rafmacalaba/datause-encoder-data huggingface dataset:
are themselves subject to significant variation, especially during periods of crisis.

The coefficient on the liquidity channel retains its sign and statistical significance in the first two of the four specifications, and approaches statistical significance in the final two ( _p_ = 0 _._ 134 and _p_ = 0 _._ 159, respectively). Its magnitude is of the same order of significance as the benchmark: the range of coefficients is between -0.02 and -0.04. Overall, we regard the findings reported in this section as broadly corroborative of the benchmark results. More importantly, given the insignificance of the interest rate even when it is instrumented, we conclude that concerns about endogeneity arising from its omission from the benchmark specifications are not generally well founded.

# **6 Conclusion**

In this paper, we have examined the transmission channels for financial shocks that affect international bank lending to developing countries, using the 2007/09 crisis as a case study. Our main finding is that cross-border lending by international banks contracted primarily due to liquidity difficulties faced by these banks, and to heightened uncertainty. Another key message is that the reduction in aggregate lending to developing countries by high income banks during the crisis was not the result of changes in their sensitivity to liquidity shortages or changes in their tolerance of risk, but instead was a normal reaction to an abnormally large shock to liquidity and uncertainty.

The main shortcomings of our work here stem from our reliance on aggregate cross-border lending data. While such data encompasses much greater coverage of the total volume of lending to the developing world, our inferences were largely limited to broad averages—and, in Subsection 4.3, U.S. and EU exposures—which limits our ability to tease out idiosyncratic elements that may be of interest, such as whether solvency considerations were altogether unimportant, or whether they were more important for certain classes of financial institutions, such as those facing more severe balance-sheet difficulties. Future research along the lines of the work considered here can attempt to explore these distinctions in greater detail.

Our work here suggests that, during a financial crisis, policymakers in developing countries that host international banks may wish to supplement liquidity provision by these banks’ home nations with additional monetary support, rather than limiting their credit window to domestic banks alone. This will limit the deleterious effects of credit contraction during the crisis, especially if such international banks play a significant role in their economies. Even though such banks are ultimately foreign-owned, policymakers are nevertheless invested in their success, insofar as their countries’ continued viability may be directly related to those of the banks.

# **References**

> Bernanke, Ben S. (1983). “Nonmonetary Effects of the Financial Crisis in Propagation of the Great Depression”. _American Economic Review_ 73(3) (June): 257–276. URL `http://www.jstor.org/stable/1808111`

21
**Figure 6: Actual BREGS wages relative to the BREGS stipulated wage rate, by gender**

<!-- Start of picture text --> Round 1 Round 2<br>4 10<br>3 8<br>6<br>2<br>4<br>1<br>2<br>0 0<br>.5 1 1.5 .5 1 1.5<br>ratio of wage rate to BREGS stipulated wage rate ratio of wage rate to BREGS stipulated wage rate<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>.5 1 1.5 .5 1 1.5<br>ratio of wage rate to BREGS stipulated wage rate ratio of wage rate to BREGS stipulated wage rate<br>Male<br>Female<br>density density<br>cumulative density cumulative density<br><!-- End of picture text --> _Note_ : Based on individual questionnaire with recall over the last year. Concerns BREGS only _. Source:_ Authors’ estimates based on BREGS Survey.

30
greater potential role for the euro than for its legacy currencies (in particular the deutsche mark), and one comparable to the US dollar.

Since both Masson and Turtelboom (1997) and Kool (2000) relied on data for the deutsche mark (and other European currencies replaced by the euro), rather than for the euro itself, now that we have about 8 years of data on euro returns the exercise merits being redone. Table 5 lists the monthly returns _R_ to holding assets paying the one-month Libor rate _r_ on US dollars, euros, pound sterling, and Japanese yen, respectively, over the period January 1999-March 2007, expressed as an ex post annualized return in SDRs using the monthly change in the SDR spot exchange rate _S_ , that is Average returns from holding pounds sterling are the highest, followed by euro returns; both of these currencies appreciated against the SDR over the period. Returns to holding US dollars are somewhat lower, reflecting depreciation of the dollar over this period, while returns on yen were depressed by very low nominal interest rates. However, the US dollar benefits from the lowest variability among the currencies. There is also a strong negative covariance between dollar and euro returns.

