rafmacalaba/data-use-mentions-extended huggingface dataset:
growing season rainfall spanning November of the previous year to March of the following year. We take advantage of the long time series to compute 30-year, long-term average rainfall, and long-term measures of rainfall variability such as the standard precipitation index (SPI), rainfall stress, and coefficient of variation.

The RALS data are large data sets collected from 8,839 households in 2012, 7,934 in 2015, and 7,241 in 2019. All three RALS waves were designed to be statistically representative of the rural population at the provincial and national levels, and 6,531 panel households were successfully interviewed over all three waves. This paper only uses data for the households that formed a balanced panel. Sampling and survey details can be found in CSO/MAL/IAPRI (2012); and CSO/MAL/IAPRI(2015).

# 4.2 Variables used in the main regressions

# 4.2.1 Dependent variables

This paper aims to explain the effects of climate shocks on vulnerability and resilience. We computed poverty incidence using real household income deflated using consumer price indices (CPI) for the relevant survey years. We then estimated a level of real income that would equate the rural poverty rate in the 2015 RALS to the official national rural poverty rate reported in the 2015 LCMS. This gave us a national poverty line of ZMW 2,697 per adult, per year in 2015 kwacha. That is, a household is deemed “poor” if real per adult income is less than the threshold of ZMW 2,697 per year.

We are also interested in understanding transitions in and out of poverty between 2012 and 2019. A household is chronically poor if it is recorded as being poor in all the three survey waves. A household that has escaped poverty is one that was poor either in 2012 and/or 2015 but not in 2019, while household entering poverty is one that was not poor in 2012 and/or 2015 but fell into poverty in 2019. It is worth noting that
enhances the accuracy of users’ taxable transaction reports and minimizes the possibility of taxpayers’ underreporting. Down the line, the produced sales records can be accessed by the tax authority for compliance monitoring.

We begin by discussing a conceptual framework to consider how surveillance enhancement may affect firm behavior. The POS distribution program improves the accuracy and timeliness of taxable sales records and has a deterrent effect on businesses’ noncompliance. However, for POS adoption to positively affect tax payments, the program’s beneficiaries must install and use POS units. When left idle and/or tampered with the purpose of altering tax liability, the government’s control function running through the system becomes muted. However, studying recipients’ behavior towards adopting the assigned POS is beyond the scope of our study. This study investigates the general link between a program’s application and the obtained tax payments.

To empirically examine the influence of POS distribution on tax payment, we utilized administrative data on firm-level monthly restaurant tax receipts, the list of POS recipients, and the time of POS distribution to perform a difference-in-difference (DiD) estimation in the Indonesian districts of West Manggarai and Gorontalo.

> 1Throughout this paper, ’Gorontalo’ refers to The City of Gorontalo _(Kota Gorontalo)_ , not to be mistaken with The Regency of Gorontalo _(Kabupaten Gorontalo)_ or The Gorontalo Province.

1
past crises resulted in the exit of both stronger as well as weaker firms (Foster et al., 2016; Hallward-Driemeier & Rijkers, 2013). If there were cleansing effects from GFC foreign shocks, we would expect the increases in exit estimated in Brazil to be higher for less efficient firms. To test this hypothesis, we estimate a variant of Equation (6) where, in addition to allowing the firm shock variable in 2008 to have a coefficient that varies over time, we allow the firm shock variable in 2008 to be interacted additionally with one of three proxies for firm efficiency: firm size in 2007, firm labor productivity or one of three firm total factor productivity (TFP) measures obtained either by Levinsohn & Petrin (2003), Ackerberg et al. (2015) or Wooldridge (2009) estimation.<sup>38</sup> The probability of exit due to the GFC firm shocks is significantly lower for larger firms and for more productive firms (see Appendix Table D1).

> 37Net revenues and profit rates are based on Brazil’s PIA manufacturing survey. Exit, the logarithm of firm size defined as the total number of workers employed by the firm in each year and the logarithm of the firm total wages defined as the sum of monthly real wages across all workers employed by the firm in each year are based on Brazil’s RAIS starting worker-level database. The definitions of these variables are provided in Appendix A. Our regressions rely on firm samples where the top and bottom 1 percent of the distribution of the continuous outcome variables are dropped.

