rafmacalaba/data-use-mentions-extended huggingface dataset:
75)|
||[0.064]|[0.105]|[0.167]|[0.109]|[0.010]|
|Mean if covariate = 0|1665|1790|1778|1660|1254|
|Mean if covariate = 1|1782|1568|1603|1754|1811|
|Share: covariate = 1|0.22|0.74|0.59|0.17|0.50|
|P-val: sum = 0|0.95|0.32|0.27|0.71|0.40|
|N: observations|137678|137678|137678|137678|137678|
|N: workers|474|474|474|474|474|

_Notes:_ Dependent variable is earnings. Unit of observation is worker-day. Private account is an indicator that equals 1 if the worker is assigned to the Private treatment. The omitted category is being assigned to the Control or Non-private condition. Covariate in each column is a binary indicator. Col. (1) covariate equals 1 if the worker strongly disagrees with the statement “I have difficulty saving over time for large goals because if I put money aside, someone else will need it for something urgent.” Col. (2) covariate equals 1 if the worker answers ’never’ to the question: “How often do you financially support acquaintances (not close friends or family)?”. Col. (4) covariate equals 1 if the worker does not answer “input into most or all decisions” to the question “How much input do you have in making decisions about the money you earn?”. Covariates in Cols. (1)-(4) are from baseline surveys conducted prior to the assignment of treatment status in each phase. Baseline covariates are missing for initial enrollees in Phase 1: any missing values are coded as 0, and a dummy indicating which observations have missing
# **Box 2 explains the robustness checks conducted using EU-SILC and matched administrative tax data for measuring impacts.**

|Box 2. Robustness Checks – Using EU-SILC and Matched Administrative tax data and EU-SILC to measure<br>Impacts.|
|---|
|**In a related study, Robayo-Abril, Balaban, and Wronski (2024) utilize statistical data matching techniques to**|
|**align income data from the 2020 administrative tax dataset with the 2021 EU-SILC, referencing the income**<br>**year 2020.**The aim is twofold: to assess tax regulation adherence and to examine possible overestimations in the<br>proportion of individuals earning the minimum wage, as reported in tax records. Their findings highlight a notable|
|disparity in the prevalence of minimum wage earners when comparing data from EU-SILC and tax records.|
|**We explore using linked survey administrative tax data, which could provide valuable insights for several**<br>**reasons**. On the positive side, this approach presents comprehensive coverage of unemployment, capturing the entire|
|labor force and enabling the estimation of unemployment rates, albeit differing from the LFS approach, as the EUSILC|
|is not designed to measure labor market status. Additionally, it considers both employed and informal sector workers,<br>providing a more holistic perspective. The inclusion of income poverty data further enriches the scope of findings.|
|Moreover, this method accounts for a diverse range of worker characteristics, including observable factors like|
|education levels and occupations, allowing for a more refined characterization of the potentially affected group.|
|However, the share of minimum wage workers earning between the current and the proposed minimum wage in the<br>EUSILC is tiny, significantly limiting the sample size for this analysis. The EU-SILC measures income annually.|
|Respondents report their income in a previous calendar year. EU-SILC 2021 provides information on annual income<br>
controls, we also include a dummy for gender of the child in the regression specification.<sup>18</sup> This implies that any common factors (such as cultural norms) that might affect the average schooling attainment of girls irrespective of parental socioeconomic status are absorbed as a shift in the intercept.

The estimates in the top panel of Table 1 provide strong evidence that truncation bias in the IGRC estimates is substantial for all three definitions of parental education. Consistent with the expectation based on the graphs discussed above, the IGRC estimate in the coresident sample is significantly biased _downward_ . The null hypothesis that the estimate from the coresident sample is equal to the estimate from the full sample is rejected unambiguously with P-values equal to 0.00 in all of the different cases.<sup>19</sup> The pattern is remarkably consistent, and justifies the widespread opinion that there are good reasons to expect the IGRC estimates to be biased downward due to non-random sample selection bias because of the coresidency requirement used in the household surveys.

To get a better sense of the implied magnitudes, we report bias defined as follows (using IGRC as an example), where _IGRCCR_ denotes the estimate from a coresident sample, while _IGRCF_ is the estimate from the corresponding full sample including non-resident household members.

The first column in the bottom panel of Table 1 reports the bias in the IGRC estimates from the coresident sample. The evidence is clear: the estimate from coresident sample is biased downward, and the magnitude of bias is substantial across all three indicators of parental education. The bias is the highest when mother’s schooling is the indicator of

> 18The estimates and the conclusions do not depend on the inclusion of the gender dummy.

