rafmacalaba/datause-encoder-data huggingface dataset:
||Other Middle-income|40,934|60,883|85,674|124,561|28,058|40,218|67,496|96,026|12,875|20,665|18,178|28,535|5.2|3.5|1.3|1.2|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|6|Low-income, all|11,569|13,189|20,677|28,052|3,840|4,533|9,778|15,234|7,728|8,656|10,899|12,818|13.7|13.5|7.5|4.8|
|7|Oil Exporters|1,902|1,674|1,997|2,938|1,093|650|1,573|2,729|809|1,024|424|209|4.1|8.8|2.3|0.6|
|8|Civil Conflict States|886|603|465|574|282|288|536|764|604|315|-71|-190|10.8|4.7|-0.9|-1.7|
||Other Low-income|8,780|10,913|18,215|24,539|2,465|3,595|7,669|11,741|6,315|7,317|10,547|12,799|20.5|16.0|9.1|5.9|

Note: Food and agricultural materials are defined as raw food plus agric raw materials, excluding processed food products and seafood (see classifications in Appendix Table 1).

1 = Industrial countries; 2 = Middle-income; 6 = Low-income; 3&7 = Oil exporters for middle and low-income respectively; 5 = Small island economies; 4&8 = Civil conflict countries for middle and low-income countries accordingly.

Exclude those small islands economies which are colonized and dependent territories.

Source: Based on partner data from UN COMTRADE Statistics.

40
|Tariff shocks|Y<br>Y<br>Y<br>Y|
|---|---|
|Excludingmigrants|Y<br>Y<br>Y<br>Y|

Note: This table reports the effect of FDI shock on city level school enrollment by gender. Standard errors are clustered at the city level. *** p<0.01, ** p<0.05, * p<0.1.

# 7. Conclusion

China’s prominent rise as a global manufacturing powerhouse during the past few decades has been accompanied by massive labor reallocation from agriculture to industry and services, as well as human capital accumulation. Inbound FDI has been instrumental in China’s economic development and export success. China has been the largest FDI recipient among developing countries since 1992. While many studies have assessed the productivity spillovers of FDI based on firm level data, very few papers have investigated the broader developmental impact of FDI. This paper constructs a city level panel data set spanning 1990-2005 to present the first causal evidence of the effect of FDI on local structural transformation and human capital accumulation in China. The paper exploits variations in FDI inflows across manufacturing sub-sectors and initial industry specialization patterns across cities as instrumental variables for local FDI shocks.

As far as we know, this paper is the first to analyze the causal effect of FDI on structural transformation and human capital accumulation in China at the city level. It joins the very limited literature assessing the role of FDI in catalyzing structural transformation, school enrollment and other local labor market effects. The paper highlights that in addition to trade shocks, FDI plays a pivotal role in driving local structural transformation and human capital accumulation, and the effect varies by the type of FDI.

Our results show that manufacturing FDI inflows greatly accelerated city level structural transformation and human capital accumulation. Cities that received more manufacturing FDI experienced higher increases in industry and services employment and GDP shares accompanied with faster declines in agricultural employment and GDP shares. These cities also witnessed larger improvements in high school and college enrollment rates. These results are robust to a series of robustness checks, including additional controls, changes in tariffs, SOE reform, migration, and alternative measure of FDI presence.

The mechanism for manufacturing FDI inflows to accelerate structural transformation is primarily through the expansion of demand. By integrating China into global production networks and supplying the global market, manufacturing FDI inflows created a huge pull factor that has drawn excess labor away from farms into factories and services. Furthermore, through FDI promoting local school and college enrollment by paying a wage premium for skilled workers and pushing up the skill premium in local labor markets, FDI helped increase consumer purchasing power and aggregate demand.