> 38The estimates using labor productivity or TFP are based on the the PIA survey and were thus obtained in the secure room at IBGE premises in Rio de Janeiro.

18
of the sample due to attrition over the five periods. Thomas, Frankenberg and Smith (2001) discuss the experience of the Indonesia Family Life Survey, which explicitly tracked movers, and do find that those who move are different in terms of initial characteristics than those who stay. Although they do not examine whether changes in income or

> 2 Luttmer actually examines mobility in expenditure, rather than income, and uses income and subjective well-being as instruments for expenditure.

3
-|---|
|||Native|Rural|Urban|Native|Rural|Urban|
|Chiapas|1,983|605.7|338.1|<br>210.7|<br>30.5%|17.0%|<br>10.6%|
|Guerrero|2,253|749.1|764.2|<br>62.8|33.2%|33.9%|<br>2.8%|
|Hidalgo|2,143|1,127|636.8|<br>51.0|52.6%|29.7%|<br>2.4%|
|Oaxaca|1,770|624.1|229.7|<br>23.8|35.3%|13.0%|<br>1.3%|
|**Average**|**2,037**|**776.4**|**492.2**|<br>**87.1**|<br>**_38.1%_**|**_24.2%_**|<br>**4.3%**|

Source: PARE's database.

* Unit cost for primary schools in native communities, SEP.

** See Table 12.

> 5 The percentages are obtained as follows: the difference in cost increase between native and rural areas (and native and urban areas) is divided by the cost increase in rural (urban) strata.

14
3|22.4**|
|<br># of competitors|14.6|35.0|15.0|14.1|0.9|
|**Individual Characteristics**||||||
|Owner age|33.5|9.0|33.4|33.6|-0.2|
|<br>Married / Living with someone|80.1|39.9|86.1|71.0|15.1***|
|<br>HH decision making index (0-100)|84.2|19.8|86.7|80.4|6.3***|
|<br>Main provider of income to household|76.9|42.2|94.9|49.6|45.3***|
|<br>Literate|<br>91.5|<br>27.9|<br>92.9|<br>89.3|<br>3.6***|
|Primary school completed is max education|35.6|47.9|36.6|34.2|2.4|
|<br>Secondary school completed is max education|24.3|42.9|23.9|25.0|-1.2|
|Higher education completed|53|224|45|65|-20**|
|<br>High capture|.<br>36.2|.<br>48.1|.<br>35.8|.<br>37.0|.<br>-1.2|
|<br>Financial Literacy knowledge (0-1)|0.43|0.2|0.44|0.42|0.0***|
|<br>
. Once they initiate care late, then it is hard to get them to return multiple times in a short period of time. From the providers perspective all this effort only yields them an additional $0.37. Moreover, providers can still get the payments for providing tetanus vaccine and malaria prophylaxis as long as women get any care.

Finally, we find no evidence that P4P led to increases in contraceptive prevalence rate, despite the relatively high payment rate for new acceptors. This may be due to the fact that we measured actual use at the time of the household survey, which is influenced more by resupply rates than by initial use rates, while P4P payments were made for initial acceptors of contraceptives only.

22
of 50-60%|8.34|1.13|5|10|1001|
|Income share of 60-70%|9.84|0.93|7|12|1001|
|Income share of 70-80%|11.91|0.64|9|14|1001|
|Income share of 80-90%|15.39|0.91|13|18|1001|
|Income share of top 10%|30.02|7.93|18|52|1001|
|Financial Reform Index|10.36|6.34|0|21|2638|
|Bank Liberalization|1.26|1.19|0|3|2638|
|Social pact|0.24|0.47|0|5|1637|
|Labor law|41.85|18.98|6|94|2335|
|Social reform|0.05|0.22|0|1|1586|
|Pension reform|0.02|0.13|0|1|1585|
|Money and quasi money growth (%)|35.26|272.63|-100|12513|6223|
|Private credit (log)|22.71|2.80|10|31|7094|
|Price level of capital stock|0.50|0.35|0|4|7436|
|Infation (%)|31.90|396.14|-18|23773|5914|
|Incidence of Part-Time Employment|14.07|6.98|2|39|908|
|SSA benefts (log)|2.47|0.48|0|3|988|

Table 1: Summary statistics.

