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2
Challenges in Measuring Labor Market Conditions Across Countries
PRESENTATION
Economists Martín Rama and Raquel Artecona clearly highlighted the difficulty they encountered in obtaining, evaluating, and organizing accurate, timely information on labor markets around the world. Summarizing their paper (Rama and Artecona, 2002), they noted that people hold strong views on the effects of globalization and the impacts of labor market regulations, but these views are generally based on limited evidence. Some data are available (from censuses, household surveys, etc.), they said, but these data are plagued with problems. Rama and Artecona’s goal was to gather existing data for developing countries, analyze it, select what was internationally comparable, and make it available to researchers in a ready-to-use format.
Rama presented their results—a database of labor market information on 121 countries, classified into seven broad groups.1 The database includes information on most “larger” countries, as well as countries with relatively good data and those with which Rama and Artecona were personally familiar. Because the boundaries of many nations have changed over
1
The seven groups include the industrial or developed countries and six geographic regions defined by The World Bank: Sub-Saharan Africa; East Asia and the Pacific Islands; Eastern Europe and Central Asia; Latin America and the Caribbean; the Middle East and North Africa; and the South Asian region.
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the past 50 years, the data were selected, and adjusted, to reflect national boundaries in 1999. Labor market information was averaged for 11 five-year time periods, from 1945–1949 to 1995–1999.
Describing the database variables, Artecona said that they sought to include information on labor market outcomes, as well as to “provide, hopefully, some light on the debate about [whether] labor market interventions are good or bad.” The variables included 44 indicators in seven broad groups: (1) labor force; (2) employment and unemployment; (3) wages and productivity; (4) conditions of work and benefits; (5) trade unions and collective bargaining; (6) public sector employment; and (7) labor standards (see Table 2-1). Artecona said that when they were selecting indicators, they chose those with clear definitions. The database does not include information on the share of the informal sector in the total labor force because there was no clear definition of what constitutes informal employment. Artecona said they drew on a wide variety of cross-country and country-specific sources, including unpublished statistical information gathered during their research for the World Bank.
Confronted with many different, and sometimes conflicting, data sources, Artecona and Rama considered three alternative strategies:
to document every break, discrepancy, and change in definition;
to recalibrate the data in order to make it fully consistent; or
to exercise judgment in selecting data that appear comparable and to report these data without comments.
Artecona argued that the first strategy is self-defeating because no one uses labor market data that are extensively documented to conduct research. The second strategy is not practical, she said, except for a few indicators, such as wages. This left the third strategy. Carrying out this strategy, Artecona and Rama first constructed a raw database, with all relevant labor market information from all sources. Second, they organized the data into 121 country files, including those variables from the “raw” database that were related to the 44 labor market indicators. When sources conflicted, they selected the “better” source, based on what they stated was their expert knowledge of countries and data sources. They also dropped variables, observations, and sources that were clearly out of line with other sources. Finally, they summarized the information in each country file in the form of five-year period averages, from 1945–1949 to 1995–1999.
Rama noted that, although he and Artecona were still adding to the
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TABLE 2-1 Variables in the Database of Labor Market Indicators Across Countries
1. Labor force
LFTALL
Total labor force, in thousands of persons.
LFTAGR
Labor force in agriculture, in thousands of persons.
LFTIND
Labor force in manufacturing, in thousands of persons.
LFRALL
Labor force participation rate, in percentage of the population aged 15 to 64.
LFRMLE
Male participation rate, in percentage of the male population aged 15 to 64.
LFRFEM
Female participation rate, in percentage of the female population aged 15 to 64.
2. Employment and unemployment
EMTALL
Total employment, in thousands of persons.
EMTIND
Employment in manufacturing activities, in thousands of persons.
UNRALL
Total unemployment, in percent of the labor force.
UNRMLE
Male unemployment, in percent of male labor force.
UNRFEM
Female unemployment, in percent of female labor force.
UNTFST
Unemployed seeking their first job, in thousands of persons.
HRSWRK
Hours of work per week, in nonagricultural activities.
3. Wages and productivity
WGEAGR
Wage per worker in agriculture, in current U.S. dollars per year.
WGEIND
Labor cost per worker in manufacturing, in current U.S. dollars per year.
PRDIND
Value added per worker in manufacturing, in current U.S. dollars per year.
WGEGOV
Wage of government employees, in current U.S. dollars per year.
WGEMIN
Minimum wage, in current U.S. dollars per year.
4. Conditions of work and benefits
MATLVE
Duration of maternity leave, in equivalent days with 100 percent earnings.
ANNLVE
Minimum annual leave with pay after one year of work, in working days.
ACCDNT
Number of workers injured at work, in thousands of persons per year.
SSTYPE
Mandatory coverage of the statutory pension scheme.
SSCONT
Social security contributions by employer and employee, in percent of salaries.
SSCVGE
Active contributors to old-age pension schemes, in percent of the labor force.
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SSREVN
Social security contributions by employers and workers, in percent of GDP.
UNBRPL
Initial unemployment benefit, in percent of earnings before job loss.
UNBDUR
Maximum duration of continuous unemployment benefits.
