rafmacalaba/datause-encoder-data huggingface dataset:
common markets or common products. Robustness checks also rule out SSS from other domestic ...rms, or from large ...rms regardless of FDI status. In a placebo exercise, random matching of domestic ...rms with FDI suppliers yields no signi...cant or meaningful results, further lend credits to the ...ndings of this paper when it only relies on information of the top three local suppliers at the end of sample period. When the fraction of a ...rm’s top three local suppliers that also sell to FDI ...rms is used as an alternative measure of the in‡uence of FDI siblings, the results are very similar and consistent with SSS. This paper also con...rms that in the garment sector of Bangladesh, FDI ...rms use local intermediate inputs more intensively relative to domestic ...rms, which satisfy the necessary condition for FDI spillovers to materialize, according to Rodriguez-Clare (1996). To further supplement the econometric results of this paper, the local suppliers listed in the ...rm survey were interviewed. These suppliers indicate that not only does working with FDI clients help them improve their e¢ ciency, product quality and variety, they also sell to their domestic ...rm clients these better quality and new variety products. These results are consistent with the ...nding of shared-supplier spillovers from FDI ...rms.

It is important to highlight that my analysis is not about comparing the performance of domestic ...rms with more FDI siblings with those that have less or no FDI siblings, as that could be driven by selection bias due to ...rm size, and productivity. All the analyses are done with ...rm ...xed e¤ects and ...rm speci...c time trends, so the estimations only rely on variations within a ...rm across time that is over and beyond ...rm speci...c time trend. In addition, the within estimations also control for ...rm-supplier relationship which is ...xed at the end of the sample period, and the identi...cation of SSS only depends on the detrend market share movement of the FDI siblings, not the choice and/or the numbers of local suppliers. Finally, while ...rms within the same industry often share intermediate input suppliers, some intermediate input suppliers may also serve ...rms in di¤erent industries. Thus, while related, SSS is not equivalent to horizontal spillovers. The quote from the general manager of a cutter knife ...rm in Malaysia, presented at the beginning of this paper, nicely provides such an example.<sup>6</sup> Horizontal spillovers of FDI typically were identi...ed by relating the performance of domestic ...rms to the overall industry presence or market shares of all FDI ...rms in the same industry, an identi...cation strategy that has been made standard due to an in‡uential

> 6 Another simple example of intermediate inputs that could be used in di¤erent industries are zippers, which are used by the apparel ...rms to make jackets, or shoes ...rms to make boots, or luggage ...rms to make bags.

4
# Figure 2: Educating Girls and Economic Development

<!-- Start of picture text -->
1. VICIOUS CYCLE OR 2. VIRTUOUS CIRCLE<br>Women are expected less education Women & Men are Girls & Boys<br>only to play sometimnes lessexetdohaerewl<br>traditional role food, than boys health care or many options and good productivity educated cared forand<br>Household Level<br>Household Level I<br>Women are equipped only for traditional role: I for traditional I role: ~~~~~~for Women & Meon high-producUvity many options In more are equipped work:<br>. marry out early * hrlater<br>. many children * smaller. healthier.<br>low productivity rmore eduated families<br>low earning-<br>high labortore parUcipation<br>'1<br>.1<br>LARGE BURDEN OF DEPENDENCY<br>SMALLER BURDEN OF DEPENDENCY<br>• halt the population under * large portion of public resources of Just next geaeration for basic health and schooling 18 years old .ieeded * * * one third of population under 18 years oldsmsller portion of publlc resources needed just for basic more resources health available for and schooling addiUonal<br>chooling and better health care<br>Macro-Economy Level<br>Macro-Economy Level<br>| POVeRTY ['CH I LOW ECONOMIC<br>CHILD<br>CHILD PROGRESS<br>MORTALITY<br>HIGH / MORTALrfY m<br>_LOW J<br>FERTIUTY<br>FXnLr<br><!-- End of picture text -->

Source: Herz et al. 1991. "Letting Girls Learn." World Bank Discussion Paper 133.
**Table 6: Estimated greenhouse gas mitigation potential in 2030 by sector at or below $20/tCO2e.**

||Total|Developing<br>Countries|
|---|---|---|
|Agriculture|1.60|1.10|
|Forestry|1.25|1.05|
|Energy|1.90|0.80|
|Buildings|5.50|2.85|
|Transport|1.75|0.13|
|Other|1.50|0.97|
|Total|13.50|6.90|

