rafmacalaba/datause-encoder-data huggingface dataset:
# **Minimum wages are relatively high in CEE and low in CIS**

Virtually all transition countries have in place minimum wages, although their “bite” measured by the ration of the minimum wage to the average wage differs considerably across country groups. In he European transition economies minimum wages are pitched at a relatively high level, on average around 40 percent of the average wage (Figure 13). The minimum ranges from about one-third of the average wage in Bulgaria and Romania, to over 50 percent of the average wage in Slovakia to two-thirds of the average wage in Slovenia. These minimum wage/average wage ratios are well within the OECD range, although in Europe the ratio is usually higher than in non-EU OECD members.

# **Figure 13  The “bite” of the minimum wage varies across ECA countries**

**Minimum Wage as Percentage of the Average Wage 2002**

<!-- Start of picture text --> 70.00<br>60.00<br>50.00<br>40.00<br>30.00<br>20.00<br>10.00<br>0.00<br>AzerbaijanBelarus Russia CIS-7MoldovaArmenia BRUEstoniaBulgariaRomania Poland Czech RepublicLatvia Slovakia CEEHungaryUkraineLithuaniaSlovenia<br>percent<br><!-- End of picture text --> Source: UNICEF TransMONEE database; Bank staff calculations.

In SEE in most cases there is no national minimum wage. However, minimum wages are fixed at the industry level by collective agreements between trade unions and employer representatives. In addition, these countries apply the minimum social security contribution threshold, which tends to be substituted for the minimum wage.<sup>14</sup> In the CIS national minimum wages are pitched at a much lower proportion of the average wage, with the notable exception of Ukraine, where currently (2002) the minimum wage accounts for 44 percent of the average wage, which is in the upper end of the CEE range.

> 14  The minimum social security contributions threshold is the lowest notional wage which is used as the basis to calculate the amount of social security contributions.  In principle workers can received wages that are lower than the threshold, although in practice this seems to be rare.

36
that liberalization of agricultural products marketing induced entry in subgroups of the food marketing channel, but not in groups where entry barriers were high. Vakis et al. (2003) measured the transaction costs occurring when a farmer sells his crops on the market. They showed that the level of transaction costs will determine which market (farmgate, local or distant market) farmers enter.

The literature’s relative disregard of entry costs in and out of commercial agriculture is understandable given that those costs can be expected to be small in magnitude, as poor farmers are unlikely to switch altogether to sophisticated input-intensive farming. However what matters is not the absolute magnitude of entry costs but their magnitude relative to the income of subsistence farmers, in particular when credit markets are deficient so that sunk costs must be self-financed. Moreover, populations on the fringe of markets are likely to be vulnerable ones, and even well-wishing reforms can have the unwanted effect of destroying fragile markets, as changes in relative prices can be too small to cover switching costs or make intermediation profitable. When middlemen’s trucks no longer come, rural roads are left to decay and entire regions can shrink into subsistence. A report by Oxfam (Oxfam-IDS 1999) argued that this is precisely what happened in Zambia after the maize reform (cited in Winters 2000).

This paper is an attempt to bridge the literature’s gap. We propose a method to evaluate entry costs based on a simple asset-return model of farm specialization, the gist of which is as follows. Suppose that being in subsistence or in the market depends on a given household characteristic _z_ . High- _z_ farmers are in the market whereas low- _z_ are in subsistence, with cutoff _z_<sup>+</sup> . Each sector of activity can be thought of as an asset whose dividend is the net value of production (evaluated at market prices for subsistence farmers, leaving aside for the moment the issues involved in that valuation). The asset’s price is the present discounted value of future dividends and expected capital gains. If the market asset is worth more than the subsistence one for some _z < z_<sup>+</sup> (i.e. for a subsistence farmer), the difference provides a lower bound on the market entry cost for that farmer. For the marginal ( _z_<sup>+</sup> ) farmer, it is just equal to the entry cost.