SVCPAY
Mandatory severance pay after three years of employment, in months of salary.
5. Trade unions and collective bargaining
TUMMBR
Total trade union membership, in percent of the total labor force.
TUCVGE
Coverage of collective bargaining agreements, in percent of salaried workers.
STKNBR
Number of strikes and lockouts per year.
STKWRK
Annual number of workers involved in strikes and lockouts, in thousands.
STKHRS
Annual work-days lost to strikes and lockouts, in thousands.
6. Public sector employment
EMPCGT
Employment in the central government, in thousands of persons.
EMPGGT
Employment in the general government, in thousands of persons.
EMPPSR
Employment in the public sector, in thousands of persons.
7. Labor standards
ILOCNV
Cumulative number of ILO conventions ratified by the country.
CHLDLB
Ratification of ILO Convention 138, on child labor.
FORCLB
Ratification of ILO Convention 29, on forced or compulsory labor.
ABOLFL
Ratification of ILO Convention 105, on the abolition of forced labor.
EQLREM
Ratification of ILO Convention 100, on equal remuneration.
DISCRM
Ratification of ILO Convention 111, on discrimination.
ORGNZE
Ratification of ILO Convention 87, on the right to organize.
BRGAIN
Ratification of ILO Cconvention 98, on the right to bargain collectively.
database, it already had very good coverage for indicators on labor force, employment, and labor standards (see Table 2-2). For example, it included more than 700 observations for employment in agriculture and industry and labor force participation rates. On the other hand, coverage was weaker for indicators on trade union activities and conditions of work. For example, Rama said that one variable for which it was difficult to find data was severance pay after two years of uninterrupted private employment. Across world regions, the database provided good coverage for Latin America and South Asia, but “much worse” coverage of labor market information for Sub-Saharan Africa, the Middle East, and North Africa. Using
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TABLE 2-2 Coverage by Variable and Region in the Draft Database of Labor Market Indicators Across Countries (as of July 2002)
Region
Countries
LFTALL
LFTAGR
LFTIND
LFRALL
LFRMLE
AFR
23
212
164
162
140
140
EAP
12
114
77
74
74
65
ECA
18
99
74
59
105
105
INL
23
223
175
153
153
138
LAC
21
196
147
144
126
126
MNA
19
174
131
126
102
102
SAS
5
49
35
35
30
31
ALL
121
1067
803
753
730
707
UNTFST
HRSWRK
WGEAGR
WGEIND
PRDIND
AFR
23
10
17
15
97
87
EAP
12
13
26
13
71
59
ECA
18
13
25
51
66
34
INL
23
48
91
16
150
155
LAC
21
33
38
15
119
118
MNA
19
13
7
7
84
83
SAS
5
3
8
26
27
28
ALL
121
133
212
143
614
564
SSCONT
SSCVGE
SSREVN
UNBRPL
UNBDUR
AFR
23
38
24
34
4
4
EAP
12
27
19
17
4
8
ECA
18
24
14
37
13
17
INL
23
46
9
107
49
56
LAC
21
56
58
81
8
20
MNA
19
43
23
25
7
14
SAS
5
9
8
0
1
1
ALL
121
243
155
301
86
120
EMPGGT
EMPPSR
ILOCNV
CHLDLB
FORCLB
AFR
23
41
24
105
253
253
EAP
12
27
15
85
126
126
ECA
18
19
25
98
178
159
INL
23
42
26
252
253
253
LAC
21
35
17
150
231
231
MNA
19
26
17
106
208
208
SAS
5
10
8
46
55
55
ALL
121
200
132
842
1304
1285
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LFRFEM
EMTALL
EMTIND
UNRALL
UNRMLE
UNRFEM
140
15
31
16
5
5
74
51
59
55
31
31
105
59
58
28
16
16
138
187
183
155
193
157
126
53
56
78
61
61
105
36
21
31
14
14
30
21
18
25
18
18
718
422
426
388
338
302
WGEGOV
WGEMIN
MATLVE
ANNLVE
ACCDNT
SSTYPE
37
41
52
28
30
34
22
26
22
10
35
19
20
33
32
14
21
18
33
102
69
43
56
46
25
64
75
29
50
42
17
30
37
18
23
29
13
5
14
9
6
8
167
301
301
151
221
196
SVCPAY
TUMMBR
TUCVGE
STKNBR
STKWRK
STKHRS
13
67
7
30
28
30
3
56
5
40
33
31
3
43
4
18
17
18
0
181
50
63
66
88
12
75
10
46
46
39
17
42
0
11
11
11
22
23
0
27
27
27
70
487
76
235
228
244
ABOLFL
EQLREM
DISCRM
ORGNZE
BRGAIN
253
253
253
253
253
126
126
126
126
126
178
160
160
178
178
253
253
253
253
253
231
231
231
231
231
208
208
208
208
208
55
55
55
55
55
1304
1286
1286
1304
1304
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Chile as an example, Rama explained how a country file is organized in the final database. Although analysis and comparison of variables in the larger “raw database” sometimes led easily to selection of the best source, this was not always the case. Fortunately, in Chile a study already existed in which researchers had tried to reconcile the figures. This study provided the information Rama and Artecona needed to select the best estimate of unemployment to include in the Chile country file in the final database.