31
ajor economies in the world have pledged to become carbon neutral by 2060. Electrifying the transportation sector coupled with a cleaner electricity grid is Mconsidered a key pathway to reach carbon neutrality. The diffusion of electric vehicles (EVs) faces multiple economic and technological challenges including the high upfront purchase cost of EVs, limited driving range, the lack of adequate charging infrastructure, and the inherent and perceived uncertainty about this new technology (Krutilla and Graham, 2012; Carley et al., 2013; Li et al., 2017a). Despite these challenges, global EV sales reached 4.2 percent of the new vehicle market (or 3.2 million units) in 2020 after a decade of growth beginning from the introduction of the Chevrolet Volt and Nissan Leaf as the first mass-market EV models into the U.S. market in late 2010. Nevertheless, the diffusion process needs to accelerate significantly in order to reach the EV adoption goals set by many countries and regions (Figure 1).

What are the key market and policy drivers behind the spatial disparities of global EV diffusion? What are the important lessons from the first decade of the market growth? Understanding these questions has important implications for the next phase of development in the global EV market and for addressing pressing climate change and local air pollution challenges. This study aims to answer these questions based on, to our knowledge, the most comprehensive data ever complied on the global EV market including model-level EV sales by country, vehicle attributes, charging infrastructure, detailed EV policies, and demographic and socio-economic variables by country from 2013 to 2020. Our analysis focuses on 13 countries with the highest EV sales, accounting for 95% of total global EV sales during this period.

# **Global EV Patterns**

After rapid growth in the past decade, the global EV stock reached 10 million in 2020. Between 2013 and 2018, the global EV market saw rapid expansion - sales increasing by over 50% each year. In 2019, the growth rate stalled temporally but quickly regained strength. By 2020, EV sales around the world reached nearly 3 million, representing a 38% increase from 2019 despite the COVID-19 pandemic. Figure 2 depicts the annual EV sales by country. China is by far the largest EV market during 2016 to 2019, accounting for about 40%-60% of global sales. In 2020, Europe overtook China and became the largest EV market, with a market share of 43%. Germany, United Kingdom, France, Norway, Netherlands, and Sweden all saw sizable growth in EV sales from 2018 to 2020, albeit the relatively low market share for each country. The global market share of EVs in the United States reduced from 47% in 2013 to 10% in 2020.

1
treatment in a consistent way across countries. This benefit comes at the limitations of using fever as a proxy for malaria.

**Table 1. Summary and background information of data used from DHS surveys**

||_Yearof_<br>_survey_|_Number_<br>_of_<br>_households_|_GNPper_<br>_capita-1990s_<br>_average_<br> _(PPP,$1995)_|_Under-5_<br>_mortality_|_Popula-_<br>_tionbelow_<br>_thepoverty_<br>_line_|_Year_|_Popula-_<br>_tion_<br>_below$1_<br>_aday_|_Popula-_<br>_tion_<br> _below_<br>_$2a day_|_Year_|_Analysis_<br>_of_<br>_incidence_|_Analysis_<br>_of_<br> _treatment_|
|---|---|---|---|---|---|---|---|---|---|---|---|
|_WesternandC _|_entralAfri_|_ca_||||||||||
|Benin|1996|4,499|798|184|33|1995||||_V_||
|BurkinaFaso|1992/3|5,143|813|187|||61|86|1994||_-_|
|BurkinaFaso|1999|4,812|813|224||||||_-I_||
|Cameroon|1991|3,358|1,412|126|40|1984|||||_-I_|
|Cameroon|1998|4,697|1,412|146||||||_V_||
|C.A.R.|1994/5|_5,551_|1,085|157|||67|84|1993|_V_<br>||
|Chad|1996|6,840|829|201|64|1995/6||||_V_||
|C6ted'lvoire|1994|5,935|1,372|150|||12|49|1995|_V_|_Vf_<br>|
|Ghana|1993|5,822|1,646|119|31|1992|||||_V_|
|Ghana|1998|6003|1,646|110||||||_V_||
|Mali|1995/6|8,716|653|252|||73|91|1994|_V_||
|Niger|1992|5,242|723|318|63|1989/93|61|85|1995||_V_|
|Niger|1997|5,242|723|303||||||_V_<br>||
|Nigeria|1999|7,647|737|133|34|1992/3|70|91|1997|_V_|_V_|
|Senegal|1992/3|3,528|1,223|131|33|1991|26|68|1995|V|_V_|
|Togo|1998|7,517|1,337|144|32|1987/9||||_V_||
|_EasternandSo_|_uthern Afr_|_ica_||||||||||
|Comoros|1996|2,252|1,586|113||||||_V_||
|Kenya|1998|8,380|948|105|42|1992|27|62|1994|_V_|_V_|
|Madagascar|1992|5,944|751|163|70|1993/4|60|89|1993||_V_|
|Madagascar|1997|7,171|751|164||||||_V_||
|Malawi|1996|2,798|526|234|54|1990/1|||||_-V_|
|Mozambique|1997|9,282|612|219|||38|78|1996|_V_||
|Rwanda|1992|6,252||150|51|1993|36|85|1983/5|_Vf_|_v_<br>|
|Tanzania|1991/2|8,327|446|141|51|1991|20|60|1993||_Vf_|
|Tanzania|1996|7,969|446|145||||||_V_||
|Uganda|1995|7,550|934|156|55|1993|37|77|1992|_V_||
|Zambia|1992|6,209|703|191|86|1993|73|92|1996||_V_|
|Zambia|1996/7|7,286|703|192||||||_V_||
|Zimbabwe|1999|6,369|2,460|90|26|1990/1|36|64|1990/1|V|_V_|
|Average*||6,081|1,003|171|48||46|77||||
|<br>Std.dev.*||1,785|461|56|17||21|14||||
|Median*||6,003|813|156|47||38|84||||
|Maximum||9,282|2,460|318|86||73|92||||
|Minimum||2,252|446|90|26||12|49||||

