rafmacalaba/datause-encoder-data huggingface dataset:
# <u>Uses of the System</u>

FCM itself primarily uses the national reports and data to strengthen its advocacy initiatives in support of the municipal sector, targeted to the Government of Canada. The system covers primarily issues of social and economic conditions and has therefore thus far been most useful in promoting those issues. As the system evolves, it is expected to expand to cover more environmental issues and address the infrastructure and service provision capabilities of municipalities, relative to their growing responsibilities.

The system has produced a large database, disaggregated at the level of the municipal government, which is used extensively by its members. Disaggregation using municipal boundaries is one of the unique features of the QOLRS. Most analyses of “local” issues outside the QOLRS system are done at the level of Census Subdivisions and Census Metropolitan Areas, and hence do not necessarily reflect the issues that a given municipal government faces. This orientation, of course, reflects the orientation of FCM, as a representative of municipal government.

Using the database, QOLRS members produce their own local reports. Members of the QOLRS use their reports and data, along with the reports prepared by FCM, to inform their municipal councils and help guide discussions on priorities, planning and budgeting, as well as to point out issues to their provincial governments. Outcomes and conditions measures, as found in the FCM system, help to point out areas in which a municipality may have to effect improvements, or can, conversely, confirm that overall targets are being reached.

It is important to recognize that this style of measurement and management feedback is distinct from more formal “performance measurement” systems, which are now in place in a number of municipalities, and are being required of municipalities by several provinces. Mandated performance measurement systems generally concentrate on process and cost efficiency.

That being said, a number of municipalities are now integrating a balance of organizational measures, employee measures, financial health indicators, and community “quality of life” indicators. One of the goals of the FCM team now is to more fully integrate the QOLRS with their own management information and performance reporting systems.

Most QOLRS participants use their local reports as a means to report to their citizens, and many use the reports as the basis for community planning sessions involving citizens at large.

Finally, the system is a network of municipal government officials, both elected and staff, who are engaged in the process of improving their management capabilities to support their communities’ quality of life.

# <u>Indicators</u>

The QOLRS currently uses 72 indicators, grouped into a set of “domains”, which are sets of indicators related principally to one aspect of quality of life or description of conditions.

Figure 1 summarizes the domains and indicators. The domains are indicated on the top row, with the columns containing the indicators. All indicators are documented and presented on the FCM website.<sup>22</sup>

> 22 See footnote 2 .

44
resource intensive products over labor force, constructed from the UN Statistics Department’s Commodity Trade Statistics (COMTRADE).

_Innovation Effort:_ Consistent with much of the microeconomic literature we employ real R&D expenditures as a measure of innovation effort. The data are derived ultimately from national surveys that use as a common definition of expenditures that include “fundamental and applied research as well as experimental development.”<sup>11</sup> The data thus include not only the basic science expected in the more advanced countries, but also investments in the adoption and adaptation of existing technologies often thought more germane to developing countries. The series are constructed based on underlying data published by UNESCO, the OECD, the Ibero American Science and Technology Indicators Network (RICYT) and the Taiwan Statistical Data Book.

Though it would be desirable to study the evolution and efficiency of both private and public R&D, we work with aggregate R&D for several reasons. First, the data sources divide R&D not into private and public R&D, but into productive and nonproductive sectors, the latter accounting for roughly 20% of the total.<sup>12</sup> The definition of “productive sector” includes both public and private for profit and not-for profit firms while “non-productive sector” includes R&D financed or undertaken by the executive branch of government.

Second, the productive/non productive split seems to occasionally lead to some critical issues in categorization. For instance, if a public company finances its R&D from retained earnings, this will count as productive sector R&D. If instead the same R&D is financed by a transfer from the treasury to the firm, it counts as “non-productive” R&D. For several countries in our sample, there were striking shifts in composition from one year to the next suggesting sensitivity to these accounting conventions. By contrast, the total R&D series were reasonably stable. The final consideration is more prosaic: many

> 11 UNESCO Statistical Yearbook (1980) pg. 742. Definitions are common to the OECD, Ibero American Science and Technology Indicators Network  (RICYT), World Bank ,and Taiwan Statistical Yearbook and all are based on the Frascatti manual definition.

