rafmacalaba/datause-encoder-data huggingface dataset:
discretion. One would thus expect more accountable governments to be more supportive of World Bank PSM projects, and these projects to perform better in the contexts of such governments.

But there are also powerful arguments that nonaccountable, authoritarian rule enables governments to push through tough PSM reforms. Empirically, it is possible to point to a number of “developmental states” with authoritarian governments that have been able to build well-performing administrations— such as China, Rwanda, and Singapore. One theoretical argument supporting this claim is that PSM reforms have a cost-benefit incidence that is particularly misaligned with electoral cycles—they tend to produce relatively certain short-term costs and uncertain long-term benefits (Schneider and Heredia 2003). Based on data from U.S. state governments, Moynihan (2008), for example, argues that democratically accountable state governments like to announce PSM reforms but tend to shy away from implementing them in full, which is costly. In brief, tough PSM reforms might be harder to do in systems with stronger checks and balances.

Levy and Fukuyama (2010) relate both arguments by arguing that the transformation of political institutions can but need not precede state capacity building. They contrast “transformational governance” with a “developmental state” trajectory. In the former, “political transformation has the potential to radically improve both the incentives and the means for state capacity building.” The latter begins with state capacity building, while the route to the “transformation of political institutions [. . .] is a long-term and indirect one.” Overall, the influence of political regimes on PSM project performance is controversial.

Arguably, PSM reform projects might be more difficult than non-PSM projects in contexts where voice mechanisms do not (or only weakly) check rent-seeking behavior (research question 3). One possible reason for this is that rent opportunities abound in the public sector (in the form of public money, contracts, jobs, and so on) and that PSM reforms are precisely about changing the rules (or the “government systems”) that determine the allocation of these rents. Whereas public sector jobs and money are under the direct control of political elites, non-PSM projects may be easier to insulate from the influence of adverse political “taking” incentives.

Compare for example a (non-PSM) road construction project and a (PSM) procurement reform project in a country marked by the rent-seeking behavior of political elites. The procurement of Bank-funded roads would be tightly monitored, limiting rent-seeking opportunities. But this would not interfere with how the government awards contracts for roads financed from the national budget. Indeed, the resulting coexistence of (at least on the surface) very different parallel procurement systems—a discretionary one for government-financed projects and a competitive one for donor-financed projects—is typical in such contexts. By contrast, the ambition of a typical PSM procurement project is very different: to change the 10
Table 1: Descriptive Statistics

||Mean|Std. Dev.|
|---|---|---|
||**Panel **|**A: Worker panel**|
|**_Outcomes_**|||
|Months worked per year (2004-2007)|9.96|3.53|
|Monthly real wages (2004-2007)|2724.7|3,817.9|
|Average months worked per year (2009 onward)|8.5|3.8|
|Average monthly real wages (2009 onward)|1.9|2.1|
|Average number of months in Bolsa Familia program (2009-2012)|0.75|2.83|
|Average payments from Bolsa Familia program (2009-2012)|28.0|221.0|
|Average number of months with unemployment insurance|0.43|1.27|
|Average payments from unemployment insurance (in local currency)|479.4|1,439.7|
|Share of workers trained (2009-2012)||3.22%|
|Share of workers in technical upgrading training (2009-2012)||1.84%|
|Number of workers trained (2009-2012)||59,672|
|Number of workers in technical upgrading training (2009-2012)||34,128|
|**_Firm shock_**|||
|Firm shock in 2008 (negative of growth in GDP of destinations)|-3.1|2.0|
|Total number of worker-year observations||3,046,074|
|Total number of unique worker IDs||342,574|
|Total number of unique frm IDs||11,453|
|Share of observations with 0 months worked per year||19.0%|
||**Pane**|**l B: Firm panel**|
|**_Outcomes_**|||
|Total employment (log)|4.6|2.4|
|Total real wages (log)|11.8|2.1|
|Avg. wages (log)|7.8|0.6|
|Net revenue (log) (2009-2014)|59.9|103.5|
|Materials (log) (2009-2014)|2.4|1.5|
|Materials per worker (2009-2014)|-1.1|1.1|
|Capital (log) (2009-2014)|3.5|1.0|
|Capital per worker (2009-2014)|0.0|0.6|
|Labor productivity (2009-2014)|60.5|105.4|
|Proft rate (2009-2014)|-1.5|1.6|
|**_Firm shock_**|||
|Firm shock in 2008 (negative of growth in GDP of destinations)|-3.2|2.7|
|Total number of frm-year observations||119,189|
|Total number of unique frm IDs||15,087|

