rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 2.B. Evolution of International Investment Positions and Share of Active Links: Portfolio Investment**

<!-- Start of picture text --> Panel A. North-North Panel B. North-South<br>35 100% 7 100%<br>90% 90%<br>30 6<br>80% 80%<br>25 70% 5 70%<br>60% 60%<br>20 4<br>50% 50%<br>15 3<br>40% 40%<br>10 30% 2 30%<br>20% 20%<br>5 1<br>10% 10%<br>0 0% 0 0%<br>Panel C. South-North Panel D. South-South<br>6 100% 6 100%<br>90% 90%<br>5 5<br>80% 80%<br>70% 70%<br>4 4<br>60% 60%<br>3 50% 3 50%<br>40% 40%<br>2 2<br>30% 30%<br>20% 20%<br>1 1<br>10% 10%<br>0 0% 0 0%<br>New links Old links Share of active links<br>USD, trillions USD, trillions<br>Share of active links Share of active links<br>2001 2003 2005 2007 2009 2011 2013 2015 2017 2001 2003 2005 2007 2009 2011 2013 2015 2017<br>USD, trillions USD, trillions<br>Share of active links Share of active links<br>2001 2003 2005 2007 2009 2011 2013 2015 2017 2001 2003 2005 2007 2009 2011 2013 2015 2017<br><!-- End of picture text --> Note: This figure shows the trends in the value of international investment positions and share of active links by block for portfolio investment. The value of positions distinguishes old and new links. Old links are those that have a positive value in 2001. New links are those that have a zero or missing value in 2001. By definition, there are no new links in 2001. Values are expressed in trillions of 2011 U.S. dollars (USD). The share of active links is the number of country pairs that have a positive value in each year over the number of country pairs with a positive or zero value in the same year. Country pairs with a missing value are not considered. Bilateral data are aggregated for all countries within a source region to all countries within a destination region.
# Table 6 Sensitivity of Consolidated Non-financial Public Sector Deficits to Change in Economic and Policy Determinants

|CHANGES IN ECONOMIC AND POLICY<br>DETERMINANTS<br>---------------------------------------------------------------------|SEMI-<br>ELASTICITY|CHANGES IN NFPS DEFICIT<br>(Percentage Point ofGDP)<br>__------__-|
|---|---|---|
|1. DOMESTIC VARIABLES|||
|PTN|-1.751|-0.071|
|INF|0.403|0.016|
|2. FOREIGN VARIABLE|||
|PXM|-2.805|-0.113|
|3.<br>POLICYVARIABLES|||
|TY<br>---------------------------------------------------------------|-6.841|-0.276<br>__------------__-|

Note: The estimations of all obtained values are based on the mean valJtes of the relevant variables.

45
# **Tables**

## **Table A.1:** Balance Checks on Village Characteristics and Electoral Data

||Hamlets|HHs|Sumatra|Java|NTB-Bali|Kalimantan|Sulawesi|Reg. voters|Candidates|Turnout|Turnout_≥_1|Herfnd.|Rounding|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|
|New village head|-0.599|-0.154|-0.057|-0.008|0.198<sup>_∗_</sup>|0.058|-0.015|-512.870|-0.195|0.043|-0.002|0.001|-0.238|
||(0.665)|(0.278)|(0.143)|(0.102)|(0.112)|(0.077)|(0.100)|(554.393)|(0.355)|(0.046)|(0.061)|(0.026)|(0.422)|
|Observations|512|509|512|512|512|512|512|512|512|512|512|512|512|
|Control mean|4.65|6.47|0.31|0.15|0.16|0.075|0.17|2229.8|3.43|0.84|0.025|0.39|1.62|
|Robust p-value|0.313|0.543|0.676|0.782|0.058|0.418|0.926|0.367|0.533|0.215|0.930|0.980|0.471|
|MSE-opt. bandwidth|19.6|20.5|21.0|27.2|19.8|19.1|22.2|20.4|22.0|21.6|31.5|31.3|18.4|
|Effective obs.|241|246|251|305|242|238|258|249|258|255|336|336|230|

