rafmacalaba/datause-encoder-data huggingface dataset:
In contrast, when we ask dropouts why they had chosen to not participate, it becomes clear that women’s responsibilities may prevent them from taking advantage of the training. 21% of women cited family obligations as the reason, while no men did. This matches reports we received at baseline, where women were twice as likely (p-value of gender difference = 0.03) to report ‘family obligations’ as the reason they had never before taken advantage of any training. Women are also seven times as likely to mention getting married as the reason for drop-out (p-value = 0.02), and four times as likely to mention transportation problem (pvalue=0.17). Men on the other hand are more likely to report administrative errors – that they did not receive the message from TEVETA to show up, possibly because migration rates are greater for men.<sup>9</sup> In summary, baseline characteristics and the attrition and drop-out analyses indicate that women participate in training in a more constrained environment. They get less financial support which puts greater pressure on their personal funds. Their attendance is slightly worse, drop-out risk is higher, and in turn MCs treat male trainees a little better during and after training (which, admittedly, may be due to gender segregation in the occupational mix rather than any particular MC’s behavior). All of this accumulates to worse treatment effects for girls compared to boys, and lower levels of (subjective) satisfaction with life after training.

# **6. Examining Drop-out and Attrition Bias using Follow-up Data on Drop-outs**

The follow-up data we collected on drop-outs yields another strategy to examine whether dropouts are selected in either a positive or negative direction. If those assigned to training dropped out because better alternative opportunities cropped up (i.e. positive selection), then we would expect the drop-out decision to be associated with better post-training outcomes. We estimate a simple OLS model, separately by gender, in which we compare outcomes for those who chose to drop out with outcomes for those who chose to continue participating in training. The right-handside variable is an endogenous choice (to drop out) that is not randomly assigned, and therefore these results cannot strictly be interpreted as causal effects. Nevertheless, the conditional

> 9 An important caveat to this discussion is that there is segregation in the types of industries/occupations that men and women select into (see Table 2).  Around 85% of training in auto mechanics, metalwork, and construction goes to men, while women are more likely to be trained in clothing fabrication or beauty.  Some of these gender differences may reflect underlying differences in employment conditions within these professions. Even so, that would imply that women are selecting into professions that are more constrained or lead to worse outcomes.

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32 international suppliers instead of relying on local suppliers. Similarly, more research is needed into cost-benefit assessments conducted by individual local firms that underlie their decisions on investments to create business relationships with MNC affiliates, absorb technologies, and improve their performance.

Related to this, more research is needed on conditions that constrain MNC-induced technology dissemination to suppliers in global value chains. GVCs are responsible for an increasing share in world trade and are linking many firms in developing and emerging countries to international markets. However, the currently available evidence indicates that most of these firms do not experience meaningful productivity spillovers that can be linked to MNCs that organize and coordinate these GVCs. This suggest that although in the short-term domestic firms can benefit from increasing sales to international markets, in the long term effects on technological development and economic growth may be lower than expected. More research is needed into examining the key factors that hinder technology dissemination and on policy measures that, in the absence of meaningful technology dissemination, host economy governments can implement to help domestic firms use the economic gains from participating in GVCs to generate technological upgrading.

There is also substantial scope to improve the data that are used to measure and analyze backward linkages and identify their impacts. On the one hand, case studies and small-scale, purposebuilt surveys provide detailed information on the nature and scale of backward linkages. However, most of these studies focus only on MNC affiliates, making it very difficult to assess to what extent foreign-owned firms differ from comparable domestic firms in their use and support of local suppliers. Also, local suppliers are typically not included in the samples, preventing analysis of the impacts of backward linkages. On the other hand, although econometric studies do provide quantitative evidence on the occurrence of productivity spillovers among domestic firms, the evidence is mostly indirect, given that it is not based on actual indicators of the scale and nature of linkages between MNC affiliates and local suppliers. Furthermore, information on firm-level characteristics is also often limited in these data sets, restricting the analysis of the impact of characteristics of MNC affiliates and local suppliers on productivity spillovers.

