rafmacalaba/datause-encoder-data huggingface dataset:
perform savings operations.<sup>32</sup> In the later case, we use a dummy variable that equals one if financial services are offered in the post offices and are handled by a separate private operator and zero otherwise. These variables are from the _Financial Access Database (2009)_ . Although the signs are as expected, none of these policies are significantly associated with broader access.<sup>33</sup> This evidence, however, is not enough evidence to suggest that these policies do not matter.

# **_5.2 Credit_**

Table 21 presents the regressions of those factors associated with loan penetration, in which the dependent variable is (the logarithm of) the number of outstanding loans in commercial banks per thousand adults in 2008. In addition to costs, the outreach in the supply of credit (unlike the supply of deposits) is constrained by risks, in particular default and agency risks. Therefore, the penetration and quality of credit depends not only on the economic conditions, but also on the financial infrastructure available to manage these risks.

We start by analyzing the relationship between measures of economic and development conditions and loan penetration. In the first regressions, we only include (the logarithm of) per capita income and (the logarithm of) population density as independent variables. Not surprisingly, GDP per capita explains a large part of the cross-country variation. Since population density is not statistically significant, in subsequent regression, we only include (the logarithm of) GDP per capita as control.

In terms of structural factors, inequality and inflation appear in the regressions with a negative sign, but neither is statistically significant. Columns (5) - (7) parallel the results of the previous sub-section, namely that better physical infrastructure is positively and significantly associated

> 32 We tried an alternative specification using a dummy variable coded one if the country allows the use of agents. The coefficient is not statistically significant. These variables, however, measure regulations at a very high level. Interviews with policymakers show that these regulations often contain detailed provisions, making implementation difficult and reducing uptake suggesting that more granular data on regulations are necessary to draw conclusions. 33 Allowing private entities to provide financial services through postal agencies enters significantly if we do not control for population density.

31
14 Hence, the effective storage is the gross storage minus the dead storage. It can be calculated from the following formula:

# Effective Storage (MCM) = MAX{Gross Storage (MCM) – Dead Storage (MCM),0}

To construct the index, the effective storage from the relevant major tanks is recorded on the 1<sup>st</sup> and 15<sup>th</sup> of every month from November to March to cover the Maha season. The normal monthly effective storage levels are specified for each tank. (The storage level data is also available on a daily basis for some tanks.) For example, for Pavatkulam tank in Vavuniya, the normal monthly effective storage levels, based on seven years of recorded data from 2011 to 2017, are as follows:

**Table 1. Normal Monthly Effective Storage Levels – Pavatkulam Tank**

|**Month**|**Nov**|**Dec**|**Jan**|**Feb**|**Mar**|
|---|---|---|---|---|---|
|**Effective storage**<br>**(MCM)**|9|16|19|17|14|

Source: Author constructed, based on MCM data from Irrigation Department of Sri Lanka.

The effective storage level of major tanks can be used as a parameter for more accurately identifying yield stress for farmers with access to irrigation from them. The indexed pay-outs would then only take place in a situation where adverse weather conditions have occurred while tank storage levels were also low in the same season. For example, if the actual average monthly effective storage level is less than a threshold level (e.g. less than 90 percent of the normal monthly effective storage level) that month can be considered a ‘low tank level’ month. Depending on the crop and its reliance on irrigation, it is possible to estimate how many such ‘low tank level’ months will result in crop yield stress. The indexed pay-outs can be structured accordingly.

The table below sets out different scenarios, noting which ones would lead to pay-outs and which would not, using both the number of ‘low tank level’ months and the rainfall index.

Column A represents the number of months (over November to March) during which the average monthly effective storage level is below a specified threshold (i.e. the number of Low Tank level months).

Column B represents whether a dry spell event has been triggered (or not) based on the rainfall conditions as measured by weather stations or rainfall gauges. Typically, dry spell events are for
where  _it_ represents a firm's TFP known only to itself and  _it_ is an iid error (see, e.g., Arnold 2005). Since  _it_ is correlated with the choice of labor and capital inputs, ordinary least squares (OLS) estimation of (5) _inter alia_ suffers from the so-called simultaneity bias (or transmission bias), leading to inconsistent estimates of  ,  and log _Ait_ , respectively.

To successfully cope with the simultaneity problem when estimating TFP at the firm leve,l the econometric literature offers various methodologies.<sup>11</sup> In case that the firm-specific systematic component in the error term is time-invariant (i.e.  _it_ =  _i_ ), standard fixed-effects estimation would allow to consistently estimate  and  and thus, TFP would be accurately measured. However, in case of low within-firm variation the parameters of the production function would be only weakly identified and in case of  _i_ varying over _t_ the fixed-effects estimates would also be inconsistent.

