rafmacalaba/datause-encoder-data huggingface dataset:
|||||Private<br>Firms<br>in the US|Baysinger and Butler<br>(1985)|
|---|---|---|---|---|---|
||Board independence|-|Operating<br>performance|Listed Firms in<br>the US|Bhagat and Bolton (2008)|
||Board independence|-|Firm<br>performance|Listed Firms in<br>Vietnam|Vo and Nguyen (2014)|
||Board size|+|Firm<br>Performance|Listed Firms in<br>Vietnam|Vo and Nguyen (2014)|
||Board size|-|Firm<br>Performance||Cheng (2008)|
||Board size|-|Firm<br>Performance|Listed Firms in<br>UK|Guest (2009)|
||Board size and<br>composition|+|ROE|SOEs in Kenya|Miring’u and Muoria<br>(2011)|
||Board composition<br>(majority<br>nonexecutive<br>directors)|+|Firm<br>performance|Listed Firms in<br>Sri Lanka|Heenetigala and<br>Armstrong (2011)|
||Board<br>composition<br>(politically connected<br>CEOs)|-|Stock price<br>Earnings growth<br>Sales growth|Partially<br>Privatized<br>SOEs in China|Fan, Wong, and Zhang<br>(2007)|
||Board<br>composition<br>(politically connected<br>directors)|-|(+) Employment<br>(-) Performance|Local<br>public<br>utilities in Italy|Menozzi, Gutiérrez<br>Urtiaga, and Vannoni<br>(2011)|
||Board<br>composition<br>(board quality)|+|Efficiency|Commercial<br>SOEs<br>in<br>Lithuania|Curi, Gedvilas, and<br>Lozano-Vivas (2016)|
||Board committee|+|Firm<br>performance|Listed Firms in<br>Sri Lanka|Heenetigala and<br>Armstrong (2011)|
||Remuneration|+|Stock price<br>performance|Listed Firms in<br>Japan|Bauer et al. (2008)|
|**Performance**<br>**Monitoring**|Performance<br>orientation &<br>professional<br>management|+|performance|SOEs in LAC|Andrés, Schwartz, and<br>Guasch (2013)|
|**Transparency**<br>**and**|Financial disclosure|+|Stock price<br>performance|Listed Firms in<br>Japan|Bauer et al. (2008)|
|**Disclosure**|Transparency Index|+|Efficiency|Commercial<br>SOEs<br>in<br>Lithuania|Curi, Gedvilas, and<br>Lozano-Vivas (2016)|
|**Protection of**<br>**Shareholders**|Shareholder rights|+|Stock price<br>performance|Listed Firms in<br>Japan|Bauer et al. (2008)|

Research on SOEs has found a positive relationship between corporate governance and performance of SOEs. Interestingly, most studies on SOEs indicate that the characteristics of the board of directors are significant in predicting SOE performance. Fan, Wong, and Zhang (2007) examined the relationship between board composition and financial performance of newly partially privatized SOEs in China and found that politically connected CEOs negatively affected the financial performance of SOEs. Similarly, Menozzi, Gutiérrez Urtiaga, and Vannoni (2011) investigated the effects of board composition in 114 Italian local public utilities on employment and performance. The results demonstrated that politically connected directors significantly increased employment but decreased performance. Curi, Gedvilas, and Lozano-Vivas (2016) assessed the impacts of corporate governance on the efficiency of commercial SOEs in Lithuania and found that good corporate governance, particularly board quality and strategic planning, enhanced firm efficiency. Jurkonis and Petrusauskaitė (2014) investigated Lithuanian SOEs and 4
# Table of Contents

|1.|Introduction|1|
|---|---|---|
|2.|A Description of the Malagasy Tax System and a Comparison with Other Countries|2|
|3.|A General Equilibrium Tax Model|10|
|4.|An Evaluation of Tax Revenue Loss|1S|
|S.|lhe Relative Efficiency of Alternative Tax Instruments|22|
|6.|Conclusions|28|
||Refwrences|30|
||Appendix: The Madagascar Data Base|32|