As indicated by findings from some recent studies that benefit from using rich data sets, one way forward is to conduct larger-scale, purpose-built surveys to obtain information on the extent and nature of inter-firm linkages, as well as information on performance indicators of local suppliers. The World Bank Enterprise Survey (WBES) could play a central role in this endeavor, given its extensive coverage of a large variety of developing and emerging economies. The growing evidence identifying backward linkages as the main channel transmitting productivity from MNC investments suggests that
Table 8: Average changes in welfare with lower mobility frictions (%)

|||Low|er mobility fricti|ons|
|---|---|---|---|---|
||(1) Baseline|(2) Both|(3) Regions|(4) Sectors|
|Aggregate|32.45|37.25|36.54|32.88|
|Agriculture|24.55|29.73|28.68|25.25|
|Manufacturing|34.78|39.49|38.79|35.19|
|Services|32.15|36.95|36.26|32.57|

_Notes_ : Table reports for each model specification the average of changes in present discounted lifetime utility as a percentage of the initial annual labor income, weighted by the initial employment share of a labor market in 2003.

# **6 Matching Untargeted Patterns in the Data**

We provide additional evidence for the importance of the channels discussed herein and the model’s fit in replicating these channels. Our model predicts that labor mobility should be highly correlated not only with trade shocks, but with most aggregate shocks. The pattern along which labor mobility in data changes over the business cycle is consistent with our model’s prediction, but different from what other structural labor mobility models in the trade literature predict.

We first define a stylized “positive aggregate shock” _z_ , as a shock that increases the number of job options, _Nt_<sup>_k_,by</sup><sup>_ρk>_0forall</sup><sup>_k_.51,whichmeansthataftertheshock,thenumberofjoboptions,</sup> therefore the number of sampled tasks, increases everywhere. The amount of increase can vary by labor market. As we assumed in Section 3, this shock does not change the underlying moving cost structure and is realized after workers earn their wage at _t_ . Also, we further assume _μ_<sup>_k_</sup> 0 _,t_<sup>_>_0</sup><sup>_, ∀k_;and</sup> that for a given _k_ , there exists _l_ such that _m_<sup>_k,l_</sup> _t >_ 0. Note that we do not assume that all entries in the transition matrix are non-zero. We assume that there are at least one stayer and one mover for each row. Then, we can state the following proposition about the “positive aggregate shock”.

**Proposition 1.** _A “positive aggregate shock” as defined above increases the (i) arithmetic mean and the (ii) geometric mean of the ratio of within-labor-market movers to stayers in a job within formal labor markets. I.e.,_

Proofs of all propositions are provided in Appendix A.8. Proposition 1 implies that economywide positive shocks increase the average churning within labor markets. In fact, we can identify changes in the number of job options directly from changes in internal churning. We extend this idea

> 51 _k_ _<u>∂</u>_ More precisely, we define _ρ ≡ ∂z_<sup>_N_</sup> _t_<sup>_k_.</sup>

36
2003). Moreover, inflation reduced real wages substantially because the break with the Convertibility Plan meant that labor market adjustment occurred more through wages than through increased unemployment. Unemployment arose largely from the formal sector, with an increase in employment in the informal sector and particularly in low paying temporary jobs. In late 2001, the government introduced the safety net program _Plan Jefas y Jefes de Hogar Desocupado_ (Jefas) leading to a slight reduction in extreme urban income poverty in Argentina (Galasso and Ravallion 2004). Finally, in 2003 the economy started picking up, new employment began to be created, and prices stabilized.

In terms of location, poverty is distributed roughly along two dimensions in Argentina; (1) within provinces along a population density gradient running from dispersed rural to urban, and (2) across regions. Argentina has fairly steep declining gradients in living conditions from more developed urban areas, through the urban periphery and smaller towns (grouped rural areas), to the more remote rural areas. This poverty location pattern is similar to other Latin American countries such as Mexico. In rural localities in Mexico with fewer than 2,500 people, over 40 percent were extremely poor compared with those localities with 2,500-15,000 people where 21 percent were poor in 2002 (Verner 2006).

