rafmacalaba/datause-encoder-data huggingface dataset:
60 RENT CONTROL IN KUMASI, GHANA market, because it assumes households consume some of each of the two substitutes. The most thorough treatment of the possible effects of rent controls on related uncontrolled markets is the recent paper by Fallis and Smith (1984).

Fallis and Smith actually develop two related models, one for rent control regimes which exempt new units from price controls, and one for regimes with vacancy decontrol provisions. Their short-run models predict that under most conditions excess demand spills over into the uncontrolled market, and, in the short run, driving up the uncontrolled price. In the long run, they implicitly assume an elastic supply function that implies a reduction in the quantity of housing services from the controlled sector, and an expansion in the uncontrolled sector, narrowing the wedge between prices.

They also present an empirical test of the model using data from Los Angeles (1969-1978). Following Rosen and Smith (1983), they assume that there is a straightforward relationship between rental rates, R, operating expenses, E, and the vacancy rate, V, estimated as: Rt - -6.25 + .078 Et + 34.09 (l/Vt) + 26.49 (l/Vt-) (3.30) (1.64) (4.12) (3.10) where dots indicate time derivatives and t-statistics are in parentheses. Rent control was introduced in Los Angeles at the end of this period, 1978. The estimates are used to forecast what rents would have been in the absence of controls, and the forecast compared with rents in the controlled and uncontrolled sector. After two years, controlled rents had risen by 10 percent less than the forecast, and uncontrolled rents by 22 percent more, confirming the hypothesis that rent control increases prices in the uncontrolled sector in the short run.

# E. Two Approaches to Measuring Costs and Benefits

In light of the above, what empirical approach will we take to measuring costs and benefits in Kumasi? As noted earlier we will present two alternative approaches to measuring the static costs and benefits of controls. The first and simplest static model estimates the difference between actual housing expenditures and what we'd expect them to be in the absence of controls, using a cross country model calibrated with uncontrolled markets. The simple method does not decompose expenditure into prices and quantities, but it does have the advantage of yielding information about the effect of rent regulation on the "uncontrolled" market (to be defined below). The second method does estimate prices and quantities, using the method of hedonic indexes.

# Cost-Benefit Using a Cross-Country Model

The first, simple approach can be expressed without any jargon with the following two questions:
# **Decomposing Response Errors in Food Consumption Measurement: Implications for Survey Design from a Survey Experiment in Tanzania**

Jed Friedman,<sup>a</sup> Kathleen Beegle,<sup>a</sup> Joachim De Weerdt,<sup>b</sup> and John Gibson<sup>c</sup>

