rafmacalaba/datause-encoder-data huggingface dataset:
use tables derived from Boolean algebra and fuzzy sets analysis<sup>6</sup> to assess interaction effects in small-N studies of (say) the conditions under which revolutions or institutional transitions occur (see Thelan and Mahoney, 2011; and Goertz and Mahoney, 2012).

The more modest objective of this note is to provide an overview of how a systematic integration — of qualitative and quantitative methods what is frequently called taking a ‘mixed methods’ — approach can usefully enrich an impact evaluation, especially as it pertains to assessing the effectiveness of investment climate interventions. It proceeds as follows. Section 2 outlines the distinctive characteristics (and strengths and weaknesses) of qualitative and quantitative approaches, and argues for the importance of assessing empirical findings regarding an intervention’s efficacy against a theory of change. Section 3 considers how the unique insights drawn from qualitative methods can be integrated throughout the different stages of an evaluation. Section 4 provides a framework to denote when qualitative methods are especially useful, focusing on the context of investment climate and private sector development interventions. Finally, section 5 provides an overview of the most common qualitative data collection techniques and provides some concrete examples of how they have been deployed in investment climate impact evaluations.

# **2. Why are mixed-method approaches useful for impact evaluation?**

The main rationale for the systematic integration of qualitative and quantitative methods is that both approaches complement the others’ limitations; this is particularly so with regards to the ‘breadth’ and ‘depth’ of information that together is needed to optimally describe and explain complex phenomena. In this way, integrating qualitative methods in impact evaluation helps reveal the ways in which different causal mechanisms—singularly or in combination—generate observed outcomes and thereby enable evaluators to assess the intervention’s theory of change; i.e., both whether and _how_ impact is achieved (Bamberger et al, 2008). Table 1 below summarizes the key ways in which both approaches are used in data collection, design, and analysis and interpretation.

> 6 Such tables typically array a series of binary variables (i.e., coded 1/0) to assess when particular combinations of otherwise complex factors (e.g., the presence or absence of a financial crisis, dependence on natural resource rents) are associated with specific outcomes (e.g., transitions to democracy).

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generated from local-level efforts to reduce greenhouse gas emissions, or are there potential benefits at multiple scales? If there are benefits at multiple scales, as well as costs at these scales, applying the updated theory of collective action needs to take these into account for analyzing proposed solutions to climate change.

**Are Only Global Benefits Generated from Reducing Greenhouse Gases Emissions?** Greenhouse gas emissions are the result of an extraordinarily large number of actions taken at multiple scales. As mentioned above, decisions within a family as to what form of transportation to use for various purposes, what car to purchase, and what investments to make for power consumption within the home all have small effects on the global atmosphere. Similar decisions within firms are also important, as buildings in general account for “more than 70 percent of the electricity use and almost 40 percent of greenhouse gas emissions in the United States” (Fuller et al., 2009, citing several U.S. Department of Energy reports). Proposals for substantial increases in energy taxes at a national level (to comply with proposed international agreements) are strongly urged as the only way of changing the decisions of individuals and families, as they make decisions based on individual and family household budgets and do not take the external costs they generate into account.

Without shared knowledge about the external costs of these actions, shifts in preference functions to take into account benefits for others, and reduced discount rates, no change at a small scale can be expected. As the scientific community has achieved a higher level of agreement about human impacts on the global atmosphere, knowledge of the effects of individual and family actions is becoming more and more available. Discussions within the family and with neighbors in a community about actions that can be taken locally to reduce greenhouse gas emissions are also important factors leading to the potential for change. Local 14
least five PE financed PIPEs during the 2000-2009 period.<sup>6</sup> Our data on SEOs come from the Security Data Corporation’s (SDC) New Issues Database on equity issues and covers the same countries and the same period as the PIPEs sample. Appendix A documents the sample selection process and filters applied to each database in detail.

While AVCJ and SDC provide transaction specific data such as the issue date and the amount raised, they do not provide accounting or stock price related information about the issuers. We match our sample of PIPEs and SEOs to the Worldscope database using SEDOL as the unique identifier to obtain accounting data and to the Datastream database to obtain stock return as well as trading volume data.<sup>7</sup> Datastream is also the source for country level stock returns. We extract data on the number of analysts covering the firm by matching the combined sample with the IBES database. Appendix B provides detailed definitions for each variable.