3
15 income levels for intcrnational comparisons than<sup>using exchange rates.12</sup> Fcr example, Kravis and Lipsey<sup>(1990, p.4) argue that "the purchasing power of</sup> the currencies of low income countries<sup>is much greater than that indicated by</sup> exchange rates ... the real income per capita of the<sup>Asian countries was twice</sup> that suggested by exchange rate<sup>conversions and that of the Central and South</sup> American countries was half again<sup>as much as the exchange rate conversions</sup> indicated." Even so, Kravis and<sup>Lipsey (1990) suggest that methodological</sup> problems could cause estimat's to<sup>be off by 20-25% for low-income benchmark</sup> countries and 30-35% for non-benchmark<sup>countries.</sup> Although ICP prices may provide<sup>more easily comparable estimates of</sup> income levels in a given year<sup>than one can obtain using exchange rates, it is</sup> much less clear whether ICP prices<sup>should be used to compute growth rates or</sup> economic ratios like the share of<sup>investment or government spending in GDP.</sup> We first discuss growth rates<sup>-following closely the presentation in</sup> Kravis and Lipsey (1990, p.32).<sup>The most common way of computing real growth</sup> rates is by deflating each country's<sup>nominal GDP figure by its own GDP price</sup> deflator and, then, determining the<sup>growth rate of this real GDP statistic.</sup> Since every country's deflator<sup>is defined by a different basket of goods, the</sup> growth rate of every country<sup>measures the change in a basket of goods that is</sup> different from that measured<sup>by all other countries. Thus, this own-currency-</sup> computed growth rate answers the question<sup>"How much change has there been in</sup> the quantity of the base year bundle<sup>of goods produced in a country?"</sup>

**t2** The organizers of the ICP, Robert<sup>Summers, Robert Lipsey, Irving Kravis,</sup> and Alan Heston, are very careful<sup>to document the procedures they use in</sup> constructing indexes, and they<sup>highlight any problems about which they think</sup> users of the data should be aware.
# **3.2 Econometric specification**

To causally estimate the impact of _Formosa_ , we employ two different data sets: (1) the Labor Force Surveys 2015-2016 (LFS), which provides information on fishery earnings and employment, and (2) VIIRS Boat-Detection Module, which provides satellite data on boats detected at night in coastal Vietnam. To analyze the LFS, we perform a set of difference-indifferences (DiD) regression analysis of the form: where the subscripts refer to an individual _i_ surveyed in month _m_ in 2016.<sup>18</sup> _yim_ is the dependent variable at the individual level. We investigate the effect of _Formosa_ on several primary fishery earnings and employment, including monthly fishery and total incomes, weekly work hours, and the probability of working secondary jobs. The standard DiD indicator terms are As mentioned, our analysis exploits both the location-specific and industry-specific characteristics of _Formosa_ . Therefore, we measure the disaster’s impact by employing two separate sets of control groups in all subsequent regressions:

1. Geographic control group: all individuals who worked in saltwater fishery before May 2016 and lived in the non-affected region. We purposefully narrow the geographic location of our control group to only the southern provinces distant from the Formosa’s location in order to mitigate any possibility that part of the control group could be contaminated due to potential spillovers of _Formosa_ . Specifically, we include all fishery workers living in the provinces South of Phu Yen, with Phu Yen being approximately –

600 km away from Hue the southern-most _Formosa_ -affected province.<sup>19</sup> 2. Industry control group: all individuals who worked in _Formosa_ -unaffected industries before May 2016 and lived in the affected region. To identify unaffected industries, we cross-check on different government’s official sources regarding _Formosa_ compensations to the affected industries. In the main analysis, we adopt three major industries that

> 18 We also report results for a placebo test using data from the LFS 2015.

> 19 We also experimented with including all provinces south of Hue, as well as including an additional province of Quang Nam in the control group and obtained robust estimates. Results available upon request.

11
# **I. INTRODUCTION**

Economists have long puzzled over why there are such astonishing differences in productivity across both firms and countries. For example, US plants in homogeneous industries like cement, block-ice, white pan bread and oak flooring display 100% productivity spreads between the 10<sup>th</sup> and 90<sup>th</sup> percentile (Foster, Haltiwanger and Syverson, 2008).

A natural explanation for these productivity differences lies in variations in management practices. Indeed, the idea that “managerial technology” affects the productivity of inputs goes back at least to Walker (1887) and is central to the Lucas (1978) model of firm size. Yet while management has long been emphasized by the media, business schools and policymakers, economists have typically been skeptical about its importance.