_Note:_ The worker-level statistics are based on the sample of workers in firms with a non-missing GFC firm shock measure in 2008 used in the main regressions. For Bolsa Familia, unemployment insurance, and training programs the statistics are based only on beneficiaries. Real wages are expressed in local currency in 2010 prices. The firm-level statistics are based on all firms with non-missing GFC shock measure in 2008 used in the main regressions. All statistics for which a time period is not shown in parentheses cover the 2009-2017 period.

34
_How do evidence gap maps compare with other evidence mapping/ synthesis products?_ The 3ie evidence gap map is a recent addition to the range of approaches available to support evidence informed policy making. The approach draws on the principles and methodologies adopted in existing evidence mapping and synthesis products. For instance, by focusing on a sector or subsector they have a broad substantive scope similar to evidence maps, scoping studies and systematic maps. However, gap maps focus on studies assessing intervention effectiveness, as well as systematic reviews of such studies, while other mapping/ scoping approaches may include a broader range of evidence to address questions other than those of intervention effectiveness (Arksey and O’Malley, 2005; Clapton and Coren, 2007). Moreover, most other mapping/ scoping approaches are generally not intended to provide inputs to policy and practice (Grant and Booth, 2009), but rather focus on mapping the existing evidence (Arksey and O’Malley, 2005; Bragge et al., 2011; Clapton and Coren, 2007), with the purpose of identifying research gaps and specific research questions. Evidence gap maps are developed to both identify research gaps and to provide a resource for informing policy and practice.

The gap maps also share some features with synthesis products with a more narrow scope, such as rapid reviews/ evidence assessments, systematic reviews and overviews of reviews. In particular, the evidence gap maps draw on the strategies for systematic searching and critical appraisal developed for these synthesis approaches (Becker and Oxman, 2011; Khangura et al., 2012; The UK Civil Service, 2008; Waddington et al., 2012). But the broad scope of gap maps means they are more limited in terms of the depth of analysis they provide as compared to systematic reviews and in most cases, rapid evidence assessments and overviews of reviews.

The Evidence-Based Policing Matrix developed by Cynthia Lum and colleagues (2011) is probably the methodology with most similarities to evidence gap maps. It is based on a systematic search, includes high quality impact evaluations and critical appraisal of included studies. It is the only other evidence product we are aware of that combines a graphical visualization of the evidence according to a policy-relevant framework and provides users with direct links to user-friendly summaries of included studies. However, the matrix only includes primary studies, while gap maps also include systematic reviews. The Evidence7
# **2.3.1. Common characteristics**

The firm’s size is one of our common characteristics. We categorize firms into one worker (primarily selfemployment), two workers, 3-4 workers, or 5-24 workers. Workers may be paid or unpaid. Industry, at the one-digit level (or aggregated further to present meaningful descriptives), is available in all three data sets. The 2012 and 2018 ELMPS and EcC have the calendar year the firm started, while the EsC has the calendar year if 2010 or later and then switches to decades (e.g. 2000-2009). We therefore create a harmonized firm age variable that is single years of age from 0-7 years old, and then 8-17 years old, 18-27 years old, 28+, or do not know.<sup>14</sup> We further have information on the governorate, which we present in terms of regions: Greater Cairo, Alexandria and Suez Canal, Lower Egypt, and Upper Egypt.<sup>15</sup>

# **2.3.2. Characteristics in the Economic Census**

In the EcC, we have particularly rich detail on firm characteristics. In addition to the common characteristics described above, we include indicators for having unpaid workers and wage workers. We also measure the share (percentage) of workers who are female. Since we have detailed information on value added, invested capital, and labor, we analyze the relationship between formality and the log of capital per worker and the log of value added per worker. We also include information on whether the firm is a sole proprietorship or some type of collective ownership arrangement.

