rafmacalaba/datause-encoder-data huggingface dataset:
13 The significant shifts in the distribution of firms have compelled us to truncate the census data to minimize measurement bias.<sup>17</sup> We conducted several simulations to arrive at an “optimal” threshold according to which the distributions of firms across the three periods are similar. As we varied the threshold between 5 employees and 20 employees, we found the “optimal” threshold to be 10 employees (figures 5 and 6). The truncation implies a loss of about 2.4% of total value-added and 3% of total employment during the 3 census periods. Setting a threshold may be the “second-best” option because there is a trade-off between eliminating the bias from unexplained shifts in firm size distributions and losing information from small firms.

**Figure 5:** Distributions of Log of Employment **Figure 6:** Firms Covered in the Census at the at the Threshold of > 10 Employees                    Threshold of > 10 Employees

<!-- Start of picture text --> .4<br>.3<br>.2<br>.1<br>0<br>2 4 6 8 10<br>Log employment distribution<br>2010 2005<br>2000<br>Density<br><!-- End of picture text --> <!-- Start of picture text --> 6,000<br>5,000<br>4,000<br>3,000<br>2,000<br>Number of firms<br><!-- End of picture text --> <!-- Start of picture text --> 2,563<br><!-- End of picture text --> <!-- Start of picture text --> 2000 2005 2010<br>>100 workers 20–99 workers 11–19 workers<br><!-- End of picture text -->

**Figure 7:** Percent of Firms by Industry, 2000 and 2010

a. 2000 b. 2010 <!-- Start of picture text -->
1.3 1.9 0.1 10.2<br>10.4 17.4<br>17.8<br>3.6<br>17.0<br>23.3<br>20.7<br>4.4<br>4.7 44.3<br>6.4 16.6<br>F&B Textile Wood F&B Textile Wood<br>Petchem Metal M&E&C Petchem Metal M&E&C<br>Transport equip Others Transport equip Others<br><!-- End of picture text -->

Source: Department of Statistics Malaysia (DOSM).

Note: F&B includes the food and beverage industries. Textile includes the textile, apparel and leather related industries. Wood includes the wood, paper products, and furniture industries. Petchem includes refined petroleum, basic chemical, other allied chemical products, rubber and plastic industries. Metal includes non-metallic mineral, basic metal and fabricated metal industries. M&E&C includes machinery and equipment, electrical, electronics and computer, and measuring equipment industries. Transport equip include motor vehicles, bodies for motor vehicles; trailers and semi-trailers and parts and accessories for motor vehicles industries. Others include jewelry, musical instruments, sporting goods, medical and dental instruments, other industries not elsewhere classified.

17 Based on our meeting with the Department of Statistics Malaysia (DOSM), the shifts were due to two reasons: an increased coverage of small and medium enterprises (SMEs) in 2003; and a change in the filtering process of identifying active firms during the 2010 census.
index in order to have the same base year as U.S. GDP. Following Ilzetzki et al. (2013), the data used are deviations of non-stationary variables from their linear trend. The results remain unchanged using a quadratic trend.<sup>12</sup>

# **3 Results**

I estimate a VAR with 2 lags, where the number of lags is chosen using the Bayesian information criterion. I test the exogeneity of _y_ 2 _t_ with a likelihood ratio test. The results of the test, not shown here, do not reject the null hypothesis of a block exogeneity at the 5-percent level. The following subsection analyzes the IRFs to the identified shocks, assesses GDP multipliers associated with spending and tax increases without and including exogenous variables _y_ 2 _t_ in the model, studies the impact of fiscal policy shocks over two sub-periods (before and after 1990:Q4), compares the results to existing estimates, and finally compares spending and tax multipliers in order to draw some policy conclusions.

## **3.1 IRFs to shocks**

The IRFs to the identified shocks are displayed in the subsequent figures. The shaded areas around the median IRF are the 68 percent confidence regions constructed from 5,000 draws of parameters from the Normal-Wishart distribution. For each draw, the IRFs are computed at each horizon and the 16<sup>_th_</sup> , 50<sup>_th_</sup> and 84<sup>_th_</sup> quantiles are chosen to construct the confidence intervals. The green bands represent the imposed sign restrictions.

