rafmacalaba/datause-encoder-data huggingface dataset:
setting that are not sufficiently explained by our indicators of unionization. Attending to the marginal effects of nominal wage freezes, these constraints seem even more important than those imposed by the wage setting environment.

# **6.  Conclusions**

We have examined the importance and determinants of six strategies firms might use to cut their labour costs, using a unique survey of European firms from 12 EU countries. These strategies are: reduce or eliminate bonus payments; reduce non-pay benefits; change shift assignments or shift premia; slow down or freeze the rate at which promotions are filled; recruit new employees at lower wage level than those who left voluntarily; and encourage early retirement to replace highly paid employees with entrants earning lower wages.

We found substantial heterogeneity in the use of each of these strategies across countries and firms, depending on firm characteristics and labour market institutions. Not surprisingly, larger firms show greater margin of manoeuvre with respect to using any of these strategies in order to adjust labour costs. Similarly, different indicators of the severity of competition suggest that firms in more competitive environments are more likely to engage in several of these strategies. We found that the presence of unions in wage setting is associated with a greater use of most of the strategies. A plausible explanation is that unions limit the flexibility of wages, pushing firms towards alternative labour cost cutting strategies. However, when we controlled for different indicators of wage rigidity (either nominal wage rigidity or alternative definitions of wage indexation) the impact of unionisation on the use of these different margins subsists. Moreover, we find that firms subject to nominal wage rigidities are much more likely to use each of the six cost-cutting strategies. This indicates that there is some degree of substitutability between wage flexibility and the flexibility of other labour cost components, and that this substitutability is not limited by the presence of unions in wage setting.

22
# Online Appendix B

## (OB.1) Robustness Checks

### (OB.1.1) Mother's Education

A central nding from our analysis is that there are no signicant eects of Inpres schools on nal educational attainment of girls (measured by years of schooling) even though the eects at the primary level are substantial. One might wonder whether the conclusions would be dierent if we used mother's education in place of father's education as a measure of family background of children. There is substantial evidence that the intergenerational link is much stronger between mothers and daughters (see, for example, Smith and Smith (2013), Emran and Shilpi (2011)). We report the estimates of equations (2) and (3) using mother's education in Table B.1 below. The evidence is very similar to what we found earlier in Table 2 and Table 3 using father's education. Perhaps, most important, there are no eects of Inpres schools on educational mobility of daughters when completed years of schooling is the measure of educational attainment. But, consistent with the evidence in Table 2 and Table 3, there are substantial and statistically signicant eects at the primary level.

### (OB.1.2) Alternative Comparison Groups, and Partially Exposed Groups

The DiD design in Table 2 and Table 3 uses data on 6 years from the pre-Inpres period to dene the comparison groups. One might worry that the children from the earlier birth cohorts, say born in 1957-1958, are likely to be less comparable to the treatment birth cohorts (1968-1972) exposed to Inpres schools in the 1970s. Since we have a large data set, we can estimate the DiD model using only the more recent birth cohorts from the pre-Inpres sample without worrying about the loss of power. We estimated equations (2) and (3) using a number of such cutos to dene the pre-Inpres comparison sample. We report the estimates for these alternative samples in Tables B.2-B.4 below in this appendix. The results are robust across these alternative pre-Inpres cohorts. We also provide estimated eects for the partially exposed cohorts (1963-1967): please see Table B.5. The estimated eects are numerically much smaller as one would expect, and many are not signicant at the 10 percent level.