* Unweighted.

_Source:_ GNP per capita from World Development Indicators (World Bank 1999). Poverty rates from World Bank (2000). DHS information from DHS Final reports and updates from www.measuredhs.com (Macro International, various years).

Focusing on fever as a marker for malaria in areas or seasons of high malaria endemicity is not without justification, however. A review of the guidelines for Integrated Management of Childhood Illness (IMCI) (Gove 1997) summarizes the

> **2** While there are differences across countries, a major advantage of the DHS is the consistency in survey instrument and implementation across countries.

_3_
**Table 5: Transition Matrix Based on the Synthetic Panel 2010/2018**

|**Status in 2010, 2018**|**Lower-bound estimate (%)**|**Upper-bound estimate (%)**|
|---|---|---|
|_NEET, NEET_|46.66|35.95|
|_NEET, non-NEET_|4.35|17.82|
|_Non-NEET, NEET_|0.00|10.71|
|_Non-NEET, non-NEET_|48.98|35.51|

_Notes:_ results are restricted to the sample of individuals aged between 15 and 24 in 2010 and between 23 and 32 in 2018. “Lower” and “Upper” refer to the two bounds on mobility; for immobility, “Lower” is the upper bound and “Upper” is the lower bound. Upper-bound estimates are based on 50 replications. Individual-level sampling weights are applied.

The estimated NEET rates for 2018 can be directly derived from the transition matrix by adding up the share of people in two groups: (NEET, NEET) and (non-NEET, NEET). For instance, the lowerbound estimate of the NEET rate in 2018 amounted to 46.66 percent, which is the estimate displayed in the respective cell in Table 4. Similarly, the upper-bound estimate consists of the chronically NEET (35.95 percent) and the (non-NEET, NEET) group (10.71 percent), resulting in the estimate displayed in the respective cell in Table 4 (46.66 percent).

The results suggest that the NEET condition tends to persist, as is hinted at in the paper’s title. Comparing the share of NEET in both periods to those who were NEET in 2010, we found that those NEET in 2010 had a 70 to 90 percent probability of remaining NEET after 10 years, and only a 10 to 30 percent probability of escaping from the condition. This impression of substantial immobility is confirmed by the complementary results of the non-NEETs in 2010. The chances after 10 years of staying non-NEET are 80 to 100 percent; indeed, the risk of becoming NEET is between 0 and 20 percent. Ten years is quite a long period and yet we observed very little mobility during that time.