> 19We, however, note here that the formal test of equality of estimates may not be very useful in our context. Even with very small numerical difference between the estimates from the full and coresident samples, one can reject the null hypothesis of equality simply because the standard errors are extremely
)|
|Observations|7,156|6,346|7,276|7,276|
|R-squared|0.855|0.877|0.852|0.852|
|**Forward links**|||||
|DVAR|0.220***|0.190***|0.119**|0.191***|
||(0.0173)|(0.0203)|(0.0503)|(0.0465)|
|FVADP|0.233***|0.264***|0.231***|0.232***|
||(0.0259)|(0.0274)|(0.0257)|(0.0257)|
|DVAR*Air freight|138,451<br>(498,564)||||
|DVAR*Rail||0.0001**<br>(4.7e-05)|||
|DVAR*Port|||0.0222**<br>(0.0097)||
|DVAR*Road||||0.0062<br>(0.0091)|
|Constant|5.065***|5.053***|5.186***|5.152***|
||(0.171)|(0.217)|(0.189)|(0.184)|
|Observations|7,164|6,347|7,284|7,284|
|R-squared|0.872|0.896|0.870|0.870|

Source: OECD ICIO database. *** p<0.01, ** p<0.05, * p<0.1. Notes: Robust standard errors in parentheses. _FVAX_
are consumed by fewer than 4 percent of households are not included (tobacco leaf, gul, and other smoked tobacco, khoer, lime, and other smokeless products). The models incorporate a set of demographic controls, as well as a control for urban and rural location.

To estimate the demand system and the price elasticities, one needs information about the prices of the various tobacco products. However, disaggregated data on prices to estimate the elasticities are not available. The HIES 2016/17 unit values obtained by dividing the total values reported by quantities may be used to infer information about prices. As Deaton (1997) points out, unit values not only capture information about prices, but are also affected by household choices on quality. In addition, unit values are measured with errors because they involve a calculation based on a ratio between values and quantities. Partially to address this concern, median values are calculated for the unit values across districts separated by urban and rural areas. This exploits the fact that the survey is representative across districts and provides a reasonable number of observations to calculate the median values.<sup>13</sup> − The quadratic AIDS estimates indicate that the price elasticity for cigarettes is 1.3. Comparing across deciles, one finds, similar to previous research results, that the elasticities are larger among the poorest − − ( 1.36 for the first decile and 1.23 for the richest deciles). The average price elasticity for bidis is − 1.22, and there is not a clear gradient across deciles.

> 13 In the cases where the number of observations was less than 30, the medians of the full districts are used.

11
the challenges posed by strategic reporting, given the sensitivity of registration status, and should not be viewed as precise measures. These are intended to summarize current official estimates of the nonregistered and temporarily registered populations, and do not imply any judgment as to their accuracy.

**Table 2: Estimated of Non-permanent Registration Status Mahalla Records and Implied Population Totals**

||Estimated<br>Share<br>Illegal|Implied<br>Population|Share of<br>National<br>total|Temporary/<br>Other<br>Register|Implied<br>Population|Share of<br>National<br>Total|
|---|---|---|---|---|---|---|
|Andijan|0.2%|2,716|<br>0.6%|0.1%|1,824|<br>0.4%|
|Bukhara|2.4%|45,011|<br>9.9%|0.0%|41|<br>0.0%|
|Fergana|0.6%|21,097|<br>4.6%|0.1%|4,821|<br>1.1%|
|Jizzakh|0.2%|3,274|<br>0.7%|0.4%|5,664|<br>1.3%|
|Namangan|0.3%|8,704|<br>1.9%|0.4%|11,527|<br>2.6%|
|Navoi|1.2%|11,952|<br>2.6%|0.9%|8,596|<br>2.0%|
|Karakalpakstan|0.9%|16,
all households increased, and that for user households decreased, with quintile level (Table A2.16).

Table A2.16: Shares of total household expenditure on LPG for all households and for purchasing households in Sri Lanka (%)

|**Quintile**|**Rural**|**All households**<br>**Urban**|**Total**|**U**<br>**Rural**|**ser households**<br>**Urban**|**Total**|
|---|---|---|---|---|---|---|
|**1**|0.1|0.5|0.1|3.6|4.2|3.8|
|**2**|0.2|0.9|0.2|2.9|3.6|3.1|
|**3**|0.3|1.2|0.4|2.5|3.1|2.6|
|**4**|0.5|1.6|0.7|2.0|2.7|2.2|
|**5**|0.8|1.6|1.0|1.5|1.9|1.6|
|**All**|0.4|1.4|0.5|1.8|2.3|2.0|

_Source:_ Authors’ calculations.

