rafmacalaba/datause-encoder-data huggingface dataset:
devaluation expected by market participants. In order to obtain a direct measure of the monetary policy response to speculative pressures, I rely primarily on changes in interest rates under the control of the monetary authority (i.e. central bank discount rates) as a measure of policy. A drawback of this measure is that discount rates are only one of many instruments that the monetary authorities have at their disposal to resist speculative pressures. I therefore also check the robustness of the results using a variety of other noisier indicators of the stance of monetary policy.

Second, there may be important non-linearities in the effects of interest rates on speculative pressures, and ultimately on the outcome of the attack. For example, the credibility of the monetary authority's signal of its intent to defend the currency may depend on the economy's ability to withstand the contractionary effects of tight monetary policy, or on the quantity of reserves held by the monetary authority. In this case, simple correlations between measures of monetary policy and the outcome of speculative attacks may obscure any effects of policy present only in certain subsamples of speculative attacks. I take into account the possibility of episode-specific variation in the effects of monetary policy by splitting the sample along various dimensions, and by interacting measures of monetary policy with episode-specific characteristics.<sup>3</sup> Third and perhaps most important, the policy decisions of the monetary authority are themselves endogenous, and are likely to depend on both episode-specific characteristics that determine speculative pressures, and on speculative pressures themselves. Consider an economy that is vulnerable to a speculative attack, perhaps because its real exchange rate is overvalued or its reserves are low relative to its shortterm obligations. If attacks on vulnerable currencies are both more likely to succeed, and also are more likely to provoke a strong interest rate defense on the part of a "tough" monetary authority committed to maintaining the fixed exchange rate, one might expect to find large increases in interest rates during successful attacks, and conversely, small increases in interest rates during failed attacks. This endogeneity 3A second possible source of non-linearities is in the time dimension, if, for example, the signaling value of tight monetary policy becomes less credible over time. Since I will be relying on the relatively low-frequency monthly data available for this large sample of speculative attacks, there is unlikely to be enough time series variation in each episode to identify non-linearities over time in the effects of monetary policy.

3
Welfare Analysis of Changing Notches: Evidence from Bolsa Fam ́ılia<sup>_∗_</sup> Katy Bergstrom<sup>_†_</sup> , William Dodds<sup>_‡_</sup> , and Juan Rios<sup>_§_</sup> _Keywords: bunching, jumping, notches, MVPF, bounds, sufficient statistics JEL: H31, H53, I38, O12_

> _∗_ Some of the ideas in this paper were part of a chapter of Juan’s dissertation at Stanford University, titled “Welfare Analysis of Transfer Programs with Jumps in Reported Income: Evidence from the Brazilian Bolsa Fam ́ılia”. We are grateful to members of Juan’s dissertation committee Douglas Bernheim, Raj Chetty, Luigi Pistaferri and Florian Scheuer for their advice and guidance. We also want to thank Pierre Bachas, Jose Maria Barrero, Alexander Gelber, David McKenzie, Berk Ozler,<sup> ̈</sup> Peter Phillips, and seminar participants at the NBER Public Economics Meeting, Stanford University, the World Bank, the University of Auckland, and Brookings for their comments and suggestions. This research was supported in part using high performance computing (HPC) resources and services provided by Technology Services at Tulane University, New Orleans, LA. 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.

> _†_ World Bank. Email: kbergstrom@worldbank.org.

> _‡_ Tulane University. Email: wdodds@tulane.edu.

> _§_ Uber. E-mail: juanriosriver@gmail.com
-14-

Furthermore, the income share of the bottom 40 percent households in Bulgaria exceeds the OECD average share by nearly 30 percent (see Table 5a). Among the Eastern European countries, Bulgaria's income distribution is slightly more equal than in Poland and Hungary, even though the data for Poland and Hungary are for 1989--prior to the dramatic economic changes of the past several years. Recent data on income concentration (Gini coefficient) in some Eastern European Countries are shown on Table 5b. Bulgaria's income distribution is less concentrated than both Poland and Hungary but slightly more concentrated than Romania.