## **JEL: C81, D12**

Keywords: Food consumption, Household surveys, Response error, Recall, Telescoping Author affiliations:<sup>a</sup> World Bank;<sup>b</sup> University of Antwerp and KU Leuven;<sup>c</sup> University of Waikato We wish to thank Francisco Ferreira, Alberto Zezza, two anonymous referees, and seminar participants at the Food and Agriculture Organization of the United Nations. Support from the Strategic Research Program is gratefully acknowledged.
|**HS**<br>**code**||||**Year**|||
|---|---|---|---|---|---|---|
|**2 digits**|**_Type of products_**|**2001**|**2002**|**2003**|**2004**|**2005**|
|||22<br>|22<br>|22<br>|22<br>|22<br>|
|65|_HATS_|19.2|15.9|18.1|18.3|18.4|
|||_(3.3)_|_(7.2)_|_(4.6)_|_(3.3)_|_(3.5)_|
|66|_UMBRELLAS_|9<br>19.2|9<br>19.6|9<br>19.7|9<br>19.0|9<br>19.1|
|||_(1.7)_|_(1.3)_|_(0.9)_|_(2.0)_|_(1.9)_|
|67|_BIRD SKIN FEATHERS_|8<br>20.4|8<br>23.1|8<br>24.4|8<br>22.8|8<br>22.5|
||_,_|_(10.4)_|_(10.9)_|_(16.0)_|_(6.2)_|_(5.2)_|
|68|_STONE PLASTER_|76<br>12.6|76<br>12.3|76<br>12.5|76<br>13.0|76<br>12.9|
||_,_|_(4.0)_|_(3.9)_|_(4.0)_|_(3.6)_|_(3.9)_|
|69|_CERAMIC PRODUCTS_|51<br>16.7|53<br>17.7|52<br>16.8|52<br>16.4|52<br>17.2|
|||_(6.0)_|_(10.0)_|_(5.3)_|_(5.5)_|_(5.2)_|
|||134<br>|134<br>|134<br>|134<br>|140<br>|
|70||13.2|13.2|13.1|13.1|13.1|
||_GLASS AND GLASSWARE_|_(3.2)_|_(3.2)_|_(3.3)_|_(3.2)_|_(3.4)_|
|||63<br>|63<br>|63<br>|63<br>|63<br>|
|71|_Natural or cultured pearls precious or_|19.7|19.7|19.7|18.4|17.5|
||_,_|_(2.5)_|_(2.5)_|_(2.5)_|_(5.0)_|_(5.8)_|
|||432|432|432|432|432|
|72|_STONE PLASTER_|3.0|3.3|3.4|3.4|3.5|
||_,_|_(2.2)_|_(2.1)_|_(2.1)_|_(2.1)_|_(2.1)_|
|||296|298|297|297|305|
|73|_Products from ferrous metals_|10.7|11.6|11.6|11.5|11.3|
|||_(5.2)_|_(4.8)_|_(4.8)_|_(4.9)_|_(4.9)_|
|||69<br>|69<br>|69<br>|69<br>|69<br>|
|74|_C d d_|3.2|3.6|3.7|3.5|3.6|
||_uprum an proucts of cuprum_|_(1.9)_|_(1.7)_|_(1.7)_|_(1.8)_|_(1.8)_|
|||18<br>|18<br>|18<br>|18<br>|18<br>|
|75||9.0|10.1|10.2|10.0|9.3|
||_Nickel and products of nickel_|_(5.1)_|_(5.0)_|_(5.5)_|_(5.3)_|_(5.1)_|
|||65|65|65|65|65|
|76||<br>13.5|<br>13.4|<br>13.6|<br>13.4|<br>13.5|
||_Aluminum and products of aluminum_|_(6.7)_|_(6.4)_|_(7.0)_|_(7.0)_|_(6.4)_|
|||13|13|13|13|13|
|78|_Lead and its products_|2.6|3.2|3.3|3.8|4.2|
|||_(2.4)_|_(2.4)_|_(2.2)_|_(2.1)_|_(1.8)_|

52
10 <!-- Start of picture text --> PARTIAL SCATTER OF RESIDUALS<br>Growth Us Gini Coefficients<br>0.08<br>1am<br>0.06<br>EGY<br>0.04 ON 5 04<br>CYP<br>0.02 ION T1H<br>rl"mN EWc IRL NUN r N<br>0! R SWYS<br>SUR Crii7 NLPL rz<br>-n<br>-0.04<br>-0. 06<br>-0.25 -0.7. -0.IS -0.1 -0.05 -1.387SE-17 0.05 0.1 0.IS 0.2<br>Glnl Residuds<br><!-- End of picture text --> Figure 3 The dependent variable is the least squares growth of rate of GDP per capita (where GDP is taken from BESD, the World Bank data base). The independent variables are initial GDP per capita in constant dollars, from Summers and Heston (1991) (SGDPPC70), primary and secondary enrollment rates lagged ten years (CPRIM60 and CSEC60), the average number of revolutions and coups per year between 1970 and 1985 (REVC7085), the number of assassinations per million population per year between 1970 and 1985 (ASSP7085), the deviation of the price level for investment in 1970 from the sample mean (PPI70DVN) from Summers and Heston (1991)4 and average government share of GDP between 1970 5 and 1988 (SGOV7088V).

" This is the price purchasing parity measure divided by exchange rate relative to the United States.

" Barro (1991) uses governmentshare of ODP from Summers and Heston (1988) less experditureon defenseand education. The measure used here, from Summers and Heston (1991), is lightly different in that it does not exclude expenditure on defense and education. The measure used in Berro is not used since it is available only over a shorter subperiod and for a smaller subseample of countries.
# Appendix A – Implementation of the Propiska System in Uzbekistan

For more details, please see the qualitative review of the propiska system that accompanies this paper.

## **Local requirements for registration in Uzbekistan**

Permanent or temporary registration (“постоянная или временная прописка”) in Uzbekistan is regulated by the Presidential Decree on Additional Measures for Passport System Development in the Republic of Uzbekistan No. UP-4262<sup>29</sup> dated 5 January 2011. The registration of citizens at the place of residence is carried out by the internal affairs bodies (IAB). All citizens are required to have a permanent place of registration.

