rafmacalaba/datause-encoder-data huggingface dataset:
# **_3.2 Evolution of Trade Facilitation Measures in Southeast Asia, 2000-2005_**

It is also important to analyze the trade facilitation environment in the context of recent reform efforts. As in Wilson et al. (2005), we source our trade facilitation indicators from the annual _Global Competitiveness Report_ issued by the World Economic Forum. Based on a large survey—over 11,000 business leaders in 125 countries—the CGR presents perception indices covering various aspects of infrastructure quality, trade policy, governance, and regulatory reform. Scores are calculated based on responses to survey questions in which executives are asked to indicate their opinion on a scale of 1 (bad) to 7 (good). The survey nature of these data means that we should be cautious in interpreting changes from one year to the next: small differences may well reflect sampling error rather than genuine substantive differences.

In light of changes in the survey questions over time, as well as data availability for ASEAN member countries, we choose to assess regional progress on trade facilitation through the lens of four indicators. Our approach is broadly similar to that of Wilson et al. (2005). To capture physical infrastructure, we examine the quality of maritime port infrastructure and air transport infrastructure. As an indicator of customs administration, we use the extent of irregular payments connected with import and export permits. And we use the quality of competition in the Internet Service Provider (ISP) sector as a proxy for services sector infrastructure.<sup>9</sup> In the case of our two transport infrastructure indicators (Figures 7-8), it is difficult to see any clear trend over time. Singapore is consistently ranked very highly for its port infrastructure, while Malaysia and Thailand appear to have improved slightly over time. The remaining countries for which we have data have remained approximately stable, with the possible

> 9 The first three indicators are also used by Wilson et al. (2005). However, the Wilson et al. (2005) indicator for service sector infrastructure does not appear in later GCRs, so we are forced to use an alternative measure.

- 9 -
WPS3229

# **HOUSEKEEPING AND PLUMBING: THE INVESTABILITY OF EMERGING MARKETS**

## JEPPE LADEKARL AND SARA ZERVOS<sup>∗</sup>

**Abstract:** This paper looks at the investment allocation process employed by portfolio investors in emerging markets.  In particular, it examines the first of a two-stage decision process: first, investors create a sub-set of investable countries to be analyzed later in further detail; second, they weigh expected returns versus risk and subsequently allocate their funds. This study hypothesizes that the determination of whether a country is investable or not is influenced by a number of factors, especially related to size, quality of “housekeeping” (macro policies, political economy, local financial markets, corporate governance, etc.) and efficiency of “plumbing” (legal and regulatory framework, custody, clearing and settlement, taxes, etc.).  By interviewing many types of these investors in both the United States and the United Kingdom, we delve into their decision-making processes as well as attempt to uncover the factors they indicate matter most in defining the investable universe.  We determine the relative importance of such housekeeping and plumbing factors while highlighting the role of external issues, such as index benchmarking and U.S. foreign policy.  We recognize from the outset that the most profound effects on investment flows, or the required minimum expected returns, arise from improvements or deteriorations in macro policies.  However, at the margin, improvements can be made in country policies that will, for a given macro situation, improve the ability of a country to attract international investment flows.

### **JEL Classification Code:** F36, G11, G15, G18, H36, K22

**Keywords:** Portfolio Investors, Portfolio Flows, Emerging Markets, Investability, Investment allocation, Investment guidelines, Index Benchmark

## World Bank Policy Research Working Paper 3229, March 2004

_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 to get 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. Policy Research Working Papers are available online at http://econ.worldbank.org._ ∗ Jeppe Ladekarl (jladekarl@worldbank.org), and Sara Zervos (szervos@worldbank.org) are both from the World Bank’s Financial Sector Vice Presidency. The authors are grateful to Messrs. Glaessner, Jonasson, Lybek, Vittas, and numerous asset management professionals in London, New York and Boston who shared their insights and experiences and provided comments and suggestions. Alessandra Campanaro provided excellent research assistance.
A. Effects of procedural restrictions <!-- Start of picture text -->
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Source: Holmes & Hardin (2000)
included in the available observations after which storm surges were modelled due to the limited period of the available records (Muis et al. 2016). In Vietnam, the GTSR dataset shows barely any flooding along the North and South Central coast which does not accurately reflect actual flooding experienced in these regions. This can likely be explained by the absence of wave setup in the GTSR approach in combination with a topography characterized by relatively steep slopes on this coastline section (Bunya et al. 2010).