It had been widely anticipated that the transition from a command economy to a market economy would result in a worsening of income distribution. Comparing Bulgaria's Gini coefficient and income distribution by decile in 1992 with estimates available from earlier years does not support predictions that income inequality would worsen quickly. Furthermore, income is considerably less concentrated in Bulgaria than other lower-middle income countries, providing a relatively egalitarian base for future income growth.
Figure 1 gives density functions for the poverty lines, survey means and medians. The skewness evident in Figure 1 is as one would expect. The poverty lines are skewed further to the right than the medians, which are skewed further than the means. The range in national poverty lines is large, from $0.69 to $36 per day. The overall mean line is $7.82 per person per day (s.e.=$0.68; n=146). (For the non-OECD countries, the mean is $4.71 ($0.32; n=122).) The median is $4.38 and the mode is $3. While the World Bank’s $1.90 line is well below the mode, it is clearly in a fairly dense part of the distribution (Figure 1). If we construct a band around the Bank’s $1.90 line of (say) $1.80-$2.00 we find four countries (with their poverty lines): India ($1.82), Indonesia ($1.88), Ethiopia ($1.99) and Nepal ($2.00). The World Bank’s international line is approximately Indonesia’s line. China’s national line is slightly above this group, at $2.29.

Recall that the Bank’s $1.90 line is an update to 2011 prices of the $1.25 line proposed by Ravallion et al. (2009). In our new data set the mean poverty line of the poorest 15 countries in terms of the survey mean is $1.67 at 2011 PPP, slightly below the Bank’s line. But one would not want to make too much of this difference. The $1.90 line is the mean for a somewhat larger group of countries, which could be considered justified by the fact that we have a larger data set of national lines than used by Ravallion et al. (2009). If one focuses instead on the poorest 25 (about the same proportion of the 122 non-OECD countries) then the mean national line is $1.91, almost exactly the Bank’s 2011 line.

We do not, of course, have national lines for all country-year combinations; indeed, our 145 national lines account for only 10% of the number of estimates we will require of national poverty measures by date. So predicted values are needed to obtain a complete set of lines for our upper bound. In past work the (equally-weighted) mean has been the main predictor.

We will introduce a more general formulation of the comparison income later, but for the present descriptive purpose we also focus on the relationship between the national lines and the survey mean. Figure 2(a) plots the data for the full sample (including OECD).<sup>19</sup> Figure 2(b) gives the lines for the non-OECD countries but using instead a log scale for the mean to avoid the bunching up at low levels evident in Figure 2(a). Most countries are also identified. Of course, there are comparability problems and measurement errors in the national lines. But the pattern is clear: national lines tend to rise with the overall mean. For example, while the mean for the

> 19 These are mostly consumption means for developing countries, and mostly income means for OECD countries. However, this does not make any difference in the relationship (on adding a control variable for the type of survey).

9
in the Electronics industry and the IT service and ITES industry are much more productive than their purely domestic counterparts. Triple mode firms in the IT/ITeS and electronics industries are respectively 49 and 35 percent more productive than firms not integrated into GVCs. On the other hand, the productivity premiums associated with intermediate and high levels of GVC integration are relatively lower in the textiles and apparel and chemical industries.

Table 6: Estimated Productivity Premium for GVC participation and Service Imports by Sector

