rafmacalaba/datause-encoder-data huggingface dataset:
in mediating the eects of the minimum wage policy. In their model the increase in the minimum wage aects mostly low-productivity rms, for which the minimum wage is more binding. The results show that these rms cut employment creation as the increase in the minimum wage squeezes their prot margins. The easier recruiting environment in turn induces higher-productivity rms to increase hiring. As a result, in their model the minimum wage primarily reallocates employment from lower- to higher-productivity rms rather than to unemployment. The magnitudes of their estimated eects of the minimum wage on inequality are driven by how binding the minimum wage is together with the extent of rm productivity dispersion in Brazil. The authors argue that they nd muted negative eects of the minimum wage on employment and aggregate output due to the heterogeneous eects of the minimum wage across the rm productivity distribution. However, Haanwinckel and Soares (2020) obtain dierent results. Specically, they nd that the decline in unemployment between 2003 and 2012 would have been greater in the absence of a minimum wage rise in the magnitude observed in the data. They also show that the decline in informality in Brazil would have been considerably larger if the minimum wage had not increased. To the extent that higher informality is associated with lower productivity, their results suggest that the increase in the minimum wage might have been detrimental to productivity growth. It thus remains an open issue how the rise in the minimum wage aected informality and productivity in Brazil during the 2000s. In any case, it should be noted that in Brazil, an increase in the minimum wage has signicant scal eects since many social benets, including pension payments and unemployment insurance, are indexed to the minimum wage. Hence, possible positive eects of an increase in the minimum wage have to be compared to the associated scal costs and their implications for investment and innovation decisions.

# 4.2.3 Misallocation of labor between the public and private sectors

There is a large body of evidence showing that for many countries the structure of wages and pensions and the labor law legislation are dierent for public and private employees. Such dierences aect the occupational choice of agents and might generate some type of misallocation. This is particularly relevant for Brazil, a country with a high public sector premium.<sup>20</sup> Cavalcanti and Santos (2021) develop a life-cycle model with endogenous occupational choice and heterogeneous agents to study the implications of an overpaid public sector in Brazil. The paper investigates whether dierences in labor compensation and labor legislation between private and public workers aect individuals' occupational choice, investment and

> 20See Bank (2017c).

19
Appendix Box 1: Misallocation metrics Define the share weighted aggregate productivity for a group of establishments as: where n is the number of establishments, si is the share of establishment _i_ in the group _g_ such that ∑ni=1 si = 1, and φi is the measure of productivity for establishment _i. g_ could be any group, such as a district, district-industry, and so on. One could use many variants for the measure of productivity and the “share.” In our work, we use a combination of OLS- and LP-based TFP estimations. For the shares part, we use two main factors of production, land and buildings and labor. In addition to these inputs, we also calculate the shares in terms of the value of external loans accessed by the plant.

There are many ways to decompose aggregate productivity (Melitz and Polanec, 2013), but we choose the Olley Pakes (1996) methodology. In particular, aggregate productivity can be written as: n Φg = φ̅g + ∑i=1(si −s̅g) (φi −φ̅g)=φ̅ + ng Cov(sig , φi) 1 n where φ̅g = n ∑i=1 φi is the unweighted productivity mean across establishments in group _g_ and s̅ is the mean establishment share, sig is the factor or value-added share of establishment _i_ in group _g_ and ngis the number of establishments in that group.

Thus, a measure of allocation efficiency can be written as: Eg = Φg −φ̅g =ng Cov(sig , φi) As explained, efficiency is maximized when most productive establishments have the highest input shares and hence the covariance is maximized. Conversely, the extent of misallocation can written as the negative of allocation efficiency, that is, For our misallocation metrics “without the industry aggregation step” where the LP-style TFP estimates are used or the one where we use residuals for TFP from simple OLS estimation, we directly compute Mg at the level of district.

