rafmacalaba/datause-encoder-data huggingface dataset:
Source: Own computations using June 2010 (EFSA II) surveys. Notes: The sample is restricted to the households residing in the six strata that cover areas directly affected by the earthquake. *Displaced persons after the earthquake.

Table 8 presents the characteristics of host households in the non-directly affected areas. After the earthquake, the percentage of households who hosted displaced people is 24.5% among the poorer and 63.8% among the richer. The average number of people hosted is 3.2 on average and does not vary much according to household wealth. Most of them are adults (2.3 out of 3.2). In June, about half of the hosted people were still living in host households.

**Table 8. Host households, by pre-earthquake quintile of wealth**

||||||Amonghost|households<br>|<br>||
|---|---|---|---|---|---|---|---|---|
|Pre-<br>earthquake<br>wealth<br>quintile|Number of<br>households|% Host<br>households|Number<br>of<br>persons<br>hosted*<br>(mean)|Number<br>of<br>children<br>hosted<br>(mean)|Number<br>of adults<br>hosted<br>(mean)|Number<br>of elderly<br>people<br>hosted<br>(mean)|Some<br>hosted<br>people<br>still<br>remain|All<br>hosted<br>people<br>still<br>remain|
|Poorest|151|24.5|3.3|1.3|2.0|0.0|8.1|27.0|
|2|109|26.6|3.1|1.1|2.1|0.0|3.4|27.6|
|3|130|36.2|3.6|0.7|2.7|0.1|23.4|19.1|
|4|130|60.8|3.2|0.9|2.2|0.0|20.3|44.3|
|Richest|130|63.8|3.2|0.7|2.4|0.0|19.3|43.4|
|Total|650|42.3|3.2|0.9|2.3|0.0|17.1|35.6|

Source: Own computations using June 2010 (EFSA II) surveys. Notes: The sample is restricted to the households residing in the three strata that cover non-directly affected areas. *Hosted persons after the earthquake.

# _(h) Emergency assistance allocation_

Table 9 (in Appendix) shows the percentage of households receiving assistance. In directly affected areas, 43.9% of households benefited from assistance, with little variation according to wealth quintile, except for the poorest: only 38.2% of them received assistance. On the contrary, in non-directly affected areas, the percentage of households benefiting from assistance is higher among the poorest households (12.0%) than among the richest ones (3.1%). One factor that may explain the difference in assistance allocation between the richest and the poorest is the probability of experiencing an asset loss, which was higher for the richest than for the poorest households. However, although government and agencies may have compensated households according to their losses, they should have also targeted with priority the less wealthy households. Also, in the early weeks after the earthquake, assistance programs may have faced difficulties when it came to efficiently targeting households, or they may simply have chosen not to do so.

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to respond the survey.<sup>37</sup> For Senegal, we explore whether after controlling for observable characteristics, there are significant differences in average technology sophistication in GBFs between firms that required a larger number of attempts to be contacted (top quartile) and those that did not. Table A.19 shows that there are no statistically significant differences in technology sophistication between the two groups.

Table A.19: Comparison of technology sophistication between high and low number of attempts

|VARIABLES|Senegal|Senegal|
|---|---|---|
|Top quartile of attempts (4 or more)|-0.021<br>(0.020)|-0.027<br>(0.019)|
|Observations|1,753|1,666|
|_R_<sup>2</sup>|0.377|0.437|
|Controls:|||
|Sector FE|Y|Y|
|Size-group FE|Y|Y|
|Region FE|Y|Y|
|Age||Y|
|Exporter||Y|
|Foreign owned||Y|

Note: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Data are from the Senegal FAT survey with information on the number of attempts to complete interview at the firm level. Technology sophistication is regressed on a dummy for the top quartile of the number of attempts (4 or more) with controls for the stratification (sectors, size groups, and regions) and/or firm characteristics (age groups, exporter, and foreign owned). Robust standard errors in parenthesis.

Third, we compare firms that were in the first sample list provided to enumerators and those in subsequent lists. Table A.20 show that there are no statistically significant differences between the two groups.