Our central independent variable is CBI. We use a recently updated version of the CBI measure as proposed in Bodea and Hicks (2015 _b_ ). Following the coding protocol of Cukierman, Miller,
reference points for the NSOs and survey practitioners in low- and middle-income countries that may adopt (a) questionnaires and fieldwork protocols similar to ours, including the goal of minimizing the use of proxy respondents while collecting personal information on adults; and (b) the use of paradata and empirical methods presented in this paper to improve further efficiency and quality of future surveys.<sup>3</sup> The paper is thus structured as follows. Section 2 discusses the LSMS+ data as well as how the paradata was extracted and cleaned. Section 3 provides descriptive statistics related to time costs of individual modules in the LSMS+ in comparison to household modules and a land module example of using interview length and budgets to make design decisions. Section 4 conducts a multi-level model analysis to understand how the interviewer effect contributes to variation in interview length. Lastly, Section 5 concludes.

> 3 Since the implementation of the surveys that inform our analyses, Survey Solutions issued multiple updates that also have a bearing on the paradata file structure. While researchers may not be able to leverage our syntax files with ease for the purpose of analyzing their own Survey Solutions paradata, our syntax files can be made available upon request. Our paradata, however, cannot be shared, as they contain confidential information that is excluded from the public use survey datasets.

5
economical volumes at the time of discovery, as measured by Rystad Energy’s Ucube database.

> 3 The average Brent oil price was $52.4 per barrel (bbl) and average US gas price was $3.87 per million British thermal units (mmBtu).

> 4 This is clearly a simplification as it ignores what amounts of resources found are recoverable, the costs and profits of extraction, etc. However, it provides a ballpark estimate of their maximum revenue potential. 5 We had to use 2011 data for South Sudan because it did not exist before then.

2
# **3. Data and Empirical Strategy**

_Data_ To test the existence of an asymmetric response to exchange rates and explore three complementary explanations, we use data sets at the macro and product-destination levels.

We use macro-level data for assessing export adjustment to changes in the RER from 1996 to 2019 – the choice of the period being of the maximum length the data allow. Pakistan’s exports at the quarterly level are obtained from the International Monetary Fund’s (IMF) International Financial Statistics - until end-2002 - and from the State Bank of Pakistan (SBP) starting from 2003; annual export data for robustness are obtained from the World Bank; the RER is obtained from Bruegel’s data set (Darvas 2012). Because quarterly data for global income for the period of analysis were not available, we proxy them by using the combined GDP of China, the United States, Japan, and the EU-28, which, altogether, account for 68 percent of the world’s income.<sup>9</sup> GDP for China is obtained from the Bureau of Statistics of China, the United States’ from the U.S. Bureau of Economic Analysis, Japan’s from the Economic and Social Research Institute, Cabinet Office of Japan, and the EU’s from Eurostat; exchange rates used to convert foreign GDPs to U.S. dollars are from the IMF.

We use product-destination-year level data for checking robustness of our macro-level results and testing our proposed hypotheses on the drivers of the asymmetric export response. We obtain product-destination level exports from UN Comtrade at HS6 level for 2003 to 2017. The choice of the period is again determined by data availability. We compute BRERs based on nominal exchange rates taken from the Bank of International Settlement (BIS) and price indices taken from

> 9 The sum of these economies represents 67.9 percent of total GDP and 53.0 percent of total Pakistani exports in 20032017.

14
location. There are also outcome measures for children that can be linked to mother‘s marital status but only for children who reside with their mothers. These include weight for height percentiles, school enrollments and whether daughters are treated differently from sons. Although I cannot link non-cohabiting children with mother‘s marital status, I can compare enrollments of children who do and do not live with their mothers.