Suppose that these four currencies (which are the components of the SDR) constitute the set of available assets for the portfolio of reserves, and that a country’s imported consumption basket is composed of the goods of the US, UK, Japan and the euro area in the same proportions as they appear in the SDR basket. Assuming that all assets are risky, and that holders of reserve assets are risk averse, the mean-variance model implies that a reserves portfolio should be allocated among the four currencies in proportions embodied in the vector _x_ given by where ρ is a vector of expected real returns, Ω is the covariance matrix of returns, and _1_ is a vector of ones. By construction the proportions _x_ sum to unity, i.e.

This model can be used as a benchmark for a stylized developing country’s portfolio—one whose consumption basket corresponds to the SDR basket<sup>8</sup> . Using the realized returns, expressed in terms of the SDR, minus the rate of inflation on the SDR (the consumer price indexes for the US, UK, Japan and the euro area, weighted together), we can calculate such a country’s optimal reserves portfolio. Optimal currency shares are

> 8 The model is more problematic when applied to industrial countries, since they include the countries that issue the reserve currencies. The United States, currently at the center of the international monetary system, does little intervention and hence holds few reserves.  European central banks hold substantial reserves, mainly in dollars, since (aside from the euro) the dollar is the only significant reserve currency.

11
# **4.1 Heterogeneity Effect**

We explore here whether country heterogeneity affects our results by focusing on national cultures (Section 4.1.1), regulatory burden (Section 4.1.2), and perceptions of courts as barriers to business (Section 4.1.3).

# **4.1.1 National Culture**

We use Hofstede’s national culture measures to test whether national culture influences the corporate ethical behaviors of digital-tech-oriented firms. For each cultural dimension, we split the sample above and below the median value to examine whether: (i) long-term orientation (“ _ltowvs_ ”) and individualism (“ _idv_ ”) affect the relationship between digital-tech-oriented firms and emissions monitoring; (ii) power distance (“ _pdi_ ”) and uncertainty avoidance (“ _uai_ ”) affect the relationship between digital-tech-oriented firms and training; and (iii) masculinity (“ _mas_ ”) and long-term orientation (“ _ltowvs_ ”) affect the relationship between digital-tech-oriented firms and the presence of a female top manager.

Our analysis includes specifications with year*country fixed effects. Tables 7 and 8 show that, although cultural factors are positively associated with monitoring emissions and training, the intensity of these factors does not appear to be a determinant. Specifically, the _digital-tech orientation_ variable is always positively and significantly correlated with the monitoring of CO2 emissions ( _mon_emi)_ and the training variable, regardless of whether we are considering countries above or below the median value of the cultural trait. Conversely, national culture influences our results when we focus on governance on the employment of female top managers.

16
**<u>Table A3. OLS Cross Section Estimates of Employment and Export Sales</u>**

|Dependent variable<br>ln(Employment)|(1)<br>2013|(2)<br>2016|(3)<br>2020|
|---|---|---|---|
|ln(export sales)|0.082***<br>(0.01)|0.088***<br>(0.01)|0.058***<br>(0.01)|
|ln(value of machinery)|0.043***|0.022**|0.171***|
||(0.01)|(0.01)|(0.02)|
|ln(cost per worker)|-0.113***|-0.016|-0.224***|
||(0.03)|(0.05)|(0.03)|
|ln(age of firm)|0.395***|0.332***|0.134***|
||(0.06)|(0.07)|(0.03)|
|Foreign owners (yes=1)|0.434***|0.297*|0.086|
||(0.11)|(0.17)|(0.13)|
|Female owners (yes=1)|0.233**|0.053|0.109|
||(0.11)|(0.11)|(0.08)|
|Registered (yes=1)|0.346***|0.306*|0.164***|
||(0.09)|(0.16)|(0.06)|
|Training (yes=1)|-0.918***|-0.520***|-0.803***|
||(0.11)|(0.11)|(0.11)|
|Foreign tech (yes=1)|0.240*|0.243*|0.300***|
||(0.14)|(0.15)|(0.10)|
|Imported inputs (yes=1)|0.286***|0.612***|0.224***|
||(0.07)|(0.09)|(0.05)|
|Indirect exporter||||
|<br>(yes=1)|-0.477***<br>(0.13)|-0.514***<br>(0.20)|-0.318**<br>(0.16)|
|Constant|3.994***|2.534***|3.595***|
||(0.46)|(0.61)|(0.50)|
|Sector dummies|Yes|Yes|Yes|
|Region dummies|Yes|Yes|Yes|
|Observations|1,767|1,084|2,676|
|R-squared|0.410|0.462|0.405|