### **3.1.1 Effects of a business cycle shock**

Defined as a shock for which Canadian GDP, private consumption, private investment and net tax revenue increase for four quarters following the shock, the IRFs to this shock are shown in Figure 1. The IRFs of these variables remain positive many quarters after the shock. In particular, the response of consumption tends to be highly persistent and behaves in a hump-shaped fashion. This behavior of consumption can be interpreted as a consequence of a higher degree of habit formation. The buoyant response of net tax revenue (net tax revenue increases at higher rate than GDP) can be interpreted as a result of progressive marginal income tax rates. Because government spending here does not incorporate transfers, the response of government purchases is not countercyclical. Instead, as the resources in the economy increase, public investment and current consumption increase too. The decline of the price level is less intuitive, since all components of aggregate demand increase. However, this response could be a result of a countercycliclal response of monetary policy (the interest rate increases in response to the shock) to dampen the effect of aggregate demand on the price level. The response of the net-exports-to-GDP

> 12Although keeping variables in level in the VAR-system would implicitly account for any existing cointegration relationship, variables are detrended because the VAR-system is not stationary with variables in levels.

8
**Table 2. Impact of Changes in International Food Prices on the Import Bill, 2010**

||% of 2010 GDP|% of international reserves|
|---|---|---|
|MENA|||
||Food<br>0.62|1.44|
||Grains<br>0.27|0.63|
||Oils<br>0.17|0.40|
||Meat<br>0.04|0.09|
||Sugar<br>0.14|0.32|
|GCC economies|||
||Food<br>0.25|0.45|
||Grains<br>0.07|0.12|
||Oils<br>0.06|0.12|
||Meat<br>0.02|0.04|
||Sugar<br>0.09|0.17|
|Developing oil|||
|exporters|||
||Food<br>0.78|2.28|
||Grains<br>0.33|0.98|
||Oils<br>0.22|0.61|
||Meat<br>0.05|0.14|
||Sugar<br>0.19|0.55|
|Oil importers|||
||Food<br>1.15|4.39|
||Grains<br>0.59|2.32|
||Oils<br>0.35|1.23|
||Meat<br>0.05|0.24|
||Sugar<br>0.15|0.59|

_Source_ : World Bank for international commodity prices and 2010 GDP, IMF for international reserves and USDA for import demand.

Dependency ratios give important insights into MENA‟s overall exposure to price shocks. It shows that, from a macroeconomic perspective, the impact of high international food prices on GDP and international reserves is sizable for most developing MENA countries, mainly because of the big role of grain imports for domestic consumption. This finding is consistent with MENA‟s high grain import dependency, the high grain import share, and the large increase in world grain prices compared with world meat prices. International prices of oils and sugar jumped, in some cases, by more than the prices of grains but their lower share in imports implies a smaller effect on the import bills.

This analysis gives an idea about the relative importance of different food commodities on macroeconomic indicators such as aggregate imports, yet it ignores second-round effects and may heavily misrepresent the impact on consumer prices,

-11-
To calibrate _hz_ and _bz_ , I use firm-specific R&D data as a proxy for the innovation effort. As documented in Aw, Roberts, and Xu (2011) **,** firms that invest in R&D on average increase their productivity over time. Boothby, Lau, and Songsakul (2008) and Ranasinghe (2014) documented the patterns of R&D expenditure for U.S. firms based on a data set provided by the National Science Foundation, and they found that R&D (when measured as employment) is increasing. Following Ranasinghe’s (2014) strategy, I calibrate the curvature parameter _bz_ of the innovation cost to target average R&D spending by firms that have between 500 and 1,000 employees relative to average R&D spending by firms that have between 1,000 and 5,000 employees. The scale parameter _hz_ of the cost of R&D is closely related to the total spending on innovation. Following the literature, I target the ratio of R&D to GDP in the United States, which for 2016 was equal to 2.7 percent.

**Bribing parameters** . Based on the data provided by the WBES, total bribes increase with firm size (measured as employment), with large differences across firms. For example, the average total bribe paid by firms with 50 to 100 employees is 40 percent of the average total bribe paid by those with 100 to 500 employees.<sup>20</sup> In contrast, firms with 100 to 500 employees spend on average 80 percent as much on bribes as do firms with 500 to 1,000 employees. Based on these features of the data, I calibrate the curvature parameter _bz_ of the cost of rent-seeking to target average bribes for firms that have between 500 and 1,000 employees relative to average bribes for firms that have between 1,000 and 5,000 employees. I calibrate the probability of keeping a firm’s distortion, _ψ_ , on the basis of the percentage of firms that have faced bribes in Argentina. Intuitively, the higher the value of _ψ_ , the lower the incentives to engage in bribing in the model. A list of parameters and targets is given in table 4.

