rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

High and potentially downward rigid reservation wages – below which unemployed persons would not accept a job – may be a key reason for high and persistent unemployment rates. North Macedonia’s unemployment rate currently stands at 20.8%, though it has continued to decline since its peak of 36% in 2006. The decline – especially during the Global Financial Crisis - has been mainly driven by the government-supported expansion of foreign production capacities in the country and frequentlygenerous programs for subsidizing employment, though also potentially interfering with the absence of a census since 2002 and the accelerated emigration from the country since then. The level of the unemployment rate in North Macedonia also reflects structural characteristics of the labor force: a large share of low-skilled job seekers, accompanied by sizable skill mismatches, prevents fast and smooth transition to decent employment.

Still, the role of reservation wage for unemployment should be viewed in at least two important aspects. First, there is almost no evidence about the trend of reservation wages, nor about the way of their formation given personal and labor-market circumstances. The reservation wage in nationallyrepresentative surveys – like the Labor Force Survey – has been asked only in 2016, making the exploration of the job-search relationship between reservation wage, personal traits and institutional aspects (e.g. the generosity of the unemployment benefits) difficult so far. Second, despite the level and structural characteristics of unemployment in the country, still the decline in a decade has been sizable.

This paper has two objectives: first, to understand the determinants of reservation wages in North Macedonia; and second, to measure the gap between reservation and market wages. Both will enable understanding of the formation of reservation wages and its importance for tailoring of policies to alleviate its potentially adverse role for unemployment. We rely on two years of the Labor Force Survey – 2016 and 2017, despite maintaining only a pooled cross-section structure due to the short period on disposal. Given the constraints, the approach is only exploratory in nature, seeking to reveal facts about reservation wages in North Macedonia which were either unknown or assorted with anecdotal evidence only.

Results suggest that it is mainly supply factors which shape reservation wages in the country. Higher education attainment sizably increases future wage expectations, especially when persons are younger and with short unemployment spells. Then, reservation wage declines with age and with longer spells of unemployment. On the other hand, the insignificance of demand factors for reservation wage formation may suggest that job seekers either lack information about local conditions, or – more importantly – they are reluctant to fast accommodate, considering them substandard. The latter is especially the case for low-skilled individuals, who consistently value their skills higher than what the market offers and set too high expectations. On the other hand, highly-skilled individuals, despite 2
Figure 1. Did Incumbents Manipulate Close Elections?

<!-- Start of picture text --> −.05 −.04 −.03 −.02 −.01 0 .01 .02 .03 .04 .05<br>Margin of victory<br>Mean Nonparametric fit<br>.1<br>.08<br>.06<br>pdf<br>.04<br>.02<br>0<br><!-- End of picture text --> _Source:_ Data for the study comes from field reports of the Armed Forces of the Philippines (AFP) and election returns from the Philippines Commission on Elections (COMELEC). _Note:_ The figure presents the probability density of incumbent mayors’ margin of victory in the 2007 election. Municipalities were grouped into 20 bins of equal width according to the incumbent’s margin of victory. Each scatter dot represents one bin. Its horizontal coordinate represents the midpoint of the bin, and its vertical coordinate represents the fraction of municipalities for which the incumbent’s margin of victory was within the bin. Dashed lines are linear fits, separately estimated on both sides of the eligibility threshold. Solid lines are nonparametric fits from a local linear regression that uses triangular kernels with a bandwidth of 5 percentage points, separately estimated on both sides of the eligibility threshold.

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# **4. Construction of the Main Variables**

## **_4.1 Short-term Performance_**

To examine the hypothesis by Shin and Stulz (1996) that investment policy of segments in diversified firms differs from that of similar specialized firms, we employ the "chop-shop" procedure of Lang and Stulz (1996) to construct our short-term and long-term performance measures. In addition to adjust for sectoral differences in performance, the measures can be interpreted as the performance of a multi-segment firm relative to singlesegment firms in its industries. These measures allow us to compare the performnance differences between multi-segment (diversified) and single-segment (focused) firms and associate the perfornance differences with their different investment strategies.

