rafmacalaba/datause-encoder-data huggingface dataset:
_Table 2.: Completed studies and completion ratesL by region, 1972-1982 and 1982-1989_

|Studi3s|Africa|Asta|LA|C"'|EMENAI'|
|---|---|---|---|---|---|
|Initiat3d 1972-1982||||||
|Completed 1972-1982|29 (32%)|21 (33%)|t'2|(21%)|11 (19%)|
|Completed 1982-1989|17 (19%)|20 (31%)|12|(21%)|10 (17%)|
|Total|46 (52%)|41 (64%)|24|(42%)|21 (36%)|
|Initiated<br>1982-1989||||||
|Completed<br>1982-1989|50 (42%)|38 (31%)|53|(53%)|43 (44%)|

**Source:** Tan (I992). **Tabic 1, Annex I**

, Completed studies as a **percentage** of planned **studies** for that region 1982-1989include **Brzil LN** 2180 **(Skills** Formation) for which 23 out of 30 **studies have** been **comnpleted.** 3 Figures for 1972-1982 **include Pakistan** in **EMENA** region in order **to** be **consistent with** current **Bank regional** country grouping.

16. Table 3 compares the numbers and distribution of planned studies between regions over the two time perioas. The distribution indicates a shift in research interest away from Africa toward a more equal emphasis across regions.

_Table 3. Distribution of planned studies by region, 1972-1982 and 1982-1989_

||Afric|a|As|ia<br>LA|C|EMENAL/|Tota|l|
|---|---|---|---|---|---|---|---|---|
|1972-1982|91|(34%)|64|(24%)<br>58|(21%)|59 (21%)|272|(100%)|
|1982-1989|119|(27%)|120|(28%)<br>99|(23%)|98 (22%)|436|(100%)|

Source: Tan (1982). Table I; Annex I l Figures for 1972 982 include **Pakistan** in EMENA region in order tr ^ consistent with current Bank regional country grouping. Parentheses indicate **percentages** of all planned studies for that time **peiiod.** _6_
20

# **S2 Table. Intervention effects on WASH institutions index and index sub-components**

|||Control||In|tervention|||CI<br>Lower|95%<br>Upper|
|---|---|---|---|---|---|---|---|---|---|
|Outcomes|n|Mean|SD|n|Mean|SD|ITT|Bound|Bound|
|WASH institutions index|185|0.00|1.00|144|0.46|0.75|0.40|0.16|0.65|
|Committee (y/n)|185|0.70|0.46|144|0.97|0.16|0.21|0.10|0.32|
|Committee mtg freq*|88|2.91|1.49|104|2.62|1.61|-0.29|-0.79|0.20|
|WASH expenditures (CDF<br>per month, IHS)|130|2.12|4.07|140|3.28|4.58|1.14|0.07|2.20|
|Track health (y/n)|185|0.69|0.46|144|0.84|0.37|0.16|0.07|0.25|
|Track sanitation(y/n)|185|0.74|0.44|144|0.78|0.41|0.05|-0.06|0.15|

ITT = intention-to-treat effect estimate. CDF = Congolese francs. IHS = inverse hyperbolic spline. Effects are estimated with models that include controls for randomization blocks based on province and number of villages per cluster. There were 121 clusters in total. The WASH institutions index was calculated by rescaling each variable in the index (e.g., presence of WASH committee) so that higher values imply better outcomes, then standardizing relative to the control group, following Kling _et al_ . Effects are in standard deviation units. The index values range from -2.35 to 2.12. *Committee meeting frequency is coded 1-6, where 1=weekly, 2=Fortnightly, 3=Monthly, 4=Every 3 months, 5=Every 6 or more months, 6=No regular schedule, based on needs
Note also that when λk=0 we retrieve our original basic model in which we assume that 2 perception errors are independent across sources and have variance equal to σk .

In order to incorporate the effects of shared prejudices on our estimates of governance, we first need to obtain estimates of the parameter λk for each source, i.e. an estimate of the importance of shared prejudices in source k's assessment of governance. Unfortunately we cannot estimate this model in general, because the statistical model in Equation (1) is not identified. Intuitively, the problem is that if we observe a high correlation between two data sources, we have no way of knowing if this is because they both place a high weight on common prejudices, i.e. they both have a high value of λk, or whether instead both sources have a low variance of the error term, 2 i.e. σk is low for the two sources.

