rafmacalaba/datause-encoder-data huggingface dataset:
to drive through all the streets of the sampled sub-counties. The mobile monitors were also active 24 hours a day and produced an average of 30 (median 31) measurements an hour for a total of 119,011 in our sampled sub-counties.<sup>18</sup> By moving across space, the mobile monitors allow us to measure the spatial variation in pollution at a highly granular level within the city. By virtue of being fixed in one location, stationary monitors allow us to precisely measure the time variation in pollution. In Section 5, we describe how we use both types of measurements in our empirical approach.

**Checks and descriptives.** We present two descriptives about our pollution data. First, in Appendix Figure A15, we show that the average PM2.5 readings of mobile and stationary monitors in the same sub-county are positively correlated (the correlation is 0.34, significant at the 1% level). This positive correlation reassures us about the internal validity of our measurements.<sup>19</sup> Second, in Figure 1, we report average and median pollution readings by hour of the day, from both stationary and mobile monitors. The figure reveals that: (i) the stationary and mobile measurements track each other closely, which reassures us about the quality of our data; (ii) average levels of PM2.5 are high in our sample, oscillating between 30-90 micrograms per cubic meters, which lines up well with the average of 50 micrograms per cubic meter reported by the World Bank in 2017 and mentioned in Section 2; (iii) there is a strong cyclicality in pollution within the day with peaks at rush-hour in both mornings and evenings, which indicates that, as expected, the main source of pollution in these urban areas is road traffic rather than economic activity, something that we explore further and confirm again in Section 6. This hourly pattern is robust: we reach the same conclusion if we use the stationary or mobile readings, and if we use the average or the median readings.

# **4.3 Firm Survey**

The second data source is a novel survey of manufacturing firms that we conducted in Uganda. The survey took place in all our target 52 sub-counties between September 2018 and July 2019 and was implemented by our partner NGO, BRAC Uganda, in partnership with the Ministry of Trade. Within each target sub-county, urban and semi-urban parishes were surveyed. The survey is described in detail in Bassi et al. (2021). Here we summarize again the key elements of the sampling of firms and survey design, and then focus on those aspects that are particularly relevant for this paper.

> 18In those sub-counties where the stationary and mobile monitors overlap, all active mobile monitors were within proximity of a stationary monitor. The median distance between a mobile pollution measurement and the closest stationary monitor is 2.345 km and 95% of measurements are within less than 7 km from a stationary monitor.

> 19 Of course, we expect the correlation of the pollution readings from stationary and mobile monitors within sub-counties to be less than one as the mobile monitors were potentially hundreds of meters away from the stationary monitors at times.

13
discuss below, we find that in many other cases expert assessments and household survey responses do in fact correlate quite well across much larger samples of countries.

We note also that well-designed survey questions regarding corruption have become increasingly specific. For example, in some years questions in the Executive Opinion Survey of the World Economic Forum have asked firms to specifically report the fraction of contract value solicited in bribes on public procurement contracts. Greater attention is also being paid to techniques that enable respondents to report more truthfully to sensitive questions. For example, questions about corruption put to firms are often prefaced by "in your experience, do firms like your own typically pay bribes for.....?". Innovative techniques such as randomized response methods are used to protect the confidentiality of individual responses by allowing respondents to "camouflage" their response to sensitive questions by generating some of their responses at random based on the outcome of a coin toss, although they have not yet been widely used in large cross-country surveys.<sup>15</sup> A related concern has to do with surveys of firms or individuals carried out in authoritarian countries where respondents might legitimately be fearful of responding truthfully to any question that might be interpreted as critical of the government.