As noted in main text, we compute the baseline misallocation index at the district-industry level to ease the comparisons across industries. This measure basically defines the group as a districtindustry and hence the misallocation index Mg is first calculated at a district-industry level and 39
Dabla-Norris, Era, Jim Brumby, Annette Kyobe, Zac Mills, and Chris Papageorgiou. 2012. “Investing in public investment: an index of public investment efficiency.” _Journal of Economic Growth_ 17: 235–66. https://link.springer.com/article/10.1007\%2Fs10887012-9078-5\#citeas.

Datt, G. 1998. “Computational Tools for Poverty Measurement and Analysis.” _International Food Policy Research Institute_ .

Deaton, A. 1999. “Commodity Price Stabilization and Growth in Africa.” _Journal of Economic Perspectives_ 13 (3): 23–40.

Dellink, R., J. Chateau, E. Lanzi, and B. Magne. 2017. “Long-Term Economic Growth Projections in the Shared Socioeconomic Pathways.” _Global Environmental Change_ 42: 200– 2014.

Gauthier, Bernard, and Albert Zeufack. 2009. “Governance and Oil Revenues in Cameroon.” _Oxcarre Research Paper_ 38: 1–58.

———. 2012. “Cameroon’s Oil Wealth: Transparency Matters in _Oil Wealth in Central America_ .” _International Monetary Fund_ , 155–70.

Harding, Torfinn, and Anthony J Venables. 2013. “The Implications of Natural Resource Exports for Non-Resource Trade.” OxCarre Working Papers 103. Oxford Centre for the Analysis of Resource Rich Economies, University of Oxford. http://ideas.repec.org/p/oxf/oxcrwp/103.html.

Hartwick, John M. 1977. “International Equity and the Investing of Rents from Exhaustible Resources.” _The American Economic Review_ 67 (5). JSTOR: 972–74.

Hsieh, C.T., and P.J. Klenow. 2009. “Misallocation and Manufacturing Tfp in China and India.” _The Quarterly Journal of Economics_ 124 (4): 1403–48.

IMF. 2012. “Macroeconomic Policy Frameworks for Resource Rich Developing Countries.” https://www.imf.org/en/Publications/PolicyPapers/Issues/2016/12/31/Macroeconomic-Policy-Frameworks-for-Resource-RichDeveloping-Countries-PP4698.

———. 2019. “The World Economic Outlook (Weo).” https://www.imf.org/external/pubs/ft/weo/2019/01/weodata/index.aspx.

Kubota, Megumi, and Albert Zeufack. 2020. “Assessing the Returns on Investment in Data Openness and Transparency.” _Policy Research Working Paper, Office of the Chief Economist, Africa Region, World Bank Group_ 9139: 1–32.

Lange, Glenn-Marie, Quentin Wodon, and Kevin Carey. 2018. “The Changing Wealth of Nations 2018.” The World Bank.

https://openknowledge.worldbank.org/bitstream/handle/10986/29001/978146481046 6.pdf?sequence=4&isAllowed=y.

52
# **<u>Table 1: Coverage of Registry or Cadaster in Main City by Region, 2017</u>**

||<br>**EAP**|<br>**ECA**|<br>**LAC**|<br>**MENA**|**OECD**|**SAS**|**SSA**|**Total**|
|---|---|---|---|---|---|---|---|---|
|Most plots in main city registered|56.0|57.7|18.8|47.6|87.1|14.3|14.6|42.1|
|Most plots in main city mapped|52.0|65.4|34.4|47.6|93.5|14.3|12.5|45.8|
|Cadaster is digital|28.0|46.2|28.1|14.3|87.1|14.3|4.2|32.1|
|Cadaster and registry linked|32.0|76.9|31.3|81.0|87.1|14.3|31.3|51.6|
|No. of countries|25|26|32|21|31|7|48|190|

_Source:_ Data from 2017 ‘Doing Business,’ www.doingbusiness.org.