In order to make progress we therefore need to impose some identifying assumptions. As an illustration we do this for the case of K=3 data sources which allows us to obtain very intuitive closed-form solutions for all of the relevant parameters as functions of the observed correlations in the data. We impose two key identifying assumptions. The first is that the variance of the overall error term is the same for all 2 2 three sources, i.e. σk =σ for k=1,2,3. This is mostly for convenience because it provides us with a natural benchmark: if shared prejudices play no role, then we would weight the three sources equally in our estimates of governance since the signal-to-noise ratio would be the same in all three.

The second assumption is more important. We assume that two of the three sources share some common prejudices but the third source does not. In particular we assume that λ1=λ2=λ and λ3=0. This assumption is based on the following interpretation of our two main types of data sources: expert assessments, and surveys of firms or individuals. It seems plausible that expert assessments might be prone to some form of "group-think" in which their shared preconceptions of countries affect their estimates of governance. At the same time, such "group-think" is less likely to be present in survey data where respondents are less likely to have access to the ratings provided by commercial risk rating agencies.

21
# 4.4 _Main explanatory variable_

Our main explanatory variable is a proxy for the competition that a formal firm faces from informal or unregistered firms. The WBES asked firms if they competed against informal firms. As discussed in detail in Section 3.1, we use the “cell average”, or the proportion of all manufacturing SMEs in a country-year cell that compete against informal firms, as our main measure of informal competition ( _Informal Competition_ ). The mean value of _Informal Competition_ equals 0.70, and the standard deviation is 0.16. As mentioned above, we also use the proportion of all SMEs in the service sector in a country-year cell as a proxy for the level of informal competition faced by the manufacturing SMEs in the country-year.

# 4.5 _Controls_

We control for several potential covariates of informal competition that may also have direct effects on quality certification. This helps raise our confidence against the omitted variable bias problem. The motivation for the various controls is based on the existing literature. Table A2 in the Appendix provides a detailed definition of the controls mentioned below.

# 4.5.1 _Baseline controls_

We employ several controls to minimize the possibility of omitted variable bias in our main result. In the baseline model, we begin by controlling for dummy variables for the country to which the firm belongs (Country fixed effects). This ensures that our results are unaffected by time-invariant country characteristics and that the identification of our main result comes from differences in informal competition across time (WBES surveys) for a given country rather than from across countries. Next, we control for a set of dummy variables for the year the WBES was administered 20
issuer only grows to one dollar and five cents. This difference persists even after adjusting for market-wide stock movements.

In Table 7 we report both the raw returns as well as CARs for three different event windows of 21 days, 7 days and 3 days around the announcement date. On average, PIPE issuers experience a raw return ranging from 7.64 percent over a 21 day window to 3.64 percent over a 3 day window. Adjusting for market movements reduces the returns slightly but they continue to be significant at the one percent level. These results suggest that markets do indeed perceive PE investors to provide valuable certification or monitoring services. However, it is worth comparing the market reaction of PIPE issues to that of SEO issues. Unlike in the US – but consistent with evidence from Hong Kong, Special Administrative Region of China and Japan – SEO issues in our sample are also associated with significant positive performance.

Yet while both SEO and PIPE issuing firms on average experience a positive announcement effects, investor identity appears to matter. Regardless of the window we pick, both the raw return and the CAR for an average PIPE issuer are two to three times larger than for an average SEO issuer. These differences are significantly different from zero at the one percent level for shorter windows and at the five percent level for the 21-day window. This suggests markets perceive PE investors to bestow certification or provide monitoring services above and beyond the ability of SEO investors.

While the event study results show that markets appear to reward PE investments, factors other than the choice of equity-selling mechanism may be driving the announcement returns. To examine the relationship between announcement-related return and equity-issue mechanism in a multivariate context, we estimate a general specification of the following form: 20
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<!-- Start of picture text --> Average Prices, Sausage I<br>(Decem ber 1991 -- August 1992)<br>160.-<br>1><br>120- ............. ...... ................. ;;-44: .........<br>8 1........ .......... .................. .......................<br>60 --- ................................----.--.--..................................................................<br>. le<br>I<br>40-... ...-.............................................................................................................<br>20 4 ------------------------------------------------------------------------------------------------------------------<br><!-- End of picture text --> <!-- Start of picture text --> DeC91 01/28 02111 02/25 03/31 0414 04/28 05/12 06102 06/16 06)30 07/14 07128 08111 08125<br>Date<br>| Maket Price State Price |<br><!-- End of picture text -->

33
ordered cities is compelling. Further, the demand on state capacity is relatively small. Hence, as Romer argues, this type of policy is a relatively safe bet.<sup>18</sup>