This research is a follow-up activity from a technical assistance mission to Madagascar in February 1989 carried out under the auspices of the joint UNDP/WB Trade Expansion Program. The views are those of the authors and should not be attributed to their respective affiliations. We thank Philippe le Houerou and a referee for helpful comments.
# **Box 4: Preventive, Diagnostics and Treatment Services for People with HIV/AIDS in Colombia**

<mark>1. Prevention 2. Diagnostics – Detection Assessment and detection Screening tests Confirmatory tests 3. Care Care programs Referrals to HIV/AIDS experts 4. Preliminary Assessment Anamnesis Revision by systems Physical exams Preliminary interventions 5. Antiretroviral Treatments Launching of the therapy ARV selection Therapy modification Therapy failure Use of genotipification test Optimization 6. Infected Patient Follow-up Clinical follow-up Laboratorial follow-up 7. HIV and Pregnancy Detection ARV therapy and pregnancy Preliminary evaluation and infected pregnant woman follow-up Newborn prophylaxis Follow-up of infected woman’s son 8. Opportunistic Diseases Prophylaxis 9. Exposition at Risk Occupational risks Non-occupational risks</mark>

**Source:** Ministerio de la Protección Social. Resolución 3442 de 22 de septiembre de 2006.

The HIV/AIDS response in Colombia is formally based at the national level upon a number of key policies, including: (i) the National Public Health Plan ( _Plan Nacional de Salud Pública 2007-2010_ , PNSP); and (ii) the National Response Plan to HIV/AIDS 20082011 ( _Plan Nacional de Respuesta a VIH/SIDA 2008-2011_ , PEN).<sup>36</sup> It is supported by a number of central coordinating bodies, including CONASIDA, the MPS, the UNAIDS Thematic Group, and the Country Coordinating Mechanism ( _Mecanismo Coordinador de País_ ).

> 36 Both the PNSP and the PEN constitute the programmatic framework at the national level. The PNSP has a broader scope than the PEN, in that it is a public health plan in addition to being an HIV/AIDS plan. For its part, the PEN currently in place replaced the previous Inter-sectoral National Response Plan to HIV/AIDS 2004-2007 ( _Plan Interesectorial de Respuesta Nacional ante VIH/Sida de 2004-2007_ ).

32
in values (thousands of US dollars) and quantities.<sup>13</sup> Trade data at the HS 6-digit level allows us to account for export failures at a detailed product level which would otherwise be undetected when using more aggregated industry data. In our estimations, we focus on the manufacturing exports to the United States during the 1995-2011 period.<sup>14</sup> The three time-varying variables (initial export, total export, number of suppliers) are measured at the beginning of the export spell. The terms ‘given year’ or ‘time’ thus refer to the initial year of a given export spell. _Initial export_ is measured at the productcountry-time level and represents the export value of a given product exported by a given country to the US market in the initial year of exporting. The variable captures the levels of mutual trust and expectations about the potential future exports that exist among the trade partners at the beginning of a new trade relationship. _Total export_ is measured at the product-country-time level and represents the overall value of the country’s exports of a given product to the world market in a given year. It serves as a proxy for the size and overall export performance of a given exporting country in that given product. _Number of suppliers_ is measured at the product-time level and counts the number of countries that export a given product in a given year to the United States. The variable can represent both the level of competition and the potential market size for a given product in the US destination market.

A dummy variable called _Multiple spell_ is measured at the product-country level. It equals one if a given country exports a given product to the United States during two or more separate export spells, identifying instances when the country at some point stops exporting a product but later re-enters the US market with the same product again. The inclusion of this variable accounts for the possibility that repeated exits and re-entries into exporting of the same product to the same destination might affect this product’s chances for export survival at various time horizons.

> 13 We do not need export values for measuring the export survival rate but we do use them for constructing some of the trade-related control variables.

> 14 We start our sample period in 1995 due to the high number of missing values before 1994. In particular, we are using BACI in HS classification from 1992 that covers the period 1994-2011. As the survival analysis relies on the length of export spells, we cannot use the data from the initial year. This leaves us with the data for 1995-2011. We finish our sample period in 2011 to match the time period for which we have data for all our industry-level variables.

16
contractors. This might well have been a tall order given that the local leaders in charge of implementing the scheme often overlap with the set of people who have been employers in the past. For example, the Mukhiya, acting as a contractor directly or via a close ally, can maintain similarly exploitative relations in implementing the scheme. The scheme officially bans contractors, but they are common, with half or more of the responding workers reporting that contractors were present at worksites.