> 12 The median for countries with both series is 21%.

6
Table <u>3:</u> CAFTA-DR effects disentangling specific effects with the US

||(1)<br>Allgoods|(2)<br>Allgoods|(3)<br>Allgoods|(4)<br>Manufacturing|(5)<br>Agriculture|(6)<br>Services|
|---|---|---|---|---|---|---|
|RTA|0.13<sup>_∗∗∗_</sup>|0.13<sup>_∗∗∗_</sup>|0.13<sup>_∗∗∗_</sup>|0.13<sup>_∗∗∗_</sup>|0.12<sup>_∗∗∗_</sup>|0.06<sup>_∗∗∗_</sup>|
||(0.03)|(0.03)|(0.03)|(0.03)|(0.03)|(0.01)|
|RTA_×_ CAFTA|-0.25<sup>_∗∗∗_</sup>|0.13<sup>_∗∗∗_</sup>|0.13<sup>_∗∗∗_</sup>|0.16<sup>_∗∗∗_</sup>|-0.07<sup>_∗∗_</sup>|-0.01|
||(0.04)|(0.03)|(0.03)|(0.04)|(0.03)|(0.03)|
|RTA_×_ CAFTA_×_ US||-0.44<sup>_∗∗∗_</sup><br>(0.05)|||||
|RTA_×_ CAFTA_×_ US as Exp|||-0.57<sup>_∗∗∗_</sup><br>(0.06)|-0.58<sup>_∗∗∗_</sup><br>(0.06)|-0.29<sup>_∗∗∗_</sup><br>(0.07)|-0.13<sup>_∗∗∗_</sup><br>(0.04)|
|RTA_×_ CAFTA_×_ US as Imp|||-0.32<sup>_∗∗∗_</sup>|-0.33<sup>_∗∗∗_</sup>|-0.07|-0.15<sup>_∗∗∗_</sup>|
||||(0.06)|(0.06)|(0.05)|(0.05)|
|TOTAL CAFTA|-0.12<sup>_∗∗∗_</sup>|0.26<sup>_∗∗∗_</sup>|0.27<sup>_∗∗∗_</sup>|0.29<sup>_∗∗∗_</sup>|0.05|0.05|
||(0.03)|(0.05)|(0.05)|(0.05)|(0.04)|(0.03)|
|TOTAL CAFTA - US||-0.17<sup>_∗∗∗_</sup><br>(0.03)|||||
|TOTAL CAFTA - US as Exp|||-0.30<sup>_∗∗∗_</sup>|-0.30<sup>_∗∗∗_</sup>|-0.24<sup>_∗∗∗_</sup>|-0.08<sup>_∗∗∗_</sup>|
||||(0.05)|(0.05)|(0.05)|(0.03)|
|TOTAL CAFTA - US as Imp|||-0.05|-0.04|-0.01|-0.09<sup>_∗∗_</sup>|
||||(0.04)|(0.04)|(0.03)|(0.04)|
|Observations|425984|425984|425984|425984|425984|425984|

**Note:** All goods refer to manufacturing and agricultural products. All regressions use the PPML estimator with exporter-time, country-pair and importer-time fixed effects. Border-time controls are also included in all regressions. Robust standard errors, clustered at the country-pair level, are in parentheses. _∗ p <_ 0.10,** _p <_ 0.05,*** _p <_ 0.01

15
<!-- Start of picture text --> MAGDALENA<br>250<br>200<br>150<br>100<br>50<br>0<br>-50<br>-100<br>-150<br>-200<br>-250<br>M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08<br>PUERTO SUAREZ<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>-50<br>-100<br>-150<br>-200<br>M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08<br>Average for 1960-1990 (milimeters)<br>Precipitation Anomalies compared to<br>Average for 1960-1990 (milimeters)<br>Precipitation Anomalies compared to<br><!-- End of picture text -->