**Figure 1:  Productivity Policy Space**

<!-- Start of picture text --> Likelihood of Being a<br>Critical Market Failure<br>Early Urban Planning<br>Infrastructure Cluster Road Mapping<br>Education Mechanisms for Public-<br>National Innovation System Private Dialogue<br>Zones and Corridors Strengthening Government<br>Coordination Capacity<br>Sectoral Targeting with<br>Sectoral Subsidies with<br>Tariffs<br>Sundown Clauses<br>Ex-Nihilo Cluster Stimuli<br>Likelihood of Successful<br>Implementation<br><!-- End of picture text -->

> 18 Paul Romer (2017) presentation to WB SD network.

17
traditional measure attained its lowest level since 1950 and was 3.5% of GNP below the average of the period 1951-1993, the adjusted measure was 0.5% of GNP below that average. Section 4 describes the behavior of the main components of Colombian private savings, an exercise that is very useful to understand its recent behavior. It shows whether consumption is more important than disposable income to explain changes in private savings and identifies which components of consumption/disposable income matter most (i.e. durable or non-durable consumption, interest or non-interest income). The main findings and conclusions are presented in Section 5.

# **2. Methodological Issues**

The Colombian National Accounts contain valuable information when compared to those of other developing countries. In fact, they have been constructed since 1950 and consumption can be divided between durable and non-durable goods since 1965. In addition, savings can be divided between corporations and households, and corporate savings can be divided between public and private enterprises ensuring that the coverage of the public sector does not distort the measures of public and private saving and disposable income3. Nevertheless, these accounts suffer from many of the problems mentioned by Schmidt-Hebbel and Serven (1996). Among them it is worth mentioning the following: (i) Private consumption is the residual that balances the income-expenditure identity and saving is the ultimate double residual. Therefore, it reflects all the inaccuracies incurred in estimating income and expenditure. In the case of Colombia these problems might be important due to the presence of drug trafficking. On the one hand, income might be underestimated since it does not take into account a large percentage of the value _3_ However, between 1950 and 1969 the National Accounts were constructed by the Colombian Central Bank using a different methodology than those constructed by the National Statistical Department (DANE) for the period starting in 1970. The main differences between the two series are: (i) A wider coverage of the public sector in the National Accounts constructed by DANE; (ii) the private saving measure calculated by the Central Bank and DANE are net and gross measures, respectively. However, the National Accounts constructed by the Central Bank present the depreciation of physical assets and, therefore, the gross saving measure can be easily calculated. The Graphs and Tables presented in this paper are consistent with the gross measure of savings. For a detail description of the differences between the National Accounts constructed by the Central Bank and Dane see: Resumen de la Misi6n de Expertos Franceses (1984), Lora (1987), Agudelo (1991).

3
Figure (4.2) Real Overvaluation and Indirect Taxation of Agriculture in Sudan <!-- Start of picture text --> 80.0<br>60.0 -<br>40.0-<br>20 00 - <<br>-20.0 , , , i , , , , , , , , , , , , ,<br>1970 1972 1974 1176 1978 1980 1982 1984 19i6 1988<br>-- Ovsrvaluatlon 4-NPRI<br><!-- End of picture text -->
13 dependent variable, in part because theories of political budget cycles (such as the theory of vote-buying in Hanusch and Keefer 2013) emphasize pre-electoral spending, and in part because Brender and Drazen (2005) find that political budget cycles are in particular driven by expenditure. They do not detect cycles in revenue and accordingly, the cycle in the budget balance they document is likely to be driven by higher spending.

Because the party age variables are available over the period 1975 - 2008, while Brender and Drazen’s data are at most available from 1960 to 2001 and for many countries only the 1990s, our main specifications rely on an expanded version of their spending data. This uses information from the _International Finance Statistics_ (IFS), compiled by the International Monetary Fund (IMF), which has become available since their research. For overlapping years, the correlation between our extended expenditure measure and Brender and Drazen’s is 0.99, with very minor differences probably due to subsequent revisions by the IMF of data they used. We also extend sample coverage to the following countries: Albania, The Bahamas, Botswana, Croatia, Ghana, Kenya, Latvia, Malta, Nigeria, and Thailand.<sup>7</sup> Overall we manage to gain up to 307 observations compared to the Brender and Drazen data set. We also, however, report results using their original data.

