rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix A Data Sources**

**ifn** order to achieve maximum cross-country comparability, we try to use the same statistical sources. Calculations of the foreign cost of borrowing are based on external debt statistics from the World Bank _Debtor_ Reporting _System._ To the extent possible, stocks of central government domestic debt and holdings of these stocks by the monetary authorities and deposit money banks are taken from the IMF Government Financial _Statistics_ (GFS). This is possible for 8 countries, and the preferred concept of consolidated central government is used for all these countries except Jordan, where only budgetary data are available. GFS data on total interest payments are broken down into their foreign and domestic components by locating a country-specific source with this information. Total interest payments from this country source approximate, but do not always equal, the GFS total.

For another 7 countries, country-specific data on the domestic debt stock are quite close within ten percent-to GFS data, and the domestic interest payments used are consistent with this debt stock. For another 6 countries, domestic debt stocks are significantly different-more than ten percent-from GFS data (higher for 3 and lower for 3) but are the only central government domestic debt figures for which associated interest payments could be identified. Finally, GFS has no data on domestic debt for 3 countries (Algeria, Mexico and Portugal) and therefore direct comparison with GFS standard definitions is not possible. Again, budgetary data are occasionally used when consolidated accounts, which include social security and extra-budgetary funds, are not available. Country-specific sources for these data and central government revenues are shown in Appendix B. Data on GDP, population, and the GDP deflator are also taken from GFS, and exchange rates are from the IMF International _Financial Statistics._ Below we describe in detail the calculations performed to obtain the government revenue from financial repression, and the sources used for each country. For each country we prepared a worksheet, whose columns contain the years for which data is available, and rows contain the items reported below.

21
# **1. Introduction**

The success of central banks in achieving price stability during the last two decades has renewed the academic interest in the cost of low inflation. Following Tobin (1972), if workers resist nominal wage cuts a rate of inflation that is too low might result in higher unemployment, since increases in the price level facilitate relative wage adjustments. A sizeable literature identifies substantial resistance to nominal wage cuts in the US.<sup>1</sup> The European evidence, led by the International Wage Flexibility Project (Dickens et al., 2007) suggests lower levels of downward nominal wage rigidity (DNWR) than those observed in the US, but higher resistance to real wage cuts, a feature labelled downward real wage rigidity (DRWR). While the behavioural determinants of DNWR have been extensively studied in the literature<sup>2</sup> little is known about DRWR. Similarly, there is little evidence regarding the characteristics of firms that are typically associated with each type of rigidity.

The aim of the current article is to analyse the incidence and causes of downward nominal and real wage rigidity. For this purpose, we use a novel major firm-level survey that contains detailed qualitative information for 15 EU countries. The survey was carried out within the framework of the Wage Dynamics Network (WDN), a research network sponsored by a consortium of central banks of the EU and coordinated by the European Central Bank. The sampling and stratification (discussed in the next section) was designed to be representative at the country level, and the questionnaire was harmonised across countries. This is the first firm-level survey with a harmonised design covering a large number of countries including detailed information on the extent of wage rigidities.

Using an extensive micro-level survey has several advantages for our purposes. Most importantly, it allows us to examine the relevance of firm characteristics in the determination of rigidities, exploiting information that is usually unobservable in administrative and household data previously used in the literature. Moreover, the coverage of a large number of sectors and countries enables us to assess the importance of product and labour market characteristics in the determination of nominal versus real rigidities. Previous research, based on aggregate or sectoral data, has demonstrated that the institutional environment, e.g. the characteristics and coverage of collective bargaining or the extent of employment protection, is significantly correlated with wage rigidity (Dickens et al., 2007; Holden and Wulfsberg, 2007 and 2008). We benefit from the detailed firm-level information available to us to extend

> 1 See among others Blinder and Choi (1990), Kahn (1997), Card and Hyslop (1997), Altonji and Devereux (1999) and Lebow et al. (2003).

> 2 See e.g. Blinder and Choi (1990), Bewley (1994), Agell and Lundborg (1995, 2003) and Campbell and Kamlani (1997).

