rafmacalaba/data-use-mentions-extended huggingface dataset:
>-20 80 -20 80<br>-40 60 -40<br>60<br>-60 40 -60<br>40<br>-80 20 -80<br>-100 0 -100 20<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>Stringency of<br>Mobility around<br>Period of interview lockdown<br>transit stations<br>restrictions<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br><!-- End of picture text --> Data from Google (2020) for the mobility trends and Hale et al. (2020) for the lockdown restrictions. The mobility trends are smoothed using 7-day moving averages.

36
)|(0.0064)|
|||||[0.0060]|[0.0073]|[0.0067]|
|Intercept|7.72|6.85|-0.86|7.72|6.84|-0.88|
||(0.15)***|(0.15)***|(0.21)***|(0.17)***|(0.17)***|(0.24)***|
||[0.31]***|[0.41]***|[0.27]***|[0.36]***|[0.47]***|[0.29]***|
|No. Observations|2444|2772|5216|2444|2772|5216|
|||**Estima**<br>|**tes of Mobility**|**from Quadrat**|**ic CEF**<br>||
||**Absolute**|**Mobility**|||**Relative**|**Mobility**|
||**Sons**|**Daughters**|||**Sons**|**Daughters**|
|**_ES0 _**|7.72|6.85||**_IGRC_**|0.26|0.33|
|**_ES6 _**|9.28|8.83|||||
|**_ES9_**|10.06|9.82|||||
|**_ES16_**|11.88|12.13|||||

Notes: (1) The data used are CFPS 2016 with
corruption was considerably _lower._ If these results are replicated at the international level, it suggests the potential for considerable bias in measures such as the CPI.

We further probe whether expert opinion about overall corruption accurately reflects observations reported by firms. Firms probably know better, whether and to what degree they themselves are exposed to corruption. At the same time, firms might underreport based on fear of discovery (Thompson and Shah, 2005). Bias may also lead to over-reporting in answer to questions such as „firms like yours.‟ As Svensson (2005) argues, this might not matter much for cross-country comparisons, if such misreporting is not systematically related to country characteristics. We use the enterprise surveys (ES) of the World Bank to test this hypothesis.

For our exercise we compare rankings. Even if firms were systematically to underreport corruption in highly corrupt countries, this need not change that country‟s ranking if underreporting is similar across countries. Of course, it could be that in some corrupt countries firms try to understate the extent of corruption, whereas in others they do not care to do so. We cannot control for such an effect, but highlight some intriguing cases.

Enterprise surveys, which by now cover 125 countries ask firms whether “an informal gift of payment [was] expected or requested” for eight transactions, to obtain licenses for operation, construction and imports, when paying taxes or applying for a government contract as well as connections to electricity, water and telephony. The correlation between these different indicators of bribery ranges from 0.41 to 0.82 (Table 1 reports selected correlations). Not being very strongly correlated, it thus seems that they may capture a range of information about bribery.

**Table 1:** **<u>Correlation of selected rankings for bribe payments</u>**

|**_Transaction_**|Construction<br>permit|Operations<br>license|Government<br>Contract|
|--
of 2001<sup>6</sup> , providing 58 quarters of data. Over this period the ENEU expanded coverage from 16 cities in 1987 to 34 cities by the end of 1992 and 44 cities by the second quarter of 2001. We include all 39 cities present by the end of 1994, although our results are robust to restricting the sample to just the 16 cities present in all years.

The ENEU only collects data on labor earnings for each household member in their principal occupation. We add this over household members and deflate by the Consumer Price Index for the relevant quarter from the Bank of Mexico to obtain real household labor earnings. To focus only on households for whom labor earnings are likely to be a main source of income, we restrict our sample to households with heads aged 25 to 49 years old. On average two percent of the observations have household labor income of zero. Using data from the ENIGH income and expenditure survey, which does include non-labor sources of income, we calculate that labor income represents 95 percent of total monetary income for urban households with heads in the 25-49 year old age range. In Section 5.2 we examine how mobility in labor earnings compares to estimated mobility in full income and in expenditure.

For our panel data analysis we then have 54 five-quarter panels, beginning with the panel of 3930 households which were sampled from the first quarter of 1987 through to the first quarter of 1988, and ending with the panel of 11,158 households that were sampled from the second quarter of 2000 through to the second quarter of 2001. We use unbalanced panels. Attrition is comparable to dwelling-based labor force surveys in other developing countries. Ten percent of households are observed for only one quarter, while approximately 65 percent of households can be followed for all five quarters.

We form pseudo-panels based on the household head’s year of birth and education level. Cohorts are defined by the interaction of five year birth intervals and three education levels (primary schooling or less, 7 to
Results are similar when relying on the other three allocation rules for the destruction of housing at the local level.