59
Table D.1. Effects of Guerrilla Territorial Control on Other Transformations of Night Light Luminosity

||Transf|ormations of N|ight Light (2013)|Literacy Rate|
|---|---|---|---|---|
||Logarithm|Level (Raw)|Weighted by Pixel Area|(2007)|
||(1)|(2)|(3)|(4)|
|Guerrilla control|-0.218***|-1.710***|-1.710***|-0.0212***|
||(0.0294)|(0.339)|(0.339)|(0.00501)|
|Observations|3,652|3,652|3,652|3,637|
|Bandwidth (Km)|2.266|2.266|2.266|2.266|
|Dependent mean|2.810|30.725|30.725|0.810|

_Note:_ The table presents the results of equation 1 using different transformations of night light luminosity. The unit of observation in all Columns is the census tract. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. We use the algorithm of Calonico, Cattaneo and Titiunik (2014) to set the bandwidth and weight using a triangular kernel. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

Table D.2. Effects of Guerrilla Control on Years of Education by Age Cohort

||Years of|Education|
|---|---|---|
||In School|Not In School|
||Age at War|Age at War|
||(1982-92)|(1982-92)|
||(1)|(2)|
|Guerrilla control|-0.346***|-0.160|
||(0.121)|(0.113)|
|Observations|3,635|3,635|
|Bandwidth (Km)|2.266|2.266|
|Dependent mean|7.860|4.410|

_Notes:_ The table presents the effects of guerrilla control on the years of education by age cohort. Column 1 estimates the effect for the sample of people who during the war period were school age. Column 2 does the same but uses the sample of people who during this period were not school age. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. We use the algorithm of Calonico, Cattaneo and Titiunik (2014) to set the bandwidth and weight using a triangular kernel. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

77
Policy Research Working Paper 5160

# **Abstract**

Firms have multiple options at the time of adjusting their wage bills. However, previous literature has mainly focused on base wages. This paper broadens the analysis beyond downward rigidity in base wages by investigating the use of other margins of labor cost adjustment at the firm level. Using data from a unique survey, the authors find that firms make frequent use of other, more flexible, components of compensation to adjust the cost of labor. Changes in bonuses and non-pay benefits are some of the potential margins firms use to reduce costs. The paper also shows how the margins of adjustment chosen are affected by firm and worker characteristics.

This paper—a product of the Office of the Regional Chief Economist, Latin America and the Caribbean Region—is part of a larger effort in the department to understand the functions of labor markets in a comparative perspective. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at jmessina@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
against poverty in 16 out of 27 country spells. The differences, however, are statistically significant only in 12 cases—9 in favor of FHHs, and 3 in favor of MHHs. So here again, in the majority of cases (24 out of 27), FHHs did better or no worse than MHHs.

In sum, poverty has fallen more rapidly in FHHs. Allowing for the diversity among FHHs shows marked differences in poverty changes among them but no systematic patterns. The living standards of the various types of FHHs followed dissimilar paths across countries and time periods with no one type consistently outperforming the others.

Poverty is falling overall, the incidence of FHHs is rising and on balance, FHHs have seen faster poverty reduction. Is the share of the poor accounted for by FHHs also falling over this time period? Our findings indicate a mixed picture (Table 8 and Figure 7). In 14 country/year spells, there was an increase in the share of the poor living in households headed by a woman, while in the remaining 13, an almost equal number of country/year spells, the share fell. A similarly mixed pattern is evident in each of Africa’s macro-regions.

# **7. Conclusion**

Living standards have risen and poverty has fallen considerably across Sub-Saharan Africa since the late 1990s. There have been concerns that some groups with above average poverty rates may be left behind. The paper has focused on female headed households and asked how this group has fared during this period. Drawing on the micro data from virtually all available national surveys for the region, the paper demonstrates that the share of households headed by women is rising over time across the continent. However, a higher prevalence of female headed households is consistent with the finding that poverty is falling. We find that higher GDP itself reduces female headship. But other things are changing across Africa. Changes in demographic and population characteristics, social norms, education and the nature of the family appear to be encouraging female headship.