# **2.3.3. Characteristics in the ELMPS**

In the ELMPS, we can identify additional detail about firm characteristics and owners. We consider the owner to be the person who worked the most on the enterprise.<sup>16</sup> We include the owner’s sex, age group (<30, 30-49, 50+), education (illiterate, less than secondary, secondary, higher education), and occupation (professional/technical, other white collar, blue collar craft, blue collar non-craft<sup>17</sup> ).

In addition to owner characteristics, we use firm characteristics from the ELMPS enterprise module. Specifically, we have current capital (categorically), the type of the workplace (e.g. office, workshop, etc.), and sole proprietorship (household) vs. collective ownership (partnership). Using the number of workers and the net revenues of the enterprise going to the household, we calculate net revenue/worker as a 14 Since the ELMPS 1998 and 2006 waves have start years categorically, when analyzing dynamics (which we can only do using the ELMPS) we switch to harmonized categorical start years.

15 Greater Cairo includes the governorates of Cairo, Giza, and Qalyoubia. Alexandria and the Suez Canal includes Alexandria, Port-Said, Suez, and Ismailia. Lower Egypt includes Damietta, Dakahliya, Sharkiya, Kafr El-Sheikh, Gharbiya, Menofiya, and Behira. Upper Egypt includes Beni Sueif, Fayyoum, Menia, Assuit, Souhag, Qena, Aswan, and Luxor.

> 16 This generally overlaps with who makes the most decisions, a variable added in 2018 (Krafft, Keo, & Fedi, 2019).

> 17 Individuals who did not report being employed in the preceding three months were classified as blue-collar noncraft.

6
yield where MPK here denotes the physical marginal product of each type of capital. Using the assumption of a Cobb-Douglas production technology (equation (8)) and multiplying the above relations by the appropriate capital stocks yields Using _kh,i_ and _kh,n_ to denote aggregate ICT and NICT capital stocks and summing the above relations across industries delivers Dividing the two expressions yields predicted values for _pph,nh,ikkh,nh,i_<sup>asafunctionof</sup><sup>_<u>r</u>_</sup> _r_<sup><u>+</u></sup> +<sup>_<u>δ</u>_</sup> _δ_<sup>_<u>n</u>_</sup> _i_<sup>andthe</sup> industrial composition as reflected by the country and industry specific levels of output, _Yh,j_ : Equation (12) allows us to conduct a simple test for the hypothesis that cross-country heterogeneity in the industrial composition may play an important role in explaining the cross-country differences in ICT abundance. Specifically, we use equation (12) to compute predicted values for ICT abundance that vary across countries exclusively because of differences in industrial composition. To accomplish this we first estimate industry specific capital income shares for the US based on the BEA’s fixed asset accounts to proxy _αj,i_ and _αj,n_ . These estimates are constructed based on the aggregate estimates by Eden and Gaggl (2014). Specifically, we use estimates of the aggregate marginal products of ICT and NICT and multiply them by the industry specific ICT and NICT capital output ratios.<sup>15</sup> We then assume that _δn_ = 0 _._ 06 and _δi_ = 0 _._ 2, the average values

> 15Notice that this approximation results in an implied aggregate capital share that is more than 100% for a few countries and industries, since the aggregate marginal product is only an approximation of the true industry specific

22
Since the micro data from POEA does not include skill levels, we calculate average education levels by occupation using the 1992-2003 Survey of Overseas Filipinos (SOF),<sup>7</sup> and assign each occupation the average education level. We use this to then construct skill quartiles of aggregated occupational cells in our data. The average years of education for occupations in the first quartile is 11.6 years, 12.8 years for the second quartile, 13.8 years for the third quartile, and 15.1 years of education for the fourth quartile. One sees notable differences in the wages that a worker of a given skill level can earn across destination countries. For instance, OFWs in the first skill quartile in Saudi Arabia receive an average wage of $336 per month, whereas OFWs of the same skill level in Japan earn an average monthly wage of $1,505. This large variation across destination countries holds for the more skilled quartiles as well. The highest skilled workers in Saudi Arabia earn $553 per month, whereas in Japan these OFWs earn $1,661 on average each month.