We focus on PIPEs and SEOs by industrial companies and exclude transactions by financial companies, insurance companies and real estate entities. To ensure that minimal accounting data is available for the firm in Worldscope, we only keep those transactions that have information available on sales, operating income, total debt, and market capitalization in the fiscal year immediately preceding the issue date. Finally, to make sure that we focus on economically meaningful transactions, we retain only those transactions for which the total proceeds from the equity issue are greater than one million US dollars (in 2008 constant US dollars).

> 6 More countries are covered in the AVCJ database; however, only the nine countries in our sample have a nonnegligible number of PIPE issues after we apply the sample filters described in Appendix A. The countries in AVCJ that we exclude are Malaysia, Philippines, Indonesia, New Zealand, Vietnam, Pakistan, Sri Lanka, and Macau. 7 AVCJ does not report SEDOLs; thus, we hand-match the issuing firm name to firms covered in Worldscope. The SEDOL number associated with the firm is then assigned to the PIPE issuing firm. This SEDOL number is subsequently used for matching with the Datastream and Worldscope databases. The SDC New Issues Database, on the other hand, provides SEDOLs.

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**1**

# I N V E S T I N G A C R O S S B O R D E R S : D O F D I - S P E C I F I C R E G U L AT I O N S M AT T E R ?

“Happy families are all alike; every unhappy family is unhappy in its own way.” – Lev Nikolayevich Tolstoy, _Anna Karenina_ , 1878

# 1.1 introduction

Foreign Direct Investment (FDI) remains one of the most important forms of crossborder capital flow into developing countries: in 2009, FDI inflow amounted to more than US$510 billion, exceeding inward remittance (US$307 billion) and development aid (US$91 billion).<sup>1</sup> As shown in Figure 1, however, just nine countries have accounted for about 60 percent of FDI inflow into developing countries over the past decade.<sup>2</sup> In an era when almost all countries in the world welcome FDI, allowing an average foreign equity ownership of 90 percent across all sectors (Table 22), this cross-country asymmetry deserves explanation that is beyond the obvious such as countries’ sizes and growth prospects.

In this paper, I focus on the policy and institutional determinants of FDI using a new cross-country data set, Investing Across Borders (IAB) 2010 _,_ drawn from World Bank Group (2010). The data set consists of indicators of FDI regulation that

> 1 The figures on FDI, remittances, and development aid are from UNCTAD (2011), World Bank (2010), and World Bank (2011), respectively.

> 2 With the onset of the global financial crisis in 2007, FDI inflow into developed countries fell from a peak of US$1.31 trillion in 2007 to US$602 billion in 2009, whereas the more modest fall in developing countries occurred with a lag of one year, from a peak of US$658 billion in 2008 to US$511 billion in 2009. FDI flow increased rapidly from 1990 onward reaching the first peak of US$1.4 trillion in 2000. There was a dip in 2003, which is attributed to the drop in the share prices of high-technology companies (Helpman 2011). According to UNCTAD (2011), total FDI stock globally stands at a record US$19.1 trillion as of 2010.

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and reported. This has been pioneered by the French government, as described in the box below: _Box 1 The Introduction of Procurement Cards by the French Government_ The French government introduced procurement cards to improve flexibility, and reduce commitment processing, and imprest accounts. The government partnered with a French bank to introduce procurement card services within the framework of a public market. Two types of cards were introduced:

- Level 1 cards: These cards are used for occasional purchases, such as a shop or online purchases, that can be made outside public markets. They can only be used with registered vendors.

- Level 3 cards: These are used for purchases made within the framework of registered public markets. They require software implementation on the vendor side, so as to transmit transaction information to the partner bank.

A limit of EUR 2,000 per transaction is applied that spending units can purchase up to. The cards may be regarded as “credit cards”, as the vendor is paid immediately by the Bank and the Bank gets reimbursement from treasury on a monthly basis. All card transactions are aggregated monthly by service, to generate a “record of bank transactions”. These records are sent electronically to the FMIS (Chorus), where they generate payment requests. All payment requests are checked and approved by services, to generate accounting records, and the refund payment to the bank.