<!-- Start of picture text --> Figure 4.1: Poverty and Indigence Poverty in Urban Areas in Argentina (P0)<br>1990–2003 (percent)<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Households below poverty line Households below indigence line<br>Population below poverty line Population below indigence line<br>Source: Adapted from PRODERNOA (2003), based on INDEC data.<br>1990 1993 1995 1998 1999 2000 2001 2002 2003<br><!-- End of picture text --> 16
# **1. Introduction**

In the 1980s and 1990s it became quite fashionable to regard natural resources as a curse whose potential benefits were entirely wasted. Perhaps this view reached its high point with the research on cross-country growth by Sachs and Warner (1997, 2001), in which a measure of specialization in natural resource exports entered the growth equation with a strongly negative coefficient. Bleaney and Nishiyama (2002) confirmed this result. Since then, it has been recognized that some countries, such as Botswana, seem to have managed their natural resources quite well and achieved rapid growth, and that, despite particular examples of gross mismanagement, the overall experience is varied (e.g. IMF, 2012). Authors such as Brunnschweiler and Bulte (2008) questioned whether natural resources were in fact associated with slower growth after all; in part the debate turned on how natural resource endowment was measured. Mehlum, Moene, and Torvik (2006) showed that the quality of institutions was critical to the correlation between natural resource endowments and growth; they added an interaction term between natural resources and institutional quality to their cross-country growth regression and found that it had a significant positive coefficient. The magnitudes were such that natural resources were estimated to be bad for growth in the presence of poor institutions, but good for growth where there were good institutions. Variations across regions also must be recognized: resource-rich Latin America and Africa have grown far more slowly in recent decades than resource-poor East and South Asia, so natural resource effects may sometimes be confounded with unobserved regional factors. Moreover, institutional quality measures tend to be based on human judgment rather than hard data, so they may be somewhat endogenous to macroeconomic performance.

Nevertheless, it is clear that in some cases natural resource endowments are wasted. But why? Van der Ploeg (2011) provides a recent extensive survey. There is some evidence that the natural resource curse is worse in presidential than in parliamentary political systems. Price volatility of natural resource exports may also be a negative factor. Resources may induce voracious rentseeking and civil conflict. Van der Ploeg (2011) discusses the empirical evidence in relation to these hypotheses in some detail. But when he comes to “unsustainable government policies,” which, presumably, refer primarily to fiscal policy, he seems to find very little empirical evidence to draw on.

Providing such evidence is the main aim of this paper. We compare fiscal performance in resourcerich and resource-poor countries in a way that, as far as we are aware, has not been done in previous research.

2
Table 6: Estimations of GQ improvements for urbanization of manufacturing sector

||Organ|ized sector urb|an share|Unorga|nized sector u|rban share||Total urban sh|are|
|---|---|---|---|---|---|---|---|---|---|
||Plants|Employment|Output|Plants|Employmen|t<br>Output|Plants|Employment|Output|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|||A. Base s|patial horizon|measuring ef|fects relative|to districts 50|+ km from the|GQ network||
|Post GQ upgrades *|0.062|0.061|0.019|-0.022|-0.000|-0.086|-0.021|0.044|0.015|
|Nodal district|(0.049)|(0.052)|(0.063)|(0.035)|(0.033)|(0.109)|(0.028)|(0.045)|(0.052)|
|Post GQ upgrades *|-0.015|-0.034|-0.031|0.004|0.010|-0.004|0.004|0.013|-0.020|
|District 0-10 km from GQ|(0.029)|(0.032)|(0.038)|(0.028)|(0.031)|(0.042)|(0.028)|(0.026)|(0.031)|
|Post GQ upgrades *|0.005|0.011|0.004|-0.013|-0.005|-0.034|-0.013|-0.007|-0.045|
|District 10-50 km from GQ|(0.042)|(0.044)|(0.061)|(0.037)|(0.040)|(0.049)|(0.036)|(0.036)|(0.041)|
|District and year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|924|924|924|1062|1062|1062|1077|1077|1077|
|||B. Extended|spatial horiz|on measuring|effects relativ|e to districts 2|00+ km from t|he GQ network||
|Post GQ upgrades *|0.047|0.030|-0.004|-0.039|-0.015|-0.090|-0.037|0.034|0.021|
|Nodal district|(0.053)|(0.054)|(0.068)|(0.037)|(0.036)|(0.111)|(0.029)|(0.046)|(0.056)|
|Post GQ upgrades *|-0.030|-0.064|-0.054|-0.014|-0.005|-0.008|-0.012|0.003|-0.013|
|District 0-10 km from GQ|(0.034)|(0.035)|(0.045)|(0.030)|(0.034)|(0.047)|(0.030)|(0.029)|(0.036)|
|Post GQ upgrades *|-0.010|-0.020|-0.019|-0.031|-0.020|-0.038|-0.029|-0.016|-0.039|
|District 10-50 km from GQ|(0.046)|(0.046)|(0.066)|(0.038)|(0.042)|(0.053)|(0.038)|(0.038)|(0.045)|
|Post GQ upgrades *|-0.053|-0.084|-0.070|-0.033|-0.028|0.003|-0.031|-0.025|-0.003|
|District 50-125 km from GQ|(0.048)|(0.051)|(0.058)|(0.033)|(0.034)|(0.043)|(0.033)|(0.031)|(0.043)|
|Post GQ upgrades *|-0.008|-0.040|-0.021|-0.033|-0.027|-0.017|-0.030|-0.013|0.028|
|District 125-200 km from GQ|(0.044)|(0.049)|(0.062)|(0.030)|(0.032)|(0.042)|(0.029)|(0.027)|(0.040)|
|District and year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|924|924|924|1062|1062|1062|1077|1077|1077|