For this report, a new set of coastal flood maps for Vietnam has been created that tries to reflect flood risk more accurately. It is based on a global digital elevation map with a resolution of 90 meters at the equator that has been corrected for several errors typically present in spaceborn elevation models (Yamazaki et al. 2017). In order to arrive at predictions for flood extent and depth, digital elevation data is combined with averaged provincial surge levels. For each province and each return period, an average surge level is calculated based on modeled frequency curves. These curves have been generated by statistically postprocessing the results from storm surge modeling with a wide range of possible typhoons (Ministry of Agriculture and Rural Development, 2012). The flood depth in the new coastal flood map is then calculated following s −d , in which s reflects the average storm surge level in province for return period , and d reflects the average altitude in grid cell of the digital elevation ii,tt jj ii,tt mm map used. Whenever s −d ≤0 the corresponding grid cell value is set to 0, indicating no flooding. jj ii tt mm jj The output has a raster format with a resolution of about 3′′ resolution (~90 m at the equator). Following ii,tt jj mm this simple methodology, flooding depth for the 28 coastal provinces are calculated for events with return periods of 5, 25, 50, 100, 250, and 500 years (figure 2.1.b). Just as with the pluvial and fluvial flooding maps no information on existing flood protection measures has been incorporated into this modelling.

However, extensive public protection infrastructure such as dikes exist in many provinces in Vietnam, aimed the mitigation of both at riverine and at coastal flooding. Given the absence of flood protection modelling in the flood maps described above, using them as they are would result in an overestimation of flood exposure. Unfortunately, incorporating flood protection infrastructure into flood models at a national scale is still prohibitively difficult. This analysis thus uses a novel approach based on knowledge collected for the main report combined with qualitative expert assessment to estimate the flood risk, while accounting for existing protection infrastructure (section 4.1).

**Figure 2.1: Riverine and coastal flood risk in Vietnam**

_a) Riverine (fluvial and pluvial) flooding_ _b) Coastal flooding_ 5
of the market (as reflected in imports) to items at or near the overall national average. 7/ The import value criteria was therefore added in recognition that value added per employee was not an infallible guide to South-North comparative advantage.

While there is a theoretical basis for postulating a positive relationship between labor intensity ana developing countries' exports, there are certain types of products where other factors may be more important determinants of whether manufacturing activity is located in developed or developing countries. Some production processes, for example, are often located near areas of raw material production, particularly if transportation costs for manufacturing inputs greatly exceeds those for higher stage (semifinished or fabricated) goods. This locational influence might be especially important for manufactured processes that require relatively large inputs of bulky commodities like wood and metal ores. In other cases, transport costs might dictate that manufacturing activity be located closer to centers of consumption. This would be the case where nominal transport costs are relatively low on production inputs, but are more important for the final

- 7/ The reader should note that there is an inverse relation between the numeric value of the index defined in equation (1) and the labor intensity of a given product. That is, the lower the numeric value of the index the higher the labor intensity of the product. It also follows that products with very high index values are capital intensive in production. The selection of items based on value added per employee in the U.S. was supplemented by detailed examination of manufactures imported by developed from less developed countries to see if additional products needed to be taken into accounc. On this basis, several items such as batteries, lamps and miscellaneous manufactures were added to the NBER list since relative value added in other countries appeared below the United States average. However, a major conclusion of this portion of the analysis was that products manufactured by labor intensive processes in the United States were also manufactured by relatively labor intensive processes in other countries. Lary used these findings to justify extensive use of United States production statistics as a guide to factor-proportions.
including in the estimations relevant moderator variables.<sup>9</sup> Formally, the error term in (2) can be expanded and written as ν _is_ +ε _is_ , where ε _is_ is sampling noise, and ν _is_ is heterogeneity in treatment effect across studies due to differences in countries, sector, time period, type of data and econometric methodologies, etc. Given a set of moderator variables _Mk_ capturing these differences, we assume that there is no single true effect, but rather that it may vary depending on a number of characteristics of the underlying sample in any given study or on the technical characteristics of this study.<sup>10</sup> We can write: Equation 2 thus becomes: Simply adding the whole set of moderator variables produces very noisy estimates and is likely plagued by multicollinearity issues, as some of these variables are highly correlated. To address this issue, we apply several methods to select an appropriate model with a smaller subset of control variables. First, we implement two alternative LASSO procedures, the rigorous penalization approach in Belloni et al. (2012, 2014), and LASSO in which the tuning parameter is chosen by 10-fold cross-validation.<sup>11</sup> Second, we use Bayesian Model Averaging, either including covariates with posterior inclusion probabil9Note that in some cases, moderator variables also vary within studies.