The registration requirement for citizens to stay temporarily in Tashkent city is regulated in Annex 1 of Resolution of the Cabinet of Ministers No. PKM-41<sup>30</sup> dated 16 February 2012. According to the Resolution if this city is Tashkent and the length of stay in it does not exceed 5 days, then the citizen is not obliged to obtain a temporary residence permit. To obtain a temporary residence permit, a citizen must submit documents to the appropriate authorities within 5 days from the date of arrival in Tashkent. A temporary residence permit in the city of Tashkent can be obtained for a period of 5 days to up to 6 months.<sup>31</sup> In some cases, temporary registration can be granted up to one year at the discretion of the reviewing officer.

There are two methods of getting a residence permit at the time of this writing: the traditional inperson application, and the new electronic application that is open to temporary residents of Tashkent on a trial basis.

## **The traditional method**

Under the traditional method, one must apply to the Department of Migration and Citizenship Registration (DMCR) for a specific area in the city of Tashkent. The list of documents required may include:

- a statement in the form established by the Ministry of Internal Affairs;

- passport;

- the identity card of the soldier under the contract or the identity card of the officer of the Armed Forces of the Republic of Uzbekistan (for servicemen residing outside the barracks and ships);

> 29 http://www.lex.uz/docs/1729274

> 30 http://www.lex.uz/docs/1960398

> 31 https://kun.uz/ru/news/2018/10/03/eksperty-rasskazali-kak-polucit-vremennuu-propisku-v-taskente-za-24-casa

48
The main result obtained from this counterfactual simulation is that TFP losses are 9.6 percent larger when innovation is allowed to adjust optimally to the introduction of bribing. The economy exhibits higher dispersion in revenue productivity, more firms that are less productive, and a reduction in average productivity of 19.5 percent. On the basis of these results, I conclude that the dynamic effect of bribing on innovation amplifies the misallocation that comes from bribing.

**Table 8. Decomposition of effects**

||**Bribing economy**|**Bribing, keeping innovation constant**|
|---|---|---|
|**_Aggregate statistics_**|||
|Output|95.60|96.53|
|TFP|96.99|97.28|
|Consumption|94.05|95.01|
|**_Firm-level statistics_**|||
|Average size|98.12|98.98|
|Output per firm|88.81|95.2|
|Average productivity|97.28|97.81|
|Number of firms|111.43|102.21|

_Note:_ All results are relative to the benchmark economy in which there is no bribing. TFP = total factor productivity.

# **6. Conclusions and Policy Recommendations**

Since Bils, Klenow, and Ruane (2018); Hsieh and Klenow (2009); and Restuccia and Rogerson (2008), the misallocation of resources across firms has been given a major role in the macro development agenda while keeping the distribution of productivity given exogenously. In Hsieh and Klenow (2014), the authors tried to understand why average productivity is lower in poor countries, using a framework similar to that of Hsieh and Klenow (2009) but in a dynamic environment, and therefore making the distribution of productivity endogenous. This paper contributes to this strand of the literature by presenting a general equilibrium model of bribing to highlight bribing’s effects on the economy through misallocation (a static effect) and on the distribution of firm-level productivity through reductions in innovation (a dynamic effect). The paper’s quantitative model is guided by establishment-level data on bribing and captures 30
enced historically during a crisis period. Secondly, we take out countries for which the selected minimum values were positive, that is, when the change in GDP growth during the crisis years was greater than zero. This is necessary to avoid the inclusion of a country experience that contradicts the definition of a systemic financial crisis.<sup>16</sup> Thirdly, we employ an outlier robust method to compute the average response of the countries that have a crisis trimmed means. The trimmed means experienced financial by computing are calculated in such a way that only the data points that fall within the 2.5<sup>_th_</sup> and the 97.5<sup>_th_</sup> percentile are included in the computation. This ensures that only the center of the empirical data, where 95% of the probability mass lies, is included in the averaging. The of extreme values on the is thus eliminated. The influence average response effectively censoring rules applied to CPI inflation, the lending rate and changes in the exchange rate are analogous to the ones applied to real GDP growth.

One last requirement that is imposed is that for each country a full set of macroeconomic Stress scenarios needs to be present. That is, we require data for each of the four macroeconomic variables (real GDP growth, CPI inflation, the lending rate and the change in the exchange rate) to be available for the trimmed mean response to be computed. If, for instance, data on lending rates are not available for a particular country, while the remaining three macroeconomic variables are present, then this scenario, and potentially the country itself, is excluded from the calculation of the average crisis response. The resulting model free Stress scenario based on the historical cross-country crisis experience

> 16 To provide an example of such a scenario, consider Lebanon which is identified by the Laeven and Valencia (2008) database to have gone through a Systematic Banking and Currency Crisis in 1990. However, (annual) real GDP growth from 1989 to 1991 was _−_ 42%, 26% and 38%, respectively for these three years. So GDP growth was an astonishing 26% and 38% for the years from 1990 to 1991 (the year of the crisis and the following year) while GDP growth was negative before the financial crisis. This example of the actual historical experience of Lebanon is clearly an exception that does not follow the typical scenario of a deep downturn in economic activity during a financial crisis. Including such a country experience in the Stress scenario construction would thus not be very informative.