Note: potential given in Gt CO2e per year. Mitigation from burning agricultural residue is attributed to sector in which the fuel-use takes place. The IPCC estimates that mitigation opportunities for this class of project at 1.26 Gt CO2e per year. Source: Barker et al. (2007), Smith et al. (2007) Table 7: Carbon stored in topsoils by land-use in ECA

|Land use|Area(Km<sup>2</sup>)|Min. Carbon(Mt)|Max. Carbon(Mt)|
|---|---|---|---|
|Low density grazing|2,179,504|3,043|7,863|
|Mixed agro-forestry|3,331,691|7,777|16,627|
|Moderate to high density crops and livestock|5,727,060|9,915|22,204|
|Unprotected virgin forest|5,368,165|14,535|30,730|
|Unmanaged|4,759,824|13,555|29,443|
|Other|2,348,108|6,497|13,811|
|Total|23,714,351|55,322|120,678|

Source: FAO-UNESCO (2007); FAO 2010) and authors‘ calculations 44
# **Introduction**

Trade is critical to economic growth, and ports are critical to trade. Indeed, ports handle about 80 percent of global trade by volume and more than 70 percent by value (IMO, 2012). In a globalized world in which technology and know-how can be easily acquired and the constant search for the most efficient supply chain drives trade flows, the performance of a country’s transport infrastructure relative to that of competing countries is a crucial determinant of global competitiveness and growth.

Transport costs (defined as all shipping expenses of internationally traded goods from origin to destination country) represent a major component of trade costs. Clark, Dollar, and Micco (2004) find that bilateral trade falls by 22 percent when transport costs rise from the 25th to the 75th percentile of countries in their data set. Korinek and Sourdin (2009) report that a doubling of transport costs is associated with a decline in import volumes of 66–80 percent. Limao and Venables (2001) show that increasing transport costs by 10 percent reduces trade volumes by more than 20 percent.

The efficiency of port operations has a direct impact on the efficiency of the entire logistics chain along domestic and international freight corridors—and hence on transport costs. Efficient ports have shorter turnaround (loading and unloading) times and lower handling costs. Port efficiency is an important determinant of transportation costs: Doubling port efficiency reduces costs by as much as halving the distance between countries (Wilmsmeier, Hoffmann, and Sanchez 2006). A 0.1 increase in port efficiency decreases maritime transport costs by 0.9–3.8 percent (Micco and Pérez 2002; Clark, Dollar, and Micco 2004; Wilmsmeier, Hoffmann, and Sanchez 2006; Blonigen and Wilson 2008).

South Asia’s trade almost doubled in the past decade, with trade as a percentage of GDP increasing by 18 percentage points between 2000 and 2014. Since 2000 the region has also enjoyed the second-highest economic growth in the world (after East Asia), growing at an average annual rate of 6.8 percent (World Development Indicators Database).

Despite this progress, trade accounted for a smaller share of GDP in South Asia (47 percent) than in East Asia (55 percent) in 2014, and South Asia’s economic competitiveness continued to lag that of other regions. Global indicators, such as the Global Competitiveness Report, point to shortcomings in the infrastructure endowment in the region, with ports one of the weakest links. How much would improvements in port performance reduce transport costs and increase trade in South Asian countries?

The literature on maritime transport costs has struggled to identify a consistent measure of port efficiency, often resorting to ad hoc measures that, by construction, do not control for the actual use of port inputs (Fink, Mattoo, and Neagu 2002; Micco and Pérez 2002; Sanchez and others 2
Tabie 1: IWECRPATION TESTS FOR HE LONG MUM: UNTRANSFORCED l^rA