A challenge in implementation is that there are limited numbers of points of sale. Vendors must be registered (levels 1 & 3) and must implement software to transmit public market references (level 3 only). Verification and approval tasks can be time consuming and may result in delays in processing the refund payment to the bank.

_Source:_ Ministère de l’Action et des Comptes Publics – Agence pour l’Informatique Financière <u>de l’Etat.</u> In many countries this type of flexibility is de facto already in place as they use manual or excel based systems, allowing them to capture and control spending ex-post but without the integration between the payment process and the recording process. There is thus little guarantee of the integrity of the expenditure data. An example of this is Malawi, where the lack of integration has undermined confidence in the system entirely (World Bank 2016a). With the increased penetration of the banking sector, an innovative approach integrating spending, recording, and the FMIS is possible, which would facilitate the same level of flexibility used in a manual system, while capturing all transactions and thereby guaranteeing good expenditure information. Using such an approach therefore would allow a shift toward the eastern quadrants where spending units have adequate flexibility, whist maintaining financial accountability. Such an approach will implicitly also lead to greater facility autonomy and should be recognized through an explicit delegation of power.

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two lines. First, we conduct standard two-group comparison tests and assess whether statistically significant differences exist between the relevant bank groups (e.g. state versus private, domestic versus foreign, listed versus non-listed). These statistical tests provide useful initial insights into how ownership and bank performance are associated, although they have to be interpreted with care, because bank performance is determined by many factors in addition to ownership.

Therefore, in a second step we turn to bank-level multivariate panel regression analysis in which we analyze ownership while simultaneously controlling for various bank characteristics. For our regressions we employ simple Ordinary Least Squares (OLS) on pooled annual bank data for the period 2001-08. Throughout our regressions we also include country- and time-fixed effects to mitigate omitted variable bias. By introducing these fixed effects we aim to control for general country conditions to which the banks are exposed throughout the sample years.

We also account for the possibility that the results could be driven by countries that have a larger number of banks. Therefore, we also conduct regressions where each bank-year observation carries a weight that is inversely proportional to the number of banks in its banking system in that particular year. We also relax the independency and homoskedasticity assumptions that are required by OLS by reporting three standard errors variations: 1) Huber/White robust standard errors and robust standard errors corrected for possible intra-group correlation for which we consider 2) a bank- or 3) a country-level grouping of bank-year observations<sup>7</sup> .

Most of our data are taken from Fitch’s Bankscope database and include unconsolidated statements of commercial banks in MENA. The sample roughly comprises 600 bank-year observations of about 120 banks in 9 countries for the period 2001-08. As noted before, our sample consists of banks in Algeria, Egypt, Jordan, Lebanon, Libya, Morocco, Tunisia, Syria, and Yemen. Table 3 provides an overview of variable definitions and their sources. Table 4 shows pairwise correlations. Appendix A shows the number of banks for each country by year.

Our main dependent variables can be grouped into four clusters. First, we consider _general profitability and interest-related_ factors. We investigate the standard profitability indicators Return on Assets (ROA) and Return on Equity (ROE). We use the Net Interest Margin (NIM) to 7 Results for country-level groupings are not reported but are available from the authors upon request. 9
Table 5: **Heterogeneity: Prevailing news sentiment and numerosity of migrationrelated news**

||Share <br>who|of migrants i<br> are long-sta|n FMP<br>yers|
|---|---|---|---|
||(1)|(2)|(3)|
|Tone of the news at destination (during months when)||||
|_Prevailing news sentiment is_||||
|Mostly negative|0.0070||0.0072|
||(0.0242)||(0.0243)|
|Same number of negative and positive news|-0.0254||-0.0240|
||(0.0233)||(0.0235)|
|Mostly positive (and)|-0.0573*|||
||(0.0324)|||
|Many news about migration|||-0.0777**<br>(0.0383)|
|Few news about migration|||-0.0070|
||||(0.0493)|
|_Among published news articles_||||
|Many news about migration||-0.0607**<br>(0.0246)||
|Few news about migration||-0.0048||
|||(0.0198)||
|Other controls|Yes|Yes|Yes|
|FMP FEs|Yes|Yes|Yes|
|Year-month FEs|Yes|Yes|Yes|
|Country of destination FEs|Yes|Yes|Yes|
|Country of origin FEs|Yes|Yes|Yes|
|Number of Observations|1,568|1,568|1,568|