The WVS confidence measures pose particular data challenges. The WVS data are only available from the late 1990s, with gaps. Unfortunately, neither the original nor the extended IFS data have sufficient coverage for estimation in this case because the IMF statistics division changed its fiscal accounting methodology during the 1990s. Expenditure data using the old methodology continued to be compiled, but only until the late 1990s and early 2000s for most countries. All of our estimates, like those of Brender and Drazen, rely on these data. Expenditure data using the new methodology begin in 1990 for some countries, but for most countries, data coverage under the new methodology begins only

> 7 We only include democracies, i.e. countries that score seven on both the DPI’s legislative and executive indices of electoral competitiveness.
$1.4 billion. Given that Afghanistan’s GNI is $67.5 billion PPP (https://datacommons.org/place/ country/AFG?utm medium=explore&mprop=amount&popt=EconomicActivity&cpv=activitySource, GrossNationalIncome&hl=en), this loss represents an annual 2 percent decline in the national in- come. These bans on women’s education and work are projected to significantly impact future schooling, wage growth, and national income growth.

It is important to note that Afghanistan’s severe instability has created a labor market with unique characteristics. Since the 1970s, instability has caused widespread disruptions, severely affecting educational and employment opportunities, particularly for women. The return of the Taliban to power led to a ban on education for girls above the sixth grade and university education, as well as a prohibition on work for women. These actions jeopardize the substantial progress previously made in expanding educational and employment opportunities for women. Wars, invasions, conflicts, and legislative and regulatory changes have continually affected the development of the education system, leading to labor market fluctuations. These factors contribute to the decline in economic growth, and the long-term economic implications of this human capital decline will persist unless the bans on women’s education and employment are lifted.

14
# **Table A1.1 Comparison with other studies: Survey-HFCE gap**

||||**All**||||**C**|**onsum**|**ption**||||**Incom**|**e**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Group**|**N**|**Gap**|**S.E.**|**Sig**|**t-test**<br>**v. orig**|**N**|**Gap**|**S.E.**|**Sig**|**t-test**<br>**v. orig**|**N**|**Gap**|**S.E.**|**Sig**|**t-test**<br>**v. orig**|
|Ravallion (Original)|88|-0.17|0.04|***||52|-0.07|0.06|||36|-0.33|0.04|***||
|Ravallion (Precise match)|54|-0.19|0.04|***|0.27|38|-0.18|0.05|***|1.55|16|-0.20|0.06|***|1.81|
|Ravallion (Country match)|72|-0.19|0.03|***|0.27|49|-0.18|0.04|***|1.52|23|-0.21|0.06|***|1.53|
|Deaton (Original)|543|-0.12|0.03|***||277|-0.14|0.03|***||266|-0.10|0.03|***||
|Deaton (Precise match)|280|-0.16|0.03|***|1.01|151|-0.18|0.04|***|0.89|129|-0.16|0.04|***|1.29|
|Deaton (Country match)|507|-0.15|0.04|***|0.61|261|-0.17|0.04|***|0.54|246|-0.16|0.03|***|1.41|

**Note:** The table presents compares estimates from Deaton (2005) and Ravallion (2003) – labelled “Original”. We present estimates for the same countries and time periods. The “Precise match” shows estimates using only observations from the same countries, years and survey type as in the original study. “Country match” shows estimates using observations from the same countries found in the original studies, within the same time period. The t-test compare our estimates with the original estimates, assuming independence.
PA 7 QAtjr = PVAE,; > QVAEt j 7 + PINTA,;7QINTAt} 7 + TCijr <!-- Start of picture text -->
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| ‘Sa re _ | Government |<br>1 | . Dae . oo | | Consumption<br>| Other Trading Sectors Trading Sectors Sectors / Inter-regional ETS \ | | Investment<br>| Coal, Coil, Petro, Chem, Chem, < >\\ Market )\ | Saving<br>| Nmm, Metal, Gas Metal, Gas Gas NN CO, price price ~~ —‘Yf, |<br>| XK : yj a | | XX . . yy, |<br>TT —- ee eX eX ' - ——-_!<br>( Non-Trading Sectors \ —————> aGommodiay. Market—— <—__> Export & | / Rest of of \<br>‘_ (Non-electricity) | Import | World<br>NX -/ ——— LH J NS 7<br><!-- End of picture text -->