In each of these exercises, we find no statistical difference in the number of employees, technology sophistication, wages, and share of workers by skill and education between firms in the group that proxies for the response sample and the group of firms that proxies for the non-response sample.<sup>38</sup>

**Minimizing enumerator bias** . To minimize the potential for enumerators to introduce biases when administering the survey, we conduct in each country the same standardized training and piloting prior to going to the field. We also conduct ex-post tests to identify

> 37Behaghel et al. (2015) infer the reluctance to participate in the survey from the number of attempts that it take for a firm to accept the request.

> 38See Table A.18 to A.24 in Appendix A.

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literature is sometimes inconclusive. In Argentina, for instance, Galiani and Sanguinetti (2003) do not find a positive association between tariffs and wages (though they do find a significant association with import penetration measures). Currie and Harrison (1997) and Harrison and Hanson (1999) are other examples where tariffs show up insignificant in wage regressions. In Attanasio, Goldberg, and Pavcnik (2004), on the other hand, tariffs have a significant impact on the industry premia and overall wages, and in Revenga (1997), real wages are also found to be affected by tariffs.<sup>8</sup> This finding helps to explain part of the decline in the labor share of GDP that took place in Argentina during the late 1970s. Galiani and Gerchunoff (2003) report that, after the liberalization of the late 1970s, the share of labor income on GDP dropped from 45 percent to around 30 percent. Moreover, they show that this share never recovered to the historical levels observed up to the pre-seventies. The tariff elasticities reported in column (3) of Table 1, for instance, suggest that a one percent increase in the average tariff can cause average wages to increase by roughly 0.35 percent. These estimates imply that the liberalization of the 1970s, when tariffs were reduced by 70 percentage points, could have reduced wages by 24.5 percent, a decline that can account for an important fraction of the observed drop in the labor share.

# 3.1 Reforms and the Skill Premium

In this section, we examine an empirical model where tariff reforms can have an impact of the skill premium. We estimate the following model: This model differs from (1) in that we add interaction terms between the trade policy variable (the log of tariffs, ln τ ) and the educational attainment dummies (dSigjt). The coefficients of these interactions, φg, can be interpreted as the differential impact of trade on the wage of

> 8The literature on this topic is very rich. Our review of the evidence is necessarily short, to avoid distracting attention from the main results of our paper. There are very good surveys on the trade-wages link, including Goldberg and Pavcnik (2005b) and Winters, McCulloch, and McKay (2004).

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8 investing across borders: do fdi-specific regulations matter?

framework. And they associate FDI inflow with _general_ quality of institutions, not quality that is _specific_ to FDI.<sup>11</sup> Bergstrand and Egger (2009) summarize the vast literature on the theoretical and empirical foundations of gravity models of trade and FDI. I am, however, not aware of any paper that addresses the three problems mentioned above in a study of the institutional determinants of bilateral FDI. As already mentioned, I use new data on regulations that are specific to FDI and the new empirical methodology that has been applied to export flows by Helpman et al. (2008).

Central to the paper’s methodology is the exploitation of the presence of zero flows between trading partners to estimate productivity cut-offs of firms that export abroad profitably. In trade, zero flows inferred from databases like the United Nations Commodity Trade Statistics Database (COMTRADE) are flawed because unreported figures are conflated with confirmed zeros. Baranga (2009) estimates that roughly 20 percent of the sample used by Helpman et al. (2008) has been misclassified. In contrast, the database – OECD (2011) – that I use for FDI clearly distinguishes between missing and zero flows between investment partners. This greatly enhances the appropriateness of the use of the new methodology in this paper.

# 1.3 model

The model is adapted from the gravity-type trade model of Helpman et al. (2008) and Anderson & Van Wincoop (2003). The derivation of the model – for FDI – is detailed in Appendix A. A representative consumer prefers variety (v) and maximizes a Constant Elasticity of Substitution (CES) utility function U = [� x(v)<sup>ρ</sup> dv]<sup>1/ρ</sup> subject to aggregate expenditure (E). All varieties have a constant demand elasticity, σ = 1−1ρ<sup>>1.Afirmincountryiproducesoneunitofoutputwitha</sup>

> 11 One exception is Wei & Shleifer (2000), but they acknowledge their coding of incentives and restrictions on FDI to be coarse and limited to less than 50 countries.
wet and dry areas. The differences are significant and pronounced. For example, the SD of rainfall that poor households in wet areas experience is 17% higher compared to the rich (above median) households. For temperature variability, the picture is mixed: in dry areas the poor have experienced less variability, while they have experienced more in the wet areas.

