rafmacalaba/datause-encoder-data huggingface dataset:
## **4.7 Collect, verify, and analyze data**

Data collection, data verification, and data reliability are crucial to the credibility and sustainability of an EPRD program. Put simply, unlike any other regulatory approach (including command-and-control and market-based instruments), errors and mistakes that may change the rating categories of the facilities are simply not acceptable. Indeed, to the extent that social and market pressure may be brought upon rated facilities,<sup>13</sup> the ratings need to be timely and accurate.

In addition to data and statistics available in various records and self-reports accumulated over a number of years, an environmental performance rating and public disclosure program may be complemented with an additional data collection exercise if budget allows, which may include:

- Surveys and visits of targeted facilities to fully understand the nature of the effort (or lack thereof) undertaken by the facility in terms of pollution control;

- Additional, frequent, and numerous samplings of the facilities’ pollution discharges in order to assess as accurately as possible the true status of compliance relative to regulatory standards; and

- Discussions with local residents and communities to better understand the local residents’ perception of the facility’s environmental performance.

With needed information and data in hand, a well-established information management system should be in place to support the program. In particular, record saving and data processing should be facilitated by a computerized system.

We recommend doing all that is reasonably possible to ensure that the data used for purpose of rating the environmental performance of the targeted facilities are reliable and credible, and ensure that the analysis of the data is accurate. Collect additional primary or secondary data when there is significant doubt and possibility. In a situation of uncertainty about the accuracy of the data that may change the rating categories of some companies, it may be preferable to avoid publicly disclosing the ratings of those companies until the uncertainty can be resolved.

# **4.8 Determine final ratings**

Once all the information available has been thoroughly assessed and analyzed, ratings can then be determined according to the devised rating methodology. It is particularly important at this stage that the Technical Review Committee plays its crucial role to ensure that the rating is correct, adequate, and accepted by members of the committee. Attention needs to be paid to the possibility of corruption as some polluters may be powerful enough to change the data or the ratings.

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- _High income in 07:_ Dummy variable indicating household's weekly earning was more than Rp 500,000 (US $50) in 2007 or not. (from the 2007 household survey)

- _Ln(boat length pre-tsunami):_ Log of the length of pre-tsunami boat (in meter, outliers above the 95 percentile are trimmed). (from retrospective data in the 2005 household survey)

- _Ln(engine capacity pre-tsunami):_ Log of the boat engine capacity pre-tsunami (in HP/PK, outliers above the 95 percentile are trimmed).  (from retrospective data in the 2005 household survey)

- _Total fish catch/length pre-tsunami:_ The total amount of fish (which is the sum of reported small fish and tuna catch excluding arthropods, in kilos, outliers above the 95 percentile are trimmed) before the tsunami divided by the length of the pre-tsunami boat (in meter). (from retrospective data in the 2005 household survey)

- _In-shore fishing skill:_ Dummy variable indicating a household which was engaged in in-shore fishing (near shore or within lagoon) before the tsunami.(from retrospective data in the 2005 household survey)

# **3. Village characteristics:**

- _Number of NGOs in 07:_ Total number of aid agencies which were operating in the village in 2007. (from RAN dataset)

- _Number of aid projects in 07:_ Total number of aid projects in the village in 2007.  (from RAN dataset)

- _Total housing aid in 07:_ Total number of temporary and permanent houses constructed by 2007. (from the 2007 village head survey)

- _Number of boat aid in 07:_ Total number of boats received from donors by 2007. (from the _Panglima Laot_ survey in 2007)

- _Number of captains in 05:_ Number of surviving fishermen working as a boat captain in the village right after the tsunami in 2005.  (from the 2005 village head survey)

- _D_Banda Aceh/D_Aceh Jaya:_ Dummy variable indicating households living in Banda Aceh/Aceh Jaya or not.

- _Fraction of fishing population in 07:_ The faction of village workers in the fishing sector in 2007. (from the 2007 village head survey)

- _Credit union in village in 07:_ Dummy variable indicating the existence of credit unions in the village in 2007 or not.  (from the 2007 village head survey)

49
* Pr( _w_  1 _w_  _w_ (0))  1 . That is, targeting the wealth poor when there is no intra-household inequality assures that one reaches all those households with undernourished individuals. By contrast, given that

* _w_ is an increasing function of  , when  approaches its maximum value, a high level of household wealth will be needed to assure that enough of the household’s resources “trickle down” to avoid undernutrition in women and children. (This is clear if one considers again the example when  represents intra-household inequality.) Specifically, _w_ *)1(  _w_ max and Pr( _w_  1 _w_  _w_ max )  Pr( _w_  1) .