# **3.2 Macro Data**

Data on annual real GDP (constant 2000 US$) over the sample period were obtained from the World Development Indicators database and the CIA World Factbook. These data are then matched to the POEA data based on destination country and year of departure. Over the sample period, destination countries in our sample experience vastly different rates of GDP growth as well as varied fluctuations in growth. For instance, during the Asian Financial Crisis, Asian countries such as Japan or South Korea faced dramatic reductions in GDP growth, whereas Middle Eastern destinations such as Bahrain or Kuwait maintained fairly stable growth. Figure 3 plots real GDP growth in the top 10 destinations for OFWs. In addition to the differences in growth rates in 1997 during the Asian Financial Crisis, another period of high volatility was during the Global Financial Crisis, which by 2009 had affected some destinations more than others.

> 7 The Philippine Labor Force Survey is administered annually to a nationally-representative sample of households. The SOF is administered as a rider to the LFS if the household reports having any members working overseas, and contains information on migrant demographics, overseas occupation and location, and remittances (all reported by the household remaining behind in the Philippines).

11
# **3. Experimental design**

We use a three-person public goods experiment to investigate the role of nonbinding agreements. Our basic design builds on the work by Gächter and Herrmann (2011) and Rustagi _et al_ . (2010), who both conducted artifactual field experiments using a standard oneshot public goods experiment with rural and urban samples in Russia and Ethiopia, respectively.

Our subjects participated in two one-shot experiments: (i) a standard public goods game and (ii) a public goods game proceeded by the possibility to form a cooperative nonbinding agreement among all group members. It should be noted that both one-shot public goods games included stranger matching (i.e., groups were reshuffled between the games) and that this feature was publically known to all subjects when making their decisions. This matching procedure ruled out any strategic motives to cooperate in the first public goods game.

In the standard public goods game each subject was asked how much of the endowment she would like to contribute to the public good, while the remainder was kept as her private income. The total amount given to the public good by all three group members was then multiplied by 1.5, and the resulting amount was shared equally among the members. Thus, the marginal per capita return to the public good equaled 0.5. This return creates the familiar conflict between the self-interested choice and the social optimum, which characterizes the public good. It is in each player’s material self-interest to keep the entire endowment, whereas it is in the collective interest that all players contribute their entire endowment to the public good since the marginal social return is 1.5.

We conducted our experiments with farmers in Nepal and Ethiopia, who have little or no formal education, and to facilitate their understanding, we used the local currency directly rather than an experimental currency together with an exchange rate. Each subject was 7
|||||(1.75)|(0.20)|
|---|---|---|---|---|---|
|Latin Am.||||-0.811|-0.447|
|||||(3.04)***|(0.70)|
|MiddleEast||||-0.338|-0.064|
|||||(1.08)|(0.11)|
|_Barriers_*Latin<br>America|||||-0.674|
||||||(1.09)|
|_Barriers__Africa|||||-3.201|
||||||(2.34)**|
|_Barriers__High<br>Inc.|||||-0.078|
||||||(0.16)|
|_Barriers_*East<br>Asia|||||-0.118|
||||||(0.20)|
|_Barriers_*Middle<br>East|||||-0.794|
||||||(1.43)|
|_Barriers_*Eastern<br>Europe|||||1.655|
||||||(2.79)***|
|Constant|-35.753|-35.043|-35.369|-27.627|-60.129|
||(9.24)***|(9.08)***|(8.23)***|(3.71)***|(3.81)***|
|Observations|510|510|510|510|510|

Robust z statistics in parentheses

* significant at 5%; ** significant at 1%

Note: See text and Appendix for definitions of variables. The excluded regional dummy variable in model (5) corresponds to South Asia, which in this sample is India. The excluded Leamer commodity group in all specifications is mining and petroleum.