|VARIABLES|(1)<br>Automobile<br>Industry|(2)<br>Electronics<br>Industry|(3)<br>Textles and<br>Apparel|(4)<br>Chemicals and<br>Pharmarceutcals|(5)<br>IT and ITeS|
|---|---|---|---|---|---|
|Limited GVC partcipaton|0.1268*|0.1288***|0.0990**|0.0611*|0.1470|
||(0.0755)|(0.0332)|(0.0467)|(0.0355)|(0.1006)|
|Low GVC partcipaton|0.1500***|0.1497***|0.1065***|0.0722**|0.1818*|
||(0.0539)|(0.0420)|(0.0267)|(0.0281)|(0.1004)|
|Intermediate GVC partcipaton|0.3822***|0.1948***|0.1141*|0.0834***|0.2307**|
||(0.1471)|(0.0549)|(0.0616)|(0.0252)|(0.0938)|
|High GVC partcipaton||0.3024***|0.1304*|0.0364|0.3958**|
|||(0.1070)|(0.0771)|(0.0550)|(0.1588)|
|(Log) Service Input||||||
|Expenditures|-0.4907***|-0.4217***|-0.4682***|-0.3899***|-0.2204*|
||(0.0921)|(0.0891)|(0.0596)|(0.0440)|(0.1178)|
|Service Intensity|0.0401|0.0518|0.0469|0.0518**|0.0089|
||(0.0688)|(0.0379)|(0.0291)|(0.0230)|(0.0790)|
|Complex Service Intensity|0.1553***|0.1666***|0.1163***|0.0873***|0.0039|
||(0.0358)|(0.0290)|(0.0238)|(0.0143)|(0.0648)|
|Service Import Dummy|0.1372*|0.1180***|0.0682***|0.0857***|0.0917*|
||(0.0750)|(0.0277)|(0.0220)|(0.0165)|(0.0483)|
|Export Service Dummy|0.0392|0.2053|0.0279|0.2464**|0.2289***|
||(0.0355)|(0.2665)|(0.2790)|(0.1164)|(0.0668)|
|Size|0.3854***|0.3990***|0.3238***|0.3779***|0.3878***|
||(0.0648)|(0.0481)|(0.0369)|(0.0295)|(0.1102)|
|Constant|0.8519|0.2286|0.3826|0.0133|-0.7903**|
||(0.5491)|(0.3442)|(0.3792)|(0.2316)|(0.3504)|
|Observatons|3,977|6,520|9,040|12,199|886|
|R-squared|0.260|0.308|0.145|0.294|0.1900|
|Number of company_code|543|1,144|1,329|1,759|260|

Note: Firm size is measured by log annual sales. Complex services intensity is measured as share of expenditures of complex services and, respectively, ITES on total service expenditures. Firm, industry and year fixed effects are included. The sample includes all manufacturing and service firms and spans the years from 1995 to 2016. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. The R-squared corresponds to the overall adjusted R-squared.

15
The existing price indexes for Mexican states do not allow comparisons of cost of living across states. For this reason we create two indices using the ending module of the National Survey of Household Income and Spending (ENIGH 1992/8 Encuesta Nacional de Ingresos y Gastos de los Hogares). Since food likely comprises a larger share of the consumption basket in LDCs,<sup>6</sup> we generate a Laspeyres Index of a consumption basket of 200 items where the price and basket of reference are the national average in 1992. The housing price index was created using hedonic prices for rented houses only and is analogous to those used in the industrial country literature. Housing characteristics include number of rooms; kitchen; bathroom; electricity; telephone; water drainage and potable water; and type of walls, floors and ceilings and community size. Both indexes were included separately and as an average measure of the cost of living and were included free standing to allow for the effects described above. The most satisfactory results were gained with an average measure and these we report.

<u>Amenities:</u> Price indices, however, may also simply reflect amenities available in the new area, implying a positive relation with migration decisions. Further, as Roback (1982) showed, they affect equilibrium wages as well and hence should be included as part of the net utility change of moving from one region to another. A measure of amenities across Mexican states was created by the National Institute of Economic and Geographical Information using the 2000 General Census of Population and Housing. However, it includes measures of labor market tightness as well as migration variables (percentage of resident population who were born in other states and percentage residing in other states), variables whose influence we are trying to separate out. We therefore create a new index incorporating information on the percentage of the population living in urban areas, mortality rates, health infrastructure (number of nurses per capita, doctors per capita, hospitals per capita, beds) education (students per teacher in primary, secondary schools), and infrastructure (percentage and education of houses with electricity and without water drainage). We identified four significant factor loadings

> 6 The ENIGH gives information on expenses for all items, but only unit price and quantity consumed  for food items.  Nevertheless the survey also includes House Characteristics which allow us to estimate also Housing.