Notes: Robust t-statistics in parentheses. *** p<0.01, ** p<0.05, * p<0.1 45
# **Table 11: Regression Results: Perceived Quality of Electricity Supply**

||Perceived Qu|ality of Electricity Supply|||
|---|---|---|---|---|
|Procedures Getting Electricity|-0.333***|-0.330***|-0.135*|-0.130*|
||(0.089)|(0.089)|(0.058)|(0.058)|
|Private utility||017||0266|
|||.<br>(0.252)||.<br>(0.148)|
|Ln GNI per capita|||0.828***|<br>0.831***|
||||(0.04)|(0.04)|
|Constant|6.361***<br>|6.291***<br>|-1.818**<br>|-1.956**<br>|
||(0.518)|(0.527)|(0.607)|(0.600)|
|R-squared|0.097|0.1|0.694|0.699|
|Observations|139|139|139|139|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1|||
||Perceived Qu|ality of Electricity Supply|||
|Time Getting Electricity (Log)|-0.687***|-0.690***|-0.149|-0.152|
||(0.188)|(0.189)|(0.114)|(0.116)|
|Private utility||<br>0.253||<br>0.296|
|||(0.262)||(0.152)|
|Ln GNI per capita|||0.843***|0.844***|
||||(0.04)|(0.04)|
|Constant|7.624***|7.563***|<br>-2.012*|<br>-2.101**|
||(0.849)|(0.855)|(0.783)|(0.795)|
|R-squared|0.075|0.08|0.682|0.689|
|Observations|139|139|139|139|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1|||
||Perceived Qu|ality of Electricity Supply|||
|Cost as % of GNI pc|-0.367**|-0.366**|-0.053|-0.046|
|Getting Electricity (Log)|(0.137)|(0.137)|(0.037)|(0.038)|
|<br>Private utility||<br>0.083<br>||<br>0.27<br>|
|||(0.210)||(0.152)<br>|
|Ln GNI per capita|||0.794***|0.805***|
||||(0.06)|(0.06)|
|Constant|6.664***|6.633***|<br>-1.954**|<br>-2.176**|
||(0.841)|(0.821)|(0.688)|(0.713)|
|R-squared|0.381|0.382|0.683|0.689|
|Observations|139|139|139|139|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1|||
||Perceived Qu|ality of Electricity Supply|||
|Country rank on the ease of getting|-0.018***|-0.018***|-0.005*|-0.005*|
|<br>electricity<br>|(0.002)|(0.002)<br>|(0.002)|(0.002)<br>|
|Private utility||0.208||0.279|
|||(0.228)||(0.150)|
|Ln GNI er caita|||0756***|<br>0760***|
|p p|||.<br>(0.07)|.<br>(0.07)|
|Constant|6.153***|6.086***|<br>-1.511*|<br>-1.639*|
||(0.185)|(0.195)|(0.727)|(0.726)|
|R-squared|0.367|0.371|0.695|0.701|
|<br>Observations|139|139|139|139|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1|||