**ROBORE**

<!-- Start of picture text --> 350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>-50<br>-100<br>-150<br>-200<br>M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08<br>Average for 1960-1990 (milimeters)<br>Precipitation Anomalies compared to<br><!-- End of picture text --> <!-- Start of picture text --> ORURO<br>200<br>150<br>100<br>50<br>0<br>-50<br>-100<br>M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08<br>RIBERALTA<br>300<br>200<br>100<br>0<br>-100<br>-200<br>-300<br>M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08<br>RURRENABAQUE<br>400<br>300<br>200<br>100<br>0<br>-100<br>-200<br>-300<br>-400<br>M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08<br>Average for 1960-1990 (milimeters)<br>Precipitation Anomalies compared to<br>Average for 1960-1990 (milimeters)<br>Precipitation Anomalies compared to<br>Average for 1960-1990 (milimeters)<br>Precipitation Anomalies compared to<br><!-- End of picture text --> 24
- 30 -

As an interesting aside, some ol the standard models of investrment in the literature (accelerator, flexible accelerator, neoclassical, putty-clay,<sup>partial adjustment, and profits models)</sup> were estimated in the levels on Egyptian data.40 The results in<sup>the levels indicated that some</sup> combination of putty-clay, profits and partial adjustment wt,.ld produce<sup>a well-fitting investment</sup> function. However, once the data were differenced, virtually all of the models<sup>collapsed. This</sup> implies that what has been interpreted as causality in a number<sup>of empirical studies of investment</sup> may have been spurious correlations between trended variables.

The recent literature on cointegiation and stationarity testing provides a more<sup>rigorous</sup> framework for avoiding spurious regression while retaining<sup>long run information about the</sup> equilibrium relationship in the levels. Essentially, the intutition behind<sup>cointegration is that</sup> econometric results are legitimate only when time series are stationary.<sup>4'</sup> Therefore it is necessary to test the time series properties in order to deternisne what degree<sup>of differencing, if</sup> any, is necessary to de-trend the data. Once stationarity is achieved, if somne<sup>linear combination</sup> of the variables results in a "white noise" error term, the series are said to<sup>be cointegrated. This</sup> implies that it is possible to explain the evolution of the time series through<sup>the interaction of a</sup> set of non-trended data that results in an error term that is random, thereby<sup>leaving nothing left</sup> to explain econometrically.

40 See Shafik, 1989 for these results.

4' For a survey of the literature, see a special issue of the Oxford Bulletin of Economics<sup>and Statistics</sup> with articles by Hendry, 1986; Granger, 1986; Hall, 1986; Jenkinson, 1986; as<sup>well as work by Dolado and</sup> Jenkinson, 1987; and Engle and Granger, 1987.
- Holbrook, A., M. Green, and J. Krosnick. 2003. Telephone versus Face-to-Face Interviewing of National Probability Samples with Long Questionnaires: Comparisons of Respondent Satisficing and Social Desirability Response Bias. _The Public Opinion Quarterly_ . 67(1), pp. 79-125.

- <mark>Holm, S., 1979. A simple sequentially rejective multiple test procedure.</mark> _<mark>Scandinavian Journal of Statistics</mark>_ <mark>, pp.65-70.</mark>

- Hynes, Stephen, Nick Hanley and Riccardo Scarpa.  2008.  Effects on Welfare Measures of Alternative Means of Accounting for Preference Heterogeneity in Recreational Demand Models. _American Journal of Agricultural Economics_ , Vol. 90, No. 4 (Nov., 2008), pp. 1011-1027.

- <mark>Kaminska, O., McCutcheon, A.L. and Billiet, J., 2010. Satisficing among reluctant respondents in a cross-national context.</mark> _<mark>Public Opinion Quarterly</mark>_ <mark>, 74(5), pp.956-984.</mark>

- Karen Nylund-Gibson & Katherine E. Masyn 2016 Covariates and Mixture Modeling: Results of a Simulation Study Exploring the Impact of Mis-specified Effects on Class Enumeration, _Structural Equation Modeling: A Multidisciplinary Journal_ , 23:6, 782-797.

- Kovac, Mitja  and Rok Spruk. 2016. Institutional development, transaction costs and economic growth: evidence from a cross-country investigation. _Journal of Institutional Economics_ , 12: 1, 129–159.

- Knack, S., and Keefer, P. 1997. Does social capital have an economic pay-off? A cross country investigation. _Quarterly Journal of Economics_ 112.4: 1251–1288.

