rafmacalaba/datause-encoder-data huggingface dataset:
show the highest contraction shares which explains why their net employment growth share is lower than SMEs. This is visually clear in Figure 2, which shows an appreciable peak for mature establishments; together mature SMEs (29%) and large (39%) make up nearly 68% of job contraction, both a possible indication that their longer tenure in the market may dissuade them from exiting altogether (as opposed to younger SMEs, in line with models set out by for instance Jovanovic 1982).

**Table 6: Job Growth Shares by Establishment Size-Age**

|(a) Net E|mploymen|t Growth Sha<br>|re<br>||||||
|---|---|---|---|---|---|---|---|---|
|||Based o<br>|n Initial<br>|||Based on<br>|Average<br>||
||100 em|p. cutoff|250 em|p. cutoff|100 em|p. cutoff|250 em|p. cutoff|
||SME|Large|SME|Large|SME|Large|SME|Large|
|Young|48.6|5.4|51.8|1.6|46.8|9.2|53.2|2.8|
|Mid-age|17.3|3.6|18.0|2.7|13.2|8.5|17.1|4.5|
|Mature|23.3|1.7|23.6|2.3|15.9|6.4|20.0|2.4|
|(b) Gros|s Job Creat|ion Share<br>Based o|n Initial|||Based on|Average||
||100 em|p. cutoff|250 em|p. cutoff|100 em|p. cutoff|250 em|p. cutoff|
||SME|Large|SME|Large|SME|Large|SME|Large|
|Young|34.1|5.3|37.3|2.1|32.3|7.2|36.9|2.6|
|Mid-age|15.6|4.7|17.6|2.8|13.6|6.8|17.0|3.4|
|Mature|26.1|14.1|32.3|7.8|22.6|17.6|30.4|9.8|
|(c) Job C|ontraction|Share|||||||
|||Based o|n Initial|||Based on|Average||
||100 em|p. cutoff|250 em|p. cutoff|100 em|p. cutoff|250 em|p. cutoff|
||SME|Large|SME|Large|SME|Large|SME|Large|
|Young|9.0|5.0|11.2|2.8|9.9|4.1|11.7|2.2|
|Mid-age|12.0|6.3|15.5|2.8|14.2|4.2|16.7|1.6|
|Mature|29.0|38.7|44.1|23.7|32.8|34.9|46.4|21.3|

20
Figure 1: Equity Investment Limits in Countries with Second Pillars, 2009

|**Equity In**|**vestment Limits i**|**n Second Pillar Pen**|**sion Systems**||
|---|---|---|---|---|
|(% of Tota|l Assets)||||
|||10<br>20|30<br>40<br>50<br>60<br>70<br>80<br>90|100|
|**Hungary**|Conservative||||
||Balanced||||
||Aggressive||||
|**Poland**|Single||||
|**Slovak R.**|Conservative||||
||Balanced||||
||Aggressive||||
|**Estonia**|Conservative||||
||Balanced||||
||Aggressive||||
|**Chile**|Fund A||||
||Fund B||||
||Fund C||||
||Fund D||||
||Fund E||||
|**Mexico**|Basic Fund 1||||
||Basic Fund 2||||
||Basic Fund 3||||
||Basic Fund 4||||
||Basic Fund 5||||
|**Peru**|Fund 1||||
||Fund 2||||
||Fund 3||||

Source: World Bank 54. One element that requires consideration is that the majority of the investment regulations have moved towards limits without minimum thresholds. Until its recent nationalization, Hungary was probably the only system that required minimum investment limits. In Chile there were thresholds for investments in equities in the past, but these were replaced by an approach which requires the most conservative funds always to have less equity than the most aggressive ones. This change is due to the fact that in the previous legislation there was the possibility that a risky fund might have less equity than a conservative fund. Slovakia also requires more conservative funds to have less equity than riskier funds.

55. By not imposing limits on equities, the market is left to decide the exposure to that asset class, which in no case ensures optimal allocation. For example, in the case of Slovakia, balanced and aggressive funds are allowed to be invested in equities up to 50 and 80 percent, respectively. However, in 2006 the actual allocation amounts were only 6 and 8 percent for the balanced and aggressive funds respectively. In 2009 the actual equity participation in the portfolios was further reduced to approximately 1 percent of the total assets. Since then, differences among the three types of funds are negligible.

56. The imposition of minimum thresholds for certain asset classes requires a clear understanding of depth of the market, the pension system risks, from the regulatory authority that many countries are not willing to assume. If the regulatory authority does not have a clear expectation of pension system risks, it is even more unlikely that the contributors may have one. In the absence of a minimum investment thresholds for certain asset classes, portfolio allocations that might emerge from the mere imposition of a ceiling can be very varied. In addition, this can lead to erratic allocations in the behavior of equity allocations, which might not be related to the strategic positions, and which are very difficult to justify not only from an optimality point of view. Perhaps the only advantage in avoiding the imposition of lower limits is to give the feeling of less intervention.