42
Table A.8: Parameter Estimates: Chemical, Rubber, Plastics and Fuel Products

||VC|M|P-|R|
|---|---|---|---|---|
||coef.|s.e.|coef.|s.e.|
|Labor Share Equation: Wage|0.05<sup>_∗∗∗_</sup>|(0.01)|0.02<sup>_∗∗_</sup>|(0.01)|
|Labor Share Equation: Capital|0.08<sup>_∗∗∗_</sup>|(0.02)|0.07<sup>_∗∗∗_</sup>|(0.02)|
|Labor Share Equation: Energy Price|0.003|(0.01)|0.02<sup>_∗∗_</sup>|(0.01)|
|Labor Share Equation: Materials Price|0.03<sup>_∗∗∗_</sup>|(0.01)|0.02|(0.01)|
|Labor Share Equation: Output|-0.07<sup>_∗∗∗_</sup>|(0.01)|-0.06<sup>_∗∗∗_</sup>|(0.01)|
|Labor Share Equation: Time Trend|-0.003<sup>_∗∗∗_</sup>|(0.0005)|-0.004<sup>_∗∗∗_</sup>|(0.0005)|
|Labor Share Equation: Constant|0.3<sup>_∗∗∗_</sup>|(0.005)|0.3<sup>_∗∗∗_</sup>|(0.005)|
|Energy Share Equation: Wage|-0.02|(0.03)|0.005|(0.02)|
|Energy Share Equation: Capital|0.04|(0.03)|0.06<sup>_∗_</sup>|(0.03)|
|Energy Share Equation: Energy Price|0.08<sup>_∗∗∗_</sup>|(0.02)|0.04<sup>_∗_</sup>|(0.02)|
|Energy Share Equation: Materials Price|-0.1<sup>_∗∗∗_</sup>|(0.02)|-0.1<sup>_∗∗∗_</sup>|(0.03)|
|Energy Share Equation: Output|0.03|(0.03)|0.01|(0.03)|
|Energy Share Equation: Time Trend|-0.001|(0.001)|0.00007|(0.001)|
|Energy Share Equation: Constant|0.3<sup>_∗∗∗_</sup>|(0.01)|0.3<sup>_∗∗∗_</sup>|(0.01)|
|Euler Equation for Capital: Adjustment Term|0.8<sup>_∗∗∗_</sup>|(0.1)|0.9<sup>_∗∗∗_</sup>|(0.1)|
|Euler Equation for Capital: Wage|1.5<sup>_∗∗∗_</sup>|(0.2)|1.2<sup>_∗∗∗_</sup>|(0.2)|
|Euler Equation for Capital: Capital|0.2|(0.3)|0.2|(0.3)|
|Euler Equation for Capital: Energy Price|0.8<sup>_∗∗∗_</sup>|(0.2)|0.6<sup>_∗∗∗_</sup>|(0.2)|
|Euler Equation for Capital: Materials Price|0.1|(0.2)|0.2|(0.2)|
|Euler Equation for Capital: Output|-2.6<sup>_∗∗∗_</sup>|(0.2)|-2.6<sup>_∗∗∗_</sup>|(0.2)|
|Euler Equation for Capital: Time Trend|0.05<sup>_∗∗∗_</sup>|(0.009)|0.02<sup>_∗∗∗_</sup>|(0.008)|
|Euler Equation for Capital: Constant|-2.2<sup>_∗∗∗_</sup>|(0.09)|-2.2<sup>_∗∗∗_</sup>|(0.08)|
|Number of observations|26|3|26|3|
|Labor Share Equation: _R_<sup>2</sup>|0.|91|0.|91|
|Energy Share Equation: _R_<sup>2</sup>|0.|89|0.|88|
|Euler Equation for Capital Stock: _R_<sup>2</sup>|0.|95|0.|94|
|LR Test: _φl_ =_φe_ =_φm_ =_ζ_ = 0, _χ_2 (pval)||36.73|(0.00)||

Notes. VCM: Vintage Capital Model. P-R: Pindyck and Rotemberg (1983) Model. coef.: estimated coefficient. s.e.: standard error.<sup>_∗∗∗_</sup> p _<_ 0.01,<sup>_∗∗_</sup> p _<_ 0.05,<sup>_∗_</sup> p _<_ 0.1 43
Trade, Global Value Chains and GDP Comovemement:

# An Empirical Investigation

François de Soyres<sup>1</sup> and Alexandre Gaillard<sup>2*</sup>

> 1 _Federal Reserve Board_

> 2 _Toulouse School of Economics_

> *We thank the World Development Report 2020 team for their comments. The views in this paper are solely the responsibility of the authors and should not necessarily be interpreted as reflecting the views of the Board of Governors of the Federal Reserve System or of any other person associated with the Federal Reserve System.
because it is the successful rural areas which are mostly likely to have become classified as urban during the course of the decade.