We measure a firm's short-term performance by its profit margin, calculated as one minus the costs of goods sold over sales. We first use the sub-sample of single-segment firns in each country to compute the median profit margin for each two-digit SIC code industry. We then multiply the sales share in each segment of a firm by the corresponding industry median profit margin. We sum the sales-weighted profit margin across segments to obtain the imputed profit margin of the firm. Lastly, we subtract the imputed profit margin from the actual profit margin to obtain the industry-adjusted excess profit margin (EPM).<sup>8</sup> In the computation of industry median profit margin, we restrict the number of single-segment firms to be at least three. In some cases, we do not have sufficient number of firms to compute the median profit margin. In these cases, we use the median profit margin of broader industry groups as defined by Campbell (1996). This procedure avoids the loss of observations. Table 2 compares the differences in the excess profit margin measures between single- and multi-segment firms. Overall, multi-segment firms are less profitable than single-segment firms. The difference in mean and median EPM between the multi- and single-segment firms is negative and statistically significant at the 1-percent level.

## [Table 2 here]

The excess profit margin is more appropriate than other accounting income variables for the purposes of this study, since it is perfectly correlated with average variable cost (defined as 1 - profit margin) which is widely used by micro economists to proxy factor productivity changes (see, for example, Young (1992) and Clerides, Lach and Tybout (1998)).

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# **References**

- Bandiera, Oriana, Iwan Barankay, and Imran Rasul (2005) ‘Social preferences and the response to incentives: Evidence from personnel data.’ _Quarterly Journal of Economics_ 120(3), 917–962

- (2013) ‘Team incentives: Evidence from a firm level experiment.’ _Journal of the European Economic Association_ 11(5), 1079–1114

- Beaman, Lori (2016) ‘Social networks and the labor market.’ In ‘The Oxford Handbook of the Economics of Networks’

- Beaman, Lori A. (2012) ‘Social networks and the dynamics of labour market outcomes: Evidence from refugees resettled in the U.S.’ _Review of Economic Studies_ 79(1), 128– 161

- Beaman, Lori, and Jeremy Magruder (2012) ‘Who gets the job referral? Evidence from a social networks experiment.’ _American Economic Review_ 102(7), 3574–3593

- Bentolila, Samuel, Claudio Michelacci, and Javier Suarez (2010) ‘Social contacts and occupational choice.’ _Economica_ 77, 20–45

- Brown, Meta, Elizabeth Setren, and Giorgio Topa (2016) ‘Do informal referrals lead to better matches? Evidence from a firm’s employee referral system.’ _Journal of Labor Economics_ 34(1), 161–209

- Burks, Stephen V, Bo Cowgill, Mitchell Hoffman, and Michael Housman (2015) ‘The value of hiring through employee referrals.’ _Quarterly Journal of Economics_ pp. 1–36

- Calv ́o-Armengol, Antoni (2004) ‘Job contact networks.’ _Journal of Economic Theory_ 115(1), 191–206

- Calv ́o-Armengol, Antoni, and Matthew O. Jackson (2004) ‘The effects of social networks on employment and inequality.’ _American Economic Review_ 94(3), 426–454

- Calv ́o-Armengol, Antoni, and Matthew O Jackson (2007) ‘Networks in labor markets: Wage and employment dynamics and inequality.’ _Journal of Economic Theory_ 132(1), 27–46

- Calv ́o-Armengol, Antoni, and Yves Zenou (2005) ‘Job matching, social network and word-of-mouth communication.’ _Journal of Urban Economics_ 57(3), 500–522

- Dustmann, Christian, Albrecht Glitz, Uta Sch ̈onberg, and Herbert Br ̈ucker (2016) ‘Referral-based job search networks.’ _Review of Economic Studies_ 83(2), 514–546

- Fafchamps, Marcel, and Alexander Moradi (2015) ‘Referral and job performance: Evidence from the Ghana colonial army.’ _Economic Development & Cultural Change_ 63(4), 715–751