15
lower schooling attainment of populations in these countries compared to Western Europe, Canada and the US, the overall quality (measured as education and wage gap to natives) of the typical US immigrant has declined. The US immigration policy instruments considered to have contributed to the change in national mix are the removal of the national-origin quota system and the high importance given since 1965 to family-related preferences for admitting immigrants. Using Immigration and Naturalization Service data, Jasso et al. (2000) find a reversed trend in US immigrants’ quality especially since the 1980s, change attributed primarily to recent developments in the US immigration policy geared towards giving more priority to highly educated immigrants.

# **_THE OUT-MIGRATION SELECTION BIAS_**

A topic that has received attention in the literature, but was insufficiently explored due to data concerns is the selection bias imposed by return migration. Most studies estimate assimilation based on US Censuses, which unfortunately do not allow control for the impact of out-migration. One solution to this problem would be to rely on longitudinal and immigration data instead. At least in the early years, these sources lacked the degree of detail and the level of coverage available in the census data, caveats rendering them much less useful for implementing econometric models that explain immigrants’ performance. However, recent research based on longitudinal data has proven more successful in estimating assimilation while controlling for return migration.

Among the studies that attempted to evaluate the magnitude of return migration, frequently cited ones are Warren and Peck (1980) and Warren and Kraly (1985) which estimate that 30 per cent of the foreign-born US residents leave the country within a decade or two after arrival. In addition, Jasso and Rosenzweig (1982) calculate as of January 1979 a cumulative net emigration rate of 50 per cent for the 1971 cohort of legal US immigrants, by country and area of origin. With respect to out-migration selectivity, one of the findings of Borjas’s 1989 longitudinal study focusing on scientists and engineers states that people with worst performance are more likely to emigrate out of the US. Relying on longitudinal data as well, Hu (2000) and Lubotsky (2007) also show that US out-migration is negatively selected. On the other hand, Jasso and Rosenzweig (1988) infer, using data from the Immigration and Naturalization Service (INS), that the most skilled workers are also the most likely to become out-migrants. Using both US Census 1980 and Immigration and Naturalization Service (INS) data, Borjas and Bratsberg (1996) estimate for 70 countries the out-migration rates as of 1980 for migrant flows who arrived during the 1970s and conclude that immigrants tend to return to wealthy countries which are not too distant from the United States and that the return migration process accentuates the type of 10
**Figure 6. Livestock Risk Layering**

<!-- Start of picture text --> Estimated return period<br>Disaster Response<br>program<br>1 in 25 years<br>Base Insurance<br>Product<br>1 in 5 years<br>Self-retention<br><!-- End of picture text --> Source: authors.

BIP and DRP payments are based on the _soum_ mortality rate, as reported by the National Statistical Office of the Government of Mongolia. The attachment point (strike) and the exhaustion point (cap) of the BIP are set according to the estimated return of period mentioned above. Therefore, the attachment points should vary from 7% to 10% depending on the species and the _soums_ and the exhaustion points should vary from 25% to 30%. In the proposed insurance policy to be pilot tested, only one strike at 7% and two caps at 25 or 30% are offered to the herders for the sake of simplicity, but it is expected that at least two strikes (7% and 10%) will be proposed to the herders in the near future. Box 3 describes how BIP and DRP work.

# **Box 3. How BIP and DRP work**

<mark>Herders pay a premium based on the value of their animals reported and the relative risk in the</mark> _<mark>soum</mark>_ <mark>that they select. The</mark> _<mark>soum</mark>_ <mark>is selected based on herder’s knowledge of where his/her animals are most exposed during the year. Herders are able to insure between 25 and 100 percent of the estimated value of their animals. Payments begin once the predetermined threshold of mortality for the</mark> _<mark>soum</mark>_ <mark>(strike) and species is exceeded. The payment rate is capped once the mortality rate exceeds the exhaustion point (cap). BIP payments are the product of the payment rate times the value insured. Formally, the BIP indemnity schedule is</mark> _<mark>I</mark>_<sup>_BIP_</sup> <mark>(</mark> _<mark>MR</mark>_ <mark>) = (sum insured) x min { max [</mark> _<mark>MR</mark>_ <mark>−</mark> _<mark>strike</mark>_ <mark>0, ] ,</mark> _<mark>cap</mark>_ <mark>−</mark> _<mark>strike</mark>_ <mark>}</mark> , where MR is the reported <mark>mortality rate.</mark> <mark>DRP payments use the full value of animals as the base of value insured. The DRP pays for losses beyond the exhaustion point. Formally, the DRP indemnity schedule is</mark> _I_<sup>_DRP_</sup> ( _MR_ ) = (value of animals) x max [ _MR_ − _cap_ 0, ] .