Another potential difficulty in cross-country surveys of firms and individuals are cultural biases. It is often argued that respondents in different countries might have different norms as to what does or does not constitute corruption, and so their responses are not comparable across countries. Presumably however these cultural biases should not be present in cross-country expert assessments that are deliberately designed to be comparable across countries. And in many cases it turns out that surveys and expert assessments tend to produce very similar cross-country rankings. In Table 6 of Kaufmann, Kraay and Mastruzzi (2006b) we document sizeable correlations between expert assessments and the World Economic Forum's Executive Opinion Survey, for six different dimensions of governance. And a glance at Table 2 provides similar examples as well: for example the correlation across countries between the assessments of WMO, a commercial rating agency, and the Executive Opinion Survey, regarding

> 15 See for example Azfar and Murrell (2006) for an assessment of the extent to which randomized response methods succeed in correcting for respondent reticence, and an innovative approach to using this methodology to weed out less-than-candid respondents.

22
employment as the narrow one in the following text, as opposed to the basic one described earlier.

Therefore there are three estimation procedures used to identify the workers in LFS matching of the shadow workers from the CASE 2007 database:

- Estimations where basic definitions were used and the contract variable was included into the model specification

- Estimations where basic definitions were used but without the contract variable in the model specification

- Estimations with narrow definitions of shadow employees and workers used and with the contract variable used in model specification.

Simple Probit and Logit models have been estimated for variables representing various definitions of shadow employment on the left hand side and all variables created on the right hand side of the model in the basic case. The models have been estimated using the CASE_2007 dataset. In the estimations the employees, the self-employed and the employers have been excluded from the sample. The mining sector has been excluded from all estimations as there were no shadow employees working in this sector and hence it predicted failure (i.e. legal employment) in 100% of cases.

The estimated coefficients have then been used to predict the probabilities of working in shadow employment in the LFS 2003-2008. As a result 12 variables with various shadow employment proxies (probabilities) have been obtained for each employed in the LFS samples:

- 4 for the basic case (from logit model for employees only, from probit model for employees only, from logit model for employees and self-employed and from probit model for employees and self employed),

- 4 for the respective models without ―contract‖ variable in specifications and

- 4 for respective models where narrow definitions of shadow employment where applied (i.e. excluding those with verbal contracts).

# **3. Results on the General Trend in Shadow Employment in 2003-2008**

The resulting general trends estimates for the share of shadow employment in total employment are very similar for both logit and probit models (see Table 1 – all rows). The results (in terms of trends) do not significantly differ between shadow employees and shadow workers either. The observed absolute values differ obviously as for the latter the definition of shadow employment is broader. (See Table 1 – all rows.) There are significant (and obviously expected) differences in absolute values of estimated average probabilities depending on definition of shadow employment used (broad (basic)including those with verbal contracts only vs. narrow – without those with verbal contracts) (compare Table 1 rows 1-8 with rows 9-12.). The trends however are very similar to those observed for the basic case.

On the other hand there are significant differences in time trends observed depending on model specifications for basic definitions of shadow employment (compare Table 1 rows 1-4 with rows 5-8).
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# **References**

Ahmed, S., B. Coulibaly, and A. Zlate. 2017. “International Financial Spillovers to Emerging Market Economies: How Important Are Economic Fundamentals?” _Journal of International Money and Finance_ 76 (September): 133-152.

Aizenman, J., M. Binici, and M. M. Hutchison. 2016. “The Transmission of Federal Reserve Tapering News to Emerging Financial Markets.” _International Journal of Central Banking_ 12 (2): 317–356.

Albagli, E., L. Ceballos, S. Claro, and D. Romero. 2019. “Channels of US Monetary Policy Spillovers to International Bond Markets.” _Journal of Financial Economics_ 134 (2): 447-473.

Arbatli, E.C., M. Firat, D. Furceri, and J. Verrier. 2022. “U.S. Monetary Policy Shock Spillovers: ” Evidence from Firm-Level Data _._ IMF Working Paper 22/191, International Monetary Fund, Washington, DC.

Arteta, C., M. A. Kose, F. Ohnsorge, and M. Stocker. 2015. “The Coming U.S. Interest Rate Tightening Cycle: Smooth Sailing or Stormy Waters?” Policy Research Note 15/02, World Bank, Washington, DC.