7
<!-- Start of picture text --> Nigeria<br>co<br>TM<br>+<br>~<br>oo<br>ram)<br>©t=) —N<br>oh<br>a |<br>~<br>co<br>co<br>1950 1960 1970 1980 1990 2000 2010<br>year<br>—— Observed Values oeee—— Simulated values<br><!-- End of picture text -->
**Table 2. The level of indebtedness of top-lossmakers**

||**_199_**|**_2_**|**_199_**|**_3_**|**_199_**|**_4_**|**_199_**|**_5_**|**_19_**|**_96_**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Top-25 lossmaker**|**s MEAN**|**MED**|**MEAN**|**MED**|**MEAN**|**MED**|**MEAN**|**MED**|**MEAN**|**MED**|
|LOSSK*|0.71|<br>0.37|0.68|<br>0.33|0.92|0.47|2.09|0.45|<br>5.45|<br>2.72|
|LTDEBTK|0.76|<br>0.28|0.74|<br>0.27|1.08|<br>0.02|0.45|0.01|<br>1.77|<br>0.00|
|STDEBTK|0.46|<br>0.24|0.21|<br>0.12|0.65|0.10|2.93|<br>0.07|<br>0.68|<br>0.03|
|STDEBTAK**|0.46|<br>0.03|<br>0.47|<br>0.05|0.71|0.04|1.20|<br>0.00|<br>3.35|<br>0.00|
|LTDEBTAK|0.28|<br>0.01|0.39|<br>0.01|0.01|0.00|0.01|<br>0.00|<br>0.24|<br>0.00|
|**Top-100**lossmaker|s**MEAN**|**MED**|**MEAN**|**MEDI**|**MEAN**|**MED**|**MEAN**|**MED**|**MEAN**|**MEDI**|
|LOSSK*|1.55|<br>0.41|<br>0.58|<br>0.29|0.97|<br>0.39|2.11|<br>0.43|<br>3.47|<br>1.09|
|LTDEBTK|2.70|<br>0.26|<br>0.66|<br>0.13|0.87|<br>0.01|0.29|<br>0.00|<br>0.68|<br>0.00|
|STDEBTK|0.55|<br>0.20|<br>0.29|<br>0.13|0.56|<br>0.10|1.36|0.14|<br>0.71|<br>0.08|
|STDEBTAK**|1.02|<br>0.13|<br>0.39|<br>0.06|0.38|<br>0.03|0.48|<br>0.01|<br>1.33|<br>0.00|
|LTDEBTAK|0.48|<br>0.01|<br>0.24|<br>0.00|<br>0.04|<br>0.00|0.04|<br>0.00|<br>0.14|<br>0.00|

* Note that for 1993-95 we have net loss as LOSS to Ltassets ratio, whereas for 1996 we had gross loss to Itassets ratio.

** Note that the STDEBTA/K ratio consist of total debt arrears for 1996, that is why the number is so large for 1996.

Source: Authors' calculations based on micro data set for 1993 - 1996, NSI, National Statistical Institute, Sofia, Bulgaria The conclusion from this sub-section is that the problem with excessive debt was not just a stock problem but a flow problem as well, despite of all debt write-offs and high inflation, and that the origin of this flow problem is most probably the presence of SBCs in Bulgaria during the observed period. Berglof and Roland (1998) present a sequential model of SBCs of the bank-firm relations in transition economies. The SBCs create an incentive problem because the company will not exert high restructuring efforts if there are refinancing prospects instead of liquidation prospects. In this way, the SBCs create a moral hazard problem. In addition, banks can exhibit rent-seeking behavior. They can extract rents from the government by triggering bailouts. The latter is relevant in the case of Bulgaria, which means that in terms of the analysis of Berglof and Roland (1998) the so-called three-tier model is the case. Thus, in our interpretation of the estimation of the investment model with liquidity constraints, we must take into account that the foundation for the information problem underlying our version of equation (1) is different in case of Bulgaria compared to a typical Western market economy, since in case of Bulgaria the information problem has its origin in SBCs.

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answer them. Through this demonstration, we also hope to motivate and equip analysts to better manage uncertainty in investment lending decisions.

# **Current World Bank Project Analyses Struggle to Manage Deep Uncertainty**

Our review of prior project analyses suggests that analysts typically use traditional Predictthen-Act methods to assess project performance, and do not explore the full range of deep uncertainty. In particular:

1. Project documents contain efforts to manage uncertainties.

2. However, even though project narratives may identify several deep uncertainties, the economic analyses address only a few.