While it is beyond the scope of this paper to investigate the causes of this inertia, some indications can be already drawn from the results of the static analysis—notably, that the person’s gender, the household head’s level of education, and the well-being of households can all play a crucial role in explaining the labor market trajectory of young people. These initial conditions, we conjecture, tend to determine whether the young family member will start off as NEET—we can see this in the previous section—but they also can give some indication of his/her capacity to move or not move out of NEET.

To further explore the persistence and dynamics of the NEET condition in the Moroccan labor market, the same exercise is repeated for different population subgroups separately: male/female, wealthy and not, and levels of household head education (in Appendix). Table 6 summarizes NEET dynamics 22
9 _Table3: Affordability :Per Capita Household Expenditures on Medical and Health Care, 1992 (As percentage of Non-foodper capita expenditure)_

||_Quintile_<br>_I_|_s of house_<br>_II_|_holdper c_<br>_III_|_apita inc_<br>_IV_|_ome_<br>_V_|_Expenditure_<br>_Elasticity_|_T-ratio_|
|---|---|---|---|---|---|---|---|
|Instrument|0.04|0.04|0.31|0.41|0.44|<br>0.85|1.04|
|Primaryhealthcare|0.96|0.11|0.12|0.14|0.13|<br>1.45|1.93|
|Drugs|5.65|4.23|4.08|3.68|2.91|<br>8.11|4.56|
|Herbal medicine|0.47|0.42|0.44|0.45|0.50|<br>6.63|2.87|
|Service charges|2.05|0.92|0.62|0.76|0.60|<br>1.15|0.77|
|Othera|0.11|0.08|0.09|0.10|1.05|<br>0.94|1.23|
|Total|9.28|5.81|5.66|5.54|5.63|||

a. Home visits.

_Source:_ SSB: Urban Income Expenditure Survey, 1992.

_Table 4: Quality - Medical Personnel Input Mixes by Province (1994)_

|_Province_<br>_by quintile_<br>_ofper capit_<br>_income_|_a_<br>_Doctor_|_Special_<br>_Nurse_|_Relative_<br>_Asst._<br>_Doctor_|_Personnel Rat_<br>_Nurse_|_io(RPI)_<br>_Midwife_|
|---|---|---|---|---|---|
|I|0.81|0.77|0.82|0.67|0.74|
|II|0.94|0.95|1.07|0.90|0.61|
|III|1.34|1.23|1.34|1.26|1.05|
|IV|0.86|1.15|1.24|1.16|1.40|
|V|1.90|2.38|1.76|2.48|1.33|

RPR= (Provincial Staff/Provincial Population)/(National StafflNational Population) RPR exceeding one indicates oversupply and less ihan one indicates undersupply of medical personnel.

**_Source:_** Table 9 (Annex).
excluded instrumental variables. The reduced form becomes: where _γ_ ( _U_ ) is a vector of functional coefficients of the instrumented variables from the first stage, Π( _I_ ). If a random sample _{Ui, Xi, Z_ 2 _i}_<sup>_n_</sup> _i_ =1<sup>is a drawn from a distribution</sup><sup>_FU,X,Z_</sup> 2<sup>, then</sup> for each given point _u_ 0 and by analogy to equation 5 above, the second stage estimates of _γ_ ( _U_ ) can be generated by a locally weighted regression of _y_ on Π(<sup>ˆ</sup> _I_ ) Cai et al. (2006) find that a first order approximation, in this case _bj_ + _cj_ ( _Ui − u_ 0) is sufficient to generate efficient estimates. The estimates of _πj_ ( _I_ ) are generated in the first stage by a locally weighted regression of _X_ on _I_ under jackknife sampling on _i_ : The calculation of the corresponding variance-covariance matrix is described in Cai et al. (2006).

# **3 Data**

We use an unbalanced panel of 75 countries covering the period 1960 - 2000. The data set comprises a number of variables coming from different sources. Income, Investment, and Labor growth are drawn from Penn World Tables. The educational data from Barro and Lee.