Notes: See Table 2. Estimations in Columns 1-3 consider the urban share of organized sector manufacturing activity in Indian districts for 1994, 2000, 2005, and 2007 from the Annual Survey of Industries. Estimations in Columns 4-6 consider the urban share of unorganized sector manufacturing activity in Indian districts for 1994, 2000, and 2005 from the National Sample Survey. Estimations in Columns 7-9 consider the total urban share for 1994, 2000, and 2005 by combining the two surveys.

32
dependent variable, loans, and main variable of interest, migration, suffer from a measurement error problem, which is exacerbated when moving to first differences. Since our data do not allow a proper panel data analysis or a difference regression, we choose to focus on the cross-section dimension of our data. As a result, the analysis concentrates on differential effects rather than on the main effects.

We construct a cross section that minimizes as much as possible endogeneity concerns. First, we take a lag of ten years between the migration data and the financial data. Hence, we regress financial flows in 2000 on migration data in 1990. This way we reduce short-term endogeneity, such as new countries that were established in the 1990s and experienced an outflow of migrants and an inflow of foreign investments.

Second, we choose as our benchmark results the regression of financial investments in 2000 on migration in 1990, although we also ran similar regressions for 2010 financial data on 2000 migration data. The reason we focus on the earlier period is that our dataset indicates that many country pairs had no financial investments in the 1980s and started their investment connections only in the 1990s. For instance, if we restrict our sample to 16 countries that were active in foreign lending in 1985, we obtain around 1,400 lending-borrowing country pairs between 1985 and 1992. The number of country pairs increased significantly during the 1990s and reached 1,877 by the end of the decade. By contrast, in the following decade, there was no significant change in the number of country pairs.<sup>7</sup> Part of the increase in lending-borrowing country pairs in the 1990s is due to new countries that were formed in the 1990s, and part is due to a rise in the volume of international loans. In this respect, the migration stock in 1990 can be regarded as a predetermined variable for country pairs that had no lending activity in the 1980s and started to lend in the 1990s.<sup>8</sup> We complete our dataset with gravity variables taken from Aviat and Coeurdacier (2007) and CEPII (CHELEM dataset). Trade data are taken from Feenstra et al. (2005).

> 7. In 2010, the number of country pairs reached 1,908.

> 8. However, our results are fully robust to the use of data for the 2000-2010 period. Note that measurement errors might be larger for the earlier period due to the appearance of new countries in the 1990s.

9
suggesting that the equilibrium was moving along the demand curve, i.e. being constrained by the supply of credit, Figure 10), but not in equipment (opposite correlation). A very small minority of firms rate the access to, and the cost of, finance as a severe constraint in the business environment (Figure 16).

34. **Savings will have to increase in the medium term** . The foregoing analysis suggests that the financial sector has, so far, not been a major constraint to growth. Nevertheless it also points towards risks, suggesting that savings will have to increase in the medium-term. In particular, the strong negative correlation between GDP growth and the size of the current account deficit (Figure 6) suggests a strong dependency on foreign savings (which are very uncertain in the current international financial environment).