Aruoba, B., and T. Drechsel. 2022. “Identifying Monetary Policy Shocks: A Natural Language Approach.” CEPR Discussion Paper 17133, Centre for Economic Policy Research, London.

Bagliano, F.C. and C.A. Favero. 1999. “Information From Financial Markets and VAR Measures of Monetary Policy.” _European Economic Review_ : 43(4-6): pp.825-837.

Bauer, M., and C. J. Neely. 2014. “International Channels of the Fed’s Unconventional Monetary Policy.” _Journal of International Money Finance_ : 44 (June): 24-46.

Bernanke, B.S. 1986. _Alternative Explanations of the Money-Income Correlation._ NBER Working Paper 1842, National Bureau of Economic Research, Cambridge, MA.

Bernanke, B.S. and A.S. Blinder. 1988. _Credit, Money, and Aggregate Demand._ NBER Working Paper 2534, National Bureau of Economic Research, Cambridge, MA.

Bordo, M. D., and K. Istrefi. 2021. “Perceived FOMC: The Making of Hawks, Doves and Swingers.” NBER Working Paper 24650, National Bureau of Economic Research, Cambridge, MA.

Bowman, D., J. M. Londono, and H. Sapriza. 2015. “U.S. Unconventional Monetary Policy and Transmission to Emerging Market Economies.” _Journal of International Money and Finance_ 55 (July): 27–59.

Bräuning, F., and V. Ivashina. 2020. “US Monetary Policy and Emerging Market Credit Cycles.” _Journal of Monetary Economics_ 112 (June): 57-76.

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Policy Research Working Paper 10724

# **Abstract**

The complex relationship between climate shocks, migration, and adaptation hampers a rigorous understanding of the heterogeneous mobility outcomes of farm households exposed to climate risk. To unpack this heterogeneity, the analysis combines longitudinal multi-topic household survey data from Nigeria with a causal machine learning approach, tailored to a conceptual framework bridging economic migration theory and the poverty traps literature.

The results show that pre-shock asset levels, in situ adaptive capacity, and cumulative shock exposure drive not just the magnitude but also the sign of the impact of agriculture-relevant weather anomalies on the mobility outcomes of farming households. While local adaptation acts as a substitute for migration, the roles played by wealth constraints and repeated shock exposure suggest the presence of climate-induced immobility traps.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at apaolantonio@worldbank.org. A verified reproducibility package for this paper is available at http:// reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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earned by the firm in the first tax year following the tax holiday period. Assume that the profits of the firm at the time immediately following the tax holid4y are sufficiently large t,o absorb all depreciation allowances that have accumulated over the tax holiday period. Should the firm decide to distribute its profit during the holiday period, the dividends received by its shareholders are also exempted from personal income taxes.

These features of the treatment of tax holiday firms make the computation of the before-tax rate of return on investment complicated. The tax holiday provisions make **_Zt, r,_** and _ut_ all vary over time. Consider the computation of **_u,_** _rt_ and Zt in turn.<sup>2 6</sup> The effective statutory corporate tax _u,_ to the firm over the tax holiday period is zero since the firm is completely exempt from paying any tax on its income. However, the corporate tax rate will revert to the standard rate u after the tax holiday. Therefore, The cost of finance to the firm is given by: where we have neglected new equity issues and we treat the parameters fi, i and p as fixed. The cost of funds to the firm will differ between the taxable and tax holiday firms because of the differences in

**_Ut._**

The calculation of the present value of tax savings due to depreciation Z must take into account the carryforward from the tax holiday to the taxpaying period of accumulated depreciation expenses as well as the variable discount rate. The value of _Z,_ will vary depending upon when the investment is undertaken. For investments made during the tax holiday period, Z, will be given by: _Z,_ **_m(Ro-_** _*_ **_d_ .** _u,_ **_a iJDds_** _(16_ For t > H, the expression for _Z7_ is the same as in equation (9) above. Note that _Zt_ is a monotonically increasing function with respect to time t within the pioneer period. Using equations (14), (15) and (16) along with (13), the time profile of the before-tax rate of return on capital _rg(t)_ for a pioneer firm can 26 **This** computstion **is** adopted **from** Boadway, Chua **and Platers** (1989).
their capacity after a sales shock, but much more strongly if the shock is considered to be temporary (results not reported).