10Some of this variation may also be assigned to the publication bias, which may now vary across (set of) studies. Think, for example, of specific econometric techniques leading to less statistical significance overall and hence to lower publication or circulation probability.

> 11LASSO is a popular model selection tool. It minimizes mean squared error subject to a penalty on the absolute size of coefficient estimates. Based on the nature of the penalty the lasso sets some of the coefficient estimates exactly to zero thereby removing some predictors from the model. The degree and type of penalization are controlled by tuning parameter(s). Under the rigorous approach, the tuning parameters are theoretically grounded, guaranteeing optimal rate of convergence for prediction and parameter estimation. There is a high priority on controlling overfitting which results in parsimonious models. Alternatively, the tuning parameter can be selected using cross-validation (CV) to optimize out-of-sample prediction performance. CV methods are universally applicable but are computationally expensive. We implement these using _rlasso_ and _cvlasso_ commands in Stata.

13
# **Figure 4. Mean Value of Bank Credit by Use of Credit as Share of GDP**

<!-- Start of picture text --> Commercial Consumer Mortgage<br>LAC ECA EAP HI OTH LAC ECA EAP HI OTH LAC ECA EAP HI OTH<br>2006 2014<br>Note: Outliers addressed by removing top and bottom 1 percent of credit measures by category.<br> HI = Higher-income countries; OTH = Others: Africa (AFR), Middle East and North Africa (MNA), and South Asia (SAR).<br>50 50 50<br>40 40 40<br>30 30 30<br>20 20 20<br>10 10 10<br>0 0 0<br>Purpose of Credit Categories / GDP (%)<br><!-- End of picture text --> 25
the same topic (Models 1 - 4), and the length of subsequent speeches that are on the same topic (Models 5 - 8) — on an interaction between the speaker’s gender and the village treatment status.

Table 6: Effect of PVP on Deliberative Influence

|||||_Dependen_|_t variable:_||||
|---|---|---|---|---|---|---|---|---|
|||Next|Same|||Le|ngth Same||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|I(PVP)|_−_0.05<sup>_∗_</sup>|_−_0.05<sup>_∗∗_</sup>|_−_0.05|_−_0.06<sup>_∗_</sup>|_−_0.09|_−_0.15|_−_0.15|_−_0.16|
||(0.03)|(0.03)|(0.03)|(0.03)|(0.10)|(0.11)|(0.10)|(0.11)|
|I(Female Speaker)|_−_0.01|_−_0.01|_−_0.0001|_−_0.004|0.02|_−_0.02|0.02|_−_0.01|
||(0.03)|(0.03)|(0.03)|(0.03)|(0.11)|(0.11)|(0.12)|(0.11)|
|I(PVP)xI(Female Speaker)|0.003|_−_0.001|_−_0.01|_−_0.001|0.03|0.04|0.003|0.04|
||(0.05)|(0.05)|(0.05)|(0.05)|(0.17)|(0.17)|(0.18)|(0.17)|
|Matched Pair FE|✓|✓|✓|✓|✓|✓|✓|✓|
|Topic FE|✓|✓|✓|✓|✓|✓|✓|✓|
|Demographic Controls||✓|✓|||✓|✓||
|Infrastructure Controls|||✓||||✓||
|_p_-Score Control||||✓||||✓|
|Observations|2,099|2,099|2,099|2,099|2,061|2,061|2,061|2,061|

_Note:_<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0.01. Robust Standard Errors, clustered at the block-pair, in parenthesis. Data are taken from the full sample of villages, but include only citizen speakers. Demographic controls include: number of households, percentage Scheduled Caste, percentage Scheduled Tribe. Infrastructure controls include indicators for the presence of a primary school, secondary school, health center, hospital, clinic, medicla shop, government hospital, and bank.