|||Unit Root Test||No. of Lags<br>in AiDF|Serial Correlation<br>Test|
|---|---|---|---|---|---|
||CFRDW|OF|ALF|n|2|
|Series|(crit.<br>value<br>99%: 0.532|(crit.<br>value<br>**99%**: -4.07|(crit.<br>value<br>**99%**: -3.77|iil<br>YZt_;|LMM : x3forADF|
||95% : 0.386<br>90%: 0.322)|95%: -3.37<br>90%: -3.03)|95% : -1.17<br>90%: -2.84)|||
|LnICP33|0.398|-1.981|-1.108|5|0.424<br>|
|LnlCPAG|0.355|-1.767|-1.187|5|1.375<br>|
|LnlCPIm|0.360|-2.039|-1.152|5|0.602<br>|
|LnlCPAGF|0.423|-1.972|-1.413|4|2.559<br>|
|LaICPAGW|0.363|-1.854|-1.262|4|1.960<br>|
|TRB|0.192|-1.687|-1.431|3|1.737<br>|
|LnIIP|0.022|-1.008|-2.226|4|1.326<br>|
|LnCPIG7|0.009|2.329|-0.431|7|4.889<br>|
|LnCPIG7T 1/|0.050|-1.547|-3.054|7|4.047<br>|
|LnPOIL|0.126|-1.076|-1.373|4|2.818<br>|
|LnEXR|0.092|-0.253|-0.669|4|0.096<br>|
|LnGNP7|0.011|0.377|-2.354|7|1.326<br>|
|LnMS|0.008|4.151|-0.204|5|1.885|

Definitions: ICP33 is the World Bank's 33-commodity price<sup>Index,ICPAG is the agricultural</sup> commodity price index, ICPMi' Is the metals and minerals price<sup>index, ICPAGF is the agricultural</sup> food price Index, ICPAGW is the agricultural non-food Index; TRB is the 3-onth Treasury Bill interest rate series in real terms; IIP Is the industrial production Index for the G-7 countries; CPIG7 is the GP-weighted consumer price<sup>index for the G-7</sup> countries; POIL is the crude oil price index; EXR is the aggregate of the real<sup>Yen,ONandPounds sterling</sup> exchange values to the US dollar weighted by _GWP;_ GtiP7 is gross national<sup>product of the G-7 countries;</sup> NS is 142 of the United States plus reserves of US<sup>dollars</sup> held by foreign central banks. The price indices have been deflated by the index of unit volues for<sup>manufactured exports from developedt',</sup> developing countries. The GNP series were deflated by the GNP deflator for the respective countries.

- 1/ The CPIG7 variable Includes a trend which is proven In the estimation: CPIG7 = -2.827 **(0.049)** + 0.050T (0.002)<sup>+et</sup> R' - 0.942 D.W. r 0.050.

- (Standard errors inside the parentheses).

In this case we can proceed to test CPIG7<sup>for a unit root either by allowing the trend tobeIncluded</sup> by de-trending. This is what we did for CPIG7 and CPIG7T,<sup>respectively.</sup> et or
determinants of structural change; and (ii) empirical studies that compare the experiences of structural change across developing regions and countries.

# 2.1 Empirical studies on the determinants of structural change

McMillan _et al_ . (2014) are among the pioneer authors who have recently undertaken quantitative analysis on structural change, while examining its determinants across countries. These authors show that since 1990 structural change has been growth reducing with labor moving from high to low productivity sectors in both Africa and Latin America. However, things seem to be turning around in Africa: after 2000, structural change contributed positively to Africa’s overall productivity. Using data over the period 1990-2005, and covering 38 countries, including 29 developing countries and 9 in Sub-Saharan Africa (SSA), McMillan _et al_ . (2014) identify three factors that determine whether or not structural change contributes to overall productivity; these include: the share of primary products in total exports, competitive or undervalued currencies, and labor market rigidity.

Mensah _et al_ . (2018) use an updated and expanded version of the Africa Sector Database developed by the Groningen Growth and Development Centre to analyze the role of structural change and job reallocation in the economic growth performance of African countries over the past 50 years. The results show that productivity growth has been generally low with moderate contributions from structural change across the 1960-2015 period. However, a regional comparison shows that structural change is more rapid in East Africa than in the other regions of SSA. Using econometric analysis covering 18 countries, the paper shows that more rigid labor markets reduce job reallocation across sectors, impeding structural change and productivity growth in Africa.