# _Smoothing of raw materials_

We finally look at another type of input, namely raw materials. In the survey, firms have not been asked about changes in the use of raw materials but they were asked whether they have reduced, kept the same, or increased their inventory of raw materials since the beginning of the global financial crisis. If a firm smoothes the use of raw materials, then we expect the inventory of raw materials to increase. Therefore, firms which are not credit constrained and which expect a negative demand shock to be temporary are expected to reduce their inventory of raw materials less than otherwise similar firms. Column (3) of Table 8 shows that this is indeed the case. When we calculate the marginal effects of a negative demand shock of 30%, we find that the probability that a firm increases its inventory of raw materials is reduced by 5%.

**Table 8. Ordered logistic regression of the change in capacity and inventories of raw materials on demand shock**

||**Change c**<br>|**apacity?**<br>|**Change in**<br>**raw ma**<br>|**ventory of**<br>**terials?**<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||Credit co<br>|nstrained<br>|Credit co<br>|nstrained<br>|
||No|Yes|No|Yes|
|_Demand shock_|||||
|Change in sales (%) (10<sup>~~-2~~</sup>)|**0.02**(1.78)|**0.03**(3.36)|**0.04**(3.84)|**0.01**(1.82)|
|Change in sales x temporary(%) (10<sup>~~-2~~</sup>)|**-0.02**(1.67)|-0.01 (0.76)|**-0.02**(1.80)|0.00 (0.46)|
|_Sector (omitted: (Other) manufacturing) _|||||
|Garments|-0.13 (0.13)|-0.21 (0.26)|-0.55 (0.48)|-0.66 (0.95)|
|Trade|-0.79 (0.91)|0.07 (0.07)|0.09 (0.09)|-0.36 (0.41)|
|Tourism|-0.22 (0.27)|0.31 (0.41)|0.87 (1.04)|0.04 (0.07)|
|Other|-0.62 (0.63)|0.36 (0.45)|0.27 (0.33)|-0.65 (0.84)|
|_Size_ _(omitted: Small)_|||||
|Medium|-0.48 (0.94)|-0.34 (0.84)|-0.64 (1.10)|0.83 (1.77)|
|Large|-0.32 (0.44)|-0.22 (0.33)|-0.32 (0.46)|0.06 (0.12)|
|_Phnom Penh (dummy) _|-0.50 (0.79)|-0.36 (0.55)|0.44 (0.58)|**1.69**(2.22)|
|Number of observations|96|144|95|143|
|Pseudo R<sup>~~2~~</sup>|0.04|0.05|0.15|0.10|

Note: standard errors are clustered at the firm level and t-values are reported in parentheses. Coefficients which are significant at 10% are reported in bold.

26
Table 6: Non conditional Transitions from employment states to out of labor market and <u>unemployment by age, in percent.</u>

|||A|rgentina||Brazil||Mexico|
|---|---|---|---|---|---|---|---|
|Age||OLF|Unemployed|OLF|Unemployed|OLF|unemployed|
|15-18|Self-<br>employed|9.2|14.0|9.1|2.4|8.1|2.2|
||Formal<br>wage|4.3|8.6|1.4|1.0|5.5|2.1|
||Informal<br>wage|9.8|12.9|4.9|2.1|7.3|2.7|
|19-24|Self-<br>employed|7.5|13.0|7.6|3.5|6.6|1.5|
||Formal<br>wage|1.7|5.2|1.4|1.3|3.4|1.3|
||Informal<br>wage|6.3|11.8|4.5|3.4|5.9|2.4|