> 20 See figure in appendix A.

23
# Improving Data on Poverty in the Third World: The World Bank's Living Standards Measurement Study

by Paul Glewwe

# Table of Contents

|I.|Int|roduction|1|
|---|---|---|---|
|II.|The|History of LSMS|2|
||A.|The State of Data on Poverty in the Third World<br>Previous to !1SMS|2|
||B.|LSMS from 1980 to 1985: rhe Development of<br>LSMS Methodology|4|
||C.|Present LSMS Survey Activities|5|
||D.|Future Survey Implementation|7|
|III.|LSM|S Methodology|9|
||A.|Objective of LSMS Surveys|9|
||B.|Consumption as an Indicator of Household<br>Welfare|9|
||C.|Distinctive Characteristics of LSMS Surveys|13|
||D.|Organization and Implementation|15|
|IV.|Sel<br>LSM|ected Results from Studies on Poverty Using<br>S Data|19|
||A.|Poverty Profiles|19|
||B.|Comparability of Different Poverty Definitions:<br>Results from CMte d'Ivoire|21|
||C.|Structural Adjustment and the Poor|23|
||D.|The Effect on the Poor of Raising User Fees for<br>Health Care and Education|27|
||E.|Food Stamps, Food Subsidies, and the Poor in<br>Jamaica|30|
||F.|Persistence of Poverty in Developing Countries|31|
|V.|Con|clusion|33|
|Refer|ence|s|35|
Finally, there is little systematic information on access to, and affordability of, infrastructure services for different percentiles of the income distribution, whether over time or across countries. This makes it very hard to reach robust conclusions regarding the consequences of infrastructure development for the equality of opportunities and incomes across households. Researchers have resorted to aggregate data on access – that is, without a breakdown across income percentiles – under the implicit assumption that changes in access at the margin affect primarily the poorer segments of the population, but this may not always be the case. More fundamentally, even aggregate access figures are available only for a limited number of countries, in most cases without any geographic disaggregation, and only for recent years.

_Identification_ remains a thorny issue. The impact of infrastructure supply on growth that empirical studies aim to estimate may be confounded with increased demand for infrastructure services prompted by rising levels of income (Canning and Pedroni 2008). Analogously, while infrastructure may help reduce inequality, at the same time inequality may hamper the provision of infrastructure services to the poor. The reason is that more unequal societies devote fewer resources to the provision of public goods, including infrastructure (Alesina, Baqir and Easterly 1999). Failure to account for these forms of simultaneity can lead to overestimation of the effects of infrastructure on growth and equity. More broadly, other unobserved factors affecting both development outcomes and infrastructure accumulation may likewise lead to biases.

There is no easy solution for this problem. In theory, one could base inference on the estimation of a full structural model. However, that approach poses difficult specification choices and challenging data requirements. Esfahani and Ramírez (2003) and Cadot, Roller and Stephan (2006) present two-equation models that jointly describe the aggregate production function and the accumulation of infrastructure. The former paper highlights the role of institutional factors for accumulation decisions in a cross-country setting, while the latter puts emphasis on the political economy of investing in transport routes across French provinces. Both papers find that the contribution of infrastructure services to GDP more than exceeds the cost of providing them. Interestingly, Cadot, Roller and Stephan (2006) find that the estimated elasticity of output with respect to infrastructure (around 0.08-0.09) remains invariant regardless of whether one accounts for the likely endogeneity.

18
Fisman, R. and R. Gatti (2002), “Decentralization and Corruption: Evidence across Countries”, _Journal of Public Economics_ , Vol.83.

- Friedman, Eric, Simon Johnson, Daniel Kaufmann, and Pablo Zoido-Lobaton (2000). “Dodging the Grabbing Hand: The Determinants of Unofficial Activity in 69 Countries,” _Journal of Public Economics_ , 76: 459-493.