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**Table 10. Welfare Effects**

|||**_P_**<br>**_From 1992_**|**_ercentage C_**|**_hange in Utility_**|**_From 2005_**||
|---|---|---|---|---|---|---|
||**_Tariffs_**|**_MFA_**|**_Both_**|**_Tariffs_**|**_MFA_**|**_Both_**|
|Usc|0.01|0.17|0.20|0.00|0.28|0.31|
|EU|0.11|0.14|0.27|0.14|0.30|0.47|
|JPN|0.44|0.01|0.44|0.43|0.02|0.44|
|NICs|1.26|-0.70|0.59|1.21|-0.64|0.60|
|CHN|0.54|0.43|0.81|0.49|0.53|0.90|
|IDN|0.53|1.18|1.95|0.49|0.82|1.53|
|MYS|3.44|-0.10|3.22|3.39|-0.18|3.10|
|PHL|1.54|0.39|2.28|1.68|-0.08|1.83|
|THA|-0.09|0.38|0.54|-0.28|0.21|0.14|
|LTN|0.02|-0.07|-0.06|0.03|-0.19|-0.18|
|SSA|-0.21|-0.08|-0.32|-0.16|-0.22|-0.43|
|SAS|0.07|0.64|0.72|0.08|0.44|0.49|
|ROW|0.13|-0.12|-0.01|0.14|-0.27|-0.15|
|**TOTAL**|0.18|0.08|0.27|0.21|0.13|0.35|

||**_F_**|**Pe**<br>**_rom 1992_**|**rcentage of To**|**tal Welfare Ga**|**in**<br>**_From 2005_**||
|---|---|---|---|---|---|---|
||**_Tariffs_**|**_MFA_**|**_Both_**|**_Tariffs_**|**_MFA_**|**_Both_**|
|USC|0.84|60.12|20.77|0.25|58.41|24.65|
|EU|18.20|50.07|29.32|18.69|59.79|35.85|
|JPN|37.98|1.26|25.66|29.38|1.70|17.92|
|NICs|17.34|-21.62|5.39|20.22|-16.67|5.88|
|CHN|6.62|11.83|6.62|11.10|18.54|12.02|
|IDN|1.73|8.70|4.25|1.95|5.00|3.56|
|MYS|5.87|-0.40|3.67|8.26|-0.70|4.47|
|PHL|2.06|1.19|2.04|2.24|-0.17|1.45|
|THA|-0.25|2.36|0.98|-1.09|1.25|0.31|
|LTN|0.66|-4.68|-1.18|0.90|-8.31|-2.89|
|SSA|-0.87|-0.71|-0.86|-0.65|-1.40|-1.04|
|SAS|0.61|11.90|3.95|0.78|6.45|2.76|
|ROW|9.20|-20.03|-0.61|7.97|-23.88|-4.92|
||100|100|100|100|100|100|
|**_bn 1992_**_US$_|36,408|16,177|54,495|60,352|38,876|102,128|
|**_Share of total %*_**|67|30||59|38||

Shares do not **sum** to 100% due to interaction effects.

29
# **Table 6.1: Regression Results: Thematic Lending, AAA and Environmental Indicators**

Environmental Thematic Lending vs. Thematic Indicator Values (Limited to Countries with Environmental Lending) <u>Climate Pollution Land Biodiversity Water Policies</u> Log Environmental Indicators Climate 0.469 (2.86)** Pollution 0.460 (3.30)** Land 0.459 (3.18)** Biodiversity 0.320 (3.32)** Water 0.553 (4.33)** Policies 1.214 (6.26)** Constant -2.519 0.686 -0.572 -2.907 0.115 -0.996 (7.51)** (1.80) (1.53) (10.56)** (0.30) (3.09)** Observations 92 92 92 92 92 91 R-squared 0.08 0.11 0.10 0.11 0.17 0.31 Adj. R-Sq. 0.07 0.10 0.09 0.10 0.16 0.30 Environmental Thematic AAA vs. Thematic Index Values (Limited to Countries with Environmental AAA) <u>Climate Pollution Land Biodiversity Water Policies</u> Log Environmental Indicators

|Climate<br>Pollution<br>Land|0.680<br>(3.18)**|<br>0.297<br>(1.14)|0.367<br>(1.36)||||
|---|---|---|---|---|---|---|
|Biodiversity<br>Water||||0.144<br>(0.77)|0.522<br>(2.23)*||
|Policies||||||1.131<br>(3.34)**|
|Constant|-3.142|-1.454|-2.653|-2.163|-0.379|-1.016|
||(8.05)**|(2.87)**|(5.98)**|(4.68)**|(0.74)|(1.80)|
|Observations|63|63|63|63|63|63|
|R-squared|0.14|0.02|0.03|0.01|0.08|0.15|
|Adj. R-Sq.|0.12|0.00|0.01|0.00|0.06|0.14|

Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1% 17