_Note:_ EAP = East Asia and Pacific; ECA = Eastern Europe and Central Asia; MENA= Middle East and North Africa; SAS = or South Asia; and SSA = Sub-Saharan Africa regions.

15
25 important reputation component. However, under either case, this variable should negatively affect mobility. Finally, a market growth variable is included for two reasons. First, growing markets makes entry more attractive and easier. Second, rapid growth should increase uncertainty in interfirm relationships and makes tacit and/or overt cooperation more difficult and hence should increase mobility. It is defined as the quarterly growth rate of deposits over the 1986-1992 (1st quarter) period.

V.3. Data The data used in this study is the Central Banks General Directorate of Banking and covers the period between 1986 (I) and 1992 (I) and includes all deposit money banks. The sample contains ratios and a number of levels of variables from income statements and balance sheets with 1302 observations. For the analysis, the data has been pooled (cross-section and time series).
throughout the sectors. The second case is to exclude the raw materials sectors from the shock assuming that this sector cannot benefit at all from the transparency reform considered here.

As indicated above, the original database needs to be adjusted to accommodate the changes in irregular transfer payments. We treat them as higher nominal import tariffs. We need, therefore, to update the database to incorporate the increase in the nominal tariff rates by the amounts of additional trade costs from irregular payments. The GTAP model provides a convenient set of parameters and closure, named ALTERTAX, to adjust the tax rates while minimizing the changes in the trade and consumption flows of the original database. Appendix B discusses the technical issues in detail. Once the database is adjusted, the shocks for simulation can be implemented to _ams_ for technical progress and _tms_ for reduction in irregular payments in the model.

# **4.  Simulation Results and Implications**

## **_Impacts on Bilateral Trade_**

## _Imports_

Theoretically, cost reductions in all sectors, whether by import augmenting technical progress or reductions in irregular payments and bribes, should stimulate the substitution of demand in favor of imports against domestic goods. This should result in an increase in imports. Table 2 summarizes the simulation results on bilateral trade flows. Case 1 in the table assumes that import cost cuts from the improvement of transparency extend to all the sectors, and Case 2, to all sectors except for raw materials. Moreover, the trade data in Case 1 covers all the sectors, but Case 2 only covers the trade data in the sectors excluding raw materials. In Table 2, “APEC” indicates that the simulated impacts are calculated with only using the trade data among the APEC members, while “World”, with all the trade data in the world. One may note that the simulation in Heble, Sheperd and Wilson (2007) refers to intra-APEC trade only, corresponding to Case 2-APEC in Table 2.

20
approach to data collection, including a core census module and one or more census supplementary modules, depending on the country.

The core census module items that are important for gender-sensitive analysis of land and livestock ownership and management include the identification and location of the holding, the legal status of the holder, the sex of the agricultural holder, land tenure types and size of the holding, and livestock types and quantities. An important methodological difference between the 2000 and the 2010 rounds is that the latter introduced the concept of the subholding and sub-holder. By recognizing that an agricultural holder could be a group of people such as a husband and wife, the 2010 round aimed to improve measurement of the role of multiple household members, and especially women, in the management of a holding.

An FAO review of 86 agricultural censuses conducted between 2006 and 2013 revealed that while almost all collected information on the sex of the holder and the land tenure of the holding, only a few countries provided analysis of sex-disaggregated data in their reports. The review also covered the availability of data on sub-holdings and sub-holders. Although many countries implemented parcel-level modules, only African countries had collected data on the sex of the parcel manager or owner. Many countries faced challenges implementing the concepts of sub-holders and sub-holdings (FAO – UBOS Expert Consultation, 2014). Therefore, these concepts are not included in the WCA 2020 round, but the concept of a joint holder—or a person making decisions about the holding in conjunction with another individual within or outside one’s household—remains. In addition, the WCA 2020 guidelines recommend (1) collecting information on the sex of any household members – not limited to the holder or joint holders – making managerial decisions<sup>14</sup> on the holding, and (2) disaggregating the area of crops and the number of livestock by the sex of the person managing them and the area of land owned and the number of livestock owned by the sex of the owner.