The paper has tried to reconcile this finding with the recent aggregate reduction in poverty. Poverty has declined for both household groups but in most countries for which the data are adequate, it has fallen faster for female headed households as a whole. When the heterogeneity among FHHs is taken into account, some countries have achieved faster improvements in poverty than others but with little discernible pattern across countries. One category of FHH does well in one country or time period while another category does best elsewhere. A decomposition of the change in poverty indicates that, rather than putting a break on poverty reduction, FHHs are contributing appreciably to the overall decline in poverty despite their smaller overall share in the population.

18
22 textile exports face restrictions while the coverage ratio for clothing is about 63 percent. In contrast, under 5 percent of OECD intra-trade in these goods encounter restrictions (mainly goods shipped from Japan). The Multifiber Arrangement, special textile quotas, bilateral quotas, and voluntary export restraints account for these major differences. Nontariff barrier coverage ratios for developing countries' footwear exports are about 20 points higher than on OECD intra-trade of these goods. "Voluntary" export restraints imposed by the EU and EFTA largely account for these differentials.' Table 6 indicates the profile of nontariff protection against sub-Saharan African exports differs somewhat from other developing countries. First, only about 11 percent of African non-fuel exports face NTBs as opposed to the 17 percent average for all developing countries. The lower NTB coverage ratio is largely accounted for by the fact that most African countries' textile and clothing products are not affected by MFA restrictions. Mauritius is a noteworthy exception with $116 million, or 88 percent of its textile and clothing exports to the United States, covered by quotas -- similar restrictions were recently placed on Kenya. Only 19 percent of African textile exports face NTBs, as opposed to 53 percent for all developing countries combined, while the African coverage ratio for clothing is about 18 points below the 63 percent developing country average. This pattern is reversed, however, for several food and feed product groups where African countries encounter a higher incidence of NTBs than all developing countries. If coffee were excluded from the tabulations the African food trade coverage ratios would be considerably lower than those for developed and developing countries as a group. Coffee exports are subject to quantitative controls (voluntary export restraints) imposed under the International Coffee Agreement. Special taxes are also applied to coffee imports in several European markets.

20Evidence shows that textile and footwear restrictions have major distorting effects on the exports of developing countries who face the measures. For example, the US International Trade Commission estimates of tariff plus NTB protection for 54 broad classes of textile and clothing products. The estimates range to over 100 percent with the nontariff barrier component of total protection generally being far higher than that of tariffs. It is generally held that levels of nontariff protection against textiles and clothing in Europe are of a similar magnitude to that of the United States,
|Private credit|0.055|0.024|.|||
|---|---|---|---|---|---|
|Market<br>capitalization|0.024|0.013|0.011|.||
|Market<br>turnover|0.183|0.136|0.039|0.027|.|

Table A3. Financial Indicators, Jointness Statistic According to Doppelhofer and Weeks (2009)

|Net interest<br>margin|.|||||
|---|---|---|---|---|---|
|Bank Z-<br>score|-0.372|.||||
|Private credit|**-2.373**|-1.025|.|||
|Market<br>capitalization|0.028|-0.645|-0.521|.||
|Market<br>turnover|-0.829|0.165|-0.331|0.256|.|

57
effect of migration on trade and their findings will serve as the benchmark for comparison for the results of this paper.

The implementation of fixed effects has successfully been used to solve a number of puzzling results in the gravity model literature. For example, Glick and Rose (2002) proffer a solution to the puzzle found by Rose (2000) whereby currency union membership was associated with an increase in trade of approximately 300%. Glick and Rose reduce this to around 100%, when pair-wise fixed effects are implemented. Similarly, Baier and Bergstrand (2007) control for countries selecting into trading and entering free trade agreements with one another. Once pair-wise fixed effects are included in their estimation these authors convincingly explain the large variation – including negative results - apparent in previous studies examining the effect of RTAs on bilateral trade flows. The current paper implements trading-pair fixed effects to control for international bilateral ties (amongst other unobserved pair-wise factors) to examine the trade-migration nexus.