More recently, Martins (2019) also analyzes the pace, patterns, and determinants of structural change in the world economy. Using a data set comprising 169 countries and covering the period from 1991 to 2013, the paper finds that structural change has played a critical role in enhancing economic performance since the early 2000s, even if it remains comparatively less important than within‐sector productivity improvements. The widespread reallocation of labor from agriculture to the services sectors has been the key driver of structural change. The paper also finds robust evidence that the pace of structural change is significantly shaped by human and physical capital.

4
_55_

# TABLE 8

# Marginal Probabilities

||Domestic<br>credit growth|EQR<br>EIDEV|Capital<br>flows|U.S.<br>residual<br>intrinsic<br>bubble|Spread between<br>**10-year**and 3-<br>month t-bill|Terms of<br>trade<br>deviation|Volatility in<br>real<br>domestic<br>credit|Volatility in<br>real total<br>returns|
|---|---|---|---|---|---|---|---|---|
|Argentina|0.0798|0.0504|0.0365|-0.0196|0.1199|0.0566|0.0306|0.0523|
|Brazil|0.0698|0.0441|0.0319|-0.0171|0.1048|0.0495|0.0267|0.0457|
|Chile|0.0906|0.0573|0.0414|-0.0223|0.1362|0.0642|0.0348|0.0594|
|Colombia|0.0977|0.0618|0.0447|-0.0240|0.1469|0.0693|0.0375|0.0640|
|M6xico|0.0922|0.0582|0.0421|-0.0227|0.1385|0.0653|0.0353|0.0604|
Table 1 column 1 reports cross-region transfer multiplier estimates for permanent Social Security benet increases. In general, I nd that a $1 increase in permanent Social Security transfers to the residents of a state increases that state's relative labor income by around $1.5 contemporaneously, though the size of the multiplier can vary from 0.91.9 (about one standard error) across all specications in Table 1. In the parsimonious specication (row A1), the relative permanent transfer multiplier is almost exactly 1.5 (signicant at the 1% level). For the benchmark specication (row A2), the relative multiplier falls slightly to 1.3 (also signicant at 1%). This reects partially osetting eects of the two controls; if added individually, US GDP growth _×_ state FEs tend to reduce the permanent transfer multiplier to around 1.14 (row B1, signicant at the 1% level), whereas population growth tends to increase the multiplier to 1.61 (not reported, also signicant at the 1% level).<sup>18</sup> Table 1 Columns 2 and 3 reports cross region transfer multiplier estimates for one-o stimulus transfers, using per capita labor income or GDP (respectively) as the income measure. Unlike the scatter plots in Figure 1, the regressions control for state and time FEs, impose that the withdrawal of transfers reverses any earlier increase in income, and for labor income, pools across the 2001 and 2008 stimulus payments (recall GDP data are only available from 2005 so excludes the 2001 transfer). In general, I nd that $1 one-o transfers to the residents of a state increases that state's relative labor income or GDP by around $1/3 contemporaneously, with multipliers around $0.4 for GDP (range 0.350.47), and $0.25 for labor income (range 0.200.31). In the parsimonious specication (row A1), the temporary cross-region transfer multiplier is around 0.26 (signicant at the 1% level), rising slightly to 0.31 for the benchmark specication (also signicant at 1%). When the dependent variable is per-capita GDP growth, the multiplier for the parsimonious specication is larger at 0.39 (row A1), increasing marginally to 0.41 in the benchmark speci- cation (row A2). Both GDP multipliers are signicant at the 5% level because standard errors are larger for the GDP specication.

> 18The coecient of population growth is negative (0.37) and is statistically signicant (not reported). Population growth has little eect on temporary transfer estimates and is often insignicant.

18
_institutions include those who choose them._ To illustrate this, we present a numerical example. In the example, we assume: (a) a transition probability equal to the squared demand for the rule of law: π ( _x_ ) = (1- _x_ )<sup>2</sup> , (b) a set of values of the parameters,<sup>23</sup> and (c) a distribution of stripping abilities in which two-thirds of the agents have θ = θ _max_ and among the remainder, θ is uniformly distributed on [0.65, θ _max_ ].

Figure 5A shows that the stable equilibria of _x_ * are 0 and 0.75. In the first case, no one opposes the establishment of the rule of law, and reform occurs in period 1. In the second case, three-fourths of the agents oppose the rule of law, and reform is delayed on average for 16 periods.<sup>24</sup> Figure 5B depicts the growth paths of agents’ expected aggregate lifetime income in these two cases.<sup>25</sup> Expected aggregate income is 20 percent lower along the path of delayed reform. Every agent is strictly worse off.