Olley and Pakes (1996) and Levinsohn and Petrin (2003) developed alternative (semi-parametric) estimation procedures, which explicitly deal with the simultaneity bias when estimating production functions. The former utilizes a firm's investment decision to proxy for differences in  _it_ , while the latter one proposes the use of intermediate inputs in order to consistently estimate the production function. The Olley and Pakes (1996) estimation procedure can only be applied to firms with non-zero investments and therefore this approach typically excludes a large number of firms. In contrast, the Levinsohn and Petrin (2003) approach can be applied to all firms with a non-zero demand of intermediate inputs such as e.g., materials.

# **4.2  Estimation results for total factor productivity**

> 11For a recent survey on the estimation of TFP at the macroeconomic and the microeconomic level, see Del Gatto, Di Liberto and Petraglia (2011).

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# **1. Introduction**

Practically nonexistent a decade ago, blogs by economic scholars have become commonplace. Economics blogs, such as Freakonomics, Marginal Revolution, Paul Krugman and Greg Mankiw, have built large followings – whether measured by subscriptions in Google Reader or by average daily page views.<sup>1</sup> Cowen (2008) argues that blogs are the main way that the general public consumes economics in a given day and guesstimates that “...about 400,000 people are reading economics blogs and digesting them” on a daily basis. These blogs not only give their creators an outlet to disseminate their ideas and work immediately in a format that is more accessible, but also enable instant feedback, are easy to share on the open web, and allow the bloggers a personal style rather than the inaccessible format of academic journals (Glenn, 2003; Dunleavy and Gilson 2011).

Our motivation in examining the impact of economics blogs stems from two observations about blogs and questions that arise from these. First, it seems fair to state that “...informing is the core business of blogging.” (McKenna and Pole 2008, p. 102) This leads to the question of whether blogs improve the dissemination of research findings and whether their readers are indeed more informed. On the one hand, coupling the large readership of blogs with the argument of Cowen (2008) that the best ones are written at a level far higher than that of any major newspapers offers the promise that economics blogs may have sizeable effects on the dissemination of economic research and on the knowledge and attitudes of their readers.<sup>2</sup> On the other hand, Sunstein (2008) argues that the

> 1 As of July 2011, Paul Krugman had more than 56,000 subscribers in Google Reader, Mankiw more than 40,000, Freakonomics almost 7,000, and Marginal Revolution more than 4,000. Average daily page views for Marginal Revolution was 36,000 and for Mankiw 13,500 in June 2011 according to SiteMeter.

> 2 In this sense, economics blogs can serve a similar function to traditional media. For example, Phillips et al. (1991), taking advantage of a natural experiment, shows that articles in the New

2
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# **6. Conclusions**

The main point of this discussion is that these efficiency indicators are not just simple "academic" exercises but that they can be used to yield results with very direct applications in the regulatory process.

The results presented here stress the advantages of relying on efficiency frontiers over the usual aLternative options in the process of implementing yardstick competition. This methodology allows to control for factors over which the operators have no control (i.e. diversity of water sources or of water quality, characteristics of users). For instance, Dhaka is ranked first if the stochastic frontier is estimated using CLS. It is ranked 43 and 41 respectively, relying on more traditional indicators such as account receivable or numbers of workers per 1000 connections.

As for the impact of ownership on the efficiency of operators, both the CLS and the ML models show that the private operators are more efficient. In monetary terms, the total gains for the region of an efficiency level similar to the one achieved by private operators would be around US$85 millions per year."<sup>2</sup> This means that the gains of every new concession would be on average of around US$1.8 million per year.

# _ACKNOWLEDGEMENTS_

_We are grateful to Ian Alexander, Phil Burns, Claude Crampes, Severine Dinghem, Frannie Humplick and Martin Rodriguez-Pardina and for useful extensive discussions on the challenges of efficiency measurements for regulators and comments. Any mistake is ours and besides, this paper does not engage in any way the institutions we are affiliated with._

**12** This number was calculated using the coefficient of the dummy variable in the ordinary least square regression.
- 5 -

The remainder of the paper is organized as follows: the next section briefly reviews related literature. Section 3 describes the data and the methodology. The results of the empirical estimation are in Section 4. Section 5 concludes.

# **2. Literature Review**

In their empirical studies of bank crisis determinants, Demirgüç-Kunt and Detragiache (1998, 2002) find that countries with better institutions are less likely to experience banking crises and are less affected by moral hazard due to deposit insurance. They interpret institutional variables such as rule of law and quality of bureaucracy as proxies for supervision and regulation, but do not include direct measures of this dimension, which were not available at the time.