One reason for skepticism is the belief that competition will drive badly managed firms out of the market. As a result any residual variations in management practices will reflect firms’ optimal responses to differing market conditions. For example, firms in developing countries may not adopt quality control systems because wages are so low that repairing defects is cheap. Hence, their management practices are not “bad”, but the optimal response to low wages.

A second reason for this skepticism is the complexity of management, making it hard to measure.<sup>1</sup> Recent work, however, has focused on specific management practices which can be measured, taught in business schools and recommended by consultants. Examples of these practices include key principles of Toyota’s “lean manufacturing”, such as quality control procedures, inventory management, and human resource management. A growing literature measures many such practices and finds large variations across establishments and a strong association between these practices and higher productivity and profitability.<sup>2</sup> This paper provides the first experimental evidence on the importance of management practices in large firms. The experiment takes large, multi-plant Indian textile firms and randomly allocates their plants to treatment and control groups. Treatment plants received five months of extensive management consulting from a large international consulting firm. This

> 1 Lucas (1978, p. 511) notes that his model “does not say anything about the tasks performed by managers, other than whatever managers do, some do it better than others”.

> 2 See for example, Osterman (1994), Huselid and Becker (1996), MacDuffie (1995), Ichniowski, Shaw and Prennushi (1998), Cappelli and Neumark (2001) and Bloom and Van Reenen (2007). A prominent early example is Pack (1987), which, like the present study, deals with textile firms in developing countries. In related work, Bertrand and Schoar (2003) use a manager-firm matched panel and find that manager fixed effects matter for a range of corporate decisions. Lazear and Oyer (2009) and Bloom and Van Reenen (2010) provide extensive surveys.

1
Are politically connected entrepreneurs more likely to evade tariffs? At issue are not only inequity and fiscal losses, but also inefficiency, since tariff evasion endows perpetrators with a cost advantage over those who are compliant that is not based on performance. The question is especially relevant for developing countries, as they tend to be disproportionately reliant on revenues collected by customs to finance public expenditures (Jean and Mitaritonna 2010),<sup>1</sup> are characterized by a greater prevalence of problematic state-business relationships (Faccio, 2006), and often have weaker tax collection capacity (Slemrod and Yithzaki, 1999).

Using a unique data set in which importing firms owned by former president Ben Ali and his family confiscated in the aftermath of the Jasmin Revolution are identified, this paper examines whether politically connected enterprises were more likely to evade tariffs during Ben Ali’s tenure. Their behavior is compared with that of public firms and other private sector firms, which can be categorized into two types, notably “onshore” firms, and so-called “offshore” firms, which are exempted from having to pay tariffs provided they export at least 70% of their output (or sell it to other offshore firms) and consequently have less incentive to evade tariffs.

More specifically, politically connected enterprises are identified in firm-product-source country customs data spanning the universe of import transactions. These are merged with UNCTAD data on HS6 exports to Tunisia by country and year to compute evasion gaps (Fisman and Wei, 2004), defined as the discrepancy between exports to Tunisia reported by partner countries minus imports of the same product from that source country reported in Tunisia. Such evasion gaps are a useful proxy for the amount of imports that are underreported (or misreported) and thus not taxed

> 1 Estimates by Cantens et al.  (2012) suggest that in low income countries such as Cameroon customs revenues account for between 30% to 60% of state revenues, and that the importance of customs revenues as a source income declines when countries get richer.

2
# **Section 1: Sources and Coverage of Remittance Statistics in Developing Countries**

In general terms, a remittance transfer can be divided in three phases, as illustrated in Figure 1. The first phase occurs in the country where the migrant works and the transaction originates. A remittance transaction is originated when a migrant goes to a bank, money transfer company, credit union, or any other originating point (such as grocery store, post office, gas station, etc.) to transfer funds to relatives or friends in his home country. The second phase takes place when funds are transferred from the originating country to another foreign country. Finally, the third phase occurs when funds arrive in the destination country and are paid to the beneficiary at a commercial bank, money transfer company or any other distribution point (such as ATMs, grocery store, post office, etc).