- Gatti, R. (1999), “Corruption and Trade Tariffs, or a Case for Uniform Tariffs”, _Policy Research Working Paper,_ No. 2216, The World Bank.

- Gupta, S., H. Davoodi and R. Alonso-Terme (1998), “Does Corruption Affect Income Inequality and Poverty?”, _IMF Working Paper_ , WP/98/76, IMF.

- Gupta, S. H. Davoodi and E. Tiongson (2000), “Corruption and the Provision of Health Care and Education Services”, _IMF Working Paper,_ WP/00/116, IMF.

- Hunt, J. (2005), “Why are Some Public Officials more Corrupt than Others?”, _NBER Working Paper Series,_ #11595, August 2005.

- __________ and S. Laszlo. (2005), “Bribery: Who pays? Who refuse? What are the payoffs?”, _NBER Working Paper Series,_ #11635, September 2005.

- Jain, Arvind (2001), “Corruption: A Review”, _Journal of Economic Surveys_ , Vol. 15, No. 1 , pp. 71-121.

- Johnson, Simon, Daniel Kaufmann, John McMillan, and Christopher Woodruff (2000). “Why Do Firms Hide? Bribes and Unofficial Activity after Communism,” _Journal of Public Economics_ , 76: 495-520

- Kaufmann, D. (2000), “Governance and Anti-Corruption”, in _The Quality of Growth_ , Vinod Thomas, ed. The World Bank, 2000.

- __________, A. Kraay and P. Zoito-Lobaton (1999a), “Governance Matters”, World Bank PRD Working Paper No. 2196.

- __________, A. Kraay and P. Zoito-Lobaton (1999b), “Aggregating Governance Indicators”, World Bank PRD Working Paper No. 2195.

- __________, G. Mehrez, and T. Gurgur (2002), “Voice or Public Sector Management?”, mimeo, The World Bank, May 2002.

- Knack, Stephen and G. Anderson (1999). “Is ‘Good Governance’ Progressive? Institutions, Inequality, and Poverty Reduction,” paper presented at the 1999 Annual Meeting of the American Political Science Association, Atlanta, Georgia.

- Levin, M. and G. Satarov (2000), “Corruption and Institutions in Russia”, _European Journal of Political Economy_ , No. 16, November 2000.

- Lui, F. (1985), “An Equilibrium Queuing Model of Bribery”, _Journal of Political Economy_ , Vol. 93, No.4.

20
market and the legal system. The survey also contains information on firm ownership, sales, employees, top manager experience, whether a firm is an exporter, and firm age.

We start with all observations in WBES, and we proceed to delete firms that do not answer the audit question: “In the last fiscal year, did this establishment have its annual financial statement checked and certified by an external auditor?” We also delete (4,330) publicly listed firms because they are required to have their annual financial statements audited. Thus, auditing is not a choice that can be made by these firms. Our final sample consists of 71,677 firms in 121 countries from 2006 to 2014.<sup>7</sup> Among these, 47% of firms had their annual financial statements checked and certified by external auditors.

A common criticism of using survey data to conduct research related to firm performance and growth is that survey data are self-reported and therefore the findings may suffer from self-reporting bias. However, accounting data are more likely to be biased than survey data as the incentives to distort data are likely to be higher in financial statements because many firm-level decisions, such as tax, financing, and managerial compensations, are in part based on variables from financial statements ‐ (Beck Demirgüç Kunt and Maksimovic, 2005). Furthermore, the self-reporting nature of the WBES data is not likely to be a significant source of bias. The survey aims to evaluate the business environment instead of firm performance and growth. Even though some firm performance and growth related questions were asked, the survey was specifically designed to ask those questions at the end of the interview. This reduces the respondents’ need to justify their own performance when answering the earlier questions about the business environment.

Throughout our analysis, we control for country level governance and macroeconomic variables. Country level financial development data are from _International Financial Statistics (IFS)_ . Country-level governance indices ( _Control of Corruption_ and _Government Effectiveness_ ) are from the

> 7 The dataset includes firms from 15 different industries according to the international industry classification codes (ISIC).

6
- 14 -

rate- -were estimated to be costing the municipality an amount equal to 30 percent of current revenues.