# **Tests for selection bias**

Two drawbacks of the following analysis should be kept in mind. First, the DHS cannot shed light on the large group of widows and other women who are older than 49, and possibly include the most vulnerable. Nothing can be done to address this data problem.

Second, the DHS sample of ever-widowed or divorced women is potentially selected in the sense that it may not be random that some women experience a marriage dissolution shock at a relatively young age. One potential objection to comparing outcomes for widows and other women is that there may be selection bias whereby widows start off with worse outcomes prior to becoming widows. For example low BMI could predate marriage and widowhood. Using longitudinal data on health and retirement in the US, it has been shown that, allowing for the fact that poorer men are more likely than other men to die young (given the strong association between income and mortality), young widows are more likely to come from poorer households (Hurd and Wise 1989, Sevak et al. 2003). However, the relevance of the US case to Mali ─ where widowhood at a young age is not usually associated with the death of a young husband given large age gaps between spouses ─ is questionable. In the Mali setting, it may be that, for example, women with initially low BMI marry much older men, increasing the probability of widowhood. In either case, the ―impacts‖ on well-being predate a husband‘s demise. The key issue is then whether women who become widows were already poorer, in which case there may be no
|0.1%|
||part time,fixed term|0.7%|0.7%|0.6%|0.6%|0.6%|
||self employed|1.4%|1.1%|1.0%|1.2%|1.2%|
||NOT EMPLOYED|93.6%|93.8%|93.4%|93.3%|93.3%|

Source: Author‘s calculations based on data from Polish LFS data for years 2003-2008.

Those with full time and fixed term contracts are also the least stable groups in the labor market with only 60%-70% of employed continuing the same labor status in the next year. What seems interesting is that these are groups with relatively high probability of entering full time and open term contracts, suggesting that they may consider staying on a fixed term
that produce reliable results when data from the census can be used or when the focus is on a poverty index with an exogenous poverty line. Table 1 reports the direct estimates of three inequality indices calculated from the 2000 and 2007 HBSs. The standard error reported in brackets is computed with a bootstrap estimator based on 500 iterations.

Table 1: Estimated Indicators by Sample Unit

|Id|20|00|20|07|
|---|---|---|---|---|
|nex|Rural|Urban|Rural|Urban|
|Gini|0.349|0.408|0.353|0.430|
||(0.006)|(0.005)|(0.006)|(0.006)|
|Theil|0.240|0.325|0.228|0.361|
||(0.017)|(0.016)|(0.012)|(0.015)|
|HCR|0.161|0.209|0.177|0.194|
||(0.005)|(0.004)|(0.007)|(0.006)|

_Source_ : Authors’ calculations based on Moroccan HBS.

Before moving to regression techniques, it is necessary to identify the variables that could be used as regressors. To do this, we must bear in mind that a bias could also arise from differences between questions and answers in the surveys.<sup>3</sup> To better understand the SSIT results, the trends of dependent and independent variables should be examined to predict trends for our indicators. The sample mean expenditure per capita in 2000 was 1 _,_ 752 _._ 96$ for rural areas and 3 _,_ 514 _._ 9$ for urban areas, which increased to 2 _,_ 265 _._ 9
increase in cognitive/SEM skill is associated with $8.3/$16.5 higher monthly earnings for men. For women, point estimates are smaller, less stable, and lack statistical precision.

This association between labor earnings and skills for men varies by respondents’ migration status.<sup>4</sup> Among the 65% of men still resident in their native village (non-migrants), we find a precisely estimated _zero_ association between labor earnings and years of schooling and cognitive skills, but a strong correlation between SEM skills and labor earnings. This result is robust to multiple specifications and sample definitions. Among men who have left the village (migrants), we find a significant correlation between labor earnings and years of schooling. The differences in the association between labor earnings and years of schooling for migrants and non-migrants reflects not just the fact that LFP is higher among migrants, but also that conditional on LFP the association between earnings and years of schooling is also significantly higher for migrants. Finally, we also find a strong correlation between labor earnings and cognitive skills for men in median regressions, but this result is less stable across multiple specifications.