By invoking continuity, it is clear that Pr( _w_  1 _n_  )1 must be a non-increasing function of  over (0, 1) and strictly decreasing for some sub-intervals. As  approaches its upper limit, the probability of reaching undernourished individuals by targeting poor households is no higher than the overall poverty rate.

This model formalizes the intuition that heterogeneity, such as due to intra-household inequality or the local health environment, diminishes the scope for reaching poor individuals by targeting poor households. But how much does this matter empirically? Is the wealth effect on individual nutritional status strong enough to allow satisfactory targeting of vulnerable women and children? The rest of this paper addresses these questions.

# **4. Data**

Our data are drawn from the Demographic Health Surveys (DHS) and the LSMS. We use the most recent DHSs available.<sup>18</sup> Table 1 lists the countries included in our analyses and the year of each survey.

**_Individual nutritional outcomes:_** We study the nutritional outcomes of women and children. For women, the two variables we employ are the body mass index (BMI) (also known as the <mark>Quetelet index)</mark> , defined as a woman’s weight (in kilograms) divided by her height (in meters) squared, and an indicator for being underweight, which is set equal to one if a woman’s BMI is lower than 18.5 and zero otherwise. The DHS excludes values of BMI that are smaller than 12 and greater than 60 on the grounds that these are almost certainly measurement errors. We do the same for the consumption surveys. BMI is computed by the DHS for samples of women aged 15 through 49. For the LSMS surveys we restrict women to the same age range.  We exclude all women who report being pregnant at

> 18 Several countries had to be excluded due to older survey data that did not contain many of the key variables needed, namely the Central African Republic, Chad, Comoros, Madagascar, Sao Tome and Principe, and South Africa.

11
in the last two columns of Table 5. In column (4), where only differentiated products enter the equation, the estimation results appear to support our earlier claim that transparency is of special importance for heterogeneous goods. Column (5) shows that the coefficients decrease considerably when homogeneous goods are considered: the ITI elasticity drops by over two-thirds, while for the ETI the fall is over 50%. Whereas both the ITI and ETI have an economically strong and statistically significant impact on trade flows for differentiated goods, their impact is much weaker and statistically insignificant in the case of homogeneous goods.

In interpreting these results, we have been careful thus far to avoid references to causality. It would not be appropriate to conclude from Table 5, for instance, that improved transparency necessarily “causes” an increase in bilateral trade. This is because simple gravity results like those in Table 5 do not account for possible estimation bias due to the endogeneity of transparency with respect to trade. While Table 5 is consistent with a causal link running from transparency to bilateral trade, it is also consistent with a link running in the opposite direction: i.e., economies may tend to create more transparency trading environments because they have higher trade volumes, which leads to greater pressure for reform. Indeed, it is likely in reality that causation runs in both directions at once, and that improved transparency leads to more intense bilateral trade flows, while more trade also leads to greater transparency.

We adopt a simple instrumental variables technique to try and take account of the probable endogeneity of transparency with respect to bilateral trade. As usual, the principal difficulty lies in identifying an appropriate set of instruments for the ETI and ITI. One possible candidate in this case is colonial history (cf. Acemoglu et al., 2001).<sup>15</sup> Pre-20<sup>th</sup> Century colonization generally leaves institutional marks on the colonized area, including potentially those institutions most directly affecting the trade policy environment. Indeed, our dataset reveals that in the APEC sample, a dummy variable

> 15 Given the relatively small number of countries included in the dataset used here, it proved impossible to effectively implement alternative instrumentation strategies for transparency, such as latitude (Hall and Jones, 1999) or settler mortality (Acemoglu et al., 2001). In future work, we will expand data coverage to include a wider number of countries. We expect this will facilitate the use of alternative instruments, and thereby provide an additional robustness check on the results reported here.

