rafmacalaba/datause-encoder-data huggingface dataset:
In Table 6, we compare naive OLS confidence intervals with the more conservative Imbens-Manski confidence intervals which adjust for censoring of the regressor of interest. As expected, confidence intervals widen slightly, but patterns of significance are unchanged: those confidence intervals that did not include zero in the naive specification do not include zero after adjusting for censoring.

# **4.5.2 Non-Independence within Language Families**

A more serious inference concern arises from the fact that languages are not independent. Different tongues evolve over time from a common ancestor. Grammatical structures vary both across and within language families. Roberts, Winters, and Chen (2015) consider a range of approaches to correcting for the non-independence of modern languages. Many approaches have the drawback that they are statistically less powerful than they could otherwise be because they ignore variation in grammatical structure either within or between language families.

We propose a permutation test approach based on the observed structure of the language tree, as documented by the Ethnologue. Specifically, we cluster together languages up to the highest tree level at which we observe no variation in our treatment of interest, grammatical gender. That is, we form the largest possible clusters that are homogeneous in terms of grammatical gender. Thus, for entire top-level language families that show no variation in gender structure (e.g., the Austronesian language family), we cluster at the language family level. In intermediate cases, we designate clusters at the highest level of the tree where we do not observe variation in grammatical gender (e.g., all Western Nilotic languages cluster together; they are only a branch within the Eastern Sudanic part of the Nilo-Saharan family, which itself contains a number of other such clusters by our definition). In cases where two languages that differ in their gender structure otherwise share the same classification path through the entire language tree, we cluster at the language level.

Figure 4 illustrates this approach for a hypothetical language family. All of the languages in the Group A branch in the figure are gender languages, so they are assigned to a single 25
firms without surveying their owners provides one approach, but at the cost of including some already formal firms and some closed firms or hard to interview firms in the study. One alternative would be to conduct a baseline survey to identify which firms are informal and willing to be interviewed, and then for the government to randomly assign all geographic blocks throughout the city to treatment or control status without reference to this baseline information. A downside of this approach is that it would make it harder to measure treatment spillovers, since one could not assign firms to different treatment status within blocks, although potentially the use of GPS coordinates (Gibson and McKenzie, 2007) on each firm could allow some measurement of spillovers.

# **References**

Alcázar, Lorena, Raúl Andrade, and Miguel Jaramillo (2010). “Panel/tracer study on the impact of business facilitation processes on enterprises and identification of priorities for future business enabling environment projects in Lima, Peru – Report 5: impact evaluation after the third round”, Report to the International Finance Corporation, Mimeo.

Alm, James, Gary McClelland and William Schulze, (1992). “Why do people pay taxes?” _Journal of Public Economics_ 48: 21–38.

Almeida, Rita and Pedro Carneiro, (2012). “Enforcement of Labor Regulation and Informality”, _American Economic Journal: Applied Economics_ 4(3): 64-89.

Andreoni, James, Brian Erard and Jonathan Feinstein, (1998).“Tax Compliance”, _Journal of Economic Literature_ 36: 818-60.

Benjamini, Yoav and Yosef Hochberg (1995) “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing”, _Journal of the Royal Statistical Society Series B_ , 57(1): 289300.

Bertrand, Marianne and Francis Kramarz, (2002), "Does Entry Regulation Hinder Job Creation? Evidence From The French Retail Industry," _Quarterly Journal of Economics_ , 117(4): 1369-1413.

Bruhn, Miriam (2011) “License to sell: the effect of business registration reform on entrepreneurial activity in Mexico.” _Review of Economics and Statistics_ , 93(1): 382-86.

De Giorgi, Giacomo and Aminur Rahman (2013) “SME’s Registration: Evidence from an RCT in Bangladesh”, Mimeo. Stanford University and World Bank.

27
2 about a loss of competitiveness in several of the African franc zone countries. Furthermore the government of Cameroon has borrowed domestically at rates which are several percentage points below the yield on French Government paper.

Nevertheless, as will be shown below, closer examination suggests that consumer price inflation is largely determined by core country inflation in the long run. Convergence to core inflation is, however, slow, and in particular fiscal policy may have a small transtory influence. The limited evidence available suggests that uncontrolled interest rates do converge to core country levels. As predicted by theory variations in the volume of domestic credit are seen in most cases to spill over into the balance of payments.

A methodological note is appropriate at this point. As we are interested in time series properties of price and monetary variables, we really need to look at quarterly data in addition to the annual series. The quality of sub-annual data in the countries under examination is generally poor. However monetary and price data are available. Our research strategy is to attempt to meet the econometric problem of underspecificied equations (due to lack of suitable non-monetary quarterly data) by use of time trend and intercept shift dummy variables.