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The most interesting banking sector variable is the share of banking sector assets held by foreign-owned banks in 1999, which we interact with the dummy variables for the latter two survey rounds of the BEEPS (2005, 2008-9). The negative, significant coefficient for foreign participation level in 1999 therefore indicates that financial constraints were more severe in 2002 in countries with greater foreign participation. The positive, significant coefficient for the 1999 foreign bank participation share multiplied by the BEEPS 2005 dummy is of similar magnitude to the negative association for 2002, indicating that by 2005 constraints were similar across countries regardless of their level of foreign bank participation in 1999. However, the interaction term for the 2008-9 BEEPS is positive, significant, and twice as large (in absolute value) as the negative association for 2002, which indicates that financial constraints were substantially less severe during the crisis in countries with a high level foreign bank participation in 1999. As noted above, the pattern of results is robust to using different measures of foreign bank participation and different years between 1999 and 2002. The pattern of results therefore supports hypothesis 4, in that reported obstacles were much less severe during the early stages of the economic crisis in countries with high foreign bank participation levels at the end of the 1990s.

Hypothesis 5 was that the increase in reported financial constraints would be less severe in countries with well-established foreign bank participation. However, reported constraints were actually _less_ severe in 2008-9 than they were in 2005 and, especially, 2002 in countries with such participation.<sup>29</sup> This pattern is consistent with the notion that foreign banks had entered relatively credit-starved countries seeking profit opportunities and were trying to meet that demand in 2002. Demand was being better met by 2005 in that constraints were on par with other countries in the region, and by 2008-9 constraints were less severe than ever despite the crisis.

Our results are consistent with others from the ECA region that lending relationships for foreign banks tended to be more stable than those of domestic banks in that foreign banks were less likely to drop their clients, even in the aftermath of an acquisition, and that over time, competition from foreign banks produced changes in the lending policies of domestic banks,

> 29 That is, the coefficient for the large*2005 interaction is smaller (in absolute value) than that of the large*2008/9 coefficient.

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_5_

# iox 1

# Characteristics of SC-Based C.i'uuodity Processing Chains

Although the approach has several recognized imperfections, a number of empirical studies have utilized the Standard International Trade Classification (SITC) system to construct "commodity processing chains" (Balassa 1968, Yeats 1979, Laird and Yeats 1987). Thesw chains trace individual commodities like cocoa and coffee beans, or cotton, jute and iron ore, through successive stages with each experiencing a greater degree of processing then the former. After detailed analysis of the SITC system, the World Bank was able to construct processing chains for 48 different commodities (see Appendix 1 for details). In 1990, the individual components of these chains accounted for approximately 85 percent of all developing countries' exports.

The major advantage of an SITC-based processing chain framework for analysis is that it provides a common basis for merging trade and trade barrier information. That is, the processing chains allow one to tabulate a country's imports or exports of each stage in a given chain and match this information directly with statistics on tariffs and nontariff barriers. The following example shows the three stages of the lead processing chais, Japan's 1990 imports at each stage, as well as Japan's average (unweighted) tariff on each product. Note how the import duties increase or *escalate" as one move: from lead ore to wrought lead alloys.

||1990 Japanese Imports|||
|---|---|---|---|
|Processing Stage (SITC)|Value (Smill.)|Share(%)|Tariff (%)|
|LEAR||||
|Lead Ore (283.4)|120.3|57.1|0.0|
|Unworked Lead idloy (685.1)|87.9|41.7|3.2|
|Wrought Lead Alloys (685.2)|2.5|1.2|558|
|All Stages|210.7|100.0||

Two specific points should be noted concerning these SITC-based processing chains. First, the level of detail changes from chain to chain due to the nature of the SITC system. That is, sevedal chains like cocoa and wood pulp contain three and four stages wbile other commodities like coffee or copra only include an unprocessed and processed stage. Second, there is a problem of "leakages" in some chains, **_L.e.,_** some commodities may be production inputs for items not included in the chain's components and lost from the analysis (see footnote 3 for a specific example). It is acknowledgeJ that the problem of leakages may produce biases in SITC-basod analyses of trade barrier escalation.
Overall, the results in Table 5 provide suggestive evidence that outages negatively affect the performance of firms. Firms respond to this negative shock by reducing demand for temporary workers, and reducing wages so as to avoid (massive) lay-offs of permanent full-time staff (see Table 6). Available evidence from the relatively scant literature on the effects of electricity outages on firm productivity and labor demand lends support to the findings of this paper (Allcott et al., 2016; Hardy and McCasland, 2019; Abeberese et al., 2021). Allcott et al. (2016) for instance provide evidence of significant revenue and productivity losses resulting from electricity outages in India. Also using data on small garment firms in Ghana, Hardy and McCasland (2019) show that the effect of electricity outages on firm revenue and profitability is non-trivial. Hardy and McCasland (2019) further show that firms respond to these shortages by reducing production hours without any allocation to non-outage days, and more importantly substituting high-wage employees for low-wage employees.