- Krosnick, J. and D. Alwin. 1988. A Test of the Form-Resistant Correlation Hypothesis: Ratings, Rankings, and the Measurement of Values. _The Public Opinion Quarterly_ , 52(4), pp. 526538.

- <mark>Landa, J.T., 1981. A theory of the ethnically homogeneous middleman group: An institutional alternative to contract law.</mark> _<mark>The Journal of Legal Studies</mark>_ <mark>, 10(2), pp.349-362.</mark>

- <mark>Larsen, Bradley J. 2018. The Efficiency of Real-World Bargaining: Evidence from Wholesale Used-Auto Auctions. Stanford University</mark>

- <mark>Latin American Public Opinion Project. 2016/7. The Americas Barometer. Latin American Public Opinion Project (LAPOP), Vanderbilt University. http://www.LapopSurveys.org (accessed June 12, 2018).</mark>

- Macaulay, Stewart. 1963. Non-Contractual Relations In Business: A Preliminary Study. _American Sociological Review_ , Vol. 28, No. 1, February.

- Masyn, Katherine. 2013. Latent class analysis and finite mixture modeling. In T. D. Little (Ed.) _The Oxford handbook of quantitative methods in psychology. Volume 2_ . pp. 551-611. New York, NY: Oxford University Press.

- <mark>McMillan, J. and Woodruff, C., 1999. Interfirm relationships and informal credit in Vietnam.</mark> _<mark>The Quarterly Journal of Economics</mark>_ <mark>, 114(4), pp.1285-1320.</mark>

- <mark>Mike, Károly and Kiss, Gábor. 2017. Beyond the Informal/Formal Divide: How Do Firms Combine Contract-Enforcement Institutions? HETFA Working Papers No. 2017/21.</mark>

27
evidence supports this theory and finds that for the United States, two-thirds of the long-run wage (or the wage at the end of a career) occurred during the first 10 years employed, and that one-third of the wage increase is explained by job-to-job transitions (Topel and Ward, 1992). Similarly, it has been found that in the United States, women are less likely to switch jobs (that is, experience job-to-job transitions), and this explains about 8 percent of the gender wage gap in the United States (Royalty, 1998; Posadas, 2009). The other main theory comes from personnel economics. Employers might provide less training and fewer promotions to women, in particular during the early years of their careers, if they are expected to quit the firm because of maternity interruptions (Lazear and Rosen, 1990). Empirical evidence also supports this stream of research (Bertrand, 2011). Also supporting these theories, Kunze (2015) finds there is a family gap, as women with children are less likely to be promoted within an organization or suffer a wage penalty when changing jobs or working part-time. These results are consistent with those obtained by Francesconi (2002).

To test these hypotheses, we add a few covariates that might capture these effects, at least partially. The RLMS asks adult respondents whether they have changed occupation, place of work, or both within the last 12 months. It can be assumed that changes in place of work are associated with the onthe-job search theory, and should result in wage increases. This effect is only present for women in the median percentile. For this group, having changed place of work (but not occupation) increases earnings by almost 13 percent. Interestingly, the effect for men is smaller and not significant. Unfortunately, the RLMS asks no direct question on promotion opportunities; the survey only asks whether there has been a change in occupation within the same place of work. This latter variable, however, could indicate either a promotion within the same firm or horizontal move not associated with a promotion.

15
**Figure 2: Change in Poverty and Initial Poverty in four West African Countries**

<!-- Start of picture text --> (A) Ghana (B) Guinea<br>0.0 20.0 40.0 60.0 80.0 20.0 40.0 60.0 80.0 100.0<br>Poverty Headcount Poverty Headcount<br>(C) Niger (D) Senegal<br>20.0 40.0 60.0 80.0 100.0 20.0 40.0 60.0 80.0 100.0<br>Poverty Headcount Poverty Headcount<br>4.0 8.0<br>3.0 6.0<br>2.0 4.0<br>Change in Poverty Headcount1.0 Change in Poverty Headcount2.0<br>0.0<br>0.0<br>15.0 15.0<br>10.0 10.0<br>5.0 5.0<br>Change in Poverty Headcount Change in Poverty Headcount<br>0.0 0.0<br><!-- End of picture text --> Source: Coulombe and Wodon (2008).