4
Angrist, J., Bettinger, E., & Kremer, M. (2006). Long-Term Educational Consequences of Secondary School Vouchers: Evidence from Administrative Records in Colombia. _The American Economic Review_ , _96_ (3), 847-862.

Barrera-Osorio, F., Blakeslee, D. S., Hoover, M., Linden, L. L., & Raju, D. (2011). Expanding Educational Opportunities in Remote Parts of the World: Evidence from a RCT of a Public Private Partnership in Pakistan. In _Third Institute for the Study of Labor (IZA) Workshop,“Child Labor in Developing Countries,” Mexico City_ . Retrieved from http://www.iza.org/conference_files/childl2011/blakeslee_d6783.pdf Barrera-Osorio, F., & Raju, D. (2015). Evaluating the Impact of Public Student Subsidies on Low-Cost Private Schools in Pakistan. The Journal of Development Studies, 51(7), 808-825.

Bold, T., Kimenyi, M., Mwabu, G., & Sandefur, J. (2010). Does abolishing fees reduce school quality? Evidence from Kenya. _Centre for the Study of African Economics (CSAE) Working Paper WPS/2011-04_ .

Deininger, K. 2003. Does cost of schooling affect enrollment by the poor? Universal primary education in Uganda. Economics of Education Review 22(3):291-305 Friedman, M. (1955). _The role of government in education_ . Rutgers University Press.

Friedman, M. (1962). _Capitalism and freedom_ . University of Chicago press.

Gertler, P. J., Patrinos, H. A., & Rubio-Codina, M. (2012). Empowering parents to improve education: Evidence from rural Mexico. _Journal of Development Economics_ , _99_ (1), 68–79. Hoxby, C. M. (2003a). School choice and school competition: Evidence from the United States. _Swedish Economic Policy Review_ , _10_ (2), 9–66.

<mark>Heyneman, S. P., & Stern, J. M. (2014). Low cost private schools for the poor: What public policy is appropriate?</mark> _<mark>International Journal of Educational Development</mark>_ <mark>,</mark> _<mark>35</mark>_ <mark>, 3-15.</mark> Hoxby, C. M. (2003b). _The economics of school choice_ . University of Chicago Press.

Hsieh, C.-T., & Urquiola, M. (2006). The effects of generalized school choice on achievement and stratification: Evidence from Chile’s voucher program. _Journal of Public Economics_ , _90_ (8), 1477–1503.

Kim, J., Alderman, H., & Orazem, P.F. (1999). Can private school subsidies increase schooling for the poor? The Quetta urban fellowship program. _World Bank Economic Review_ 13(3):443–466.

Kremer, M., Brannen, C., & Glennerster, R. (2013). The Challenge of Education and Learning in the Developing World. _Science_ , _340_ (6130), 297–300. doi:10.1126/science.1235350 Lucas, A and Mbiti, I. 2012. Access, Sorting, and Achievement:�The Short-Run Effects of Free Primary Education in Kenya _American Economic Journal: Applied Economics, 4(4): 226–253_ MacLeod, B., & Urquiola, M. (2012). _Competition and Educational Productivity: Incentives Writ Large_ (No. 7063). Institute for the Study of Labor (IZA).

MacLeod, W. Bentley, and Miguel Urquiola. (2009). Anti-lemons: school reputation and educational quality. No. w15112. National Bureau of Economic Research, 16
draws a general picture of the relative evolution of FCS. One drawback of this dataset is that the agencies that collected the original data vary from one host country to another. This makes comparisons between different host countries difficult to interpret. Moreover, these aggregate data do not provide information on FCS by source-host country pairs. Since the major sources of FCS for China are different from those of Latin American countries, it is prudent to focus on some attention on source countries that are important for Latin America<sup>6</sup> .