# **5.2 Extensive Margin**

In this section, I provide evidence on how unreliable electricity provision constrains job creation on the extensive margin by showing the effects on: (i) (net) entry of firms using data from Ethiopia, and (ii) entry of foreign firms via FDI exploiting the “Dumsor” crisis in Ghana as a natural experiment.

## **5.2.1 Firm Entry**

The entry of new firms is an important channel for job creation as it leads to the expansion of the productive sectors and employment opportunities. Examining the effects of unreliable power provision on entry (and exit) of firms is however a challenge mainly due to the lack of administrative data on firms. In many African countries, firm census are scant, with the exception of Ethiopia and South Africa that conduct yearly censuses of firms. An 36
suggests that more girls than boys are hidden as students in primary schools, as the official age group for the primary school students is 7-12. This is corroborated by the results of the field visits.

Many parents in the selected research districts also noted that they decided to send their daughters to _madrasah_ following completion of primary school, and indeed in recent years _madrasah_ have been opening more seats for girls. Again, this is reflected in the literature. Some studies have pointed to parents preferring to send their daughters to _madrasah_ instead of public schools, given a preference for religious education and the ability of Islamic schools to provide values-based moral education to teach girls to become good mothers (Jackson & Parker, 2008).

For higher education (ages 19-24), the GPI for SER (School Enrollment Rate) at the national level has increased dramatically in recent decades. While the GPI in 1997 was 0.72, by 2007 it increased to 0.95. The GPI figure was about 1 from 2012 to 2014, and then stabilized above 1 during the 2015-19 period. Currently, female participation in higher education remains higher than male participation by 1 percentage point (Figure 3).

**Figure 3. Tertiary enrollment rate of population aged 19-24 by sex and Gender Parity Index**

<!-- Start of picture text --> 30 1.20<br>25 1.00<br>20 0.80<br>15 0.60<br>10 0.40<br>5 0.20<br>0 0.00<br>Male Female GPI<br>GPI<br>Percent<br><!-- End of picture text --> Source: Susenas various years.

At the subnational level, the SER for ages 19-24 is largely influenced by the availability of universities in the respective areas. For example, compared with the districts of Bangkalan and Sampang, Banjar Baru and Banjarmasin have more universities, and thus the SER for ages 19-24 is higher. The figures are even higher than the national average (24.40), which may be a result of some in-migration for these tertiary services. The SERs for Banjarmasin and Banjar Baru in 2018 are 39.07 and 37.11, respectively. In contrast, the SERs in Bangkalan and Sampang are much lower, at 16.15 and 16.19, respectively. Except for Sampang, the GPIs for SER in selected districts and provinces are above 1, which means that the SER for this age group is higher for girls than boys.

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|**Variable**|**Mean**|**Std. Dev.**|**N**|
|---|---|---|---|
|Actual minus forecast growth<br>annual over fve years|-0.472|3.616|3781|
|Absolute diference<br>annual over fve years|2.15|2.945|3781|
|Averaged actual growth rate<br>annual over fve years|3.985|3.913|3781|
|Averaged forecast growth rate<br>annual over fve years|4.457|2.054|3781|
|Table 1: Summary sta|tistics, fu|ll sample||
|**Variable**|**Mean**|**Std. Dev.**|**N**|
|Actual minus forecast growth<br>annual over fve years|0.504|6.53|583|
|Absolute diference<br>annual over fve years|3.194|5.717|583|
|Averaged actual growth rate<br>annual over fve years|5.776|6.818|583|
|Averaged forecast growth rate<br>annual over fve years|5.272|2.547|583|

Table 2: Summary statistics, resource rich countries such as Statistics Office, Ministry of Finance, Central Bank as well as common assumptions and guidance provided by a central IMF team for consistency. As the IMF website reports “because forecasts are made by the individual country teams, the methodology can vary from country to country and series to series depending on many factors”.<sup>7</sup> Despite the differences an important assumption underlying all forecasts is that the present policies of national authorities will be maintained.<sup>8</sup>

