific|
|Effects of Education, Under Constant Returns to Scale . . . . . . . . . . 33|

The authors are respectively Professor of Economics, Stanford University, Professor of Education and Professor of Public Health, University of California at Los Angeles, and Researcher at the World Bank. They are grateful to Bela Balassa, John Ham and Jacques van der Gaag for helpful comments and ShuCheng Liu and Regie Stites for competent research assistance.