<!-- Start of picture text --> Figure 1. The Remittance Industry -Key Phases<br>Originating Points in Transfer Payment Points in<br>Country A Country B<br>Money Transfer Commercial<br>Companies Banks<br>Commercial Money Transfer<br>Banks Companies<br>Transfer<br>Migrants Credit Credit<br>Unions Unions Beneficiaries<br>Post offices, Post offices,<br>grocery stores, retail stores,<br>etc. etc.<br>1 2 3<br><!-- End of picture text --> It is important to note that in most cases, banks, credit unions, post offices, exchange bureaus, and micro-finance institutions in developing countries act as “paying agents” of financial institutions and money transfer companies located in the countries where the transactions originate. Only a few banks or financial institutions in developing countries have branches or subsidiaries overseas that serve both senders and recipients of remittances.

The first issue this survey attempted to explore was to what extent the balance of payment statistics of developing countries reflect the volume of remittance flows that countries receive every year (third phase).<sup>2</sup> As shown in Table 2 below, the survey found that in many countries the coverage of remittances in balance of payments statistics is partial, because remittances

> 2 In the countries covered in this survey, remittance data for balance of payment statistics is collected by central banks. However, it is worth noting that in some other countries data is not collected by central banks but by national statistics agencies.

6
data from the 1990s for firms in six emerging economies that experienced a financial crisis (Indonesia, Malaysia, Mexico, the Philippines, the Republic of Korea and Thailand), Love, Preve, and Sarria-Allende (2007) find that firms with high short-term debt levels, an indicator of financial vulnerability due to the increased interest rates and difficulties in rolling over debt during a crisis, reduced their provision of trade credit significantly more than other firms in response to the aggregate contraction in bank credit. In comparing the crisis episodes across countries, they found that the countries that experienced a sharper decline in bank credit also experienced a sharper decline in trade credit. Their interpretation is that firms with better access to capital will redistribute the credit they receive to less advantaged firms via trade credit only if they able to, and that under the extreme scenarios imposed by financial crises, all potential sources of funds including bank credit tend to dry up. Thus, trade credit cannot serve as a substitute for other types of formal credit during extreme financial distress because there are very limited funds that could be redistributed.

# **4. Trade Credit in Advanced versus Lower Income Economies**

In the discussion in section 3, an assumption was that underlying legal and institutional foundations were sufficient to support trade credit arrangements when business disputes arise (e.g., with respect to customer repayment or supplier product quality). Much of the evidence in that section therefore focused on advanced economies, though even there, smaller, lessestablished suppliers extend longer trade credit terms to buyers (Klapper, Laeven, and Rajan, 2012) and the growth-supporting effects of trade credit were not evident for start-up firms (Fisman and Love, 2003). In the absence of reliable supporting legal and institutional foundations, the issue of trust between supplier and customer becomes even more salient and thus less developed countries provide useful laboratories to investigate how this challenge is overcome in practice.

# **4a. Trust or institutions?**

Knowledge of, and trust in, each other enables suppliers and customers to enter into trade credit agreements when supporting legal, judicial, and other institutions are inadequate. However, this is not an either/or choice and a combination of informal arrangements based on trust and formal institutions enable trade credit to flourish in very different contexts as illustrated by empirical evidence from Vietnam, post-Communist transition economies, and China.

**_Vietnam_**

17
**Table 4. Impact of EPAG on Hours and Earnings**

||**Panel 1: I**<br>Baseline|**ntensive Employme**|**nt**|||
|---|---|---|---|---|---|
||Mean|OLS||Controls|Observations|
|Number of days worked past week|2.000|1.107***||Yes|3155|
|(unconditional)|[2.758]|(0.153)||||
|Number of days worked past week|5.321|0.251***||Yes|1494|
|(conditional on IGA)|[1.604]|(0.091)||||
|Number of hours worked on a typical|2.712|1.282***||Yes|3134|
|day (unconditional)|[4.033]|(0.216)||||
|Number of hours worked on a typical|7.247|-0.240||Yes|1473|
|day (conditional on IGA)|[3.253]|(0.193)||||
||**Pa**<br>|**nel 2: Earnings**||||
||Baseline|||||
||Mean|OLS|Tobit|Controls|Observations|
|Total earnings past week (LD),|698.9|563.216***|417.799***|||
|unconditional*|[1843.0]|(124.839)|(77.518)|Yes|3178|
|Probability that total earnings past<br>week (LD) > 0|||0.157***<br>(0.024)|Yes|3178|
|Total earnings past week (log),<br>ditil*|2.592|1.316***|0.982***|||
|unconona|[3.443]|(0.197)|(0.151)|Yes|3178|
|Probability that total earnings past|||0.155***|||
|week (log) > 0|||(0.024)|Yes|3178|
|Total earnings past week (LD),|1854.0|<br>437.573**||||
|conditional on IGA|[2622.0]|(186.749)||Yes|1517|
|Total earnings past week (log),|6.877|0.162||||
|conditional on IGA|[1.403]|(0.128)||Yes|1517|
|Standard deviation in brackets. Robust st|andard errors in|parentheses, cluster|ed by classroom.|||