# **2.1 Identification strategy**

We test the extent to which growth disappointments arise following a resource discovery. In particular, we investigate the period immediately following discoveries and prior to production taking place, and how performance is associated with government policies and institutions. Our ideal test would be to randomly assign major resource discoveries to different countries and then measure their performance against some calibrated trajectory of growth. We could then observe deviations by countries with weak institutions or poor policy making in the years following a resource discovery and examine how this performance compares to their counter-factual trajectory. To mimic this set-up we exploit the giant oilfields data as having plausibly exogenous timing once we account for country and year fixed effects. This dataset has been utilized by various studies in the literature such as Harding et al. (2016), Lei and Michaels (2014) & Arezki et al. (2016).

The empirical test for a resource curse is typically formulated as the difference in long run growth

> 7www.imf.org/external/pubs/ft/weo/faq.htmq1g

> 8www.imf.org/external/pubs/ft/weo/data/assump.htm

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14 included in the subsidy programme, but not so helpful in determining who should be excluded.

- By contrast, poor-quality sanitation is not so highly correlated with poverty since only 58.9% of the poor suffer from this problem. However, it has a very low correlation with not being in poverty; only 15.7% of the nion-poor suffer from low-quality sanitation. Hence, this variable is a powerful means of excluding people who are not eligible, but it is not very helpful in identifying those who should be included.

As a result of this, the most powerful eligibility criterion is that which combines information on all of these characteristics. For example, limiting eligibility to those who fulfilled two or more of the above criteria would reduce the Type I error associated with the subsidy to a mere 6%, which is to say that 94% of those in extreme poverty would be eligible to benefit from the scheme. Although this level of performance falls short of perfect targeting, it is clearly far superior to the performance of the zonal subsidy. Hence, individual level criteria are much better at identifying those in extreme poverty than were geographical criteria.

Notwithstanding the higher success rate in reaching the genuinely poor, this criterion still entails allocating a very large number of subsidies to those who fall outside the target group. That is to say, the analysis indicates that an individual subsidy based solely on the criterion of meeting two or more of the above conditions would lead to an extremely high Type II error of 93%, meaning that the vast majority of subsidy funds would be allocated to households above the extreme poverty line. This performance is substantially worse than that of the zonal subsidy.

**Table 4.2: Potential Eligibility Criteria for Targeting Individual Subsidies**

||**Extr**<br>**pov**|**eme**<br>**erty**|**Pove**|**rty**|**Non-**|**poor**|**To**|**tal**|
|---|---|---|---|---|---|---|---|---|
||**'000s**|**O/6**|**'OOOs**|**%**|**'000s**|**%**|**'OOOs**|**%**|
|Poor-quality floor materials|10.6|88.6|24.3|78.3|187.6|56.0|222.6|58.8|
|Lack of telephone connection|9.6|79.8|21.7|69.8|113.2|33.8|144.4|38.2|
|Primary educated head of household|7.8|65.3|15.2|49.1|85.2|25.4|108.3|28.6|
|Toilet facilities located outside house|7.2|60.4|18.3|58.9|82.3|24.5|107.8|28.5|
|Lack of sanitation beyond most basic|7.1|58.9|15.0|52.7|52.7|15.7|74.8|19.8|
|**Total**|**12.0**|**3.2**|**31.0**|**8.2**|**335.1**|**88.6**|**378.2**|**100.0**|
|None of the above|0.0|(.0|1.0|3.3|98.8|29.5|99.8|26.4|
|One or more of the above|12.0|10l).0|<br>30.0|96.7|236.3|70.5|278.4|73.6|
|Two or more ofthe above|11.3|93.9|25.9|83.5|140.7|42.0|178.8|47.3|
|Three or more ofthe above|9.5|78.8|20.2|65.2|80.1|23.9|110.5|<br>29.2|
|Four or more of the above|6.8|56.1|13.2|42.6|47.6|14.2|68.3|18.1|
|All five of the above|2.9|24.1|5.2|16.9|16.2|4.8|24.3|6.4|
|**Total**|**12.0**|**[]**|**31.0**|**0**|335.1|**0**||**0**|