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Figure 2: Labor’s Share in Income <!-- Start of picture text --> (A) Aggregate Labor Income Share (B) Piketty-Saez Adjustment<br>Routine (Piketty−Saez adj.) Routine (raw CPS)<br>Non−Routine (Piketty−Saez adj.) Non−Routine (raw CPS)<br>CPS (R+NR)<br>BLS: Non−Farm Business<br>NIPA: Compensation of Employees (BEA:A033RC0)<br>NIPA: Wage & Salary Accruals (BEA:A034RC0)<br>1980q1 1985q1 1990q1 1995q1 2000q1 2005q1 2010q1 2015q1 1980q1 1985q1 1990q1 1995q1 2000q1 2005q1 2010q1 2015q1<br>Quarter Quarter<br>65 30<br>60<br>55<br>25<br>50<br>45<br>Income Shares (%) 20<br>CPS Labor Share (%)<br>40<br>15<br><!-- End of picture text --> _Notes:_ Panel A contrasts aggregate income shares (as a fraction of GDP) based on the CPS outgoing rotation groups (MORG), aggregates reported in the NIPA tables, as well as a BLS estimate for the total non-farm business sector that includes benefits, self employed, proprietors income, and other non-salary labor income. The aggregate series are drawn from FRED. The series labeled “CPS (R+NR)” is constructed from our occupation specific earnings based on the monthly CPS MORG extracts provided by the NBER. The data are seasonally adjusted with the U.S. Census X11 method. Panel B contrasts the raw earnings reflected by CPS topcoded values and our series that adjust top-coded earnings with the appropriate (updated) estimates by Piketty and Saez (2003).

To do so, we use the CPS sampling weights and construct an estimate of the aggregate wage bill at the detailed occupation level, which requires several non-trivial adjustments to the raw data. First, since the U.S. Department of Labor’s (DOL) classification of occupations changes several times during our sample period, we aggregate individuals into a panel of 330 consistent occupations, designed by Dorn (2009).<sup>11</sup> Second, and more crucial for our analysis, we follow Champagne and Kurmann (2012) and adjust top coded earnings based on Piketty and Saez’s (2003) updated estimates of the cross-sectional income distribution.

Based on these adjusted earnings numbers, we then compute the aggregate annual wage bill and divide it by nominal GDP, to construct the share of wage and salary earnings in aggregate income. As illustrated in panel A of Figure 2, the aggregate labor share based on earnings data in the CPS-MORG accounts for stable 70% of the one based on total non-farm business labor income (which includes benefits, pensions, self employed income, etc.). Moreover, the two series are almost perfectly correlated over time.

> 11We thank Nir Jaimovich for providing a crosswalk between Dorn’s (2009) occupation codes and the latest Census classification that is used in the CPS since 2011. This crosswalk is the same as in Cortes, Jaimovich, Nekarda and Siu (2014).

7
# Table S2. Features of private debt booms

|**All debt booms**<br>Amplitude<br>(Percentage points of GDP)<br>Duration<br>(Years)<br>Speed<br>(Percentage points of GDP per year)<br>**All debt booms: Completed versus On**|World<br>18<br>[8, 38]<br>8<br>[4, 12]<br>2.5<br>[1.3, 4.3]<br>**going**<br>**Completed**|Advanced<br>economies<br>54<br>[30, 82]<br>10<br>[5, 17]<br>5.1<br>[3.3, 7.4]<br>|EMDEs<br>15***<br>[7, 29]<br>7***<br>[4, 11]<br>2.0***<br>[1.1, 3.5]|World<br>**Ongoing**|Advanced<br>economies|EMDEs|
|---|---|---|---|---|---|---|
|Amplitude|18|54|14|21|59|17*|
|(Percentage points of GDP)|[8, 37]|[31, 78]|[7, 29]|[13, 41]|[29, 112]|[10, 29]|
|Duration|8|11|7|9|9|8|
|(Years)|[5, 12]|[6, 16]|[4, 11]|[3, 14]|[5, 18]|[3, 12]|
|Speed|2.3|5.0|1.9|3.1**|5.4|2.3*|
|(Percentage points of GDP per year)|[1.2, 4.2]|[3.2, 7.2]|[1.1, 3.4]|[1.7, 4.6]|[3.8, 8.3]|[1.5, 4.2]|
|**Completed debt booms: With crises v**|**ersus Witho**<br>**With crises**|**ut crises**<br>||**Without cri**|**ses**||
|Amplitude|17|65|13|19|42**|14|
|(Percentage points of GDP)|[6, 45]|[50, 131]|[5, 29]|[8, 35]|[30, 74]|[7, 29]|
|Duration|9|13|8|7**|9*|6*|
|(Years)|[5, 13]|[9, 22]|[4, 12]|[5, 11]|[5, 15]|[4, 10]|
|Speed|2.1|6.4|1.6|2.5*|4.2|2.0**|
|(Percentage points of GDP per year)|[1.0, 4.3]|[3.4, 8.0]|[0.9, 3.0]|[1.3, 4.1]|[2.8, 6.4]|[1.2, 3.5]|
|**Completed debt booms: Before 2000**|**versus After**|**2001**|||||
||**Before 200**|**0**||**After 2001**|||
|Amplitude|16|42|11|23***|76***|17***|
|(Percentage points of GDP)|[6, 32]|[26, 64]|[5, 22]|[10, 44]|[36, 133]|[8, 34]|
|<br>Duration|<br>7|<br>10|<br>6|<br>9**|<br>14|<br>8**|
|(Years)|[4, 11]|[5, 14]|[4, 10]|[5, 13]|[7, 19]|[5, 11]|
|Speed|2.1|4.0|1.6|2.6***|6.0***|2.3***|
|(Percentage points of GDP per year)|[1.1, 3.7]|[2.6, 6.4]|[1.0, 2.9]|[1.3, 5.3]|[4.4, 8.4]|[1.2, 3.9]|