* For the unconditional earnings regression, a zero was imputed for respondents who report no income-generating activities. Controls used are household size, sex of household head, asset based quintiles, community, age of adolescent, educational status of adolescent, marital status of respondent, parental status of respondent (indicator variables for adolescent having a child and being pregnant), orphan status of respondent (one parent or both parents being deceased), and month of interview at midline; all controls are from baseline data except month of midline interview. Three outliers (LD 105,000, 133,625 and 218,750) were top-coded to 28,800 and a control indicator variable (1 for the ones top-coded and 0 otherwise) was added to the regressions. Days per week over 7 days were recoded to 7 days and a covariate indicating recoded observations was added. When hours per day exceeded the 99<sup>th</sup> percentile (which is 15 hours per day), they were recoded to the 99<sup>th</sup> percentile and a covariate indicating recoded observations was added.

37
-39-

The equation (4) was also estimated for the balance of payment. The Trade surplus is replaced by the ratio between the balance of payment and CDP, _(BOPY),_ and the primary surplus of the government is replaced by the total surplus, divided by GDP _(SURY)._ The results are presented in Tables 9 and 10.

TABLE 9 SMPL 1971 - 1983 13 Observatiors TSLS // Dependent Variable is BOPY Instrument list: C LPXM(-1) RER(-l) SURY(-l) M2Y(-1) GY(-I)

|VARIABLE|COEFFICIENT|ST|D. ERROR|T-STAT.|2-TAIL SIG.|
|---|---|---|---|---|---|
|C|0.2662136|0|.1024156|2.5993466|0.029|
|LPXM|0.2001880|0|.0604843|3.3097541|0.009|
|RER|-0.3884315|0|.1223529|-3.1746829|0.011|
|SURY|0.2317518|0|.1781069|1.3011950|0.226|
|R-squared|0.854|229|<br>Mean of|dependent var|-0.107648|
|Adjusted R-squa|red<br>0.805|639|<br>S.D. of|dependent var|<br>0.045853|
|S.E. of regress|ion<br>0.020|215|<br>Sum of|squared resid|0.003678|
|Durbin-Watsonst|at<br>2.077|865|<br>F-stati|stic|17.58029|
|Log likelihood|<br>34.66|130||||

|||Residual|Plot||obs|RESIDUAL ACTUAL FITTED|
|---|---|---|---|---|---|---|
|||I<br>ff|<br>|*****|I1971<br>|0.02130 -0.09074 -0.11204<br>|
|||:<br>|<br>|<br>|***:**|I1972|0.01967 -0.08455 -0.10422|
|;<br>*****||<br>:|<br>I||:|<br>j 1973|-0.02856 -0.10747 -0.07891|
||||0<br>*****||:<br>:||<br> <br>|1974<br>|0.01125 -0.03485 -0.04610<br>|
|||:<br>*****<br>|||**:**|t1975|-0.01721 -0.10812 -0.09091|
|||:<br>*****<br>|<br>|:|<br>j 1976|-0.00041 -0.06006 -0.05965|
|||:<br>I|<br> <br>*****|:|<br>|1977|0.01150 -0.03543 -0.04693|
|||:<br>*****||:|<br>t1978<br>|-0.00040 -0.11125 -0.11084|
||||<br>:<br>*****<br>||:|I<br> <br>§1979<br>|-0.01435 -0.15183 -0.13747<br>|
|i|*:|I<br> <br>|||:|I1980<br>|-0.02328 -0.17372 -0.15043|
|||:<br>*****<br>||:|I<br> <br>g1981<br>|-0.00448 -0.16750 -0.16302<br>|
|||:<br>I|<br>|:|*****<br>I1982<br>|0.02639 -0.13616 -0.16254<br>|
|||:<br>*l||:|<br>I1983|-0.00141 -0.13776 -0.13635|