Barth, Caprio, and Levine (2001) (BCL) assembled the first extensive cross-country database on the characteristics of the supervisory and regulatory framework. The data come from a survey of bank supervisors, and measure the presence or absence of a series of regulatory features. In the first comprehensive theoretical and empirical study of alternative approaches to bank supervision, BCL (2004) find that regulatory and supervisory practices that force accurate information disclosure, empower private sector monitoring of banks, and foster incentives for private agents to exert corporate control work best to promote bank performance and stability. Specifically, in a cross-country setting they show that regulatory and supervisory regimes with these features have suffered fewer crises in the past two decades, have lower non-performing loans, and have deeper credit markets.

One limitation of this study is that survey information reflects whether laws or regulations are on the books, but not to what extent they are implemented in practice.
To shed light on the potential drivers of FDI for this region, we compare SADC with two groups of developing countries with higher FDI per capita. The factors that account for SADC’s lower FDI inflows are economic fundamentals: previous growth rates, income, phone density, and the adult share of the population. Interestingly, while income and infrastructure do not matter as much in SADC as in the rest of the world, openness matters more. This is consistent with the view that FDI to small countries is more export-oriented.

The next section discusses firm-level evidence about how foreign ownership affects firm behavior and performance. Section three relies on country-level panel data to understand what determines FDI inflows, and what explains the FDI performance of the SADC region. Section four concludes.

# **2. Micro Consequences of Foreign Ownership in SADC**

If foreign investment supports development, its effects should show up in firm performance or some indicators of "good outcomes", such as export orientation and spillover effects on domestic firms.

_A priori_ it is unclear whether the advent of foreign-owned firms improves firm performance in SADC. SADC might feature differences in the nature of FDI as well as in the complementary factors (such as business environments) for FDI to work its magic. As the domestic markets in SADC tend to be too small (except for a few countries such as South Africa) for R&D investment to make economic sense, technological spillover from R&D of foreign companies (conducted in headquarter countries of multinational companies) may improve local productivity. Moreover, capital shortages or financial constraints may limit firm size, making foreign firms more productive.

To investigate this issue and to provide a rationale for whether FDI has led to real economic benefits to local economies, we investigate how foreign ownership is related to firm performance, as measured by sales growth and total factor productivity, which capture dynamic and static firm performance, respectively. Productivity should capture 4
April 2018 and April 2019 (i.e., before and after the pilot was implemented), in our placebo experiments we compare them between April 2017 and April 2018 (i.e., one year before the implementation of the ENTRE pilot). If our main experiment is just picking up pre-existing trends, these trends should be also captured by our placebo experiments. Tables 5 and 6 show that the coefficients of the placebo regressions are all quite small in magnitude and statistically insignificant, providing strong evidence against pre-existing trends. This is further illustrated in figures 3 and 4.

Table 5. Placebo test of the impact of the program on the probability of student dropout

||<br>ITT<br>|
|---|---|
||Experiment<br>Placebo|
|**_Panel (i): Pooled Model_**||
|_ENTRE j_|-0.000<br>-0.013***|
||(0.000)<br>(0.004)|
|Observations|147,977<br>145,628|
|R-squared|0.351<br>0.331|
|**_Panel (ii): Treatment arms_**||
|_ENTRE 1 j_|-0.000<br>-0.015***|
||(0.000)<br>(0.005)|
|_ENTRE 2 j_|-0.000<br>-0.011**|
||(0.000)<br>(0.005)|
|_ENTRE 3 j_|0.000<br>-0.013**|
||(0.000)<br>(0.005)|
|Observations|147,977<br>145,628|
|R-squared|0.351<br>0.331|
|Controls at student level|YES<br>YES|
|School fixed effects|YES<br>YES|

Notes: 1) Placebo: estimates using data from 2016 and 2017, when the ENTRE program did not exist. Experiment: estimates using data from 2017 and 2018 (year of the intervention) and the preferred specification in Table 3. 2) Standard errors clustered at schoolyear level in parenthesis; 3) *** p<0.01, ** p<0.05, * p<0.1. 4) The dependent variable is a dummy variable that is equal to 1 if student _s_ in school _j_ dropped out school between year t and year t+1. 5) _ENTREj_ represents the interaction _Entrejt_ x _Postt_ where _Entrejt_ : (1= Student _s_ attends a school _j_ in year _t_ that is in the treatment group) and _Postt_ : (1 = year 2017 in Placebo; = year 2018 in Experiment; 0 otherwise); _ENTRE ij_ with i=1,2,3 captures the same interaction as _ENTREj_ but using assignment to the treatment arm _i_ instead of assignment to the general treatment group. 6) All specifications in panel (i) include the corresponding _Postt_ and _Entrejt_ dummies, while those in panel (ii) include the _Postt_ and _Entre ijt_ dummies; 7) Student characteristics used as control: age dummies, gender and risk (probability of dropout estimated in the Early Warning System).