# **2.  Sources of Historical Dependence**

In the Markovian model we have constructed, actions do not depend on history. In this section, we consider sources of historical dependence.

# 2.1 _History-dependent Payoffs_

An important example of history dependence are endogenous shifts in the distribution of types _H_ ( θ ). Consider a set of societies with initially similar distributions of types among which, for some reason, one subset began with an initially high level of asset stripping and another subset with an initially low level. If stripping assets was characterized by learning-by-doing, then behavior would be self-reinforcing. The distribution of types would diverge over time, as would the degree of support for the rule of law. As Holmes (2003, pp. 20-21) states,

> 23 _f =_ 0.05, _IL_ = 0.01, _IN_ = 0.0475, _g_<sup>~</sup> = 1.05, ~ _z_ = 0.9, λ = 0.3, and δ = 0.945.  Using (4), these values imply θ _max_ = 0.8.  The excel program is available at <u>www.econ.worldbank.org/staff/khoff.</u> 24 One can show by standard methods that given a transition probability π per period, the expected number of periods before reform is 1/ π .   If _x*_ = 0.75, then π ( _x*_ ) _=_ 0.0625, and the expected delay is 16 periods.

25 When _x_ * = 0.75, 87% of agents build value (the marginal asset-stripper demands the rule of law). To compute aggregate income, we assume that each agent has control rights over an equal share of aggregate assets. This implies that the fraction of _agents_ who strip assets equals the fraction of _assets_ that are stripped.

16
**Table 1.** Agriculture’s contribution to employment and GDP

||**GDP**<br>_1980-99_|**(%)**<br>_2000-15_|**Employ**<br>_1980-99_|**ment (%)**<br>_2000-15_|
|---|---|---|---|---|
|East Asia and the Pacific|17.4|11.2|37.2|25.3|
|Europe and Central Asia|9.7|5.3|17.3|12.6|
|Latin America and the Caribbean|11.0|7.1|19.4|15.7|
|Middle-East and North Africa|12.5|9.1|16.7|17.1|
|North America|1.3|1.2|2.9|1.6|
|South Asia|28.6|19.2|50.6|42.0|
|Sub-Saharan Africa|19.1|15.4|47.6|43.5|
|High income|4.5|2.7|9.9|5.5|
|Upper middle income|15.1|7.8|28.8|22.2|
|Lower middle income|24.1|16.9|39.9|35.1|
|Lowincome|32.8|28.9|75.0|71.4|
|**Total**|**12.9**|**8.3**|**24.1**|**19.1**|

Source: Authors’ calculations based on World Development Indicators.

From a development perspective, two main reasons constitute our rationale for focusing on agricultural productivity. The first reason is agriculture’s relevance in the short run given its size. The figures on agriculture’s contribution to the economy validate the idea that the sector is far from being marginal to the development process. Between the last two decades of the 20<sup>th</sup> century and the first 15 years of the 2000s agriculture’s contribution has declined globally both in terms of GDP and employment (Table 1). In the latter period agriculture accounted only 2.7% of the GDP and 4.5% of total employment in high-income economies. The picture is however quite different for less wealthy nations. Since 2000, agriculture averaged 28.9% of the GDP of lowincome<sup>†</sup> economies and 71.4% of total employment. Even among upper-middle income countries where agriculture downsized from 15.1% to 7.8% of GDP, the sector is still responsible for over one-fifth of total employment. Agriculture’s relative decline in terms of contribution to employment and GDP as countries graduate to higher levels of income is a remarkable empirical regularity. Nonetheless, the sector’s relevance for development is not to be questioned. While it may be trivial in high-income economies, agriculture’s contribution is still substantial in most of the developing world.

> † Low-income economies are defined as those with a GNI per capita, calculated using the World Bank Atlas method, of $1,005 or less in 2016; lower middle-income economies are those with a GNI per capita between $1,006 and $3,955; upper middle-income economies are those with a GNI per capita between $3,956 and $12,235; high-income economies are those with a GNI per capita of $12,236 or more.

3