# **_4.1 Wages received by households_**

The BREGS survey obtains wages from two sources. Block 23 of the household questionnaire asks about earnings and days of work in the week preceding the survey for each adult household member.<sup>18</sup> It differentiates between public works (PW) and other casual wage work, but does not differentiate between BREGS and other PW employment.<sup>19</sup> Wage earnings include cash payments and the value of in-kind payments. In addition, for each work activity, the questionnaire records information separately on wages that were owed and wages that had already been received. We expect one-week recall to be excellent but the data have the disadvantage that they provide relatively few observations. Since the survey was fielded in the May-July months, wage information from this Block pertains only to these months.

The second source of information about wages is from the individual level questionnaire (Block 1) which asked (up to) one male and female adult in each household about their involvement in public works specifically, including type (whether BREGS or other), days worked, wages owed, and wages received separately for each episode of public works employment over the last year. These are the data that we have already made reference to above with respect to delays in wages paid. In addition to providing details specifically about BREGS, this source gives many more observations than Block 23. The drawback is that there may be mismeasurement due to the long recall period and that this does not give wages for non-PW work.<sup>20</sup> Given the different pros and cons of each source, we make use of both.

> 18 Block 23 is adapted from the standard weekly module in the NSS Employment-Unemployment (Schedule 10) surveys. 19 Other public works employment could include BREGS, road building projects or other public works schemes run by the state Government.

> 20 To reduce sensitivity to measurement errors we treat the (very few) recorded wages over 200 Rupees per day or under 10 Rupees from both sources as missing values.

13
admissions among the insured with a 16% increase among the uninsured. The coefficient for drug is rather appreciable, although not statistically significant.

An interesting question is how the differential effects of capitation change the insureduninsured differences in service utilization described in Table 2. Figures 2-4 plot the OP contact, IP admission, and monetary value of drugs used per insured and uninsured patient. The left bars show the simple sample means while the right bars show the sample means after factoring in the effects of capitation as shown in panel B of Table 7. Capitation in all three cases narrows the gap in the intensity of treatment and resources used between the insured and uninsured. Note, however, that “intensity” is rather roughly defined and drug value reflects in-hospital drug use only.

# **_Sensitivity analyses_**

We performed several additional analyses to test the results sensitivity to data issues and assumptions (table not shown). These include: (1) excluding 5 provinces which did not report hospital data in the hospital inventory surveys in 2005-06; (2) excluding two major cities Ha Noi and Ho Chi Minh, where registration for primary care is more likely to be at the tertiary hospitals than district hospitals; (3) using alternative definition of case (Total Case=OP contacts + IP admissions); (4) dropping the autonomy variable from the covariates; and (5) dropping earlier adopters (hospitals adopting capitation before 2009) from the sample. The last test addresses the possibility that earlier adopters have time-varying unobservables (not captured by our diff-in-diff model) that are different from those of the hospitals that adopted capitation after the Insurance Law in 2008. The coefficients on the capitation variable in all cases remain similar in the order of magnitude and statistical significance as the ones presented in Tables 5-7.

22
Vegetation Index (NDVI) explains about 4 percent of the variation in per capita consumption across Sri Lanka (Engstrom et al., 2021). Daytime satellite imagery has also been shown to predict 15 to 17 percent of changes in village-level asset indices extracted from Living Standards Measurement Surveys in Sub-Saharan Africa. However, that rises to 35 percent when predicting an index of changes in asset in ownership at the village level, and 50% when that index of changes is aggregated to the district level (Yeh et al, 2020). Khachiyan et al (2021) find that models trained on satellite imagery can explain between 32 and 46 percent of decadal changes in income and population in the United States.

Data from search engines have also been used to assess people’s real-time well-being without relying on survey questionnaires. _Google Trends_ and its local equivalents (such as Baidu Trends in China and Naver Trends in the Republic of Korea), based on search frequency, provide an index for search intensity (or relative popularity) by topic or term over time in a certain geographical area. A number of recent studies have used data from _Google Trends_ to explore the changes in psychological well-being during the COVID-19 pandemic _(_ Brodeur et al., 2021; Foa et al., 2020; Ma et al., 2021 _)_ . However, these studies focus on measuring the impact of lockdowns on search behavior and do not compare search data with large-scale phone survey data. In addition, data from search engines are also not nationally representative since they are only available for areas with access to the internet and collected from users of Google for online search, while data from satellites are affected by cloud cover, raising questions about their ability to track economic changes more broadly.