To address these issues, we also use data on bilateral outward stocks from major OECD countries. These data were taken from OECD statistics and UNCTAD for the period 1990-2003.<sup>7</sup> The OECD reports the bilateral FCS of 29 OECD countries into 235 host economies in millions of U.S. dollars from 1982 to 2003. One shortcoming of this dataset is that observations for most Latin American countries are missing for some source countries. We therefore expanded this dataset using data from UNCTAD for 29 source countries into 190 host countries. For those countries for which the UNCTAD data is reported in national currency, we transformed the figures into U.S. dollars using the end of period exchange rate, which was taken from the OECD. We then use the OECD dataset unless the observations are missing.<sup>8</sup> Even after including the observations from UNCTAD, we continue to have several missing observations for some country pairs. For this reason, in sections 3, 4 and 5 we restrict the analysis to major source countries that have most of the observations for the Latin American countries. The selected source countries were Canada, Germany, France, Japan, Netherlands, Spain, Switzerland, the United

> 6 IADB [2004]

> 7 The OECD data is available from www.sourceoecd.org.

> 8 Data for Australia for the period 1990-2000 was also taken from the UNCTAD, since OECD reports data for the fiscal year.

5
19 aspects of infrastructural investment are important. Again, however, the principles of good system design are relatively obvious, and more or less identical to those in the **'smallW** local infrastructure ctse just discussed. B;sically, the idea is that while local residents should again finance - through user clarge- (Box 9) or locally-borne taxes - the costs of designing, building, operating, and maftitaining facili .es tW the extert that they receive the benefits, regional or national governments (as the cse may be) should assume the balance of the cost - that is, 'their share' of the benefits spilling over. Locally, taxes, like transfers from the central government, are often earmarked for investment. As Box 10 notes, however, this common practice is usually not a good idea.

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proportions of households regarding themselves as rich are much smaller than the other categories. On the whole, households appear to think they are somewhat better off in 2011.

In sum, objective measures of household consumption indicate almost no change in poverty rates at two snapshots in time, but subjective measures show a slight, but definitely positive trend.<sup>7</sup> There can, however, be issues with both sets of results. The former result only reflects the net change in poverty after movement out of poverty is adjusted by that into poverty. In other words, this little change does not necessarily indicate little transition of households into or out of poverty over time but can even mask a large level of churning if the degree of movements into poverty is cancelled out on the average by that out of poverty. For example, a difference of one percentage point between the percentage of households moving into poverty and those that move out of poverty can in theory occur regardless whether the current economic situation is one of low mobility (say, 6 percent and 5 percent, respectively, for the former and the latter) or of very high mobility (say, 52 percent and 51 percent). At the same time, without a specially designed questionnaire to collect data on subjective well-being, the latter approach may provide some useful but not finally conclusive evidence due to inter-personal differences in subjective evaluations (see, e.g., King et al. (2004)). These mixed results highlight the value of acquiring a better understanding of poverty dynamics.

# **III. Framework of Analysis**

# **III.1. Overview of the Synthetic Panel Method**<sup>**8**</sup>

Let xij be a vector of household characteristics observed in survey round _j_ (j= 1 or 2) that are also observed in the other survey round for household i, i= 1,..., N. These household

> 7 See also World Bank (2014d) for a recent poverty assessment report on Senegal.

> 8 We provide an overview of the synthetic panel method developed by Dang et al(2014) and Dang and Lanjouw (2013) in this section. For more details, interested readers are encouraged to read the original papers.

9
databases.<sup>25</sup> Note indeed that electricity services measured in this way is the indicator that more often fails to produce significant results (Table 1).

# **3.1.2. Indirect Effects (Questions 1.B and 1.C)**

Few papers have addressed this question. In our sample, only Hulten et al. (2000, 2005), La Ferrara and Marcelino (2000) and Duggal et al. (1999) do so. Except the last one, these contributions use a growth accounting framework. Indeed, when discussing estimates of the effect of infrastructure, a first issue is simply to recognize that empirical analysis based on some version of a Cobb-Douglas production function approach (this is the case of 15 out of the 17 production function-based studies in our review sample) has in general little to say on the indirect effects, as this specification does not permit a distinction to be made between indirect and direct effects. Growth accounting techniques suffer from a similar problem, as they are unable to discriminate the direct effect of infrastructure for reasons discussed above, namely the difficulty to attribute a price to infrastructure capital. Indeed, as infrastructure is partially a public good, not remunerated at its marginal productivity, its share of the output can only be guessed, which makes the estimates subject to caution.<sup>26</sup> Note that in most cases, growth-accounting studies find lower levels of infrastructure externalities for more developed countries or regions than for developing ones. For example, applying a similar framework to both cases, Hulten and Schwab (2000) show that US state level data displayed no significant infrastructure externalities on growth, while Hulten et al. (2005) found infrastructure to account for a significant part of TFP growth (about half of it in the case of highways and electricity) across Indian states in the period 1972-1992.