Impacts differ substantially between areas, which poses a dilemma for policy-makers. On the one hand, the desire to help households cope with the increase in food prices may lead policy-makers to implement safety net interventions in the hardest hit areas. On the other hand, these hard hit areas are not among the poorest in the country. Hence the effectiveness of the country’s overall poverty reduction strategy may be jeopardized if public resources are diverted from the poorest areas to less poor areas to address the food crisis. We turn in more details to the issue of policy responses to the food crisis in the next section.
implemented many strategies to address low compliance by firms. Identification of unregistered firms includes drawing on data from business registries, licensing regimes, and banking systems. To identify tax evasion among registered firms, DGT takes steps to determine the accuracy of tax returns, including implementing a compliance risk management model that predicts the likelihood of non-compliance. These predictions are partly based on tax audit results covering around 2 percent of registered firms each year. However, there is no public reporting on the scale of misreporting identified by these audits, nor any estimates of total tax losses if rates of identified misreporting were to be extrapolated.

# **3 Data**

The data used in this analysis comes from the 2023 WBES in Indonesia, which interviewed the top managers or owners of 2,955 firms. This sample was nationally representative of firms possessing a Company Registration Certificate (TDP) or Business Identification Number (NIB) with five or more employees with at least 1 percent of private ownership and who do not have legal status as cooperatives. Firms are selected through stratified random sampling from the 2016 Economic Census conducted by the Central Agency of Statistics of Indonesia. Stratification is based on sector, firm size (employment),<sup>7</sup> and location. The sectoral distribution covers firms from the manufacturing and major services sectors. The survey covers all 38 provinces of Indonesia, though some are aggregated, resulting in a total of 22 regional strata. The sample design ensures no more than 7.5 percent margins of error and 90 percent confidence intervals at each of the stratification levels: size, sector, and region. All interviews were conducted face-to-face with top managers or business owners in Bahasa Indonesian through Computer-Assisted Personal Interviews (CAPI) using tablets and Survey Solutions as the software for data collection. Data collection started in December 2022 and concluded in September 2023, achieving a response rate of 41.2 percent, similar

> 7The stratification by firm size is defined using the number of employees in the firm. The definitions are consistent across all countries where the WBES has been implemented and are defined as small (5-19 employees), medium (20 to 99 employees), and large (100 and more).

7
16 of input prices from the later period estimates in Table 8 has a similar dampening effect. In any case, a simple plot of relative minimum wage trends to relative employment trends is strongly suggestive of a negative relationship between unskilled employment and minimum wages over time. In sum, the empirical results indicate that minimum wage fixing has had a negative and significant impact on unskilled employment in Colombia, at least over the 1980s following and coinciding with a period when minimum wages were rising in relative importance.

# IV. Minimum Wages and Labor Market Segmentation

Coverage under minimum wage laws was granted to all Mexican workers as part of the 1974 revised labor code irrespective of the size of the enterprise in which they work (Roberts, 1991). In theory, this should cover the 50.5 percent of urban wage earners who were employed in the informal sector in 1988. The wage histograms in figures 3 and 4 suggest otherwise, since they reveal large shares of individuals whose wages fall below the minimum wage. Because the urban informal sector is quite large in Mexico it is an important outlet for displaced formal sector workers and a major employer in Mexico. Understanding the characteristics of the employed in this sector is important for this reason. Using the household data to examine differences between formal and informal sector work and workers may help to verify the establishment level results.

Table 9 examines in detail the issue of noncompliance using 1988 Mexican Household Survey data. The incidence of non-compliance with the minimum wage- defined here as the proportion of individuals reporting wages below the federally mandated minimum wage-- varies considerably according to whether the individual reports that he or she works in a formal sector private job (receives social security and works in a registered private firm) or not. As expected, non-compliance is far more prevalent in the informal sector than in the formal sector in all cases and for all categories of workers.

Despite large formal/informal sector differences, what may be most striking about the results in Table 9 is the degree of compliance that actually exists in the informal sector. Indeed, greater than 80 percent of full-time male informal sector workers earn wages in excess of the minimum wage, and nearly 60 percent of full-time female workers are so compensated. The numbers may reflect efforts to comply