_Notes_ : This table reports RD estimates of _γ_ in equation (1) obtained via the non-parametric method from Calonico et al. (2014). The dependent variable is: in column 1, the number of neighborhoods/hamlets in the village; in column 2, the log number of households residing in the village; in columns 3-7, a dummy equal to 1 if the village is located on the island of Sumatra, Java, Nusa Tenggara Barat/Bali, Kalimantan, and Sulawesi, respectively; in column 8, the number of registered voters in the most recent village election; in column 9, the number of candidates; in column 10, voter turnout (votes cast divided by the number of registered voters); in column 11, a dummy equal to 1 if reported turnout was greater than 100% in the most recent election; in column 12, a Herfindahl index of candidate vote shares; in column 13, the number of candidates with a trailing zero in their vote tally. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust bias-corrected standard errors in parentheses.
total population in 1980, the working age population share grew by more than 13 percentage points in the last 35 years. The growth received further tailwind from increasing participation rates. The aggregate participation rate rose from approximately 45 percent in the mid‐1970s to over 60 percent in the 2010s, largely due to a massive jump in the female labor force participation rate, which grew from a low base of 12 percent in 1989 to 30 percent in 2013. What has been most encouraging is that not only the quantity but also the quality of labor resources has been rising over time. The Penn World Table (PWT) human capital index for <!-- Start of picture text --> 35 Population ages 15 ‐ 64 (% of Total Population)<br>70<br>30<br>Total<br>25 65<br>Private<br>20<br>60<br>15<br>10<br>55<br>5<br>0 50<br>Source: World Development Indicators Source: World Development Indicators<br>Investment Share of GDP (%)<br>1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br><!-- End of picture text --> Bangladesh has grown by more than 1.5 percent per year on average during 1990 – 2010.

Apart from growth in underlying factors of production – capital and labor – economic growth can also be derived from advances in technology or through an efficient use of existing resources. The growth in the productivity of resources, or Total Factor Productivity (TFP), has been an important driver of growth for many countries.<sup>3</sup> The TFP growth rate for Bangladesh during 2001 – 2011 has just been above zero implying that the strong growth seen during the period was not aided by either technology adoption or through efficiency in resource allocation. On the other hand, India and China, which also realized healthy economic growth during the same period, were aided significantly by productivity with TFP growing by 1.3 percent and 2.9 percent on average each year respectively. However, the uptick in TFP growth since later 2000s is encouraging and continued reforms can help the country to move into an era of sustained positive TFP growth.

The important question going into the future is whether the country can continue on its path of accelerating growth or not. The goal is to identify areas that need reform and quantify the relative merit of alternative growth strategies. Such an analysis will help isolate factors that are most likely to be the key drivers of growth. In this paper, I use the World Bank’s Long‐Term Growth Model (LTGM) to answer these questions. The LTGM (Pennings 2016) is a simple

> 3 The estimated TFP growth series is usually volatile and is also sensitive to the method of estimation. TFP is estimated as a residual after accounting for all factors of production, and the estimates across studies are bound to differ if they employ a different production function or measure factors of production differently. For example, estimates of TFP will be different for a study that accounts for human capital based on schooling compared to a study that does not account for human capital gains that stem from schooling.

3
**SENA: A Threat to Identification?** We have not included SENA students in our analysis because SENA captured a negligible share of higher education enrollment in the early 2000s, and SENA students are not included in the student-level higher education dataset, SPADIES. Students in SENA, therefore, have been classified as "not enrolled" for our estimation. In principle, this could affect the interpretation of our findings.

Figure A3 presents the evolution of higher education enrollment in Colombia by program type. Panel (a) excludes SENA enrollment and leads to the conclusion that higher education enrollment has grown mainly through bachelor’s programs. This is consistent with SC programs having a small expansion margin. However, the story changes when we include SENA enrollment, which expands dramatically between 2005 and 2015 (panel b). Further, the distribution of SC students across cities changes over time and when SENA enrollment is included. The left side of Figure A4 shows that, before 2005, most SC enrollment outside SENA was concentrated in large cities; only later–and very slowly–did it rise in medium and small cities. SENA, however, was particularly aggressive in those markets (right side). A similar picture emerges when looking at number of SC programs rather than enrollment (Figure A5).

The fact that most SC programs were offered in large cities in the early 2000s helps explain our small expansion effects. Since those cities already had HEIs offering bachelor’s programs, those students might not have been persuaded to enter higher education just because of the opening of an SC HEI. In contrast, in smaller cities without other HEIs, such openings might have induced higher education entry. Data from later years (including more SC HEIs in smaller cities), especially if including SENA students, might have uncovered a larger expansion margin. Our results, therefore, illustrate what might happen when SC programs expand in markets in which other, more widely preferred higher education options are already available.