1 The African currency unions Most African countries entered the post-colonial era with fixed exchange rates vis-a-vis the pound sterling or the franc and fairly open capital accounts with their former colonizing powers. One by one, however, the sterling area countries abandoned this link and in 1986 the Gambia became the last African country to move from sterlinge.

5 Members of the East African Economic Community had a common currency area in the years before 1977. The Kenya, Uganda and Tanzania shillings continued to share a common parity until 1979, but by 1981 they had gone their separate ways. The Liberian currency still retains its _de jure_ link with the US dollar.
# **Section I: Introduction**

The Dominican Republic remains among the top economic performers in Latin America and the Caribbean, experiencing economic expansion at a rate of 6.6 percent in 2016 in contrast to an average contraction of 1.4 percent of GDP in the region. At the same time, poverty has declined in the last decade nearly 7 percentage points from 43.6 percent (in 2007) to 32.4 percent (in 2015). Despite this economic growth and steady decline in poverty, the Government of the Dominican Republic continues to face structural rigidities, and inadequate revenue collection sharply limits its capacity to respond to imminent natural disasters. Coupled with the likelihood of future disasters and weatherrelated shocks, the Dominican Republic is thus at risk of economic and social losses without the adoption of adequate risk reduction strategies and an enhanced management of contingent fiscal liabilities associated with disasters. As a matter of fact, based on historical data for 1961–2014, losses associated with all types of natural disasters in the Dominican Republic averaged USD 420 million annually, or 0.69 percent of 2015 GDP.<sup>5</sup> As climate change is expected to increase the likelihood, intensity, and frequency of extreme weather events in the Dominican Republic, strengthening disaster and climate risk reduction will be critical.

According to the World Bank’s Country Disaster Risk Profile, the average annual loss over the long term projected from hurricanes alone will be USD 337 million in the Dominican Republic, or 0.49 percent of 2015 GDP. A large portion of the country’s population and key productive activities–related to agriculture, energy and tourism–are situated in areas highly exposed to natural hazards. Between 2005 and 2014 the vulnerable

> 5 See MEPyD and World Bank (2015).

2
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The associations between the industry output share of Suharto family firms in 1996–97 and individual industry outcomes in the post-Suharto era (2000–09) did not change dramatically. Only the correlations with output and the profit elasticity have significantly weakened in the post-Suharto period. Correlations between the broad measure of connectedness and individual industry outcomes are also quite stable, with only a change in the correlation with the export propensity. In sum, at first glance, Suharto’s fall seems to have sparked mild improvements in competition industries with family connections to him relative to industries that were not connected.<sup>18</sup>

# **5.2 Impact of Political Turnover on Competition**

We now assess more rigorously the impact of the disruption of political connections on competition.<sup>19</sup> We use an event study analysis which exploits the collapse of the Suharto regime as a quasi-natural experiment by which the value of political connections was reduced. Our empirical specification is: where is one of the outcome variables in five-digit industry _j_ at time . These variables are the competition index jjii _Z_ and the specific indicators from which it is derived, notably entry and exit rates; the YY tt price–cost margin; profit elasticity; the Herfindahl-Hirschman index and the market share of the four largest companies; the number of firms; and prices. The key variables of interest are the interaction terms 18 See Table C1.2 in the Appendix for descriptive statistics on competition indicators during and after the Suharto era. Overall, competition seems to have improved somewhat in the post-Suharto era. The number of firms went up. Prices fell and concentration rates went down slightly, but entry rates decreased. Assessing to what extent these developments can be attributed to the fall of Suharto is difficult given that there are many confounders, such as globalization, technological progress, changing demographics, structural change etc.

19 As a prelude to that analysis, the online appendix C2 examines the relationship between changes in connectedness and changes in competition during the Suharto era, and shows that increases in the market share of connected firms were associated with attenuated competition as measured by the Z competition index which is based on 8 individual indicators of competition during the Suharto era.

20
**TABLE 7: Effect of Parenting and Classroom Quality on Child Outcomes (Instrumental Variables) - 18-Month Follow-Up** **<u>(CONTINUED)</u>**