18
# **1 Introduction**

The macro-financial shock caused by the COVID-19 pandemic precipitated a global economic recession and put severe pressure on financial markets and institutions around the world. Policy makers reacted by implementing an unprecedented array of public health, fiscal, monetary, macroprudential, and financial measures to contain the spread of the virus and support the real economy. For example, authorities introduced travel bans, mandated the closure of businesses, limited social gatherings, and scaled up unemployment and social protection programs. Financial sector authorities undertook a complementary, wide-ranging set of temporary measures to preserve the well-functioning of core markets and maintain the provision of critical financial services to the real economy, including credit and payments, while at the same time safeguarding prudent risk management standards, bank balance sheet transparency, financial resilience, and a globally coordinated response.

To date, financial sector policy measures have focused on providing liquidity to financial institutions and markets (e.g., lowering reserve requirements, purchasing financial assets); maintaining operational and business continuity (e.g. extensions of deadlines on supervisory reporting); facilitating the flow of credit and supporting borrowers that face short-term repayment difficulties either directly (e.g., lowering interest rates, introducing debt repayment moratoria, facilitating loan restructuring, offering government guarantees and loans to affected sectors) or by providing regulatory relief (e.g., encouraging banks to use available capital and liquidity buffers, allowing for the flexible treatment of non-performing loans and asset classification). Several standard-setting bodies for the financial sector have also issued guidance (e.g., encouraging and clarifying the use of the flexibility embedded in the global standards) to ensure a coordinated global response and some have deferred the implementation of components of certain global standards, notably Basel III.

This paper makes three contributions. First, to support policy formulation and evaluation by policy makers and researchers, the paper introduces a new global database and a simple policy classification framework. Containing over 3,100 individually classified measures, the database provides a comprehensive, though not exhaustive, overview of the financial sector policy response to the COVID-19 crisis, based on publicly available information. Second, the paper documents patterns of the measures taken around the world and introduces the _COVID-19 Financial Policy Response Activity Index_ , a simple country-level indicator that tracks the total number of measures taken. Third, as an initial step towards understanding the determinants of the policy response, the paper explores the association between relevant country characteristics (i.e., domestic exposure to COVID-19 and fiscal, economic, and financial fundamentals) and policy makers’ 2
comparison comes from surveys conducted in April 2013 and 2014. From these surveys, we use data on workers hired by the firms eligible for the subsidies in the three years following the subsidy period. Compared with the workers hired under the subsidy program, the workers hired later by the same pool of firms are slightly older (32.6 years) and less likely to be female (10.0 percent), though neither of these differences is statistically significant. They are much less likely to be related to the owner (10.0 percent), a difference which is significant at the 1 percent level. They are also slightly less likely to have known the owner previously (71.4 percent) and to live within one kilometer of the business (35.7 percent), though these differences are not statistically significant at conventional levels. They are paid much more (3,350 LKR per week, deflated to fall 2009 prices). On the whole, the characteristics of workers hired post-subsidy are quite similar to those hired in 2009-10 by the control firms. Hence, it appears that workers hired under the subsidy are similar except that they are more likely to be related to the owner, and they are paid a lower wage.

We also asked owners which of 10 methods they used to locate the employee hired through the subsidy program. Employers relied mostly on networks, with asking friends (50 percent), neighbors (33 percent), immediate family (21 percent) and extended family (18 percent) for referrals the most common responses. Advertising the position was very rare, used by only 1 percent of the employers, while contacting former employees (7.7 percent), and contacting friends (9.3 percent) and relatives (6.7 percent) directly about the job were used with intermediate frequency. On average, employers used two of the methods. For comparison, in the April 2013 survey, we asked those eligible for the subsidy program how they found employees hired in the two years after the subsidy period. We find similar patterns though the search intensity is somewhat higher, with employers using just over 2 methods. With the post-subsidy hires, employers were more likely to say they advertised (9.6 percent) and contacted former employees (17.2 percent), but referrals through networks of family, neighbors and friends remain the most common search methods by very large margins.

# **5 Results**

We first examine whether the wage subsidy changed the survival rates of firms, since, to the extent it did, we need to control for this in examining impacts on employment, profitability and sales. As noted above, our estimation aims to combine the advantages of combining multiple follow-up 15