21
310,000, and eligibility expires after 3 years for incorporated companies or 4 years for other registered businesses. Firms in the construction, real estate, shipping, and airline services sectors are also subject to special final tax rates on their gross turnover, instead of the CIT. There is a statutory VAT rate of 11 percent. There are various exempted or zero-tax-rated categories, including essential food, health, education, financial services, and exports. Participation in the VAT regime is only compulsory for firms with gross annual revenues of at least US 310,000, though smaller firms may voluntarily opt-in.

There is considerable evidence that incomplete tax compliance by firms is pervasive in Indonesia. Previous studies have found that nine out of ten firms were not registered with the revenue authority (Rothenberg et al., 2016), and that most registered firms reported zero net income and paid no income tax (e.g., Ikhsan et al., 2005). Audit data suggests that in some instances there is substantial misreporting between taxpayer self-declared liabilities and auditor-assessed liabilities among firms that do pay tax.<sup>6</sup> Moreover, such noncompliance seems exceptionally high among professional services corporations (Breuer et al., 2018). Recent World Bank (2024b) research has estimated Indonesia’s combined CIT and VAT compliance gap at around 3.8 percent of GDP in lost revenues per year from 2016 to 2021. This includes losses due to deliberate or unintentional underpayment of businesses’ tax obligations, such as the type of tax evasion focused on in this paper. More generally, a few studies have also estimated Indonesia’s tax losses based on estimates of the informal economy. Tax losses are calculated by applying an average tax rate to this economic activity which is assumed to have remained outside the tax net. These studies have arrived at a wide range of tax-loss estimates, from 0.7 to 3.8 percent of GDP per annum (see Tatariyanto, 2014; Mulyawan, 2017; Nizar and Purnomo, 2011; Ramadhan, 2019; Indupurnahayu and Walujadi, 2019).

The revenue authority in Indonesia, the Directorate General of Taxation (DGT), has

> 6Based on a joint World Bank and Directorate General of Taxation exercise conducted in 2017. It analyzed a sample of business tax returns, comparing income tax estimated by taxpayers with that estimated by tax auditors.

6
24 Table 1: Cotton net farm income and net export positions in 2001, and impact of removing cotton subsidies and tariffs<sup>a</sup> on cotton output, exports, net farm income and economic welfare (percent and 2001 US$m)