# **3.7 Living Standards Measurement Study – Integrated Surveys on Agriculture**

The World Bank Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) program provides technical and financial support to national statistical offices in Sub-Saharan Africa in the design, implementation and analysis of national, multi-topic, panel household surveys that have a strong focus on smallholder agriculture and that are modeled on the integrated household survey design of the LSMS.<sup>15</sup> In addition to the goal

> 14 The list of managerial decisions varies by country.

> 15 The LSMS-ISA is implemented by the Living Standards Measurement Study (LSMS), housed within the Development Data Group of the World Bank. The unit-record anonymized data and documentation associated with each survey supported by the LSMS-ISA are made publicly available within 12 months of

15
monitors can help provide policy results that are better aligned with the regulatory data.

Data from a spatially dense network of low-cost monitors can be calibrated using at least one regulatory-grade monitor to improve the measurement of PM2.5 levels and of policy impacts. Investing in a combination of low-cost and regulatory-grade monitors can improve the air quality data used for policy evaluation. In the absence of data from regulatory-grade monitors, the results suggest that satellite data can be used to calibrate the PM2.5 measurements from a network of low-cost monitors, although the results may not be as accurate in terms of measurement of PM2.5 levels or policy impacts as when calibrated with regulatory-grade data.

# **6 Conclusion**

In this paper, we compare and evaluate the three principal sources of PM2.5 data in Dakar, Senegal. We find that the low-cost monitors report substantially lower PM2.5 levels due to incomplete measurement of dust and that, at higher levels of temporal aggregation, PM2.5 levels derived from satellite data are closely aligned with PM2.5 levels from regulatory-grade monitors. Using mobility restrictions during COVID-19, we show that all three data sources provide similar estimates of the impact of a policy on PM2.5 at the city-level, particularly when measured in percent changes instead of levels. Finally, we show the value of investing in at least one regulatory-grade monitor. Developing a context-specific calibration formula provides substantial improvement over off-the-shelf calibration formulas in aligning the data from low-cost monitors with data from regulatory-grade monitors. Faced with budget constraints, an air pollution monitoring system based on one well-maintained regulatory-grade monitor complemented with a dense network of low-cost monitors can provide PM2.5 data with high spatial variation that can also be used to accurately estimate the impact of policies. Barring any data from regulatory-grade monitors, calibration of PM2.5 measurements from low-cost monitors using satellite-based estimates of PM2.5 can improve the measurement of PM2.5 levels and policy impacts as long as the calibration is conducted at a lower temporal frequency.

Our results have important implications for policy makers in rapidly growing urban areas of LMICs. Our results also speak to rural or suburban areas of higher-income countries where the coverage of regulatory-grade monitors is also sparse. Overall, the results are encouraging and point to the potential that low-cost monitors have as part of a system of air pollution monitoring. Increasing the data availability in these settings could help lead to an important expansion of the literature focused on air pollution, providing a better understanding of context-specific challenges and potential environmental policy solutions to address these.

27
consider how variations in policies would be expected to affect the housing elasticities in the various cities. The equation makes sure that all the important and interactive factors involved in an abstract model of city development are explicitly treated, and it can be crudely estimated with a minimum amount of aggregate data. That is, we use the equation to consider how basic aggregate statistics about each of the cities would affect their relative supply elasticities.