3.08 Valuations, meanwhile, were falling increasingly behind market values. The valuations made in the 1975 general resurvey were based upon market data originally gathered in 1966, adjusted for inflation. As the rate of inflation increased in the early 1980s, subsequent adjustments in values failed to keep pace. Between 1984 and 1987, inflation totalled 2,900 percent. Adjustments to unit costs were less than half that.

3.09 Distortions were also introduced in the distribution of the tax burden across neighborhoods. In 1983, the municipality began to differentiate the annual increases by zone and building size, in an attempt to reduce the tax burden on lower-income households. Expecting an inflation rate of 200 percent, the municipality capped the increase on small residential. buildings in the north zone at 50 percent. Assessments on large reside-s.. along the southern beachfront were permitted to increase by 280 percent. AP a result, by 1987 the tax base not only grossly underestimated property values, but also no longer even reflected the relative prices of properties in different locations.

# A. Resurvey

3.10 The task of resurveying Rio was assigned to the _Instituto de PlaneJamento Municipal (IPLAN)._ IPLAN is a m%inicipally-owned parastatal, which assumed the task of maintaining Rio's property tax cadastre in 1986.

3.11 From the outset, the scope of the resurvey was limited to those areas and classes of property where it was expected to be most cost effective. Geographically, its coverage was confined to higher-income neighborhoods. The entire northern zone of the city--a low-income area encompassing 22 percent of taxable properties but only 3 percent of the city's assessed value- -was excluded. Favelas--squatter settlements in higher-income areas--were also explicitly exempted. Vacant land was also not surveyed: no attempt was made to ascertain changes in parcel boundaries due to subdivision or consolidation. IPLAN anticipated that the greate3t increase in assessments would arise from the discovery of new construction and changes in building use. Thus the program focused exclusively on built-upon properties.

3.12 The program was carried out in three phases. First, the entire assessment zone was aerially-photographed. To minimize costs, the resulting photomosaic was not orthographically rectified and transformed into line maps. Instead, sections of the unrectified photomosaic itself were carried into the field and used as base maps. In the field, survey teams sought only three items of information for each building shown on the photomosaic: its street address, number of floors and its current use. No attempt was made to measure the physical dimensions of the buildings or to determine their owners.

3.13 Building dimensions were determined at the IPLAN offices on the basis of the aerial photographs. The ground-floor perimeter of each building was
from the labor force around marriage age. Economic conditions being equal, women who get married have a much higher likelihood of exiting the labor force also when controlling for children. We still know little about why this happens but it is clear that marriage is the breaking point. The third aspect is economic and related to the very weak performance of sectors that are more likely to hire women with primary and secondary education such as manufacturing and services (Verme, 2014). These factors will be explored further in the rest of the paper.

There are also two other stylized facts that may play a role in explaining FLP. One is the rising of enrollment rates at all levels of education and the resulting elongation of the period of schooling. This delays labor market entrance and increases the demand-supply mismatch if sectors that are more likely to hire well educated women do not perform, as we have seen for Morocco. The female unemployment rate has indeed increased while female participation was declining. The second factor is the astonishingly high share of women in school age out of school and out of work despite progress in education. Using LFS data for the period 2007-2011, we found less than 5 percent of men to be simultaneously out of work and out of school in both urban and rural areas. By contrast, the figures for women show that 4 in 10 women in urban areas and 6 in 10 women in rural areas aged 15 to 29 years are neither in work nor in school. This high share of young women at home has an impact on child and elderly care in households with employed women with children, an important factor to model in the econometric equations that follow.

**Figure 1 - Female Labor Force Participation Rate (Quarterly, 1999-2013)**

<!-- Start of picture text --> 50.0<br>45.0<br>40.0<br>35.0<br>30.0<br>25.0 Urban<br>20.0 Rural<br>15.0<br>10.0<br>5.0<br>0.0<br>1999 1999 2000 2001 2002 2002 2003 2004 2005 2005 2006 2007 2008 2008 2009 2010 2011 2011 2012 2013<br><!-- End of picture text --> Source: LFS (1999-2013)

# **Regional medium-term panel model**

In this section we review the results of the medium-term quarterly regressions largely following the recent literature on long-term determinants of FLP (Tam, 2011, Gaddis and Klasen, 2013, Tsani et al., 2013). Table 1 provides ten different specifications of the equations. The first four 9