To get a sense of the role played by true rural-urban migration rather than urbanization, Table 2 shows estimates obtained from the inter-censal survey (Supas) in 1995.<sup>6</sup> Only 2.8 percent of those individuals in rural villages in 1990 were living in urban villages in 1995. Moreover, the 3.6 million people who moved to urban villages between these two years were compensated in part by 1.8 million people who moved from urban to rural villages over the same period. Overall, around 5 percent of those in urban areas in 1995 had been living in rural areas five years previously. The role of “true” migration is certainly not trivial, but the much larger shifts in the shares of urban employment described above suggest that, overall, urbanization rather than rural-urban migration has been the dominant factor in changing the nature of activities undertaken by the poor and, therefore, their opportunities for finding a pathway out of poverty.

**Table 2: True Rural-Urban Migration, 1990-1995**

||||**1995**<br>||
|---|---|---|---|---|
|||**Rural**|**Urban**|**Total**|
|**1990**|**Rural**|122,037,729|3,570,511|125,608,240|
|||97.16|2.84|100|
|||98.53|5.13|64.91|
||**Urban**|1,823,701|66,089,601|67,913,302|
|||2.69|97.31|100|
|||1.47|94.87|35.09|
||**Total**|123,861,430|69,660,112|193,521,542|
|||64|36|100|
|||100|100|100|

_Source:_ Supas (1995) While the above presents suggestive cross-sectional evidence about the numbers of people working in agriculture and non-agriculture in rural and urban areas in different years, these data do not show the numbers of people actually moving from agriculture to

> 6 “True” migration means that individuals actually physically moved from a village to somewhere else. Because the _Supas_ survey asked about the respondent’s address five years ago at the village level, it is possible to tell whether they actually moved village or not. The full Census only asked for this information at the district level making it impossible to tell from Census data whether they moved from rural or urban areas.

13
# Contents

|1|Introduction|3|
|---|---|---|
|2|The Model|6|
||2.1<br>Household . . . . . . . . . . . . . . . . . . . . . . .|. . . . . .<br>7|
||2.2<br>Final Good Producer . . . . . . . . . . . . . . . . .|. . . . . .<br>10|
||2.3<br>Intermediate Good-Producing Firms<br>. . . . . . . .|. . . . . .<br>11|
||2.4<br>Commercial Bank . . . . . . . . . . . . . . . . . . .|. . . . . .<br>12|
||2.5<br>Central Bank . . . . . . . . . . . . . . . . . . . . .|. . . . . .<br>20|
||2.6<br>Government . . . . . . . . . . . . . . . . . . . . . .|. . . . . .<br>21|
|3|Symmetric Equilibrium|22|
|4|Steady State and Log-Linearization|24|
|5|Calibration|25|
|6|Procyclical Efects of Regulatory Regimes|27|
||6.1<br>Negative Productivity Shock . . . . . . . . . . . . .|. . . . . .<br>27|
||6.2<br>Negative Government Spending Shock<br>. . . . . . .|. . . . . .<br>29|
|7|Summary and Extensions|30|
|Ap|pendix A: Steady-State Solution|33|
|Ap|pendix B: Log-linearized System|37|
|Re|ferences|41|
|Ta|ble 1: Calibrated Parameter Values|43|
|Fi|gures 1 to 4|44|

2
72 images/KPDNKK/ops‐titik/ops_titik_BI_‐_22.12.2014.JPG). Although fuel prices are no longer subsidized, there are still large price differences with Thailand and other neighboring countries, largely due to the absence of fuel taxation and other levies in Malaysia other than the 6‐percent goods and services tax on 97 RON gasoline. As such, financial incentives for out‐smuggling continue.

# **Mexico**

The government uses a special tax on production and services to smooth domestic prices of gasoline and diesel, while controlling maximum retail prices of LPG through a series of decrees. Until the recent oil price collapse, gasoline and diesel prices rose at a fairly stable rate, averaging 0.2–0.4 percent a month in 2008 and rising to about 1 percent a month by 2011 and remaining at that rate through 2013. LPG prices were frozen in 2009, and then raised at a small rate until January 2015, after which it has remained the same in nominal terms (Figure 18). The prices of these fuels were effectively decoupled from international prices and exchange rate fluctuations. Thanks to the fall in world petroleum product prices, fuel price subsidies were largely eliminated by early 2015.