To understand if the lower returns to years of schooling and cognitive skills among non-migrants is a facet of this particular sample, we turn to the second dataset from Cambodia. As in Pakistan, this sample comes from a rural area (poorer than in Pakistan) and includes children who have been tracked and re-surveyed in adulthood. Here, we again find that the correlation with years of schooling is close to zero, and although the results are more imprecise, the association with SEM skills is positive. Thus, for those who have remained in the village, the labor market appears to reward skills that are only weakly correlated with years of schooling; but for those who have left their village, the labor market rewards both the cognitive skills that are produced by schools as well as years of schooling itself. This result is reminiscent of Schultz’s (
|**_Other "Non Market"_**|**_2.5_**|**_5.2_**|**_9.8_**|**_2.1_**|**_3.9_**|
|**_Other_**|**_5.3_**|**_5.8_**|**_4.0_**|**_1.1_**|_0.7_|

_Rad, Share in Investigations (Measures) /Share in World Exports_ _Source WTO, Trade and Antidumping Data Base, Mirandaetal. 1998._ 21
total movie trade is divided by X’s GDP or population, the two methods might render totally different results. GDP’s great merit is that since trade-offs are measured by market prices, we know the terms at hand. That noted, the immediate purpose of this index is to achieve the following objectives:

- 1) To shed light on patterns of cultural trade effectively.  The index would assemble cultural trade data in one place where they can easily be studied.  And as a database, it could function as a public good (for anyone who wishes to learn about patterns of international cultural trade).

- 2) Policy action: To pursue policy action that may help promote effective cultural trade policies for development.  Ranks and comparisons are not often golden gates to truth. But, in our context, if country X is number one in attracting global cultural tourism, that rank might serve as a proxy for country X to stay competitive in this sector.  This is not new.  And indeed policy makers elsewhere could consider what allows X to succeed while others, with more or less similar features, have not.

- 3) Improve research on cultural data: Anyone who has done research on the cultural or creative economy perhaps faces the same challenge that inspired the creation of this index: the paucity of data.  This can be a major challenge in convincing skeptics and policy makers to allocate scarce resources to promote the creative sector — although, of course, data availability does not automatically lead to policy priority; nor does availability necessarily lead to access, better

> 4 Panitchpakdi and Kemal Derviş 2008, pp. iii-iv. 5 Ibid., p. iii.

> 6 See Kabanda 2014.
used as a variable in other studies including Fan et al. (2009), Duvanova (2014), and Amin and Soh (2020).

Firm-level responses to the bribery question cannot be used directly in our regressions as they are likely to be endogenous to the firms’ growth experience (reverse causality problem) and firm characteristics (omitted variable bias problem). For instance, large firms are more visible and have higher ability to pay bribes. Thus, these firms may be targeted by corrupt public officials. At the same time, growth rate is also known to vary systematically with firm-size. One solution suggested in the literature is to proxy the level of corruption faced by a firm by the average level of corruption experienced by all other firms (that is, other than the firm in question) in the countryindustry cell.<sup>2</sup> Using country-industry cell average also helps to control for potential measurement error if some firms choose not to respond or misreport the regulatory burden (Pounov 2016). Thus, we define our main explanatory variable, _Overall Corruption_ , as the average of the bribe rate reported by the firms “to get things done” where the average is taken over all firms in the countryindustry cell excluding the firm in question. For later reference, the term “CI avg.” denotes the cell average at the country-industry. To ensure adequate thickness within the cells, all cells with fewer than 5 firms are excluded from the sample. As mentioned above, there are 712 country-industry cells in our baseline sample for overall corruption. In this sample, the mean value of _Overall Corruption_ equals 1.18 and the standard deviation equals 1.81.

# 2.4.2 _Petty corruption_

The ES also contains information on instances of corruption that firms experience in soliciting the following: obtaining electricity connection, obtaining water connection, obtaining construction

> 2 Reverse causality from a firm’s own growth rate to the bribe payments experienced by the other firms in the countryindustry cell is highly