**Figure 10: Investment in construction is negatively correlated with real interest rates, but investment in durable equipment is not**

<!-- Start of picture text --> 25<br>20<br>15<br>10<br>5 Durable<br>Equipment, Construction,<br>2007 2007<br>0<br>0 5 10 15<br>Investment to GDP (%)<br> Source: NIS, WDI, Staff (data for 1995-2007).<br>Real interest rate (%)<br><!-- End of picture text --> 35. **In addition, access to finance is**

**likely to be a constraint for medium-size greenfield investments** . Given Cambodian banks’ size, large loans of US$5 million or more are reportedly very difficult to mobilize. It can also be observed that almost all large investments are cofinanced by foreign investors. Anecdotal evidence indicates that credit rationing has been an element preventing diversification from garments to textiles.<sup>12</sup> Given the many constraints on such capital investments (see below), it is however difficult to conclude to what extent the financial sector is the constraint – rather than other business environment issues.

36. **Second, access to finance is a stronger constraint for agriculture** (Box 3). Only 5 percent of the loan portfolio is for agriculture (NBC, 2008). With lack of access to formal credits, many farmers rely on informal sources of financing with very high interest rates. Lack of credit is also viewed as a major reason for rice millers’ lack of working capital and outdated technology. Many financial instruments, such as factoring, warehouse receipts, and inventory credit, are missing. In addition, banks only do collateral-based lending with steep coverage requirements, amounting to two or three times the value of the loan: they simply will not lend against unsecured future cash flows, or against a signed sales agreement. Finally, loan repayment terms are not sufficiently flexible for some longer term agriculture crops, agro-forestry in particular.

> 12 This is also consistent with an analysis of the “external dependence across sectors” (the amount of capital expenditures that a typical firm needs to finance from external finance as opposed to retained earnings, cf. Rajan and Zingales, 1998). All sectors that Cambodia is specialized in (garments, but also tobacco and beverages) have a low level of external dependence, suggesting some constraint from the financial sector.

15
<!-- Start of picture text --> 4.0<br>2.0<br>0.0<br>2015 2020 2025 2030 2035 2040 2045 2050<br>-2.0<br>-4.0<br>-6.0<br>Unilateral carbon tax; Nordhaus BT; TD Unilateral carbon tax; Nordhaus BT; AD<br>Cooperative carbon taxes; TD Cooperative carbon taxes; AD<br>Unilateral carbon tax; BCA on carbon content; TD Unilateral carbon tax; BT on carbon content; AD<br><!-- End of picture text --> _Source:_ World Bank and Purdue University (ENVISAGE).

_Note:_ BAU = business as usual; TD = traditional diversification; AD = asset diversification. Red lines represent unilateral (noncooperative) climate policies involving the imposition of a Nordhaus tax in 2025; green lines represent cooperative climate polices based on traditional carbon prices. The colored lines represent the difference between consumption under a specific scenario with respect to consumption under the BAU scenario in a given year, expressed as a percentage of BAU.

High unilateral carbon taxes imposed by LCT pioneers and the traditional border taxes on the carbon content of imports initially do not encourage most FFDCs to cooperate. For a few years after the external impact occurs, free riding allows higher household consumption than putting domestic carbon taxes (figure 4) and higher growth for the entire modeling period (figure 5). Therefore, traditional border adjustment taxes may not be enough to tilt FFDCs’ incentives towards cooperation no matter what diversification strategy they choose. The analysis of general equilibrium effects sheds light on the macroeconomic adjustments to such external conditions. On one hand, unilateral steep carbon taxes in fuel importing countries squeeze import demand and prices for fossil fuels, hence transferring a portion of resource rents to fossil fuel importers as carbon tax revenues. On the other hand, however, the FFDCs’ industries using fuel as an important input benefit from stringent and asymmetric climate policies implemented by LCT pioneers. The drop of prices and revenue from fossil fuel exports reverses the resource curse and the Dutch disease, increasing export competitiveness of FFDCs’ manufacturing industry that in addition enjoys lower domestic prices of fuels, favorable exchange rates and weak foreign competitors squeezed by high carbon taxes in LCT pioneer countries. Therefore, the FFDCs industries maintain and even strengthen their comparative advantage in energy intensive products despite traditional border carbon adjustments. They increase global market share along with domestic fuel use and carbon emissions (carbon leakage).

_Figure 5: Year-to-Year GDP versus BAU in Fossil Fuel–Dependent Countries under Different Cooperation and Diversification Scenarios_ Optimal shortterm strategies: 12