||_Index of_<br>_cotton_<br>|_Net exp_<br><br>|_orts_<sup>_b_</sup>_($b)_<br>_of:_|Change<br>in|<br>Change<br>in cotton|<br>Change<br>in value<br>|Welfare<br>($|change<br>m):|
|---|---|---|---|---|---|---|---|---|
||_production_<br>_special-_<br>_ization_<sup>_c_</sup>|<br>_cotton_|_textiles_<br>_and_<br>_clothing_|cotton<br>output<br>volume<br>(%)|<br>net farm<br>income<br>(%)|<br>of<br>cotton<br>exports<br>(%)|TOTAL|<br>that due<br>to terms<br>of trade<br>change|
|**High-income countries**|**0.3**|**1.0**|**-92.0**|**-20**|**-15**|**-18**|**465**|**275**|
|Australia|3.8|1.1|-2.6|25|22|38|137|125|
|United States|0.6|2.2|-60.7|-25|-18|-29|429|443|
|EU25|0.1|-1.0|-28.8|-54|-53|-49|14|-109|
|Japan|0.0|-0.4|-14.1|1|2|62|-24|-49|
|Korea-Taiwan|0.1|-0.7|22.5|12|7|34|-61|-84|
|**Developing countries**|**3.8**|**-1.0**|**92.0**|**6**|**4**|**46**|**-182**|**-275**|
|<br>**_E. Europe & C. Asia_**|**_4.3_**|**_0.3_**|**_7.4_**|**_7_**|**_3_**|**_36_**|**_-14_**|**_-36_**|
|Turkey|11.6|-0.4|8.7|2|2|37|-86|-80|
|Other ECA|2.1|0.7|-1.3|10|9|35|72|44|
|**_East Asia_**|**_3.0_**|**_-1.4_**|**_60.4_**|**_2_**|**_2_**|**_72_**|**_-83_**|**_-127_**|
|China|4.0|-0.1|41.9|2|2|76|50|45|
|**_South Asia_**|**_14.5_**|**_-1.0_**|**_24.5_**|**_2_**|**_1_**|**_55_**|**_-96_**|**_-99_**|
|Bangladesh|14.2|-0.3|3.8|8|5|68|-11|-21|
|India|13.7|-0.6|11.9|-1|0|31|-85|-79|
|Pakistan|29.9|-0.1|6.8|5|3|61|-7|-5|
|**_M. East &North Africa_**|**_2.5_**|**_0.4_**|**_-3.3_**|**_6_**|**_6_**|**_37_**|**_19_**|**_26_**|
|<br>**_Sub-Saharan Africa_**|**_5.8_**|**_1.1_**|**_-1.8_**|**_32_**|**_31_**|**_55_**|**_147_**|**_113_**|
|<br>South Africa|0.3|-0.0|-0.2|19|21|47|-1|-2|
|Mozambique|6.1|0.0|-0.0|19|18|29|2|1|
|Zambia|11.6|0.0|0.0|4|4|11|0|0|
|Uganda|6.8|0.0|-0.0|27|26|45|4|3|
|Other Sthn & E. Africa|7.5|0.2|0.7|21|20|46|17|14|
|Nigeria|2.2|0.0|-0.7|23|21|47|-1|0|
|Other Sub-Sah. Africa|12.6|0.8|-1.6|39|37|60|126|97|
|**_Latin America & Car._**|**_1.1_**|**_-0.4_**|**_4.8_**|**_11_**|**_9_**|**_54_**|**_-155_**|**_-152_**|
|Argentina|1.1|0.1|-0.4|14|11|66|7|6|
|<br>Brazil|1.5|0.1|-0.0|10|10|58|13|12|
|Mexico|0.8|-0.5|4.0|13|11|42|-128|-136|
|**World**|**1.0**|**0.0**|**0.0**|**-1**|**-2**|**8**|**283**|**0**|

a Removal of those distortions left after the phase-out of the quotas at the end of 2004 b Exports minus imports, both valued at f.o.b. prices as in the GTAP database 6.05
characteristics such as gender or ethnicity may make a respondent more or less forthright in their responses (Haber, 2018).

# _Intraclass Correlation Coefficients_

Classical statistical models depend on the assumption that observations are independent and identically distributed (IID). Non-random interviewer measurement error violates this assumption through errors clustered by interviewer. The result is loss of precision through higher variances. The clustering of responses by interviewer is analogous to the geographic clustering that occurs through a multistage cluster sample and similarly leads to larger standard errors. This homogeneity of responses resulting from clustering can be expressed through the intraclass correlation coefficient (ICC) (Brunton-Smith _et al.,_ 2016).

The ICC expresses the share of the total sample variance that can be explained by the withincluster variance. In the case of interviewer measurement error, this is the share of the total sample variance that can be explained by interviewer-based measurement error that is not randomly distributed across interviewers. The ICC ranges from 0 to 1, with a value of zero indicating zero correlation in responses by interviewer and a value of one indicating that all response variance is due to interviewers. Intuitively, an ICC of zero implies no loss of sample efficiency (i.e. no increase in standard errors) resulting from clustering and therefore no adjustment needs to the sample to account for clustering. On the other hand, an ICC value of one would indicate that each cluster must be treated as a single observation, obviously implying a much larger sample requirement.

# _Design effects_

The ICC affects variance and, therefore, sample size requirements through the design effect, which is commonly used to adjust for clustered sample design. The design effect is the ratio of the actual sample variance to the presumed variance under simple random sampling. The design effect formula is shown below: where _DF_ is the design effect, _c_ is the size of the cluster and ρ the ICC. In the case of interviewer effects, _c_ is the number of respondents per interviewer. To correct for clustering of responses and the consequent increase in variance, the design effect is multiplied by the sample size requirement obtained through simple random sampling.

In their seminal work on interviewer design effects, O’Muircheartaigh and Campanelli (1998) used an interpenetrated design, randomly assigning interviewers to respondents in a household survey in the United Kingdom (UK). They calculated interviewer ICCs for 820 variables and found median interviewer design effects of 1.8 with a maximum of 5, implying an 80 percent and 400 percent increase in the sample size required to maintain the same variance. Brunton-Smith et al. (2016) found evidence of interviewer design effects in UK Household Longitudinal Survey data ranging between 2.5 and 3.3. Schnell and Kreuter (2005) analyzed face-to-face and mail-in 3