Two remarks follow. First, if the total effect of infrastructure on output is high overall, but externalities are low, it may be that most of the effect of infrastructure is a direct one,

> 25 See Briceño-Garmendia, Estache and Shafik (2004) for a discussion of the issue of quality indicators and an overview of existing data.

> 26 Hulten et al. (2000; 2005) attempt to disentangle these indirect effects for regional US manufacturing data and Indian manufacturing data respectively. They estimate the share of output of intermediate input by assuming it is constant over time.

25
- Dagnelie, Olivier, Anna Maria Mayda and Jean-Franc ̧ois Maystadt. 2023. Refugees, Children’s Health and Malaria Transmission in Africa. Technical report.

- Davis, C. Austin, Paula L ́opez-Pe ̃na, Ahmed Mushfiq Mobarak and Jaya Y. Wen. 2024. “Refugees Are Hosted in Highly Vulnerable Communities.” AEA Papers and Proceedings 114:75–79.

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- Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21stCentury Forest Cover Change.” Science 342(6158):850–853. **URL:** _https://glad.earthengine.app/view/global-forest-change_

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**URL:** _https://www.hrw.org/reports/2000/burma/burm005-01.htmP116_ 27103

- Hussein, Mohammed and Hanna Duggal. 2023. “What is Life Like Inside the World’s Al azeera .

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26
To investigate the robustness of our results to this issue, we use the public use sample of the U.S. Census to obtain the educational breakdown of the 14.4% of recent male migrants who have migrated with their spouse present, and are hence least likely to be reported on from Mexico.<sup>11</sup> Our sample of 55,848 males in the ENADID contains 568 recent first-time migrants. Based on an undercount of 14.4%, there should be an additional 95 migrants in the analysis. We therefore assign the education distribution observed in the U.S. Census to these 95 additional migrants, and then reweight the ENADID migrant sample so that it reflects the educational breakdown of the combined 663 migrants. For example, 17 of the ENADID sample have 16 or 17 years of education, and we estimate from the U.S. Census that 7 out of the additional 95 migrants would also have this education level. Migrants in the ENADID with 16 or 17 years education are therefore assigned a weight of 24/17 = 1.41.

Table 4 then provides the estimated 2SLS results after adjusting for undercount. Columns 1 and 2 replicate columns 5 and 6 of Table 3 for ease of comparison. Columns 3 and 4 then provide the undercount-adjusted coefficients. We see that our main coefficient of interest, the interaction between education and network size, becomes slightly more negative, and is still significant. Figure 3 plots the predicted probabilities from column 4, and compares them to the predicted probabilities for the unadjusted results in column 2. The results show that adjusting for undercounting leads to only small changes in the fitted curves. The adjusted curves are all above the unadjusted curve, reflecting that the likelihood of migration at any given education level is higher than estimated with just the ENADID data, since some migrants aren’t recorded. Secondly, we see that the effect of the larger interaction term is to shift the curves rightward slightly, resulting in more positive selection or less negative selection for a given community network. However, we still find positive selection at low levels of community migration prevalence, and negative selection at high levels, showing our results are robust to undercounting of some migrants.

# **6.2 Robustness to Endogenous Education Formation**

> 11 The U.S. Census does not provide information on whether these individuals are coming from high or low migration networks. Our weighting procedure implicitly assumes the unobserved individuals are drawn from communities in the same proportions as observed individuals. Given the relatively low level of undercount for males, and the fact that our fitted probabilities don’t change by much, we believe the general pattern of our results is also robust to alternative assumptions about the network these undercounted individuals are drawn from.

16