Note: Table shows medians, as well as interquartile ranges (i.e., two numbers in brackets), based on 515 private debt booms, of which 100 debt booms are in advanced economies and 415 are in EMDEs. Amplitude is the trough-to-peak change in the private debt-to-GDP ratio during a private debt boom. Speed is the ratio of the amplitude to the duration. *, **, and *** denote that medians between advanced economies and EMDEs in all debt booms, and between completed and ongoing booms or between completed debt booms with and without crises in respective country groups, are statistically significantly different at the 10 percent, 5 percent, and 1 percent levels, respectively.

34
This suggests using a model that, in estimating earnings, takes into account both the movement in good prices and worker characteristics. In doing so, I follow the approach of Porto (2003), with minor modifications.<sup>19</sup> The earning equation for each individual can be written as: where _Wijt_ is the wage of individual _i_ in household _j_ at time _t_ , _Pgt_ is a vector of prices for goods _g_ in time _t_ , _H jt_ is a vector of household characteristics and _Zit_ is a vector of individual characteristics. In this setup, household survey data is well suited for the estimation because it collects information on both prices and wages.

To derive the wage price elasticities, my estimation strategy follows a varying coefficient model (Hsiao 1986) where the variability of the coefficient is given by regional and time differences. The estimating earning equation, which incorporates the role of product prices and worker characteristics, is: _g_ , _r_ where _wijt_ is the observed wage of individual _i_ in household _j_ in time _t_ , _pij_ is the price of good _g_ faced by the individual in region _r_ ,<sup>_Z_</sup> _i_<sup>is a vector of individual characteristics</sup> (age, gender, household head status, and type of employment), _H j_ represents a vector of household characteristics (average expenditure by region, and the number of workers, skilled or unskilled, that the household faces in its region of residence). Finally, θ _r_ and

> 19 The difference resides in how to retrieve the coefficient of interest. In the estimation, Porto (2003) interacts prices with regional and schooling dummies but not with both simultaneously.

16
The family enterprises we study compose what is usually called the 'informal" sector of the Peruvian economy -- small businesses that are loosely organized, usually pay no taxes, and may or may not comply with the variety of other legal requirements for setting up and running a business in Peru. But the word "informal" should not be taken to mean that these enterprises operate irregularly, or that they require no particular skills, or that they make no use of purchased inputs: we discuss some of these characteristics in section 2. Because we are trying to explair, the earnings of businesses within this sector, we do not address the issue of whether these businesses are more or less productive than so-called "formal" enterprises employing wage labor, or whether they are more or less innovative. There is no presumption here that family enterprises are the dumping-ground for life's losers -- for people who could not obtain more serious jobs and therefore had to create their own livelihood. Nor do we presume that these businesses are particularly dynamic, because they operate out from under the heavy hand of government regulation. This is an interesting and important debate in Peru [Kafka (1984); de Soto (1986); Vargas Llosa (1987); World Bank (1987)], but the data obtained in the Peru Living Standards Survey of 1985, analyzed here, do not help much to resolve it. For our purposes, it is sufficient to recall that, not so many decades ago, virtually the entire Peruvian economy consisted of family enterprises, both farm and nonfarm, and that while wage employment has greatly increased in importance, as a consequence of the expansion nf the public sector and modern, large-scale private enterprises, family businesses continue to employ a large share of the Peruvian working population.