**Fully saturated model.** We investigate the sensitivity of our main findings to model specification. In particular, we estimate a _fully_ saturated model where _Hj_ interacts with the entire vector of covariates in _Xij_ . Table A8 displays estimates for this model, which can be compared to those in Table 6. We do not observe large discrepancies across specifications.

> particularly for females. We also present additional results for radii greater than 20 km in Tables A5, A6, and A7 in the Appendix. Our preferred findings (with a 10 km radius) remain largely stable across radii up to 20km. After that, the share of students with SC HEIs is above 70%, and above 90% for other HEIs. Since the majority of students have HEIs available at radii greater than 20 km, such large radii wipe out most of the identifying variation and render the estimates less informative.

18
**Figure 6: Industrial value added (over GDP) in CAR and its synthetic control, 2009-17**

<!-- Start of picture text --> 130<br>120<br>110<br>100<br>90<br>80<br>70<br>60<br>2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Year<br>Seleka occupation CAR Synthetic CAR<br>20<br>10<br>0<br>-10<br>-20<br>-30<br>-40<br>-50<br>2009 10 11 12 13 14 15 16 17<br>Seleka occupation<br>Year<br>Industry value added (base 100 =2012)<br>Gap Industry value added (index 100 =2012)<br><!-- End of picture text --> _Source: Industrial value-added series are from the World Development Indicators_ (35) _, from 2009-17. Note: In upper chart, the blue (lighter in greyscale) line indicates actual industrial value-added series, and the orange (darker in greyscale) line indicates industrial value-added synthetic series. The grey bar indicates the beginning of the civil war up to the departure of Séléka rebel coalition from Bangui (from December 2012 to January 2014). In the lower chart, the dark continuous line indicates the gap between actual and synthetic industrial value-added series (normalized to 0 in 2012), the dark dash horizontal line indicates a null gap, and the two red vertical lines indicates the period of_ 17
# **Table 6:  Robustness Across Alternative Instrument Sets**

# **(OBT Dataset, Levels Equation, Redistribution Not Included)**

|**Panel A‐‐ SYS‐GMM Estimat**|**es**|||||||
|---|---|---|---|---|---|---|---|
|**N**|**mean**|**sd**|**p10**|**p25**|**p50**|**p75**|**p90**|
|_Coefficient on Lagged Inequa_|_lity_|||||||
|GMM‐style<br>625|‐0.55|0.13|‐0.76|‐0.66|‐0.51|‐0.44|‐0.40|
|Collapsed<br>625|‐1.18|0.33|‐1.81|‐1.20|‐1.06|‐0.97|‐0.89|
|Both<br>1250|‐0.86|0.40|‐1.41|‐1.06|‐0.81|‐0.51|‐0.43|
|_Absolute value of t‐Statistic f_|_or Lagged_|_Inequality_||||||
|GMM‐style<br>625|2.49|0.35|2.05|2.23|2.45|2.74|2.99|
|Collapsed<br>625|2.85|0.48|2.30|2.52|2.77|3.07|3.63|
|Both<br>1250|2.67|0.46|2.13|2.35|2.62|2.92|3.25|
|**Panel B‐‐ Differenced Equati**|**on**|||||||
|**N**||**d**|**10**|**25**|**50**|**75**|**90**|
||**mean**|**s**|**p**|**p**|**p**|**p**|**p**|
|_Coefficient on Lagged Inequa_<br>|_lity_|||||||
|GMM‐style<br>25|0.48|0.21|0.28|0.31|0.43|0.59|0.83|
|Collapsed<br>25|2.88|2.48|1.41|1.90|2.12|3.15|3.78|
|Both<br>50|1.68|2.13|0.30|0.43|0.96|2.12|3.38|
|_Absolute value of t‐Statistic f_|_or Lagged_|_Inequality_||||||
|GMM‐style<br>25|1.07|0.40|0.70|0.77|0.99|1.28|1.78|
|Collapsed<br>25|1.88|0.54|1.12|1.71|2.08|2.26|2.41|
|Both<br>50|1.48|0.62|0.70|0.91|1.40|2.08|2.32|
|**Panel C‐‐ Levels Equation**||||||||
|**N**|**mean**|**sd**|**p10**|**p25**|**p50**|**p75**|**p90**|
|_Coefficient on Lagged Inequa_|_lity_|||||||
|GMM‐style<br>25|‐0.53|0.15|‐0.80|‐0.66|‐0.47|‐0.41|‐0.37|
|Collapsed<br>25|‐1.29|0.52|‐2.29|‐1.21|‐1.07|‐0.96|‐0.89|
|Both<br>50|‐0.91|0.54|‐1.74|‐1.07|‐0.84|‐0.47|‐0.40|
|_Absolute value of t‐Statistic f_|_or Lagged_|_Inequality_||||||
|GMM‐style<br>25|2.77|0.65|1.96|2.49|2.71|2.96|3.94|
|Collapsed<br>25|2.29|0.37|1.79|1.99|2.24|2.53|2.87|
|Both<br>50|2.53|0.58|1.90|1.99|2.50|2.83|3.44|