3. There is a disconnect between risk and economic analysis. For instance, analyses typically assume that project costs and implementation will occur as planned, even though implementation issues are often cited as potential risks to the project.

4. Projects often carry out sensitivity analyses only for the already-preferred investment option or for the option shown to perform best under a single prediction of future conditions.

5. Most project managers we contacted during this study are aware of the potential for improving economic analysis.

In sum, project analyses can be improved to help make projects more robust.

# **Methods Like RDM Can Help Make Better-Informed Investment Decisions**

The findings from the Turkey project suggest that methods like RDM can improve our understanding of the vulnerabilities and strengths of our investment options, despite deep uncertainty and a potentially surprising future. In particular, this analysis demonstrates how RDM can help analysts and decision makers

- Identify and make explicit the many deep uncertainties that may affect the performance of investment options;

- Analyze the performance of investment options across a full range of plausible futures, without needing to assign controversial likelihoods to those futures;

- Identify and focus attention on the specific combinations of conditions that determine whether an investment meets or fails to meet decision makers’ goals; and

- Use historical and scientific evidence credibly, to assess whether threatening conditions are plausible, rather than to make predictions about what will be.

Such knowledge informs rather than replaces decision makers’ deliberations. It helps them systematically, rigorously, and transparently compare their options and select one that is robust – meeting their needs in the widest range of possible futures. Decision makers can have confidence in a robust decision, even if they cannot have confidence about the future.

17
<!-- Start of picture text --> Figure 2<br>Living Arrangements of China’s Rural Elderly<br>by Age<br>60 70 80 90 100<br>Age of Elderly<br>Living Alone, but Child(ren) in Village Living w/ Adult Child(ren)<br>Child(ren) in Either Village or Household<br>100<br>80<br>60<br>40<br>Percent of Elderly<br>20<br>0<br><!-- End of picture text --> Source: RCRE 2004 Supplemental Rural Household Social Network, Labor Allocation and Land Use Survey.

29
rors clustered at the industry-region, thereby accounting for potentially serially correlated standard errors. These are very similar to the ones in our main specification. To address potential weak instrument concerns Column (3) reports results when we aggregate our 21 instruments into a single instrument. The point estimate of the effect of immigration on native employment in an industry-region increases to 0.71 with a single instrument (however, the instrument is not significant in the wage regressions). Column (4) reports a log-log specification where dependent, independent and instruments are all in natural logarithms.<sup>33</sup> As anticipated, this specification fits the data very poorly: the instruments are not significant in the first-stage, and the employment effect of immigration is not significant.

# 4.4.2 Using Labor Force Survey Data for 1990 - 2010

We have employment data for Malaysia for the period 1990 - 2010, and can therefore run our employment regression, equation (8), for that time period. Uniquely in this literature this allows us to test the robustness of our results to the inclusion of local labor market (industry-region) specific linear time trends, so as to deal with potential bias arising from long-term correlated demand shocks. We also provide results using regions, as opposed to industry-regions, as our local labor markets. We report our results in Table 5.

Panel A.1 replicates our main IV regressions including the full set of fixed effects of our main specifications (Table 3) using the data for 1990 - 2010. The point estimate of the impact of immigration on native employment in an industry-region are somewhat higher: 0.60 instead of 0.41 (standard errors are smaller and the first-stage F-statistic is 14.6). 33We add one to each variable to avoid the problem of taking natural logarithms of zero.

36
56 Kassouf (2005) provide some further insights on the conditional and unconditional marginal effects:

1. If a variable enters the first-stage but not the second-stage equation, there is no direct effect and the sign of the conditional effect will depend on the sign of the correlation (rho) between the error terms in the two equations. Where rho is positive, this results in the signs of the variables identified in the first stage being reversed in the calculation of the conditional marginal effect. In section 4.3, where conditional and unconditional marginal effects are calculated, Guatemala, Pakistan, and Sri Lanka had positive values for the rho.

2. If a variable enters the second-stage but not the first-stage equation the value of its conditional effect will be identical to that estimated in the second-stage equation (there is no indirect effect).