In this paper, we exploit a set of real-time indicators from different sources to predict changes in welfare in developing countries during the COVID-19 crisis. Six main findings emerge:

1. The selected real-time indicators predict a significant share of the cross-regional variation in the initial impacts of the crisis. Across about 300 regions in 20 countries, eight Google search terms explain a combined 36 percent of the variation in the share of households reporting total income declines since the start of the crisis between April and August 2020. Meanwhile, during the same period, 10 Google search indicators explain a combined 34 percent of the variation in the share of respondents reporting job loss since the start of the crisis.

2. Compared with initial impacts, selected real-time indicators explain less of the variation in the prevalence of income declines and employment between April 2020 and 4
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25
Table 9 presents the effects of the CLRW intervention on average TEGRA reading score; these effects were based on the estimates of equations (4) and (5) presented in Annex Table 7. Annex Table 8 presents effects for each domain of TEGRA. Positive effects of 1- and 2-years’ exposure to the CLRW intervention were found for all students, and no difference in effects was found between boys and girls. Effect sizes are larger for 2 years of exposure. For the 1-year exposure sample, effects were largest for the initial sounds, letter sounds, reading and listening comprehension domains. Positive effects were not found for the remaining domains. Two years of exposure to the intervention yielded positive effects in all domains except letter names and listening comprehension.

Table 10. Lee bound estimates of impact of CLRW on average TEGRA score

|||1 year exposure<br>sample|2 year exposure<br>sample|
|---|---|---|---|
|All students||||
||lower bound|0.09**|0.17***|
|||(0.04)|(0.06)|
||upper bound|0.24***|0.51***|
|||(0.04)|(0.06)|
|Girls||||
||lower bound|0.07|0.15***|
|||(0.05)|(0.05)|
||upper bound|0.24***|0.55***|
|||(0.05)|(0.07)|
|Boys||||
||lower bound|0.11***|0.2**|
|||(0.04)|(0.07)|
||upper bound|0.25***|0.48***|
|||(0.03)|(0.07)|

Lee (2009) tight bounds are presented. The grouping variables for the 1 year exposure sample are cohort and gender, and for the 2 year exposure sample the grouping variable is gender. Gender is not used as a grouping variable for the bounds estimated for boys and girls. Standard errors included in parentheses. Statistical significance at the 1, 5 and 10 percent levels are denoted by ***, **, and *, respectively. Standard errors are estimated using jackknife replicates.

29
have a significant impact on local economic growth. Fan, Morck, Xu, and Yeung (2009) find that inward FDI within China flows disproportionately into provinces with less corrupt governments and governments that better protect private property rights.

Overall, the evidence suggests that while informal financing is very prevalent in China, it still offers a second-best solution since there is no evidence that informal financing is associated with higher growth or productivity of firms.

# **8. Conclusion**

The last decade has seen an explosion of international finance research highlighting the issues firms in developing countries face in accessing external finance. Any review of this large body of work is bound to be subjective, reflecting the interests of the authors. Further, given the space constraints, this already long review provides only a cursory treatment of some important issues. Considering these caveats, here is what we have learned from the evidence on access to finance in developing countries:

- At the macro level, a large literature has established the importance of financial development for long-term economic growth. While some studies have suggested that entrepreneurship and innovation are the channels through which financial development affects growth, more work is required at the micro level to understand the different mechanisms through which access to external finance helps firms.

- While firms in developing countries complain about a number of business environment factors affecting their growth and operations, access to finance emerges as one of the most binding obstacles to firm growth. Small firms are more constrained than large firms in access to external finance, and are also the most likely to benefit from improvements in institutions and financial development.

- There is undisputable evidence that access to finance is a function of the underlying institutions such as good property rights protection, strong shareholder and creditor rights, stringent accounting standards, availability of high quality information, and less government interference in the form of corruption and political interference.

60
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