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commitment accounts were not denied those accounts, but they were not given information about or assistance in opening them.<sup>8</sup> An ordinary savings account is a regular OIBM savings account with an annual interest rate of 2.5%. The commitment savings account has the same interest rate but allows farmers to specify an amount and a “release date” when the bank would allow access to the funds.<sup>9</sup> Farmers who chose to open a commitment savings account were also required to have an ordinary account where uncommitted funds would be deposited.

During the account opening process, farmers stated how much they wanted in the ordinary and commitment savings accounts after their tobacco crops would be sold. For example, if a farmer stated that that he wanted MK 5,000 in an ordinary account and MK 10,000 in a commitment savings account, funds would first be deposited into the ordinary account until MK 5,000 had been deposited, then into the commitment savings account for up to MK 10,000, with any remainder being deposited back into the ordinary account.<sup>10</sup>

# **_Raffle Treatments_**

To study the impact of public information on savings and investment behavior, we implemented a cross-cutting randomization of a savings-linked raffle. Participants in each of our two savings treatments were randomly assigned to one of three savings-linked raffle conditions. These raffles provided a mechanism for randomizing information about each other’s savings balances. We distributed tickets for a raffle to win a bicycle, where the number of tickets each participant received was determined by his savings balance as of pre-announced dates. Every MK 1,000 saved with OIBM (in total across ordinary and commitment savings accounts) entitled a participant to one raffle ticket. Tickets were distributed twice. The first distribution took place in early September, and was based on savings as of August 19. The second distribution took place in November, and was based on savings as of October 22. By varying the way in which tickets were distributed, we sought to manipulate the information that club members had about

> 8 Among farmers in the control group, nobody requested an ordinary or a commitment account during the savings training at baseline. According to OIBM administrative records, eight farmers in the control group had commitment accounts by the end of October 2009 (opened without our assistance or encouragement), but none of these had any transactions in the accounts.

> 9 By design, funds in the commitment account could not be accessed before the release date. In a small number of cases OIBM staff allowed premature withdrawals of funds when clients presented evidence of emergency needs, e.g. health or funeral expenditures.

> 10 Notice that members could have revised the initial allocation of funds made during the initial account opening process when they visited the bank after the crop sale. However, we find no evidence of this behavior in practice (analysis not reported) using data from the club funds allocation sheets.

8
efforts could differences in up investigate gender performance rating, promotions, project allocations, or the impact of career interruptions due to children.

# **2 Data and Institutional Features**

The WBG’s Human Resource Longitudinal Database was assembled for the purpose of this study. It is structured in a panel from 1987 to 2015 with staff uniquely identified through a universal, permanent personnel identifier (UPI). Data from each year are a snapshot taken on June 30 that contains information on the staff’s universal personnel identifier (UPI), compensation and benefits (for instance, salaries), personal background (gender, age), professional situation (for instance, professional grade), location (HQ or country-office based), role and movements within the organization (promotions and lateral moves), and information on the yearly performance rating (SRIs) from 2000 onward.<sup>12</sup> For the purpose of applying our decomposition, we consider the annual monetary compensation of each employee, the grade at which they were hired, their gender and nationality.<sup>13</sup> Our data cover all staff employed by the WBG between 1987 and 2015. Among staff members, the WBG grades run from GA to GL (the president of the WBG). Grades GA–GD are the grade levels for administrative and client support (ACS) staff. GE corresponds to analyst level. GF and GG contain the bulk of professional technical staff. Staff in the GH level, the first leadership position at the WBG, can be either in a technical or managerial role. GI (director) through GK (vice presiables (wages) can be explained by differences in characteristics (experience or education) versus differences in returns to these characteristic (see the seminal papers Oaxaca 1973 and Blinder 1973; Fortin et al. 2011 for a recent survey of the literature; and Filmer et al. 2005 for an application to a 1997 crosssection of World Bank personnel files).

> 12The Technical Appendix and codebook provide further details.

> 13Non-monetary benefits (such as health insurance) are substantial at the WBG, but homogeneous within the sample we consider, particularly across genders (described below).

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