|**PANEL B: Classroom Quality**||||**Dependen**|**t variable:**||||
|---|---|---|---|---|---|---|---|---|
|**Sample restricted to Control and T3 (T2 +**<br>**caregiver incentives)**|**Malaw**|**i Developm**<br>**Tool**|**ental Asse**<br>**Score**|**ssment**|**St**|**rengths an**<br>**Question**|**d Difficul**<br>**naire Scor**|**ties**<br>**e**|
||||**Fine M**|**otor /**|**T**|**otal**|||
||**Langua**|**ge Skills**|**Perce**<br>**Sk**|**ption**<br>**ills**|**Diffi**<br>**(Inv**|**culties**<br>**erted)**|**Pro**|**social**|
|**Variables**|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|**Second Stage**|||||||||
|Cl Obti Id Midli|0.063|0.068|-0.054|-0.070|0.101**|0.089*|-0.039|-0.055|
|assroom servaon nex (ne)|(0.050)|(0.046)|(0.051)|(0.047)|(0.045)|(0.051)|(0.052)|(0.049)|
|Ld Ddt Vibl Bli||0.354***||0.454***||0.338***||0.168***|
|agge epenen arae (asene)||(0.037)||(0.033)||(0.038)||(0.035)|
|Missing Lagged Dependent Variable (Baseline)||||||-0.067<br>(0.271)||-0.091<br>(0.140)|
|**First Stage**||**D**|**ependent**|**variable: Cl**|**assroomQ**|**uality Inde**|**x**||
|T3|1.307***|1.313***|1.307***|1.313***|1.306***|1.301***|1.306**|* 1.301***|
||(0.162)|(0.156)|(0.162)|(0.156)|(0.163)|(0.157)|(0.163)|(0.157)|
|Laed Deendent Variable (Baseline)||0.001||-0.019||0.026||0.017|
|gg p||(0.021)||(0.026)||(0.019)||(0.017)|
|Missing Lagged Dependent Variable (Baseline)||||||-0.183<br>(0.312)||-0.183<br>(0.315)|
|F-test for Significance of T3(F-stat)|64.91|70.54|64.91|70.43|64.38|68.53|64.38|68.40|
|District-bin Fixed Effects?|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Lagged Dependent Variable and Age<br>Dummies?|No|Yes|No|Yes|No|Yes|No|Yes|
|Number of observations|876|876|876|876|873|873|873|873|

Notes: .01 - ***; .05 - **; .1 - *. OLS regressions at child level using baseline and 18-month data. Standard errors in parentheses are clustered at the CBCC level. Strengths and Difficulties Questionnaire: Total Difficulties Score had its score inverted so that a higher score is better. We use all children for whom the relevant variables are available. We weight observations using sampling weights (and tracking weights for the 42 randomly tracked observations at the 18-month follow-up). For each round, indices are stardardized using the (weighted) mean and standard deviation for the control group in that round.

50
This leads to a very important, albeit not sufficiently appreciated, difference between Concepts 2 and 3. This is not a conceptual difference but rather the difference in what is a commonly used metric of welfare. To be sure, Concept 2 inequality can be calculated across GDIs per capita or across mean disposable incomes retrieved from countries’ household surveys (HS). It is however almost never calculated using the latter because HS means are much more difficult to obtain than National accounts (NA) data. In contrast, Concept 3 inequality _must_ be based on household surveys because the only source of distributional data are, as said before, household surveys. Because there is no world-wide household survey this means that the best one can do is to combine individual countries’ surveys, and use disposable per capita income or personal per capita consumption as welfare indicators.

Now, the problem is first, that there is a definitional difference between GDI which comes from National accounts, and disposable income which comes from surveys, and second, that there was a recent tendency for these two measures not to move in unison in several important countries (see Deaton, 2005). Thus, even if everything else were fully comparable, a commonly calculated Concept 2 measure that uses National accounts data would differ from an equivalent Concept 2 measure calculated using household surveys because welfare indicators are different and because they have recently diverged for reasons that are not yet quite clear.<sup>7</sup> This review will deal only with studies of global or Concept 3 income inequality.<sup>8</sup> The easiest point of departure for an estimate of global inequality is to calculate Concept

> 7 This area—understanding why national accounts and household surveys averages move differently— represents one of  the most important areas for further research. Bhalla (2001) must be credited through his, at times single-minded, insistence on using National accounts data for highlighting this issue.

> 8 And with global inequality conventionally defined as inequality in relative, not absolute, incomes and using the conventional measures of inequality like the Lorenz curve, Gini coefficient or Theil index. The focus on absolute inequality  however has its own uses (see Atkinson and Brandolini, 2004; Svedberg, 2003; Ravallion  2004). Similarly, relative income inequality with the use of different inequality aversion parameters (reflecting in principle different welfare judgments) will produce ambiguous results even where conventional statistics yield a clear outcome (see Capeau and Decoster, 2004, Table 5).