The special tax on production and services was negative most of the time between January 2009 and August to November 2014, depending on the fuel (Figure 19). Somewhat surprisingly, premium gasoline has historically been subsidized much more than regular gasoline or diesel, with the subsidy provided by the special tax for premium gasoline being more than double that for regular gasoline in 44 months during the period shown in the figure. The difference in favor of premium gasoline became large in 2013 and 2014, rising to a nine‐fold difference in September and October in 2014, followed by the tax having the opposite sign in November 2014—premium gasoline continued to be subsidized while regular gasoline was levied a positive special tax—and again in July and August 2015. Otherwise, in 2015, following the large drop in the world oil price, the government sharply increased the tax by not decreasing retail prices. Between January 2010 and August 2015, the price of regular gasoline was decreased only once, in December 2014 by 0.5 percent. During the same period, the price of premium

**Figure 18: Retail prices of diesel and LPG in Mexico**

<!-- Start of picture text --> 16 $1.60<br>14 $1.40<br>12 $1.20<br>10 $1.00<br>8 $0.80<br>6 $0.60<br>4 $0.40<br>2 $0.20<br>0 $0.00<br>Diesel, pesos/liter LPG, pesos/kg Diesel, US$/liter LPG, US$/kg<br>U.S. dollars<br>Mexican pesos<br>Jan‐09 Jul‐09 Jan‐10 Jul‐10 Jan‐11 Jul‐11 Jan‐12 Jul‐12 Jan‐13 Jul‐13 Jan‐14 Jul‐14 Jan‐15 Jul‐15<br><!-- End of picture text --> _Source:_ <u>http://sie.energia.gob.mx/.</u> _Note_ : LPG price is for Mexico City.
Table 2 and Table 4 (evaluated at the median). The marginal e¤ects con...rm the conclusion that the IV estimates of the e¤ects of the land restrictions on wages are substantially larger compared to the OLS estimates; the marginal e¤ects are -0.46 (OLS, column (5) Table 2) and -1.41 (column (1) in Table 4). This substantial increase in the estimated e¤ect of land restrictions on wages after instrumentation is indicative of the importance of unobserved positive productivity as the main source of omitted variables bias as discussed in details earlier.

# 6.3. Additional Robustness Checks: Can Long Run Adverse E¤ects of Early-Age Malaria Exposure Drive the Results?

Although we control for education, age, and current malaria infection rates in the IV regressions, one might still worry that they might not be adequate controls for potential long term e¤ects of early-age malaria on health and education. For example, if a signi...cant proportion of the individuals in our sample belong to age cohorts that were a¤ected by malaria prevalence during 1937-1941, then they are likely to have lower educational attainment and adverse health status. We have reasonably good controls for education, as both individual and DSD level indicators of education are used in the IV regressions. However, the Sri Lanka HIES 2002 lacks good measures of health of the individuals. To check if our results are driven by long term negative e¤ects of early-age malaria, we perform robustness checks by excluding early age malaria cohorts from the sample, those born before 1947, 1950, and 1960 respectively. The oldest person in our sample is born in 1937 and the youngest in 1981. The results from estimating the IV regressions using the three subsamples are reported in Table 5. It is reassuring that the estimates are consistent with the results reported earlier in Table 4 using the full sample. Both the direct e¤ect of land restrictions and the interaction with travel time are statistically signi...cant at 5 percent or lower level. The IV diagnostics are favorable, the lowest P-value for Hansen’s J statistics is 0.24. The implied marginal e¤ects are also comparable to the estimates in Table 4.

# 6.4. The Importance of Distance in Determining the E¤ects of Land Restrictions

An interesting prediction from our simple general equilibrium model is that the e¤ects of land restrictions will die down with an increase in the distance of a village from the nearest urban center. Consistent with the prediction of the theoretical model, our estimates show that the interaction e¤ect of land restrictions and travel time has a positive sign and the e¤ect is 26