In conclusion, Rama noted that “only their use” will tell whether the data included in the database are helpful for research, and he added, “We want to report some encouraging results.” He cited several published and completed studies (Forteza and Rama, 2001; Rodrik, 1999; Freeman, 1994) that have drawn on preliminary versions of the data and have yielded meaningful results. Rama said his research with Forteza indicates that countries with relatively rigid labor markets adjust more slowly to economic reforms than those with more flexible labor markets. However, he said, “We found it is not minimum wages and mandatory benefits” that cause these rigidities, but rather the size of organized labor, which represents those who stand to lose as a result of economic reforms.
DISCUSSION
Responding to Rama and Artecona’s presentation, Yale economist T.N. Srinivasan commended “their valiant effort in putting together a data set.” However, he was not persuaded that the database would allow researchers to analyze the interaction between labor market policies and institutions on the one hand and economic growth, poverty, and inequality on the other hand. He was not convinced that the database would allow evaluation of the relationship between labor market conditions and openness to trade and foreign direct investment.
Srinivasan said he was glad that Rama and Artecona’s data set has not yet fallen into the hands of cross-country regression analysts. He was unenthusiastic about such regressions because they are based on a common regression of data of varying quality from disparate economies. Therefore, he said, it would be inappropriate to make inferences about growth from such regressions unless they allow for both measurement errors and biases in some of the explanatory variables and for the possibility that there might be a two-way relationship between growth and some of the explanatory variables. For example, higher educational attainment of the labor force might lead to faster economic growth, while faster growth would enable the soci
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ety to provide more education to its workers. Although in principle these econometric issues could be addressed, in practice it is either not possible to do so (for example, appropriate instruments are not available), or it is simply not done.
Although he acknowledged that most countries have population censuses and household surveys, Srinivasan disagreed with the authors’ claim that they were able to derive employment and unemployment data from these sources. In India, he said, the census definition of “worker” has changed over time. It is difficult to adjust for such changes and for undercounts. As for data from establishment surveys, Srinivasan said it is “not a simple exercise” to put together a sample frame for drawing random samples. He said that economic censuses for the developing countries with which he is familiar do not survey all establishments and do not “provide a sensible base from which to put together a sample frame.”
Srinivasan also noted problems with the approach of selecting the best source for each labor market variable in the database. First, for some countries, only one source may be available; by definition that source is the best, regardless of its accuracy. Second, choosing the best source “creates a selection bias” because more developed countries are likely to have more sources of data and data of better quality. As a result, selecting the best sources may affect in a nonrandom way the number of cells filled in the final data set. Srinivasan highlighted another problem: The data set does not include the informal sector, which accounts for the major share of employment in many developing countries. Without the informal sector, he said, “the data set is . . . not representative of the labor market conditions in developing countries.”
Srinivasan also said that the strategy of not providing complete documentation of breaks, discrepancies, and changes of definition in the data was inappropriate. Economists who ignore problems such as changes in definition and breaks in the data “are not interested in the data, they are interested in an observation to throw into the regression.” Their deplorable practice should not be the reason for excluding footnotes and documentation and expecting other serious users to rely only on Rama and Artecona’s expert judgments. Srinivasan said it would be better to “hang it all out there and let the user decide what is appropriate.”
Responding to Srinivasan’s comments, Rama explained why he and Artecona chose the approach they used. First, he said, he agreed with Srinivasan’s criticisms of cross-country regressions, and he said the database was not created to encourage economists to conduct more of these studies. Second, he agreed with Srinivasan that there are many problems with mea
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surement error. However, he and Artecona found that the bias introduced as a result of measurement error in the data selected for each variable was not so large that the user would learn nothing from the variable. In addition, he said, there are so many different sources of measurement error, in so many different directions, that “our sense is that it can be treated as random error.” Although the database does not provide an indicator of the relative shares of employment in the formal and informal sectors for each country, Rama noted that some of the variables in the database do provide information on informal employment, such as labor force levels, labor force participation rates, unemployment rates, and work hours. In addition, one of the variables—the average wage of casual agricultural workers—refers exclusively to the informal sector.
In further discussion, a committee member noted that the committee is charged with creating “an ongoing, living database.” He asked Rama what resources would be required to respond to the comments offered and to continue compiling and updating the database. Rama replied that a small team of experts could visit countries and arrange systematic meetings with labor lawyers, statistical officers, and others to identify and obtain the most accurate data sources. He suggested it might be appropriate to begin by visiting a core of about 30 countries. Another committee member described Rama’s response as “enormously optimistic,” because “a huge commitment of resources is usually necessary” to obtain reliable labor market data over time for just one country. Even an expert on a particular country may take a long time to fully understand such complex issues as the role of labor unions in that country’s economic growth. Developing an understanding of these complex issues internationally, in many different countries around the world, is “exceptionally difficult,” requiring much more time, funding, and expertise, he argued.
Representative terms from entire chapter:
labor market