9
intennediate variables have positive effects on GDP growth, and 3) the addition of these variables to the GDP 1ouw-ul regressions subs.a;mi ydi. **urnbl.r.e** **_thI_ iuae** **_-su_ of0t1** comumunilybankheeaulm onGDP growth. We find that the relative health of SMALL banks is positively associated with both the SME eemnplment share and the overall-bank-lending-to-GDP ratio in both developed and developing nations. We also find that these intermediate variables are positively related to GDP growth, although the relationship is generally only statistically significant for the SME employment ratio. However, we do not find substantial diminishment of the measured effects of the relative health of SMALL banks on GDP growth when the intermediate variables are included in the GDP growth regressions for developed or developing nations.

The literature on developing nations suggests that tne definition of community banks might be extended to include **At- te-owned bankq** (STAT- **bsnkc) nr** foreia.-nW.sAed **bhanL-c, (TnllPl( Mnha>L Qa,.I. Qs AATT** A bm" These potential alternative types of community banks may be able to overcome some of their disadvantages in SME lending in nations in which SMALL banks have difficulty providing sufficient credit using subsidies, by making use of decentralized organizational structures, or by employing better technologies. Our test results from developing nations suggest that larger market shares for FOREIGN banks are associated with better economic perfornance in terms of faster GDP growth, greater SME employment shares, and higher bank lending to GDP ratios, but worse econornic performance in terms of these outcomes for iarger snares for S TATE institutions. **Thesefindings** sugrst **that at** bast in some c-s, foreign-owned ban r.ay function as comrrunity banks, but that state-owned banks do not appear to play this role in an effective fashion. The evidence on efficiency ranks for STATE and FOREIGN banks do not show consistent patterns, which may reflect difficulties in measuring the efficiency ranks of these institutions. Our findings are robust to a number of changes in specification, samples, lag structures, and so forth.

Our analysis as well as the extent research in both the finance-growth and community banking literatures may help contribute to discussions of public policy toward domestic consolidation, foreign bank entry, priva-ti7tfion of stateow.ned bank!s, a-id -- pport foIr public crfiit info....a:ion **bu.aus znd"I** oULIer HmetL1ods of supporting SME lending. The extant research would appear to be consistent with favorable economic consequences from policies that promote relatively healthy SMALL banks in both developed and developing nations. Notably, policies that simply protect these banks from competition in product markets or from the markets for corporate control have been found to lead to more inefficiency and less growth (e.g., Jayaratne and Strahan 1996, 1998). In developing nations, the extant research would also appear to be consistent with favorable 26
**Table 4A: Matching results on Social Assistance** <u>[effect of participation in productive association on access to social programs]</u>

||_N treated_<br>_on support_|_N control_<br>_on support_|_ATET_|_Boostrap_<br>_s.e._|_z–_<br>_Statistic_|_95% Conf._|_Interval_|
|---|---|---|---|---|---|---|---|
|**Probability of benefiting from a social program in**||||||||
|**Agriculture (in %)**||||||||
|Kernel Matching (epanechnikov)|588|6,064|8.3%|0.015|5.67|5.4%|11.1%|
|Kernel Matching (Gaussian)|588|6,065|6.4%|0.017|3.80|3.1%|9.7%|
|Radius Matching|588|6,065|9.57%|0.011|8.99|7.5%|11.6%|
|Radius Matching, caliper(0.01)|588|6,064|8.2%|0.016|5.28|5.2%|11.3%|
|Radius Matching, caliper(0.05)<br><sup>*</sup>|589|6,064|8.30%|0.014|5.74|5.5%|11.1%|
|Calibrated cofounder|||||-|-|-|
|Pr U=1 if T=1 is 0.30; if T=0 is 0.10|589|6,065|7.7%|0.003||||
|**Probability of benefiting from a housing program (in %)**||||||||
|Kernel Matching (epanechnikov)|588|6,064|1.2%|0.008|1.62|-0.3%|2.8%|
|Kernel Matching (Gaussian)|588|6,065|0.3%|0.011|0.31|-1.8%|2.5%|
|Radius Matching|588|6,065|1.3%|0.006|2.10|0.1%|2.6%|
|Radius Matching, caliper(0.01)|588|6,064|1.3%|0.007|1.84|-0.1%|2.6%|
|Radius Matching, caliper(0.05)<br>|588|6,064|1.3%|0.008|1.61|-0.2%|2.8%|
|Calibrated cofounder<sup>+</sup>|||||-|-|-|
|Pr U=1 if T=1 is 0.30;if T=0 is 0.10|589|6,065|1.3%|0.001||||