<mark>As an example, consider a herder who has 36 sheep where the value of a sheep is 28,320 Tg (US$1 is approximately equal to Tg 1200). The herder decides to insure the total value = 28,320 Tg. x 36 ~ 1,000,000 Tg. The premium rate for the BIP, with a strike at 7% and a cap at 30%, is 1.4 %. The herder would pay 1.4 x .01 x 1,000,000 = 14,000 Tg as a premium.</mark> <mark>The mortality rate in the herder’s</mark> _<mark>soum</mark>_ <mark>during a bad</mark> _<mark>dzud</mark>_ <mark>year equals 35 %. Payment rate for the BIP is equal to 30% -7% = 23 %, and thus BIP are 23% x 1,000,000 Tg. = 230,000 Tg. Payment for the DRP equals (35%- 30%) x 1,000,000 Tg. = 50,000 Tg.</mark> <mark>In total, the herder thus receives payouts of 64,000 Tg.</mark> <u><mark>Source: Authors.</mark></u> 16
historical standards, since it is unlikely that the steady state is associated with extreme values. Therefore, we calibrate the rest of the world price at $176/ton, the average US export price in 2005—2007, a period before the very high prices of the food crisis but during a time when prices were higher than in the beginning of the decade.<sup>14</sup> The steady-state MENA price is defined by assuming that the price difference between the regions reflects transport costs, which is set at $35.55/ton based on a recent survey (World Bank, 2012).

Picking appropriate elasticities is a challenge given the large variation in elasticities reported in the literature. As a guide, we follow the literature on commodity price dynamics, which shows that observed price volatility is consistent with relatively low demand elasticities ( Roberts and Schlenker, 2009; Cafiero et al., 2011), thus, we assume the demand elasticity to be equal to –0.12, which is toward the lower end of commonly used elasticities. We consider a supply elasticity of 0.2, in line with commonly used supply elasticities for wheat.<sup>15</sup> The cost function is assumed to be quadratic, making the marginal cost function linear. We express the marginal cost function as The parameters �<sup>�</sup> and �<sup>�</sup> determine the supply elasticity (see Table 2) assuming that marginal cost equals the steady-state price.

� We specify two sets of uncorrelated supply shocks, �<sup>�</sup> ~��1, �� �. Based on historical production data, we derive an estimated distribution of supply shocks, where production in MENA is more volatile than in the rest of the world.

Interest rates and per unit storage charges are assumed to be the same for each region at 5 percent per annum and $22.40 per ton per annum. The storage costs are based on recent findings in a World Bank study on wheat markets in MENA (World Bank, 2012).

The rational expectations storage model is known to lack a closed-form solution and so has to be approximated numerically. The numerical algorithm that we use is based on a projection method inspired by Miranda and Glauber (1995) and is described in detail in Gouel and Jean (2012, Appendix D).<sup>16</sup>

> 14 Average annual prices are for wheat (US), no. 1, hard red winter, ordinary protein, export price delivered at the US Gulf port for prompt or 30 days shipment as reported by the World Bank (2011b).

> 15 See, for example, FAPRI’s elasticity database, available on the internet at <u>http://www.fapri.iastate.edu/tools/elasticity.aspx).</u>

> 16 A description of the solution algorithm and solver written for MATLAB is is freely available on <u>https://github.com/christophe-gouel/recs.</u>

11
# **Online Appendix**

|**Table A1: Descripti**|**on of Main Variables**|
|---|---|
|**Variable name**|**Description of variable**|
|R&D Activity|Dummy variable equal to 1 if the firm spent on<br>R&D activity during the last year and 0<br>otherwise.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Informal Competition (Country-level cell<br>average)|Proportion of firms in the country where the<br>firm is located excluding the firm in question<br>that report competing against informal firms.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Informal Competition (Country-industry-firm size<br>level cell average)|Proportion of firms in the country-industry-<br>firm size group or cell excluding the firm in<br>question<br>that<br>report<br>competing<br>against<br>informal firms. Country is where the firm is<br>located. Industry is defined at the 2-digit ISIC<br>Rev. 3.1 level (7 industries) and firm-size<br>group is small (11-20 full-time permanent<br>workers)<br>or<br>medium<br>(21-99<br>full-time<br>permanent workers).The size groups here are<br>based on workers at the firm three fiscal years<br>ago (or two fiscal years ago in some countries)<br>from the date of the Enterprise Survey.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Informal Competition (City-industry-firm size<br>level cell average)|Proportion of firms in the city-industry-firm<br>size group or cell excluding the firm in<br>question<br>that<br>report<br>competing<br>against<br>informal firms. City is the sub-national region<br>or city where the firm is located. Industry is<br>defined at the 2-digit ISIC Rev. 3.1 level (7<br>industries) and firm-size group is small (11-20<br>full-time permanent workers) or medium (21-<br>99 full-time permanent workers).The size<br>groups here are based on workers at the firm<br>three fiscal years ago (or two fiscal years ago<br>in some countries) from the date of the<br>Enterprise Survey.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Industry fixed effects|Dummy variables indicating the industry to<br>which the firm belongs. Industry grouping is<br>at the 3-digitISICRev. 3.1 levelas provided|

54
# **Trade data by regional partner and sector**

To obtain the shares of imports and exports from the different regions of our model, we used trade data published by the National Statistical Service of the Republic of Armenia<sup>13</sup> . The data is for the year 2007 and shows exports and imports by country and commodity.

The regions of our model are Armenia, the European Union, the CIS, and the Rest of the World. For the European Union, we took the 27 member countries as of 2007. For the CIS, we include Azerbaijan, Belarus, Kazakhstan, Kyrgyzstan, Republic of Moldova, Russian Federation, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan. Rest of the World is the residual.

The data is reported according to the Harmonized System (HS) classification at the two digit level. We mapped the HS-commodities into the sectors of our model. The exact mapping and results for both exports and imports are reported in Appendix A.

# **Tariff data—Collected rates at the tariff line level**

We received data on collected import duties (tariffs) and import values at the four digit tariff line level (again using the Harmonized System classification) from the Armenian Customs Authority. The collected tariff rates for the sectors in our model are obtained by first aggregating the four digit tariff line level tariff collections and import values to the sectors of our model. The ratio of tariff collections to import values for each sector of our model is then calculated to give estimates of the collected tariff rates, which in turn are incorporated into our dataset. The tariff rates are shown in table 4 of appendix A. Applying these tariff rates across all sectors implies that tariff revenue in the revised database is about 1% of GDP, which is consistent with collected revenues in Armenia.<sup>14</sup> Given that Armenia participates in preferential trade areas with the other CIS member states, it was necessary to make further adjustments. That is, since, in principle, tariff rates should be zero within these preferential trade areas, we set tariff collections on imports from CIS countries at zero. We then increased the tariff rates for the other regions in our model so that the overall weighted average collected tariff rate is unchanged at the tariff line level. We used the trade flow data, disaggregated by regions and sectors of our model to weight the tariff rates. This

> 13 http://www.armstat.am/file/article/ft_2nish_07_14.pdf 14

> For the year 2008, aggregate data from Armenia show that tariff collections are 1% of GDP.

17
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31
17 with minimum wage laws in the informal sector; alternatively, they may be an unrelated consequence of the steady slide in the real value of the minimum wage in Mexico since the late 1970s.

Several other facts emerge from the Table 9 means. First, male full-time workers are unlikely to earn below minimum wages regardless of whether they work in the formal or informal sectors of the Mexican economy, and regardless of their occupation, region of work, or educational status. These data are therefore consistent with the firm level data in showing a weak impact of the minimum wage on male workers, since roughly 80 percent of the unskilled employed in the firm level data are male. Second, parttime workers are far more likely to be paid sub-minimum wages than are full-time workers. This is true for both the formal and informal sectors and is especially pronounced for female workers. Third, women are more likely to earn sub-minimum wages than are their male counterparts with comparable education, labor market status, and occupation. Fourth, sub-minimum wage workers are more likely to be found in lower paid occupations (laborers, salespersons, and service workers), although these differences seem to be more pronounced in the formal sector. In the urban informal sector sub-minimum wage earners are more evenly spread across-diverse occupations, and especially so for female and part-time workers. Fifth, non-compliance is higher in the (relatively poor) South than in the Federal District (which surrounds Mexico City) and the (relatively rich) North.

To what extent are the differences between the formal and informal sectors noted above the result of labor market segmentation? To what extent do they arise from differences in the characteristics of informal workers and informal jobs? In order to better understand the origins of formal and informal sector pay differences, I used the 1988 data and evaluated mean earnings differences after controlling for competitive factors commonly associated with these differences. Specifically, OLS equations were estimated linking In hourly income to a set of variables capturing characteristics of the individual (education and experience), and of the job (occupation, industry and region). A dummy variable was included for whether or not the individual was a formal sector worker. Two definitions of formal sector