The present paper is the first to implement a panel spanning several decades to investigate direct-links ( _a la_ Gould) and third party effects ( _a la_ Rauch), both separately and simultaneously; while also crucially controlling for unobserved pair-wise factors such as bilateral ties, which may influence both trade and migration. The focus also moves away from a single country (and her trading partners) and towards groups of countries. More emphasis is placed upon the relationship between trade and migration in the context of developing countries while the longer time dimension of the panel is also superior since most papers investigate immigrant-links in the years after 1980.<sup>10</sup> Significantly, the comprehensive migration data allow the effects of immigration _and_ emigration on trade to be assessed simultaneously, the absence of one of which tends to overestimate the importance of the other.

The paper is structured as follows. The following section discusses the underlying mechanisms which are purported to drive the links between trade and migration and restates the basic framework so as to emphasize the role of the transaction costs – as opposed to preferences – which are economically more important. Section 4 outlines the specification of the empirical model, while Section 5 discusses the underlying data sources. Section 6 presents a repeated cross-section analysis and Section 7, a discussion of the issue of endogeneity. Observations are then pooled and fixed effects are added to show the nature and direction of the biases from cross-section estimates in Section 8. The analysis is then disaggregated at both the regional (Section 9) and the country level (Section 10) to highlight how dramatically the estimated effects of migration on trade change when fixed effects, used to control for unobserved bilateral ties, are considered. Lastly, Section 11 investigates the impact of third-party migrants on bilateral trade.

> 10 Notable exceptions include Gould (1994) and the papers by Dunlevy and Hutchinson (1999 & 2001).

6
operations vis-à-vis the tax authorities, regulatory harassment in the form of bribes or inspections, and perceptions of regulatory obstacles. Reliance on enterprises’ perceptions and subjective evaluation of corruption and other regulatory burdens raises concerns about the potential endogeneity between explanatory and dependent variables. To help deal with this problem, we use location averages as instruments. Because the enterprise level data consists of subjective opinions and recall data, measurement error is likely to be of concern, particularly in the bribe and corruption data. However, using grouped averages as instruments can also serve to mitigate the measurement error (Krueger and Angrist, 2001).

For robustness purposes, variables from the World Bank indicators on Doing Business are used to capture the regulatory burden at the country level.<sup>5</sup> These variables are quantitative measures of business regulations and the protection of property rights across countries in our data set. We use measures of the cost of regulation revolving around taxation, business registration, employment rigidities, property registration, and licensing. Because country fixed effects are used in our model, these country-level variables are interacted with enterprise perception variables matching each of the Doing Business variables in question. For example, Doing Business variables on tax administration at the country level (e.g. number of tax payments in a given country, or days needed to make tax payments) are interacted with enterprise perceptions of tax administration as a business constraint. A similar approach is employed for all country level variables.

We control for enterprise characteristics which may influence an enterprise’s financing portfolio; enterprise size, ownership, and legal status. The variable on ownership (foreign-owned versus domestic) was subsequently dropped, as it was consistently insignificant. Country and industry fixed effects are used throughout the estimation.

Table 1 provides a detailed description of all variables. Table 2 presents summary statistics by country of all the main variables. One can see from Table 2 that the

> 5 These are available at <u>www.doingbusiness.org.  The original methodology is developed in Djankov et al.</u> (2002) and Botero et al. (2004).

7
# **References**

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Baird, S., B. Ozler, C. Mcintosh. 2011. The regressive demands of demand-driven development, The World Bank.

Banerjee, A., Green, D., Green, J. & Pande, R. 2010. Can voters be primed to choose better legislators? Experimental evidence from rural India.

Banerjee, A., R. Hanna, J. Kyle, B. Olken and S. Sumarto (2018). Tangible information and citizen empowerment: identification cards and food subsidy programs in Indonesia. Journal of Political Economy 126(2).

Barr, A. And D. Serra (2009). The effects of externalities and framing on bribery in a petty corruption experiment. Experimental Economics 12(4): 488-503.