The paper proceeds as follows. Sections 2 and 3 describe, respectively, the data and the regression model. Section 4 presents the 3
# **Table 7 – Comparison of Self‐Reported and CR measures**

# _<u>Acres</u>_

|Level (CR)|||Ethiopia||Mean<br>Bias /|Difference|||Tanzania||Mean<br>Bias /|Difference|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||N|SR|CR|Bias|Mean CR|in means|N|SR|CR|Bias|Mean CR|in means|
|1 (< 0.05 acres)|352|0.09|0.02|0.07|307%|***|44|0.32|0.04|0.28|661%|***|
|2 (< 0.15 acres)|392|0.27|0.09|0.18|188%|***|622|0.41|0.11|0.31|288%|***|
|3 (< 0.35 acres)|351|0.40|0.23|0.17|72%|***|816|0.62|0.23|0.39|173%|***|
|4 (< 0.75 acres)|316|0.66|0.51|0.15|29%|***|323|0.98|0.49|0.49|100%|***|
|5 (< 1.25 acres)|179|0.95|0.97|‐0.02|‐2%|‐|63|1.53|0.92|0.61|66%|***|
|6 (>=1.25 acres)|99|1.42|1.90|‐0.47|‐25%|***|20|2.05|1.81|0.24|13%|‐|
|Total|1689|0.47|0.38|0.09|23%|***|1888|0.65|0.27|0.38|143%|***|
||||Nigeria||||||Pooled||||
|Level (CR)|||||Mean||||||Mean||
||||||Bias /|Difference|||||Bias /|Difference|
||N|SR|CR|Bias|Mean CR|in means|N|SR|CR|Bias|Mean CR|in means|
|1 (< 0.05 acres)|‐|‐|‐|‐|‐|‐|397|0.12|0.03|0.09|371%|***|
|2 (< 0.15 acres)|21|0.15|0.11|0.03|30%|‐|1035|0.35|0.10|0.25|247%|***|
|3 (< 0.35 acres)|73|0.39|0.25|0.14|55%|***|1240|0.55|0.23|0.32|136%|***|
|4 (< 0.75 acres)|129|0.79|0.53|0.26|50%|***|768|0.82|0.50|0.31|62%|***|
|5 (< 1.25 acres)|108|1.31|0.99|0.32|33%|***|350|1.16|0.96|0.20|21%|***|
|6 (>=1.25 acres)|153|2.56|2.87|‐0.30|‐11%|‐|272|2.11|2.44|‐0.32|‐13%|**|
|Total|485|1.38|1.31|0.07|5%|‐|4062|0.66|0.44|0.22|51%|***|

*p<.1; ** p<.05; *** p<.01

37
would then provide some guidance as to the likely course for the largest participants in terms of their market share as the sector consolidates or liberalizes. That is, could they be expected to be participants in the consolidation or will it be largely limited to smaller participants merging between themselves? Would it be expected that the larger company shares will fall and to what extent in a liberalizing environment?

**<u>Figure 8: Variation in Herfindahl Index compared to Market Share of the Larger Companies</u>**

<!-- Start of picture text --> Largest company share<br>Life Non Life<br>100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40<br>40 Data Set Average<br>30<br>30 Data Set Average<br>20<br>20<br>10<br>10<br>0<br>0 0 2000 4000 6000 8000 10000<br>0 2000 4000 6000 8000 10000 Non Life Herfindahl<br>Life Herfindahl<br>Largest 3 Company Shares<br>Life Non Life<br>100 100<br>90 90<br>80 80<br>70 70<br>60 Data Set Average 60<br>50 50 Data Set Average<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>0 2000 4000 6000 8000 10000 0 2000 4000 6000 8000 10000<br>Non Life Herfindahl<br>Life Herfindahl<br>Largest 5 Company Shares<br>Life Non Life<br>100 100<br>90 90<br>80 80<br>Data Set Average<br>70 70<br>60 60 Data Set Average<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>0 2000 4000 6000 8000 10000 0 2000 4000 6000 8000 10000<br>Life Herfindahl Non Life Herfindahl<br>Source: Staff Analysis<br>Life Top Company Share Non Life Top Company Share<br>Life Top 3 Company Share<br>Non Life Top 3 Company Share<br>Life Top 5 Company Share<br>Non Life Top 5 Company Share<br><!-- End of picture text --> 17