# **Albania (LSMS 2008)**

The data are taken from the Living Standards Measurement Survey (LSMS) 2008 administered by the Institute of Statistics (INSTAT) in the Republic of Albania. The fieldwork for the survey was carried out
|
|Miscellaneous|104.6|172.9|171.9|
|Total|101.4|145.0|144.4|

over time for each region. Other countries, such as Ghana, have a rural price index available. The urban and rural deflators for the Ghana poverty analysis are based on rural and urban price indices. Given the difference in expenditure shares between the survey and CPI baskets in Ghana, the food and nonfood price indices were weighted by the survey shares to derive the deflators. Such a procedure might be envisioned for CMte d'Ivoire, though in the absence of regional price indices over time and given the differences even in the composition of food expenditure between the survey and the CPI basket, it is not clear that the result would necessarily be more accurate.

The use of the Abidjan price deflator as the price deflator for other regions could potentially be a problem, since there is a big difference between Abidjan and the Savannah in the share of expenditure on food. The food share is 62 percent in the Savannah compared with 35 percent in Abidjan, according to the 1993 survey (table 31). The higher share on food in the Savannah was offset by a somewhat lower share of expenditure on lodging and utilities, health and education, transport and remittances. Since the price increase in lodging and utilities was far less than the overall increase in prices, this would have contributed to the Savannah having a slightly higher rate of inflation than Abidjan, if the price index were recalculated on the basis of the Savannah consumption basket.

34
|2018|606,656|132,134|116,777|60,464|144,161|253,841|30,729|444|107,240|54,611|
|2019|652,459|115,923|118,158|56,089|182,194|296,584|34,497|443|105,061|55,031|

_Note:_ This table presents counts of observations in tax records, by year and source of income information. Column (1) presents the total number of records in each year. Column (2) presents the number of individuals filing the yearly income tax declaration. Columns (3) through (8) present the number of individuals with withholding records for income from wages, services, dividends, interest, capital gain and rental, respectively. Column (9) presents the number of individual with some other record of third-party information, including exports, sales to the government and sales to firms declaring detailed VAT information. Finally, column (10) presents the number of individuals being assigned undistributed corporate profits. Since records might be available for the same individual from several different sources, the numbers of each source do not sum to the total observations in one year. Before 2011, all withholding forms were presented on paper and have not been digitized, so we do not observe those records in our sample.

26
**_II.3_ Long-term credit flows have fallen steadily and** **<u>were less pro-cyclical than shorter-term loans</u>** Overall Credit Flows to Non-Fin. European Firms _€ millions (2005Q1-2012Q4)_

**_II.4_ Increasing margins and collateral and decreasing maturities have** **<u>been instruments of choice</u>** Lending Terms and Conditions for Total Loans _Net percentage of banks indicating tightening relative to_ _<u>previous quarter</u>_

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could create moral hazard problem by having the country overstate the loss it has incurred. Parametric

> 10 This form of cat bond trigger is more analogous to a traditional insurance policy with its loss settlement process. Other triggers are on _modeled losses_ or _industry losses_ .  For _modeled losses_ , instead of dealing with Proactive's actual losses, an exposure portfolio is constructed for use with catastrophe modeling software. When there is a disaster, the event parameters are run against the exposure database in the cat model. If the modeled losses are above a specified threshold, the bond is triggered. For _industry losses_ , the cat bond is triggered when an entire industry loss from a certain peril for the insurance industry doing business in this country reaches a specified threshold.

22
POI-ICY RESEARCH WORKING PAPER 2435

# Summary findings

Poverty is intrinsically a complex social construct, and even when it is narrowly defined by a deficit oiF consumption spending, many thorny issues arise in setting an appropriate "poverty line." Suryahadi, Sumarto, Suharso, and Pritchett limit themselves to examining how poverty-defined on a consistent, welfare-comparable basis-changed in Indonesia during a series of crises that began in August 1997. Using various data sets and studies, they develop a consistent series on poverty's evolution from February 1996 to August 1999.

Specifically, they study the appropriate method for comparing changes in poverty between the February 1996 and February 1999 Susenas surveys.

To set a poverty line for 1999 that is conceptually comparable to that for 1996 involves a standard issue of price deflation: How much would it cost in 1999 to purchase a bundle of goods that would produce the same level of material welfare as the money spent at the poverty line in 1996?

Empirically, given major changes in the relative prices of food, the key issue is the weight given food prices in the price index.

Using different deflators produces a range of plausible estimates, but they produce two "base cases": one working forward from 1996 and one working backward from 1999.

If one accepts the official figure of 11.34 percent for February 1996, poverty increased from the immediate pre-crisis rate of about 7-8 percent in the second half of 1997 to the post-crisis rate of about 18-20 percent by September 1998 and 18.9 percent in February 1999.

If one begins from the best estimate of the poverty rate in February 1999 (27.1 percent), poverty rose by 9.6 percentage points from 17.5 percent in February 1996.

Since February 1999, poverty appears to have subsided considerably but-two years after the crisis started-is still substantially higher than it was immediately before the crisis.