5
countries’ practice to borrow in foreign currency, both abroad and in the domestic market when it comes to long maturities and fixed rates.<sup>3</sup> In our sample, emerging markets (especially in Eastern Europe and Latin America) tend to exhibit a high degree of liability dollarization over time. Such countries are financially fragile due to either currency mismatches (because of the currency composition of the debt) or maturity mismatches (because of the short‐term nature of the domestic currency debt). Eichengreen et al. (2002) show that countries with original sin exhibit greater output and capital flow volatility, lower credit ratings, and limited ability to manage an independent monetary policy.<sup>4</sup> Alternatively, following Sorsa et al. (2007), we construct domestic liability dollarization through the BIS reporting banks’ local asset positions in foreign currency as a proportion of GDP. For many emerging market countries, however, BIS banking data are unavailable. To overcome this drawback, we build an estimate by summing the foreign currency deposits and net foreign liabilities of the banking sector. Under the assumption that banks match their foreign currency assets and liabilities, this should be an adequate proxy for foreign currency lending to the domestic nonbank sector.

In our estimations, we mostly rely on the proxy of liability dollarization through original sin, because of drawbacks in the alternative measure. Namely, the liability dollarization calculated following the methodology in Sorsa et al. (2007) displays high values (indicating a high degree of liability dollarization) for the UK relative to other countries and negative values for Nigeria. Despite these shortcomings, we apply this second measure of liability dollarization to test robustness of the results.

While external vulnerability and liability dollarization are expected to increase the probability of sudden stops, FDI inflows are expected to lower it. FDI flows have been shown to be less volatile than other types of financial flows in crisis periods.<sup>5</sup> The lower volatility could be linked to a longer‐term investment perspective for this type of capital flows.

In terms of regional patterns of FDI, the sample shows evidence of a rapid growth of FDI inflows to emerging markets over the sample period ‐ especially to the transition economies of Eastern Europe (see Appendix 2). It is worthwhile mentioning that growth rates of FDI to mature economies tend to be lower in comparison, but also less volatile over the period under consideration.

**III. Methodology of Estimating the Likelihood and Consequences of Sudden Stops of Capital Flows**

Carefully taking into account the specific features of the dataset, we estimate a treatment effects model that specifies the effect of a sudden stop of capital flows on growth (“outcome

> 3 See Eichengreen and Hausmann (1999).

> 4 See Eichengreen, Hausmann, and Panizza (2002).

> 5 See for example Levchenko and Mauro (2006).

5
on compensation, which increases until an individual is 65 years old and then starts decreasing. Finally, an additional set of dummy variables was included in Equation 1 to take into account most relevant individual-specific characteristics: gender, marital status and urban/rural location. When focusing on gender, results show that women earn a lower salary than men (1.20 Nuevos soles per hour, or 13 percent of the average compensation). Results also show that hourly wages are higher for married employees (1.12 Nuevos soles per hour) and people living in an urban location (1.13 Nuevos soles per hour). The explanatory power of the model is in line with that of other standard Mincerian regressions.

The second estimation approach consists in estimating the Mincerian regressions separately for the public and the formal private sector (Equations 2 and 3). To do so, we followed a two-step approach to correct for potential selection bias. In the first step, probit equations are estimated to understand which variables affect the probability of working in the public sector or in the formal private sector. The first probit equation estimates the probability of working in the public sector. Therefore, its dependent variable takes value 1 if the i-th worker is employed in the public sector and value 0 if she is a formal or informal private sector employee (results are shown in the first column of Table 3). The second probit equation follows the same approach. Its dependent variable takes value 1 if the i-th worker is a formal private sector employee and value 0 if she is a public sector employee or an informal private sector employee (results are shown in the second column of Table 3).

**Table 3. Probit Estimation Results**

|**Variables**|**Public Sector**|**Formal Private Sector**|
|---|---|---|
|Secondary Education|0.762***|0.393***|
|Non Completed Post-Secondary|0.458***|0.249***|
|<br>Completed Post-Sec (non University)|0.347***|0.252***|
|Completed Post-Sec (University)|0.674***|0.261***|
|<br>Postgraduate|0.283***|-0.073|
|Age|0.105***|0.092***|
|<br>Age Squared (/100)|-0.091***|-0.120***|
|<br>Female|0.211***|-0.517***|
|Married|0.147***|0.049**|
|Urban|-0.250***|0.461***|
|Large cities dummy|-0.653***|0.416***|
|Observations|3670|4032|
|Pseudo R-squared|0.261|0.185|
|McFadden's Pseudo R2 evaluates the g|oodness-of-fit of the models.||
|<br>*** p<0.01, ** p<0.05, * p<0.1|||

All variables included in Equation 1 affect significantly wage levels. Therefore, they are all potentially relevant to affect the selection of the public/private sector and need to be included in the probit equations (Van der Gaag, Stelcner and Vijverberg, 1987). In addition, we included 11