_Notes:_ OLS regression results. Standardized OLS estimated coefficients are reported. Standardization of coefficients is obtained using the formula<sup>_sd_</sup> _sd_<sup><u>(</u></sup> (<sup>_x_</sup> _y_ )<sup><u>)</u></sup><sup>_βx_,where</sup><sup>_βx_isthepointestimateassociatedtocontrolvariable</sup><sup>_x_,while</sup><sup>_sd_(</sup><sup>_x_)and</sup><sup>_sd_(</sup><sup>_y_)</sup> indicate the standard deviation or respectively control variable _x_ and dependent variable _y_ . Standard errors clustered at the FMP level in brackets. The dependent variable _Share of migrants in the FMP who are long-stayers_ is the share of migrants in the FMP _i_ in month _m_ in year _t_ who report being in that location for more than 6 months. The variable _Tone of the news at destination_ ( _Tone News Destinationsi,m−_ 1 _,t_ ) is the average tone of the migration-related news articles published in the top destination countries for migrants in FMP _i_ in month _m −_ 1 in year _t_ .“Other controls” include: _Intensity of night lights within 5 km_ ; _Number of conflicts within 5 km_ ; _FMP with migrants planning to remain in Libya (1 = Yes)_ ; _FMP with migrants planning to return home (1 = Yes)_ . All variables are defined in Tables A1 and A2 and additional details are provided in Section 4. *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

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are good proxies for the firm’s access to information and external finance, measures of the degree of openness and technological innovation, measures of the human capital composition of the workforce and on the perceptions regarding the investment climate. The availability of several worker and firm characteristics will allow us to analyze the role of different factors in explaining the correlation between the investment in job training and firm size.

Unfortunately, survey limitations also affect the scope of the analysis behind our control. First, in most countries the surveys are representative of the formal sector, and particularly of the manufacturing sector. Since in developing countries the informal sector can reach more than half the workforce, this will naturally limit the representativeness of the analysis to the informal or non-manufacturing sectors. Still, we expect most of the job training taking place in the informal sector to be more of the type of learning-by-doing rather than formal training programs (see Johansen and Van Adams, 2004). Second, the surveys collect only information on formal training programs, leaving undocumented any informal job training taking place while on the job (e.g., learning by doing).<sup>2</sup> Therefore, we will likely underestimate the overall investment in job training, particularly among the smaller enterprises, where informal training is likely to be more important (e.g., Frazer, 2006, Velenchik, 1995, Teal 1996, Monk et al, 2008).<sup>3</sup> Nevertheless, our focus on formal training programs is still economically and policy relevant.

The paper documents several interesting findings. First, we find robust evidence of a large and statistically significant positive correlation between firm size and the investment in job training. In particular, we find that _small_ (11-50 permanent employees), _medium_ (50-250 employees) and _large_ firms (more than 250 employees) train approximately 13, 30 and 40 percentage points more than micro firms (with 10 or less

> 2 The job training supplied by the firm does not necessarily take place during the normal working period nor is it necessarily accredited. The _Enterprise Surveys_ explicitly refers to “formal programs” in the survey questionnaire does ruling out any form of learning by doing. Since this is employer provided training, we do expect that training benefits both the firm and workers simultaneously. Otherwise, one of them would prevent form engaging in this investment.

> 3 In developing countries, training in the formal manufacturing sector takes different forms. First, the technical and vocational training; Second the apprenticeship system, which is mostly informal and has more relevance in Western Africa and; Third, any other formal manufacturing sector training still carried out within firms. In spite of the importance of all these strategies for skills development there is little research in each of these topics. This paper contributes to this literature by focusing on the third channel.

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