This paper-a product of the Environment and Social Development Sector Unit, East Asia and Pacific Region-is part of a larger effort in the region to develop a national poverty strategy for
A few additional comments on the data set that we use and the data transformations that we apply are in order. The set of countries consists of a reasonable mix of developed and emerging market economies with a satisfactory north/south and continental representation. The initial cross-sectional dimension consisted of 65 countries, but due to the lack of largely GDP and GDP Deflator data, it was necessary to exclude countries that did not have GDP related data available for a long enough period.

There were further occasional data gaps that were interpolated with a linear interpolation method to keep the size of the sample as large as possible. These were occasional gaps in the lending and deposit rates of some countries, and very rarely also in the credit series. For EU countries, we have converted the relevant variables and the exchange rate to eurodenominated values prior to the 1<sup>_st_</sup> of January 1999, where the official EU conversion rates were used.<sup>22</sup> Although the real GDP data were marked as seasonally adjusted, it became evident from visual inspections of the series that for a handful of countries that were included in the final data set, this was in fact not the case. It was, therefore, necessary to use a seasonal filter to remove the seasonality in those GDP series. The X12-ARIMA seasonal<sup>23</sup> The number of time-series observations of each of the 49 countries that are included in the cross-section from 25 observations for to 118 observations for ranges Bulgaria up France. Evidently, having less than 40 observations for the time-series dimension is far from optimal, nonetheless, we chose to leave as many cross-sections in the final data set as possible. A brief summary of the number of time-series observations of the individual countries is as follows: there are only 5 countries with less than 40 observations, there are 21 countries with 100 observations or more and the remaining countries have between 40 and 92 time-series observations.

The regressor variables intended to capture the cross sectional variation
# **Aggregate effects**

## **Labor market outputs and welfare levels**

Table 5 presents information on projected changes in employment status: employed and non‐employed. The latter is a combination of being inactive or being unemployed. The size of the labor force is predicted to grow as consequence of population growth in both scenarios. The additional 1.2 million potential workers would increase pressure into a non‐dynamic labor market, particularly, in the crisis scenario where the economy would have diverged significantly from its natural path. This would represent an extra 800,000 non‐employed Iraqi workers relative to BaU levels.

Employment shares by main economic sectors change between the two scenarios. There is a shift from the manufacturing and construction sector to agriculture and services sectors. This would represent a change from more productive/higher earnings jobs to less productive/lower earnings. However, these changes most likely cover fluctuations in the composition of sectoral employment not captured by the model such as changes in shares of formal and informal employment within a sector.<sup>21</sup> The household income projections from the micro‐simulation exercise are presented in Table 7. On average, labor income per capita represents more than 65% of total income per capita in the baseline 2012. Total household income per capita decreases almost 14% in crisis relative to business as usual as a consequence of a reduction in labor income of 20%, a much smaller fall in non‐labor income and services from dwelling, almost 5 and 2 percentage points respectively. The reduction in non‐labor income between scenarios would be the result from a reduction in both types of transfers: public and private (3 and 5 percentage respectively) for the country as a whole (Figure 3).<sup>22</sup> Similar patterns are observed across regions. However, the magnitude of these impacts are higher in IS‐ affected and Kurdistan regions than in other regions. For instance, total income would
normalize the skewed distribution of outcomes (i.e. many observations of low income levels and a few observations of very high income levels).<sup>11</sup>

# **3. Rural incomes and changing living conditions**

This section shows the composition of rural income portfolios across different socioeconomic groups and regions, as well as changes in living conditions and the impediments to increasing prosperity – as perceived by households.

## **3.1 Income portfolios**

As calculated based on the VHLSS data, the following income categories are of importance in rural Vietnam:

- 1) _crop_ income: the output value minus production costs including rice, other staple crops, industrial crops<sup>12</sup> , fruit trees and crop by‐products;<sup>13</sup>

- 2) _livestock_ income: the value of animals and animal products produced (for selling and self‐ consumption) minus production costs;

- 3) _forestry_ income: the value of harvested trees and other forest products (such as firewood), hunted animals, as well as incomes from , forest protection and management minus the production costs;

- 4) _fishing_ income: the value of production and catch of fish and shrimp minus production costs;

- 5) _wage_ income: any cash and in‐kind wages and salaries including from agricultural wage employment and non‐agricultural wage‐employment in unskilled and skilled occupations;

- 6) _business_ income: revenues from businesses outside agriculture, forestry, aquaculture minus cost;

- 7) _transfer_ income include remittances, emergency assistance, insurance, donations, social benefits, support in health care and education;

- 8) _other_ income: including returns from investments, and earnings from weddings and funerals.

The data demonstrates that agriculture and other ecosystem‐based incomes remain an important income source for rural households. Crop, livestock, forestry and fishing incomes and agricultural wages make ca 45 percent of incomes of all rural households (Figure 1)