Table 7: Duration ratio of employment states to prime age workers

||**Self**<br>**employed**|**Formal**|**Informal**|
|---|---|---|---|
|**Argentina**||||
|Youth to Prime Age|0.46|0.23|0.85|
|Young Adults to Prime Age|0.54|0.50|0.97|
|**Brazil (Quarterly)**||||
|Youth to Prime Age|0.57|0.58|1.10|
|Young Adults to Prime Age|0.81|0.86|1.03|
|**Mexico**||||
|Youth to Prime Age|0.51|0.36|0.98|
|YoungAdults to Prime Age|0.70|0.59|0.96|

23
<!-- Start of picture text --> China: Time Series U.S.A.: Time Series<br>Reported Log Real GDP Reported Log Real GDP<br>Composite Estimate Composite Estimate<br>2005 2009 2015 2020 2005 2009 2015 2020<br>Year Year<br>China U.S.A.<br>12 4<br>0706 10 061815<br>1410<br>1217<br>2 0719<br>111613<br>8<br>11<br>08<br>13<br>09 0 08<br>14<br>12<br>17<br>1618<br>19<br>4 15<br>-2 09<br>09<br>Composite=Reported Composite=Reported<br>20 Composite Fitted Values 20 Composite Fitted Values<br>0 -4 20<br>0 4 8 12 -4 -2 0 2 4<br>Reported % Chg. Real GDP Reported % Chg. Real GDP<br>China US<br>Reported Composite Difference Reported Composite Difference<br>2009 7.54 5.97 1.57 -2.57 -2.45 -0.11<br>* -2.06 -0.51<br>2020 1.92 0.64 1.28 -3.55 -3.65 0.10<br>* -4.00 0.46<br>16.8 16.85<br>16.8<br>16.6<br>16.4 16.75<br>16.7<br>16.2<br>16 16.65<br>16.6<br>15.8<br>Composite Estimate Composite Estimate<br><!-- End of picture text --> Figure 7: Reported GDP versus NO2 Composite: % change in China and US _Notes:_ . The top row contains time series for log GDP or the composite estimate. The bottom row contains percentage change in each, viewed as scatter plot, by-year. China is a grade C country (GDP weight _λC_ =0.60) and US is grade A (GDP weight _λA_ =0.91). Red asterisks (*) indicate what the composite estimate, and the resulting difference for the US would be if it were treated as grade C.

25
Table 3: Validation of Topical Prevalence Using Survey Data

||Transcript Data|SurveyData|
|---|---|---|
|Water|0.1487|0.1743|
|Wages and Employment|0.0990|0.0647|
|Housing|0.0668|0.0540|
|Ration Shop|0.0735|0.0625|
|Toilets|0.0606|0.0625|
|Environment and Sanitation|0.0617|0.0511|
|Education|0.0446|0.0945|
|Funding|0.0612|0.0260|
|Women’s Issues|0.0810|0.1261|

_Note:_ Tis table presents the relative frequency of topics across both our survey and transcript data. Categories collected in the survey data were post-coded by issue area. For transcript data, documents were coded as a mixture of topics. As such, we take the share of all documents associated with that topic. Direct comparisons across the dataset was not possible for all topics, as there were only a limited set of clear analogues.

frequency of specific topics (water, housing, etc.) are roughly similar across the two datasets (Table 3). Te similar proportions (both in levels and rank) for topics with ready analogues suggests that our unsupervised methods reflect substantively what hand-coded results would yield.

# **4.2 Measures of Deliberative Equality**

Having validated the output of the topic model, we can generate a set of quantitative measures to capture deliberative quality across our sample of villages. Deliberative quality here is assessed based on the three metrics identified above — namely, the equality of participation, agenda-seting power, and responsiveness by the state.

To evaluate the equality of participation, we look at both the frequency and volume of speech by gender and position. Tat is, we can examine counts for the _number of speakers_ with each demographic category of interest (men versus women, citizens versus officials). We also examine the _length_ of speech as a proxy for the amount of floor time that speakers occupy.

To beter understand who drives the topic of conversation, we examine the sequence of speech topics to estimate the likelihood that a given speech is followed a speech that addresses the same topic. Since any given speech is modeled as a mixture across multiple documents, we focus on the 23