The proposed approach also allows to generate geographically disaggregated estimates of disaster costs. The results show that they are very heavily concentrated in a limited number of districts, especially those around the epicenters, where almost all housing was destroyed (figure 3). In relative terms, losses matter less in these locations, where only a few dwellings were left standing. On the other hand, losses are significant in the Kathmandu valley, which is further away from the epicenters. This varying mix of damages and losses provides further evidence that narrowly focusing on physical destruction can be misleading.

# **9. Comparison with standard estimates**

For the 14 districts most affected by the disaster, the cost assessment obtained using the approach proposed in this paper can be compared to that estimated from the PDNA. Overall, the results are within the same order of magnitude. However, discrepancies are significant at the district level. Clearly, they do not arise from the physical destruction of housing, given that the approach used in this paper simply allocates that physical destruction across households, without modifying the total. They rather stem from the way dwellings are valued, both before and after the disaster.

14
**Figure 3: Informal employment rates estimated using LFS**

<!-- Start of picture text --> 0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>LFS informal (broad) Rosstat official<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> <u>Source: Authors’ calculation using LFS.</u> <u>Note: In the LFS, informal employment includes workers that are employed but not at an enterprise in their primary job, workers</u> employed without a written contract in their primary job, and self-employed workers without registration. This LFS-based measure is henceforth referred to as “LFS (broad)”. Rosstat’s official measure of informal employment is estimated using the LFS and includes workers that are employed but not at an enterprise in their primary or secondary job. All estimates are weighted.95% confidence intervals are shown using dotted lines.

**Figure 4: Informal employment rates estimated using LiTS**

<!-- Start of picture text --> 0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>LiTS informal Rosstat official<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> <u>Source: Authors’ calculation using LiTS.</u> <u>Note: In the LiTS, informal employment is defined as working without a written contract in the main job. Rosstat’s official measure</u> of informal employment is estimated using the
3.7|0.1|-|-|1.8|
||Employee|4.4|3.0|0.0|-|4.0|-|0.1|-|-|2.1|
|**Other**|**Total**|**14.6**|**7.0**|**2.6**|**-**|**8.9**|**3.7**|**0.2**|**-**|**-**|**3.9**|
||Employer|35.2|30.0|34.3|27.9|14.7|31.1|16.1|24.1|33.0|11.1|
||Employee|13.4|18.5|11.0|16.5|22.7|9.0|22.1|9.0|0.0|17.8|
|**Total**|**Total**|**48.6**|**48.5**|**45.3**|**44.4**|**41.2**|**40.1**|**38.2**|**33.1**|**33.0**|**30.5**|

**_<mark>Source:</mark>_** _<mark>European Commission (2011) ''Taxes in Europe - Tax reforms'' database, http://ec.europa.eu/taxation_customs/tedb/taxSearch.html</mark>_

The pension reforms reduced pension expenditures
some cases with productivity problems caused by insufficient language skills.

With respect to gender and family status, the predictions are ambiguous because those whose family status suggests a strong preference towards flexible working time, are also likely to place high value on job security and be more risk averse in general.

# **2 Prevalence and dynamics of informal employment in Europe, 2004-2009**

In this section we use the ESS data to compare prevalence of informal employment (in the main job) across 30 European countries and years 2004 to 2009. For 25 countries we will also show (in Table A3) that ESS-based results for 2004-2006 are well in line with the results of the Fourth European Working Conditions Survey (EWCS) conducted in 2005 (European Foundation for the Improvement of Living and Working Conditions, 2007); it is worth noting

> 7 See Hazans (2011a) for econometric analysis of agents‘ sorting across all six labor market states.

6
the Citizens' Militia, the reserve corps.<br>GDP at market Prices is taken from World Tables 1995 and relates to 1993. Central Government wage bill is taken<br>from Tanzania: Role of Government: Public Expenditure Review of June 17,1994 and refers to 1993.|
|Togo|Data on Central Government, Education and Health employment are taken from Harold Schwartz files and Ministry<br>of Works and Public Functions (Ministere du travail et de la fonction publique) and relate to the situation as of<br>September 1995. Local Government employment is an estimate from Antonella Bassani in AF4CO based on her<br>Public Expenditure Review mission. Health and education are solely the responsibility of Central Government.<br>Data on State-owned Enterprises are from the IMF Staff Country Report No. 96/12 - Statistical Annex, of February<br>1996 and relate to 1995. Data on military personnel do not include enrollment in paramilitary units. The<br>Gendarmerie (750), under the control of the Ministry of Interior, is not included in our data.<br>Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers<br>to 1992.<br>GDP at Market Prices is from World Tables 1995 andisfor 1993. Wages and Salaries are from Antoine Schwartz,<br>Senior Economist, PSP, and are for 1994. Data on Average Public Sector Salary is from Antoine Schwartz, Senior<br>Economist, PSP, and relate to 1994.|
|Uganda|Data on Central Govemment, Non Central Govemment and Education employment is taken from Uganda<br>Government Payroll system, September 1996 and refer to 1996. Data provided by Dennis Hooper, Senior Adviser/<br>Civil Service Reform in the Ministry of Public Service of Kampala. Central Govemment employment includes<br>16,700 police personnel and 3201 people working in the Prison Services. It also includes employees of agencies
a conceptual discussion of alternative aggregation methods.