To do this, we make use of the results of two recent studies of transport costs in African cities, see World Bank (2002) and (2005), and other city-wide level data on the four cities. Based on this model we can also infer how much Accra’s policies have affected the housing supply curve relative to the aggregate estimate. Table 2 reports on our estimates of the various components of equation 1 and the corresponding estimate of the relative housing supply elasticity in each of these cities.<sup>12</sup>

**Table 2. Components of Housing Supply Elasticity**

||**Housing Price**<br>**ratio (p)**|**Population (n)**|**Density**<br>**persons/ km**<br>**2**<br>**(φ)**|**Pop. growth**<br>**rate (g)**|**Transport cost**<br>**USD PPP (k)**|η**= [(2/ φ**<br>**√n)(25-g)p]/k]**|
|---|---|---|---|---|---|---|
|Addis Ababa|0.43|2,973,004|5506|0.033|21.02|1.25|
|Accra|1.00|1,981,000|10708|0.034|87.77|0.43|
|Nairobi|0.63|2,940,911|4300|0.036|51.16|0.94|
|Dares Salaam|0.25|2,809,000|2017|0.045|14.38|2.83|

In a mono-centric model such as the one implied by equation (1) one important factor affecting the supply elasticity is that fact that Accra, unlike Nairobi and Addis Ababa, cannot expand in all directions. Like Dar es Salaam, and many of the U.S.

> 12  The empirical implementation of equation 1 is relatively straightforward, except as regards transportation costs, k, and the discount rate, i. For the former, we relied on the studies noted in the text to compute estimates of the cost of commuting. Our approach follows that suggested by Hamilton and Mills (1984). As for the discount rate, because, as shown in Honohan and Beck (2006), mortgage finance is almost completely absent in all 4 cities, even if the other cities have somewhat more developed financial sectors, we assume that all borrowers face a discount rate of 25 percent.

10
Eastern European countries as countries started the course of transition from different historical backgrounds, physical endowments and reform processes. One of the most difficult processes is the transformation of social contracts. Stiglitz (1999) points out: ―If ―reformers‖ simply destroy the old norms and constraints in order to ―clean the slate‖ without allowing for the time-consuming processes of reconstructing new norms, then the new legislated institutions may well not take hold‖ (p. 9). _Table A6_ reports higher institutional quality in CEE countries, than in FSU countries using six proxies of the governance indicators developed by Kaufmann, Kraay, and Mastruzzi (2004). Thus individual uncertainty was reduced leading to a better transition process with more stable institutions. As in many developing countries we might observe that transition countries are in a situation of ―over-government‖ and ―under-government‖ (Frey and Eichenberger, 1999, p. 89). There is a strong combination of interventionism and bureaucracy. On the other hand, property rights are not sufficiently secured and there is a high degree of uncertainty, reducing thus the incentive for investment. For transition countries it is difficult to find the right equilibrium of state activity, as the collapse of communism was a collapse of a vast state apparatus. A frequent use of the exit option in form of tax evasion or tax avoidance by entering the shadow economy has the negative effect of reducing the state‘s tax collection, affecting thus the revenues governments need to provide public goods and to build trustworthy institutions. Many countries might react with the problematic strategy of increasing taxes. This enlarges the shadow economy, as the incentive for enterprises to evade taxes increases; they just pay, e.g., more bribes to protect themselves (see Levin and Satarov, 2000). Campos and Coricelli (2002) stress that reforms progressed much faster in CEE countries than in FSU countries. Moreover, Martinez-Vazquez and McNab (2000) argue that, in countries negotiating their accession to the European Union, the accession intention acted as a catalyst for rapid tax reform shaped along western lines. As a consequence, one would predict significantly lower tax morale in Former Soviet Union (FSU) economies than in CEE economies. RoseAckerman (2001) reports that citizens are critical regarding the transition process and state institutions and officials and that the ―sense of stasis is highest for the former parts of the Soviet Union‖ (p. 419).

46
Figure 1 Percent males aged 15-19 with at least primary education <!-- Start of picture text --> Figure 2 Percent males aged 20-24 with at least secondary education<br>80<br>es<br>60 | — World<br>i at<br>g 40 West Africa<br>a east Africa<br>2010 ——— ——=——SSA Southern Africa<br>0<br>1970 1975 1980 1985 1990 1995 2000<br>Year<br><!-- End of picture text -->