Across all specifications, we find no evidence that PVP improves the agenda-seting power of women; point estimates are small and statistically insignificant — suggesting that the presence of this intervention does not increase the likelihood that women are able to drive conversation. Moreover, we find no evidence that PVP improves women’s ability to elicit a response from the state (Table 7), let alone from elected officials (Table 8). To ensure that these results are robust to alternative specifications of the topic model itself, we re-run the analysis with 30 topics and find largely consistent results (presented in Appendix 3B). Given that one key function of the _gram sabha_ is to provide a forum for citizens to make requests of and demand accountability from politicians, elected officials’ failure to respond to women suggests that they remain unheard within the _gram sabha_ .

Interestingly, despite women’s lack of substantive influence, Khanna et al.’s (2015) surveybased evaluation of PVP suggests that women _feel_ more efficacious afer program implementation. More specififically, when presented with hypothetical vignetes about various village and 21
Policy ReseaRch WoRking PaPeR 4691

# **Abstract**

This paper re-emphasizes the link from structural policies to enhanced macroeconomic stabilization using a small structural model estimated on quarterly data for Macedonia and Slovakia over 1995-2007. The success of macroeconomic stabilization, typically in the hands of monetary policy, is not only determined by a suitable choice of the nominal anchor, which shapes the reaction function of monetary policy, but also the constraints within which the monetary policy strives to achieve its objectives. The key attributes of the constraints to macroeconomic stabilization are economic rigidities and structural shocks. By benchmarking the estimated economic rigidities and structural shocks faced by Macedonia to those faced by Slovakia, the authors find that Macedonia has relatively weaker transmission mechanisms of monetary policy, higher output rigidity, and a lower exchange rate pass-through, and faces larger external shocks. For Macedonia, these relatively higher constraints on monetary policy together with the chosen exchange rate anchor result in greater output and inflation volatility relative to Slovakia. Hence, it appears that small, open economies with stronger economic rigidities should apply monetary policy regimes that allow for more flexible adjustments in external relative prices to enhance their macroeconomic stability.

This paper—a product of the Poverty Reduction and Economic Management Department, Europe and Central Asia Region—is part of a larger effort in the department to understand the tradeoffs and complementarities between pro-growth structural and stabilization policies. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The author may be contacted at mmelecky@worldbank.org.

_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 to get 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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table** **<u>8: Under what conditions are the marginal effects of trade shocks not significant?</u>**