# **4.2 Weather anomalies**

Our second research question is: _Is exposure to large weather anomalies in the survey year related to lower household income, and how does exposure to anomalies vary across poverty categories?_ To address this, we first look at the relationship between income and rainfall in the survey year. Figure 1 presents a distinct bell-shaped relationship.<sup>14</sup> The relationship is robust, also after controlling for other factors, such as regions (see also discussion in Noack et al., 2015).

The poorest villages are found in driest areas, but these areas also have a huge variation in average household income. The peak income is at around 2,000 mm/year, after which mean income tends to decrease.

**Figure 1: Relationship between rainfall and mean observed income, at village level**

Next, we investigate whether exposure to weather anomalies (during the survey year) had a negative impact on household income. How is exposure to weather anomalies correlated with whether households experienced lower or higher incomes than predicted? In particular, can more exposure to weather anomalies help explain why some asset-rich households move below the poverty line and become income poor? In other words, were the _income poor & asset rich_ more exposed to weather anomalies than the _income & asset rich_ ? Similarly, could _less_ exposure to weather anomalies help explain why some asset-poor households move above the poverty line? In other words, were the _income rich & asset poor_ less exposed to weather anomalies than the _income & asset poor_ ? As noted earlier, there are possible mechanisms (e.g., favorable access to forest resources) that can pull the

> 14 The graph is generated by estimating the simple polynomial relationship between rainfall and mean income in the villages during the survey period (i.e. observed, not predicted income). It is generated using the _fpfit_ command of Stata (fractional-polynomial prediction).

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- 10-

efficiency gains resulting from financial innovation and improvement in corporate governance associated with contractual savings development are likely to foster growth. In addition, while maximizing the risk adjusted rate of returns on managed funds, contractual savings institutions diversify their portfolio internationally, thus improving the allocation of capital on a global level, which, in turn, may favor growth in developing economies. Finally, faster growth, inducing a higher savings rate, would further promote growth in a virtuous cycle.<sup>20</sup>

# **_The international evidence_**

We now turn to study the behavior of contractual savings and non-life insurance institutions' portfolios in some OECD and developing countries. Figure 2 shows the importance of contractual savings around the world in terms of financial assets over GDP in 1996. In countries with long time reformed pension systems and important national provident funds, contractual savings have accumulated large amounts of financial assets (some of them higher than 100 percent of GDP). In countries that have not yet or have recently reforrned their pension system, contractual savings are less developed. In addition, in countries where enterprises carry important book reserves on their balance sheets against liabilities resulting from mandated or promised benefits to workers, have not developed their contractual savings sector (e.g., Germany, Italy, Austria, Korea) as countries of comparable level of economic development have.

**Figure 2: Contractual savings financial assets (GDP %,1996)**

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**Source:** 1998 OECD Institutional Investors Statistical Yearbook and WB institutional investors database.

Other institutional investors, like non-life insurance companies, mobilize a considerably smaller volume of savings. Figure 3 shows that even in countries with 20 For discussions on the impact of capital market development on growth see, for example, Levine and Zervos (1996), and Levine (1997).
Even though the deployment of mixed-method approaches has been increasing in economic development impact evaluations, most notably in health, to date relatively few impact evaluations can be identified as truly using a mixed-method approach. For example, only three percent of 3ie’s portfolio has used a mixed-method approach<sup>10</sup> , and neither J-PAL nor World Bank databases formally record whether mixed methods were used in a given evaluation. At present the systematic integration of qualitative and quantitative methods in the evaluation of investment climate related topics is rare. In the following sections we provide some examples of how qualitative methods have been deployed in each stage of the standard evaluation cycle. Although these studies did not use a systematic integration of methods, they are useful to showcase the value-added of qualitative methods in specific stages of the evaluation. We reiterate that, ideally, the most valid and useful findings are likely to emerge when both qualitative and quantitative methods can be integrated at different stages, enabling their systematic combination to exploit the strengths (and minimize the weaknesses) of using one method alone.

# **3. The purpose and value added of integrating qualitative methods at various stages of an impact evaluation**

This section seeks to illustrate the purposes of a mixed-method approach and how qualitative data collection and analysis might be useful in each stage of an impact evaluation.