Note: This table summarizes the distribution of point estimates of the growth effect of inequality from 2SLS estimates of the levels equation, and the corresponding (absolute) t‐statistics. These distributions are calculated over different instrument sets consisting of different sets of lags of changes in income and inequality used as internal instruments, as described in the text.

37
|**_Opportunity cost of labor_**||0.202||0.219|
|---|---|---|---|---|
|Daily wage male casual worker in||(0.229)||(0.230)|
|**_Competition_**|||||
|Between 1 and 5 competitors||-0.270<br>(0.253)||-0.276<br>(0.253)|
|More than 5 competitors||0.205<br>(0.205)||0.200<br>(0.205)|
|**_Selection Correction_**|||||
|Inverse Mills Ratio|||-0.007*|-0.008**|
||||(0.004)|(0.003)|
|Constant|-6.812*|-7.071*|-6.684*|-7.056*|
||(3.503)|(3.836)|(3.485)|(3.828)|
|R2|0.434|0.468|0.437|0.472|
|Adjusted R2|0.406|0.426|0.407|0.429|
|Observations|384|384|384|384|

_Note:_ Standard errors are robust and clustered by enumeration area. Significance at the 1%, 5%, 10% level is indicated by ***, **,*, respectively.

(1) The omitted category for the activity dummy is a catchall category “other”.

(2) Excludes grain milling, food and beverages, and distilling.

-41-
||**Sectoral Decomp**|**% of**<br>**T**<br>**osition of Ugan**|**ABLE S4.2**<br>**dan Labor F**|**% of**<br>**orce and Valu**|**e Added, 2002**|**% of**|**% of Net**|
|---|---|---|---|---|---|---|---|
|**Sector**|**Rural**<br>**labor**|<br>**national**<br>**labor force**|**Urban**<br>**labor**|<br>**national**<br>**labor force**|**Total labor**|<br>**national**<br>**labor force**|<br>**Value**<br>**Added**|
|Staples and Cash Crops|4,796,824|69.0%|115,768|<br>1.7%|4,912,592|70.7%|18.3%|
|Animal farming|260,581|3.7%|22,298|<br>0.3%|282,879|4.1%|2.1%|
|Mining and Quarrying|14,616|0.2%|5,124|<br>0.1%|19,740|0.3%|0.3%|
|Tradable Manufacturing|208,296|3.0%|64,737|<br>0.9%|273,033|3.9%|10.6%|
|Utilities|5,508|0.1%|8,861|<br>0.1%|14,369|0.2%|3.9%|
|Non-tradable local capital g|oods<br>58,774|0.8%|49,934|<br>0.7%|108,708|1.6%|11.6%|
|Non-tradable services|190,815|2.7%|222,521|<br>3.2%|413,336|5.9%|28.4%|
|Tradable services|105,806|1.5%|117,219|<br>1.7%|223,025|3.2%|8.1%|
|Public service|317,228|4.6%|188,741|<br>2.7%|505,969|7.3%|16.7%|
|Other|99,193|1.4%|97,193|<br>1.4%|196,386|2.8%||
|**Totals**|**6,057,641**|**87.2%**|**892,396**|<br>**12.8%**|**6,950,037**|**100.0%**|**100.0%**|

Sources: Uganda Bureau of Statistics 2004; