rafmacalaba/datause-encoder-data huggingface dataset:
seen as likely to result in higher earnings. But greater labor mobility is also l'kely, pari passu, to reduce the enterprise's willingness to offer training.

# Three Issues

Three linked issues emerge from the economic context.

I. The first concerns the implication of economic reforms for labor markets and hence for VTE policy: to what extent will the reforms lead to increased labor mobility and truly competitive labor markets? And, if these do appear, what will be their effect, in turn, on the willingness of enterprises to _ontinue to finance and provide training under conditions of higher labe-r mobility? (This last question has already received substantial attention by Orr and Orr, 1985.) Evidence from Liaonint and Hubei provinces can provide some partial answers to these questiorns.

On the basic question of whether the reforms will significantly increase labor mobility, managers did not appear to believe that this would become a seriour problem (or, for that matter, a substantial benefit). --e manager of an enterprise in Liaoning - with 6,000 workers felt labor mobility within the region might I.crease somewhat, but now. enough to cllow him to attract sufficient numbers of skilled workers from elsewhere. Moreover, the enterprises with relevant skills were all under the same technical ministry and industrial bureau. Herce, mobility within the industry would not represent either a net loss or net gain.

In Wuhan, another factory manager was more concerned about the problems he faced in finding work for young people who had been assigned to him (after he had lost no fewer than 4,000 employees to retirement, over half his total staff, in 1984), than about labor mobility. Strongly in evidence was the traditional view of factory employment as a lifetime commitment by the worker to a specific factory, and by the factory to each worker.

In Beijing, officials doubted that the newly-introduced contract labor system will have any major impact on the structure of the indastry's labor force for anoth_r 5 to 10 years. Even then, very little mobility of labor across geographic areas was envisaged, partly for cultural reasons ("the Chinese worker is reluctant to move from his native city"), partly for institutional reasons (he has his living quarters, pension, children's education, and wife's employment all tied to his present place of employment). There may be some increase in labor mobility among enterprises in the same city, but it is all too soon to say whether contract workers will indeed take advantage of the expiration of their contracts to seek out better opportunities. As long as wage differentials among firms remain small, inter-firm labor mobility is also likely to remain low.

On the subsidiary question of whether, given higher mobility of labor, the willingness of enterprises to commit resources to training would be adversely affected, managers, planners and

- 9-
_5_ and lack of efficiency, and decentralization and lack of monitoring. Coordination seeks to minimize this trade-off.

Studies of organizational boundaries consider two elements in the decentralizalion of decision rights (Holmstrom 1995; Hart 1995; Milgrom and Roberts 1992; and Kreps 1992). First, those with authority must also bear the responsibility for their decisions because the alignment of authority and responsibility creates incentives for optirnal decision making. Second, coordination is important in ensuring that organizations allocate the authority to make decisions to the agents best informed to make them. 'I'he benefits and costs of decentralization have been well studied.

**Table 1. Benefits and costs of decentralization**

|_Benefits_|_Costs_|
|---|---|
|Better use of information at the local level|Agency costs|
|Lower response time in adapting to local<br>conditions|Coordination costs|
|Increased motivation of managers|Costs of communication between central and<br>local units|

Source: Brickley, J. et al. 1997. Managerial Economics and Organizational Architecture, Irwin.

In health service delivery, organizational boundaries are in flux throughout the world, because of changes in medical technology, know-how, and costs, resulting in differential changes in transaction costs (Robinson 1996). Organizational boundaries have also been in flux because policy-makers have deliberately experimented with new residual control rights" (pp.30). Residual control rights are also referred to as 'decision rights' by Holmstr6m (1995), Milgrom and Roberts (1992), and Kreps (1992). The latter, shorter term is used more frequently in this paper.
also significantly increase the working-age population, although to a lesser extent than exports. The unskilled working-age-population again accounts for the bulk of this increase.

**Table 5: Exports, imports and local migration patterns with the US**

|||**IV**<br>||
|---|---|---|---|
|**(a) Exports**|Emigration<br>to USA|Returning migrants<br>from USA|Remittances<br>from USA|
|log (Exports per worker), change|-95.97**|152.3***|-1.984***|
||(38.36)|(33.16)|(0.639)|
|Observations|1,979|1,979|1,979|
|Demographic and employment controls (year 2000)|YES|YES|YES|
|Median income control(year 2000)|YES|YES|YES|
||Emigration|Returning migrants|Remittances|
|**(b) Imports**|to USA|from USA|from USA|
|log (Imports per worker), change|-14.59|22.58***|-0.257|
||(10.19)|(7.726)|(0.195)|
|Observations|1,979|1,979|1,979|
|Demographic and employment controls (year 2000)|YES|YES|YES|
|Median income control(year 2000)|YES|YES|YES|

Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. The dependent variables are: Emigration to the US = change in the number of households with emigrants to the US from the previous five-year period, from 2000 to 2010; Returning migrants from USA = change in the number of households with returning migrants to the US in the previous five-year period, from 2000 to 2010; Remittances from USA = change in the percentage of households receiving remittances from US, from 2000 to 2010. The explanatory variable is the change in the log of exports-to-workers from 2004 to 2014 in panel (a) and the change in the log of imports-to-workers from 2004 to 2014 in panel (b). Demographic and employment controls include the proportion of rural population, the proportion of population aged 15 and more who are literate, total population, and the share of employment in the primary, secondary and tertiary sectors of economic activity (all variables in the year 2000).

# 3.4 Exports, imports and the informality channel

Besides migration, informality could serve as an alternative channel through which trade affects local labor markets in low- and middle-income countries. Even when trade does not change the rate of employed workers, it could induce a movement of workers from the informal to the formal labor market. We therefore estimate the impact of exports and imports on local labor informality, defined as the percentage of employees without pension or retirement benefits at work. Focusing on our preferred specification including the relevant control variables in column (6) highlights that exports significantly reduce local labor informality (panel A). A 10 percent expansion in exports per worker lowered the informality rate by 0.24 percentage points on average. We also observe a decline of labor informality as a result of increasing imports per worker, although the coefficient is much smaller (panel B). A 10 percent expansion in imports per worker lowered informality by 0.07 percentage points on average.

17
**Figure 1. Pre-primary education gross enrollment rates (percentage)**

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1970 1980 1990 2000 2010 2020<br>Egypt, Arab Rep. World<br>Percentage<br><!-- End of picture text --> _Source:_ Authors’ creation based on World Development Indicators (World Bank, 2022a)

# _2.4.2 Education challenges in Egypt_

Generally, investment in early childhood development in the MENA region has been comparatively low (El-Kogali & Krafft, 2015). The region also tends to have the lowest scores in the world on international assessments during the primary and secondary grades (El-Kogali & Krafft, 2020). The catch-up between the earlier grades of primary school and later grades suggests that gaps in early learning may play an important role in learning and human capital challenges in the region (El-Kogali & Krafft, 2020).

Egypt has had substantial success expanding access to primary, secondary, and higher education, but education quality remains an issue (Elbadawy, 2015). Emphasis on rote memorization, large class sizes, and a focus on credentials over skills have been long-standing challenges in Egypt’s education system (El-Kogali & Krafft, 2020; Elbadawy, 2015; Moustafa, Elghamrawy, King, & Hao, 2022; World Bank, 2008). Returns to education in MENA and Egypt are low and stagnant as a result of both education and labor market challenges (Krafft, Branson, & Flak, 2019; Said, Galal, & Sami, 2022; Salehi-Isfahani, Tunali, & Assaad, 2009).

# _2.4.3 Education 2.0 reforms_

Starting in 2018, MoETE began a series of system-wide educational reforms, referred to as education 2.0 (Moustafa, Elghamrawy, King, & Hao, 2022). The new education 2.0 system was competency-based, multi-disciplinary, and aimed to foster a variety of 21<sup>st</sup> century skills. The new system also included a new approach to assessment and examination. Goals of the reform included expanding access to pre-primary education and improving the quality of education. Reforms were implemented grade by grade, starting with the pre-primary level (Moustafa, Elghamrawy, King, 6
estimated for the level of FDI (FDI per capita)25 as we!l as for the share of FDI in FDI to all developing countries.26 Except for the variable real GNP in the FDI share regressions, all variables are calculated as treans for the observations 1988-92 and weighted by population. The estimated equations have the following forn: <!-- Start of picture text --> FDI, GDP~ EX7M, 1NV CON<br>(1) + A ' +X ' + 'I' ' + ' +FXR +e<br>(2) n a+1p 3X+ +x M+pX, ++ + -,GDP, "' + e<br>fjDI APO, P, POP, POP,<br>i=,<br><!-- End of picture text --> where EXIM represents the sum of exports and imports, INV stands for gross fixed domestic capital formation, CON is government consumption, EXR is the real effective exchange rate, and the denominator POP stands for population. The subscript i indicates the ith country.

The re-estimation generates results very similar to Edwards' study as shown in Table 4.

Table 4 Re-estimating the Edwards Model

|Dependent<br>Variable|FDI**pr cap4ita**|FDI shae|
|---|---|---|
|Constant|-7.372<br>(-0.859)|1.3171<br>11.0372)|
|GDP per capita|0.0043<br>**(0.8865)**|**-0.0005**<br>(-0.5908)|
|**Trade**|0.0229 * *<br>(2.7279)|0.0011<br>(-0.8538)|
|Inves.**nent**|0.0491<br>(1.0815)|0.0217<br>(3.2524)|
|Government|**-0.04**|**-0.02**|
|Consumption|(-0.954)|(-3,21641|
|Exchange Rate|**-0.06e**<br>**(-0.722)**|40.0174<br>(-1.2893)|
|Real GO)P|_-I-_<br>I<br>1|2.1 2E-05<br>**1~~~~~3,3334)**|
|Adi. A-Squared|0.8908|0.6728|

t-statistics in **parenthes"s** "I indicates statistical significance **at the** 98%-level.

The trade variable is positive and statistically significant in the FDI per capita estimation, indicating that economic openness is an important aspect of the decision to invest abroad.

25 Edwards weights FDI by GDP rather than by population, but the results obtained here are quite similar to results when GDP is used as weight. Using population generally resulted in a better fit, possibly related to measurement difficulties in GDP, especially when calculated in dollar terms. 26 'Nhile Edwards calculates the FDI share based on OECD data, IMF data on FDI were used here.

26
32 Table A.8: Regressions for fixed investment on selected subsamples, unbalanced 5-year average panel, 1980–2009<sup>_†_</sup>

||**A.S1**|**A.S2**|**A.S3**|**A.S4**|
|---|---|---|---|---|
|Lagged investment|0.634|0.591|0.471|0.698|
||(0.10)<sup>_∗∗∗_</sup>|(0.13)<sup>_∗∗∗_</sup>|(0.18)<sup>_∗∗_</sup>|(0.21)<sup>_∗∗∗_</sup>|
|Output|1.012|1.164|1.060|1.385|
||(0.39)<sup>_∗∗∗_</sup>|(0.39)<sup>_∗∗∗_</sup>|(0.39)<sup>_∗∗∗_</sup>|(0.32)<sup>_∗∗∗_</sup>|
|Output growth|0.318|0.437|0.501|0.266|
||(0.11)<sup>_∗∗∗_</sup>|(0.14)<sup>_∗∗∗_</sup>|(0.18)<sup>_∗∗∗_</sup>|(0.21)|
|Cost of capital|-3.686|1.995|-0.431|-0.439|
||(1.94)<sup>_∗_</sup>|(1.26)|(0.84)|(1.02)|
|Trade openness|-0.099|0.243|-0.094|-0.071|
||(0.16)|(0.24)|(0.11)|(0.14)|
|Financial openness|-0.096|-0.174|-0.068|0.002|
||(0.08)|(0.09)<sup>_∗_</sup>|(0.06)|(0.05)|
|Financial|0.192|-0.025|0.117|0.154|
|development|(0.10)<sup>_∗∗_</sup>|(0.14)|(0.11)|(0.09)<sup>_∗_</sup>|
|Institutional|-0.279|0.315|0.223|0.139|
|quality|(0.18)|(0.19)<sup>_∗_</sup>|(0.10)<sup>_∗∗_</sup>|(0.17)|
|Business|0.641|-0.209|-0.085|-0.103|
|environment|(0.26)<sup>_∗∗_</sup>|(0.19)|(0.28)|(0.14)|
|Institutional|-0.041|-0.058|-0.162|0.063|
|structure|(0.14)|(0.11)|(0.13)|(0.08)|
|Wald _χ_<sup>2</sup>|33,096<sup>_∗∗∗_</sup>|3,454<sup>_∗∗∗_</sup>|18,604<sup>_∗∗∗_</sup>|51,763<sup>_∗∗∗_</sup>|
|Hansen J|16.147|26.271|33.258|29.902|
|AR(2) z|0.004|-1.191|-0.834|0.287|
|Instruments|42|42|43|48|
|N (countries)|104 (32)|220 (73)|144 (51)|177 (68)|
|Subsample?|Ind.|Non-ind.|High FD|High IQ|

> _†_ All variables are in log form. Heteroskedasticity and autocorrelation-robust standard errors are reported in parentheses. Period fixed effects and a constant term were included in the regressions, but not reported. FD = financial development, IQ = institutional quality.<sup>_∗_</sup> indicates significance at 10 percent level,<sup>_∗∗_</sup> indicates significance at 5 percent level, and<sup>_∗∗∗_</sup> indicates significance at 1 percent level.
**Figure 4: Average monthly electricity subsidy by household income deciles**

<!-- Start of picture text --> 30<br>25<br>20<br> Scenario 1<br>15<br> Scenario 2<br>10 Scenario 3<br>5 Scenario 4<br> Scenario 5<br>0<br>1 2 3 4 5 6 7 8 9 10<br>-5<br>-10<br>Source: calculated from 2004 ISSP Montenegro Household Survey.<br>Euro per household per month<br><!-- End of picture text -->

**_What share of the poor receives a subsidy?_** The five alternative subsidy programs we have considered in this analysis are very different in terms of coverage performance. Clearly, all households are subsidy recipients in Scenario 1 when everybody is charged a tariff below the cost-recovery level (Table 7). In Scenario 2 the price of the first block is below the cost-recovery level, and at the cost-recovery level for the second block. In this case all consumers are subsidy beneficiaries. In Scenario 3 all consumers receive a subsidy for the first 300 kWh; however, high volume consumers pay a higher price than the cost-recovery level for consumption in excess of 300 kWh. Since non-poor households are high volume consumers, our calculations show that overall they would be cross-subsidizing the poor. This result is unique to the countries like Montenegro, where higher income households consume significantly more electricity than low income households, making it relatively easy to implement quantity-based targeting with an IBT structure.<sup>19</sup> However, it is necessary to have a very large difference between the price for consumption below and above the threshold, and it may not necessarily be politically feasible.

A VDT in Scenario 4 would require a smaller difference between the lower and upper consumption blocks in order to remain fiscally neutral. However, this tariff scheme would perform worse in terms of coverage. As shown in Table 7, only two thirds of the poorest households would receive any subsidy. The remaining 26 percent of households in the bottom decile consume more than 500 kWh and they would pay for their consumption at the higher rate, assuming that they do not adjust their consumption level in response to the change in tariff policy.

> 19 It is common for electricity consumption to increase with income, but, as shown in Komives et. al. (2005), it is less common to see such a high discrepancy in the consumption level of the connected poor and the non-poor.

15
farming (Panel A). These positive effects are robust to the choice of the decay function in the construction of the IV.<sup>17</sup> This effect is compatible with households mainly relying on subsistence farming prior to the arrival of refugees (see descriptive statistics) and potentially responding to an increasing demand for some agricultural products from refugee settlements. Market expansion induced by refugees would allow subsistence farmers to sell some of their home production and diversify their sources of livelihood. Alix-Garcia and Saah (2009) similarly find that, compared to urban households, rural households closer to refugee camps experience a positive wealth effect which could be resulting from production and supply of non-aid food products in response to the upward price shifts. We could not find any significant effect on those initially involved in commercial farming, wage employment and nonagricultural employment. But the reduction of sample sizes and the weakness of the first-stage regressions do not allow us to draw any firm conclusion.

Table 6: Second stage: Effects of refugee presence on household welfare disaggregated by initial main source of income, - <u>with analysis at cluster level and the distance weighted refugee index at 50km from the clusters (Panel 2009 2012)</u>

||Log of welf<br>|are (consumpti<br>equival<br>|on aggregate p<br>ent)<br>|er adult<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||Pa|nel A (Subsiste|nce farming)||
|Log of refugees (50km)|0.08035|0.08138|0.09170*|0.08407*|
||(0.05125)|(0.05133)|(0.04793)|(0.04368)|
|Observations|2,850|2,849|2,532|2,532|
|Kleibergen-Paap rk Wald F|14.35|14.34|14.20|13.85|
|Root MSE|0.533|0.530|0.513|0.366|
||Pa<br>|nel B (Comme<br>|rcial farming)<br>||
|Log of refugees (50km)|0.57362|0.45739|0.31604|0.21158|
||(0.91898)|(0.78486)|(0.71053)|(0.55641)|
|Observations|148|148|133|133|
|Kleibergen-Paap rk Wald F|1.771|1.740|1.298|1.508|
|Root MSE|0.434|0.408|0.374|0.344|
||P<br>|anel C (Wage e<br>|mployment)<br>||
|Log of refugees (50km)|0.02290|0.02291|-0.02186|0.02644|
||(0.07035)|(0.07011)|(0.12617)|(0.11184)|
|Observations|843|842|533|533|

17 Table A23 (Appendix 4) shows that the effect size varies from 8% to 10% depending on the choice of the decay function (Panel A). Within the 100km buffer, the effect of refugee presence on welfare of households whose initial main source of income is subsistence farming varies from 11% to 16% depending on the decay function of the IV (Appendix 4, Table A24; Panel A).

25
In this paper, we revisit the evidence using a large panel of bilateral trade flows between 167 countries over 13 years spanning 1992-2004. Particular attention is given to the econometric specification of the gravity model in order to eliminate sources of biases common in conventional gravity models. In particular, we take advantage of the sample’s panel structure to include country-pair specific effects instead of the usual exporter and importer effects (making the identification of time-invariant country characteristics like landlockedness possible) and treat carefully the potential endogeneity of some of the explanatory variables. Using this careful specification, we assess the impact of domestic infrastructure and landlockedness on Central Asian trade and provide a decomposition of landlockness’ impact on transport costs into several components, such as overland distance to port or transit-country infrastructure.

Our empirical results show that an improvement in the own infrastructure of Central Asian countries from the level of the median Central Asian country to that of the top 25<sup>th</sup> percentile among other landlocked countries would raise exports (imports) by a modest 6.5% (8.6%).<sup>3</sup> An improvement in infrastructure quality to the level of the median coastal country would raise exports (imports) by 14.5% (19.6%). Thus, the impact of domestic infrastructure appears limited.<sup>4</sup> By contrast, an improvement in transit-country infrastructure to the level of the best 25<sup>th</sup> percentile amongst other landlocked countries would raise the representative CAC’s exports by a whopping 52%. Other dimensions of landlockedness are also great impediments to the trade of Central Asian countries. Hence, diminishing the extra overland costs (proxied by the distance to the nearest port) or enhancing the ability of a landlocked country to negotiate sea access compared to others landlocked countries would significantly increase Central Asian trade, ‘’transit monopolies’’ (single transit corridors) reducing trade significantly.

Although these estimates are subject to the usual caveats, their policy message is clear. First, transit infrastructure is a regional public good and should be managed as such. Facilitating regional cooperation between landlocked and transit countries for the upgrading of transit infrastructure should thus remain a clear policy priority for the IFIs. Second, monopolies in transit corridors are costly. Short of an often doomed quest for transit corridor

> 3 Maintaining all other variables at the level of the representative Central Asian country (median value).

> 4 And not specific to landlocked countries as the associated coefficient is the same for all countries in the sample.

4
# 1 Introduction

How do countries succeed at economic development? Many descriptions of success stories have stressed the important role of manufacturing exports as a vehicle for success. Indeed, manufacturing exports per capita have a striking correlation with GDP per capita across countries, see Figure 1. Causality could go either way in this association, or both variables may re‡ect other factors. The ...gure does support a descriptive statement that success at manufacturing exports and success at development are close to being the same thing. This naturally warrants a close examination of the characteristics of success in export.

In this paper we show that manufacturing export success shows a remarkable degree of specialization for virtually all countries. Manufacturing exports in each country are dominated by a few “big hits”, which account for most of export value and where the “hit” includes both ...nding the right product and ...nding the right market. Moreover, we show that higher export volumes are associated with higher degrees of concentration, after controlling for the number of destinations a country penetrates (i.e. absolute advantage and size). This highlights the importance of big hits. In addition, we estimate that most of the variation, and hence concentration, in export is driven by technological dispersion of the exporting country, rather than demand shocks from the importing destinations. However, given the size of the economy, developing countries are more exposed to demand shocks than rich ones.

Hausmann and Rodrik (2006), in a seminal paper which helped inspire this one, had previously pointed out the phenomenon of hyper-specialization, although only for a few countries and products, and not including the destination component, in contrast to the comprehensive scope of our work. We also make a very signi...cant addition to the Hausmann and Rodrik ...ndings, in that we characterize the probability of "big hits" as a function of the size of the hit –by a power law.

We specify a “hit”as a product-by-destination export ‡ow. We chose this categorization because some export products are shipped to several destinations, while the typical export product is shipped to few destinations (with a mode of one). A few examples of big hits and their relationship to concentration are in order. Out of 2985 possible manufacturing products in our dataset and 217 possible destinations, Egypt gets 23 percent of its total 1
<!-- Start of picture text --> wt<br>=<br> eet cet<br>tnwm LO New —— _<br>o O * io a<br>a<br>= %<br>cae7 =e = <f < a<br>Oo = _ _— =<br>£8 an ee Ne<br><!-- End of picture text --> <!-- Start of picture text --> °<br>1995 2000 2005 2010 2015<br>year<br>——— Korea USA<br>Japan — — — Germany<br>------— Australia Hong Kong, China<br>Malaysia<br><!-- End of picture text -->