## **3.1 Purposes of a mixed-method approach**

The examples below illustrate how qualitative analysis and data collection can be integrated and add value to each stage of an evaluation. When, how and to what extent they are integrated depends on the objective of the research and the type(s) of purposes the evaluation will serve. Four main purposes for a mixed-method approach are identified in the literature (see Greene, et.al. 1989, Shaffer 2013):

> 10 Better Evaluation Blog, August 2013. “Mixed methods in evaluation Part 3: Enough pick and mix; time for some standards on mixing methods in impact evaluation.” http://betterevaluation.org/blog/mixed-methods-part-3

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2

# **1. Introduction**

Households, enterprises and governments demand financial services to reallocate consumption and investment across different time periods and different states of the world. The fact that the financial sector is one of the oldest service sectors in human history suggests that the demand for (i) payment, (ii) savings, (iii) credit, and (iv) risk management services is a fundamental characteristic of exchange-based economies.<sup>1</sup> Micro-evidence in the form of financial diaries (Collins et al., 2009) has shown that even the poorest of the poor have demand for financial services, even though this demand is satisfied mostly by informal arrangements.

In spite of widespread demand for financial services, there is a high variation in the range and depth of financial service provision across countries. This paper introduces the concept of the possibility frontier, which is the constrained optimum of financial development in an economy, as diagnostic tool to assess the gap between actual provision of financial services and demand from the real sector and identify bottlenecks that prevent further financial deepening of financial institutions and markets, on the one hand, and mitigate risks of overheating in banking or capital markets, on the other hand. We will use a cross-country benchmarking exercise to illustrate the application of the frontier concept. We will then suggest several specific applications of the frontier concept. Specifically, we will assess the performance of i) the transition economies in the 1990s and 2000s, ii) illustrate the assessment of SME finance with firm-level data and iii) use the example of Egypt to document the use of different data sources to gauge the development of its financial system.

> 1 For just one example of historic financial arrangements and their development during the Roman empire see Malmendier (2009).
# **4.3. VAT rebate rate and other data**

The VAT rebate rates data are from the State Administration of Taxation. The data are available at the tariff-line level. There are many rate changes in the middle of a year. If a product has multiple rates in 2005, we use their average rates weighted by the number of days during which each rate applies. The more detailed information at levels higher than HS 8-digit is averaged first to the HS 8-digit level. The major VAT rebate rates (r) in 2005 are 0% (3.87%), 5% (9%), 13% (72.3%), and 17% (7%), with the shares of the HS 8-digit product lines under each rebate rate in parentheses. The corresponding figures for statutory VAT collection rates (t) for normal tax payers are 13% (13.3%) and 17% (84.3%). The major rates for the tax net of rebate (t-r) are 0% (7.5%), 4% (70.5), 8% (11.1%), and 17% (3.4%). In line with the WTO rules, the VAT rebate rates are always no higher than the collection rates.

The data used in our country-product level and transaction level analysis will be described in Sections 5.2 and 5.3 respectively. Other variables used in the robustness checks will be discussed in Section 6. Appendix 2 lists some descriptive statistics of the variables used in the paper.

# **5. Empirical results**

# **5.1. A product level analysis**

Table 1 reports the baseline regression results at the HS8 level, using the conservative measure of indirect export share as the dependent variable. Because our key variable of interest, (t-r), is at HS 8-digit level and we have the data only for year 2005, we cannot include HS8 product fixed effects. Nevertheless, we always include HS 4-digit level fixed effects to control for any unobserved heterogeneity at HS4 level (about 1300 products).<sup>31</sup> In assumes that the ownership structure of the products exported indirectly is the same as that of directly exported products. If this assumption is correct, then the ownership shares should not affect the indirect export shares and hence should not be included in the regressions in the first place. If the production structures are different for directly and indirectly exported products, then using the ownership based only on direct exporters will also be problematic. This is why we continue to calculate ownership shares based on total exports. Dropping these ownership variables from our regressions does not affect our main findings at all.

> 31 We do not use HS6 fixed effects because only about 8% of the HS6 products lines have more than two HS8 lines. Including HS6 fixed effects would be too demanding because they would absorb most of the variations in (t-r).

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