Source: Authors using the 2005 Nicaragua EMNV. The program, previous to perform the sensibility analysis, calculates a baseline estimate. In this case using kernel matching and only observation on the common support the baseline estimate is:<sup>*</sup> 8.7 percent; and<sup>+</sup> 1.3 percent.
10

**Figure 9:  Corporate Bonds Outstanding by Credit Ratings (9/2003)**

<!-- Start of picture text --> 1600 100<br>1400 90<br>Debt Outstanding (bln pesos) 80<br>1200<br>Number of issues 70<br>1000 60<br>800 50<br>600 40<br>30<br>400<br>20<br>200 10<br>0 0<br>Source: Sara Zervos (2004)<br>AAA AA+ AA AA- A+ A A- BBB+ BBB BB+ BB B C<br><!-- End of picture text --> Due to the minimum size requirement, bond issues have been restricted to mega large (and some large) firms. 93 percent of all bond issues still outstanding were made by mega large firms, and the remaining 7 percent by large firms. This seems to result directly from the high minimum issue size, since the latter is well above the financial capacity of all firms except the mega large ones. Figure 10 shows indeed that a US$45 million bond issue would more than double the total amount of long-term liabilities on the balance sheet of large firms, and multiply by more than 6 that of medium firms.

**Figure 10:  Bond Issues and Minimum Size Requirements compared to the Financial Capacity of Firms by Size**

<!-- Start of picture text --> Breakdown of bond Minimum issue size compared to<br>0% issuers by size total LT debt of companies<br>US$ millions<br>0% 300 281<br>250 Avg LT liabilities (in US$ 000)<br>7% 0%<br>Mega Minimum issue size<br>200<br>Large<br>150<br>Medium<br>Small 100<br>Micro 50<br>18<br>7 2 0<br>-<br>Mega Large Medium Small Micro<br>93%<br>Sources: SVS, FECUs<br><!-- End of picture text -->
# I. Introduction

Recent empirical investigations of the influence of barriers to international trade show that previous conceptions about transport costs must be re-considered. For example, theoretical models that attempt to explain the level, direction, or structure of international trade are often based on the assumption of zero transport costs, or have assumed that freight costs have a constant (neutral) impact on different products. The validity<sup>of these</sup> propositions have been challenged by empirical analyses that demonstrated nominal transport charges on North-North and South-North trade frequently exceed pre-Tokyo Round tariffs. 1/ The implications of these findings are that transport costs have (at least) equivalent retardation effects on trade

- Senior Economist, International Economics Department, The World Bark, Washington. The author would like to thank Azita Amjadi<sup>for programming</sup> and other empirical assistance John Eyre, Paul Meo, and Alasdair Sinclair for excensive comments and suggestions. The views expressed in this paper need not represent those of the World Bank or its staff.

- 1/ For example, Yeats (1976a) (1977a) examined the lavel of transport costs and tariffs on Indian and Indonesian exports to the United States and found that the former averaged between two to three times<sup>higher. For</sup> some specific products like rough and shaped wood,<sup>crude vegetable</sup> materials, preserved fruit, cotton fabrics and made up<sup>textile articlis</sup> shipped from indonesia, nominal transport costs of over 30 per cent were record. Two studies by Sampson and Yeats (1976) (1977, also show that the average ad valorem incidence of transport costs on U.K. and Australian exports generally exceed nominal tariffs. However, on South-South trade flows nominal freight rates appear to be far higher, with<sup>some studies</sup> suggesting they may reach 200 per cent or more on total bilateral trade. For a survey of available evidence see Yeats (1981).