Barr, A. And A. Zeitlin. 2011. Conflict of interest as a barrier to local accountability. CSAE working paper, Oxford University.

Bertrand, M., Djankov, S., Hanna, R. & Mullainathan, S. 2007. Obtaining a driver's license in India: An experimental approach to studying corruption. Quarterly Journal of Economics, 122, 1639-1676.

Bhargava, V. E. 2006. Global Issues for Global Citizens: An Introduction to Key Development Challenges, Washington, D.C.: World Bank.

Bjorkman, M. & Svensson, J. 2009. Power to the People: Evidence from a Randomized Field Experiment on Community-Based Monitoring in Uganda. Quarterly Journal of Economics, 124, 735-769.

Bjorkman, M. & Svensson, J., 2010. When is community-based monitoring effective? Evidence from a randomized experiment in primary health in Uganda. Journal of the European Economic Association 8(2-3): 571-581.

Bloom, N., Sadun, R., and Van Reenen, J. 2012. The organization of firms across countries. The Quarterly Journal of Economics, 127(4):1663–1705.

Dal Bo, E., F. Finan, N. Li and L. Schechter, 2018. Government Decentralization Under Changing State Capacity: Experimental Evidence from Paraguay. Working paper.

de Rochambeau, G. 2017. Monitoring and intrinsic motivation: Evidence from Liberia’s trucking firms. Working paper.

30
|||Multi-Model<br>Scenarios|IEO<br>2005|Forecast<br>IEO 1995|Errors<br>WEO 1994|Weighted Mean<br>1 : 1 : <sup>1</sup><br>2 <sup>: 1</sup><br>2|
|---|---|---|---|---|---|---|
||_σE_|15%|6%|11%|4%|9%|
|All|_σY_|14%|10%|7%|23%|13%|
|Countr.|_ρ_|0.43|na|-0.04|0.31|0.28|
||_σE_|17%|7%|13%|5%|11%|
|Develop.|_σY_|16%|11%|6%|21%|14%|
|Countr.|_ρ_|0.43|na|-0.19|0.30|0.24|
||_σE_|11%|4%|6%|1%|6%|
|Industr.|_σY_|8%|6%|9%|28%|11%|
|Countr.|_ρ_|0.44|na|0.27|na|0.36|

Table 5: Summary of parameter estimates for uncertainties _σY_ and _σE_ , and for the linear correlation of forecast errors _ρ_ . Note, however, that the latter has been derived with very few data points. Estimates derived from forecast errors represent the average of the absolute values of the relative forecast errors of GDP (respectively emissions) across all countries in Table 4 that are either in the developed or developing world. To avoid double-counting, aggregate regions such as ‘World’ were excluded in this calculation. Means (last column) are computed by taking the average of columns 2 to 5 with weights 1 : 1 : 1 _/_ 2 : 1 _/_ 2, so as to give equal weight to each of the three approaches.

can be made on the basis of the results above as to which uncertainty–emissions or output–is higher, even though for the limited data considered here, the average uncertainty on future GDP is somewhat higher than the average uncertainty on future emissions. This finding is significant because the ratio between the two variances plays a crucial role in the equations obtained in Section 4. Fourth, the most difficult parameter to estimate remains the correlation of forecast errors _ρ_ . While the evaluation of the long-term scenarios led to a remarkably large range of values, the analysis of historical forecasts produced lower values not too far from zero. On the aggregated level values for _ρ_ tend to be positive, around 0.25, with a tendency to be higher in industrialized countries than in developing countries.

# **5.2 Estimating Uncertainty about Marginal Abatement Costs**

To estimate _σC_ , one would need several estimates of the marginal abatement cost curve for the period 2013-2017, all with the same functional form. Such a set, however, is not readily available. Most of the marginal abatement cost curves currently available apply to the first commitment period only. In addition, available surveys of marginal abatement cost curves 23