5. Multidimensional poverty weights selected from information on preferences The “equal-weights” procedure is regularly used when designing national multidimensional poverty measures and always used for global measures. The use of this obviously ad-hoc procedure runs two risks. First, measures based on ad-hoc weights may yield implausible well-being comparisons in the sense that they would deviate too much from people’s views. Second, ad-hoc weights may reduce the perceived legitimacy of these measures. The paper should propose a conceptually sound method to select values for these weights that is consistent with information on preferences (see section 3.6). It should also study empirically the extent to which the obtained weights yield different well-being comparisons than “equal-weights”. The information can come from either subjective well-being data or from a one-off dedicated preference elicitation survey.

40
standard deviation of this prediction as a measure of uncertainty.

Our measure differs from the original one developed by Altig et al. (2020b) in two ways. First, it elicits only three scenarios with subjective probability distributions over the establishment’s future sales growth instead of five scenarios. Second, it focuses on a shorter horizon of 6 months instead of 12 months.<sup>18</sup> Previous research showed that this measure of uncertainty is predictive of future growth and significantly correlated with recent news about the firm’s growth expectations. We report here five main findings based on our results.

First, we find that on average businesses expectations are three times more pessimistic than those in the United States (US) with an average expected sales growth of about -9% in the cross-country dataset and -3% in the United States. Moreover, businesses are significantly more uncertain than businesses in the United States as the average growth rate of uncertainty is close to 21% which is about 4 times larger than uncertainty among US businesses.<sup>19</sup> Second, a striking finding is that uncertainty is broad-based and cuts across all firms irrespective of their size, and strikingly similar across sectors too, as shown in Figure 19 and Figure 20, with the exception of the hospitality industry (food preparation services and accommodation) where the situation is significantly worse.

Third, expectations and uncertainty clearly reflect the firm-level experience at the micro level as firms that experienced larger sales drop in the last 30 days are both more pessimistic (i.e. expect lower sales growth in the future) and have higher uncertainty (Figure 21). Similarly, at the macro level, the size of the shock experienced at the country level is correlated with both expectations and uncertainty as businesses in those countries hit by a larger crisis (as measured by the magnitude of the mobility drop) are significantly more pessimistic and have higher uncertainty (Figure 22).<sup>20</sup>
results. You would be well advised to get a new doctor!<sup>5</sup> Or imagine that a new car comes on the market that collapses all those dials on the dashboard into just one composite index, on which you are supposed to decide what to do (slow down or get fuel). You would surely not buy this car!

Essentially the same point applies to the task of prioritizing policies for fighting poverty in a given country (or other geographic area). We will naturally want to look at the country’s attainments in various dimensions, rather than focusing on its performance with respect to a single composite index. Should we be focus on promoting job creation (say) or better health and education services? Such an approach does not deny that poverty is “multidimensional.” Rather it says that forming a single (uni-dimensional) index may not be particularly useful for sound development policy making.

Consider now the MIP developed by Alkire and Santos (2010a) for the 2010 _Human Development Report_ (UNDP, 2010). They choose 10 variables for their MIP under the same three headings—health, education and living standards—as the UNDP’s _Human Development Index_ (HDI). There are two variables for health (malnutrition, and child mortality), two for education (years of schooling and school enrolment), and six for deprivation in “living standards” (namely cooking with wood, charcoal or dung; not having a conventional toilet; lack of easy access to safe drinking water; no electricity; dirt, sand or dung flooring and not owning at least one of a radio, TV, telephone, bike or car). Poverty is measured separately in each of these 10 dimensions. The equally-weighted aggregate poverty measures for each of these three main headings are then weighted equally (one-third each) to form the composite index, also echoing the HDI. A household is identified as being poor if it is deprived across at least 30% of the weighted
tenure of three top leaders, among which Hu Jintao’s tenure is the longest (2003—2012). Column 4 thus restricts the sample to Hu’s tenure. In column 5, when matching the city leader data with annual data on night lights and economic variables, in cities where multiple leaders hold the same post within a given year, we choose the PS with the longest tenure rather than the last PS as in the baseline. We also allow standard errors to be clustered at the provincial-year level in column 6, and the provincial level in column 7. Reassuringly, the political cycle effect on GDP manipulation remains similar to the baseline.