||**Type **|**Range**|**Px**|**Point source**|**Pm**|**MP**|
|---|---|---|---|---|---|---|
|_Grievance_|||||||
|Economic inequality|Contin.|[22;65]|x<26 or<br>x>58|x<46 or x>52|No diff.|No diff.|
|gini_net|Contin.|[15;75]|<32|<48|>52|No diff.|
|gini_market|Contin.|[17;80]|<37|<52|No diff.|No diff.|
|Ethnic polarization|Contin.|[0;1]|<0.37|<0.42|No diff.|No diff.|
|Ethnic fractionalization|Contin.|[0;1]|<0.28|<0.30|No diff.|No diff.|
|Religious fract.|Contin.|[0;0.7]|No diff.|<0.28|No diff.|No diff.|
|Religious polarization|Contin.|[0;0.96]|No diff.|<0.49|No diff.|No diff.|
|Any conflict in last 10 yrs|Dummy|[0;1]|No conflict|No conflict|No diff.|No diff.|
|_Political institutions_|||||||
|Elections (t-1)|Dummy|[0;1]|No diff.|Elections|No diff.|No diff.|
|Federal Govt.|Dummy|[0;1]|Federal|Federal|No diff.|No diff.|
|polity2|Contin.|[-10;10]|No diff.|>5|No diff.|No diff.|
|parliamentary democracy|Dummy|[0;1]|No diff.<br>|No diff.|No diff.|No diff.|
|Programmatic party|Bins|[0;3]|< or > 2nd<br>tercile|No diff.|No diff.|No diff.|
|Age of party in office|Bins|[1;191]|No diff.|No diff.|No diff.|No diff.|
|_Governance_|||||||
|Law|Contin.|[0;6]|No diff.|No diff.|No diff.|No diff.|
|Military|Contin.|[0;6]|No diff.|No diff.|No diff.|No diff.|
|Accountability|Contin.|[0;6]|>3|>2.2|No diff.|No diff.|
|Corruption|Contin.|[0;6]|>2.5|No diff.|No diff.|No diff.|
|Bureaucratic|Contin.|[0;4]|>1.5|No diff.|No diff.|No diff.|
|composite index|Contin.|[12;84.7]|No diff.|No diff.|No diff.|No diff.|
|_Neighbors_|||||||
|Neighbors' conflict (any)<br>|Dummy|[0;1]|No conflict|No conflict|No diff.|No diff.|
|Share trade neighbors<br>RTA|Contin.|[0;0.8]|>0.35|>0.08|No diff.|No diff.|
|Share trade neighbors|Contin.|[0;0.9]|No diff.|>0.24|No diff.|No diff.|
|_Price transmission_|||||||
|NRA (output + input)|Bins|[-0.9;1.4]|< or > 2nd<br>tercile|No diff.|No diff.|No diff.|
|NRA (output)|Bins|[-0.9;3.5]|< or > 2nd<br>tercile|No diff.|No diff.|No diff.|

_Note: the column range indicates the range of values taken by each variable in our sample; the column type groups the variables into three types, i.e. continuous, dummy (0 or 1) and bins (the variable is split into three mutually exclusive continuous ranges of values). The latter category is included for those variables for which the marginal effect of Px on conflict appeared to be non linear. The other columns report the values of the interaction for which the marginal effect of the relevant trade variable becomes not significantly different from zero (at the 10% level); “No diff.” indicates either that the marginal effect is always or never statistically different from zero across the distribution of the interactions’ values._ 33
main obstacle for conducting an empirical analysis is the absence of data on informational asymmetries within split households, which surveys of migrant or household alone cannot possibly provide. We offer a new solution to this problem. We assess informational asymmetries about income earned abroad by collecting migrants’ reports about their earnings and contrasting these reports with accounts of their earnings collected from the remittance recipients. To our knowledge, this is one of the first few studies using a matched household dataset that collects cross-reports on variables of interest.<sup>3</sup> We are able to offer new evidence of substantial asymmetry in information within split households. In our representative sample of South Indian migrant households, where husbands are migrant workers in Qatar and wives remain behind in Kerala, India, wives report on average only about 79 percent of their husbands’ earnings, which we show is unrelated to any reporting bias. We find that underreporting is not uniform across households—it is more prevalent in households with higher earning migrants. In particular, we observe that the reported earnings ratio—the ratio of the wife’s report of her husband’s earnings to that of her husband’s— decreases in migrant earnings. Importantly, we find that asymmetric information about the migrant’s income is strongly correlated with variation in remittances: all else equal, the lower the reported earnings ratio, the smaller the annual remittance sent home.

By design, existing remittance models, mainly based on the symmetric information assumption, are not able to account for any differences in cross-reports and, consequently, cannot explain the observed relationship between the reported earnings ratio and remittances.<sup>4</sup> To account for our empirical findings, we develop a model around the exchange motive for

> 3. De Weerdt et al. (2014) use a matched sample of domestic migrants and extended family in Tanzania to examine remittance motives under asymmetric information.

> 4. In Section V, we compare the predictions of alternative remittance models with our empirical findings. See Stark (1995) and Rapoport and Docquier (2006) for reviews of remittance models.

3