# **5.3 Institutional environment and performance manipulation**

Institutional quality could shape local bureaucrats’ performance manipulation. In regions with more corruption and less marketization, local monitoring such as that by media would be less likely. The likelihood of being detected when local officials manipulate GDP should be smaller, making the costbenefit analysis of performance manipulation more likely to point to being beneficial. Marketization further increases the respect for the norm of trust, which might also restrain falsifying GDP. We thus

> 39 We treat all the regressors as pre-determined except the province-year FEs and use one and two lags of the regressors as instruments for the equation in first-differences. There is no evidence of serial correlation of order two in the differenced residuals and the Hansen’s _J_ test is also passed.

> 40 In 2019, the GDP combined of these cities accounted for nearly 20% of the country’s total GDP.

21
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Table 9: Long-term Effect of FPE on Educational Achievement: DR DID estimates

|VARIABLES|(1)<br>|(2)<br>|
|---|---|---|
||Treatment group:<br>First year students|Treatment group:<br>16-18 year olds|
|Panel A: Traditio<br>|nal DR DID estimates||
|ATT (<sup>ˆ</sup>_δDRDID_)|2.8628***<br>(0.8439)<br>[1.1369, 4.5888]|0.7666<br>(0.5148)<br>[-0.3085, 1.8416]|
|Observations|14,095|14,095|
|Panel B: Improve|d DR DID estimates||
|ATT (<sup>ˆ</sup>_δDRDID_)|5.7425***<br>(0.8318)<br>[1.4052, 10.0798]||
|Observations|14,095||

_Notes_ : The point estimates of the FPE effect on achievement (OWM) and standard errors in parentheses are produced using `DRDID` R code by Sant’Anna and Zhao (2020). Bootstrapped standard errors with 1000 replications are in parentheses. 95% Confidence intervals in brackets. Significance levels are indicated as follows: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. The covariates used in the estimation of the propensity score are APS, gender, district, and age (age dummies in the first column to approximate the cohort effects, and age and age squared in the second column). Panel A reports traditional DR DID estimates that are DR consistent and locally semiparametrically efficient. Panel B reports DR DID estimates that are DR consistent, locally semiparametrically efficient
in the context of related literature. Section 2 then very briefly introduces our dataset of annotated interview transcripts. Section 3 describes our approach to using LLMs for annotation (3.1) and the supervised NLP method introduced by Ashwin et al. (2022) which we refer to as iQual going forward (3.2). Section 4 then describes LLM-based out-of-sample performance in comparison to iQual (4.1) and then shows that LLMs introduce more bias and illustrates this could cause researchers to draw incorrect conclusions (4.2). Section 5 then concludes.

# **2 Data and Qualitative Analysis**

The interview transcripts, data collection and the qualitative coding process are explained in detail in Ashwin et al. (2022), so we restrict ourselves to a very brief description here. The population we sample are displaced Rohingyas based in the Cox’s Bazzar camp and local Bangladeshi residents. Along with a standard household survey including questions on demographics and economic conditions, the data include transcripts of 2,407 open-ended interviews with subjects on their aspirations for their eldest child. The interviews were conducted either in Bengali or in Rohingya which was then transcribed into Bengali, but we work with machine translations into English. The interviews take the form of an unstructured to-and-fro of question and answer (QA) pairs the interviewer and the subject. The interviews are on average 12.6 QA pairs long, with the average answer in each QA pair being 13.7 words long.

Based on a close reading of a subset of transcripts, and following a "flexible coding" process (Detering and Waters, 2018), a coding tree was developed including 25 potentially overlapping categories, 19 of which we focus on in this paper. A full description of each code along with examples are shown in Appendix A. Following Callard (2018) the distinction between aspiration and ambition was adapted within the context and nature of "dreams" parents expressed for their children. For example, concrete and measurable dreams for child (
size, marital status of household head, age of household head, whether the household uses electricity for cooking or lighting, dummy variables for regions, and the religion of the household head, but are not shown. Standard errors in parentheses are clustered at the enumeration area. Female-only households are households with no male adult present. Household consumption is the spatially-adjusted adult equivalent scale household consumption.

Of the female entrepreneurs, 40% are divorced, widowed, or unmarried, and approximately 40% live in female-headed households (in Appendix 4). There is some evidence that the composition of enterpriseowning households changed between the survey periods. In period 1, 32% of female entrepreneurs live in households without male adults, but this percentage drops to 9.5% in period 2. The absence of male adults in the households is likely to be disadvantageous for women wanting to engage in more profitable activities. Studies have shown that spousal support or male mentors could play an important role in helping female entrepreneurs enter male-dominated and more profitable sectors (Alibhai et al., 2017, World Bank 2022).

8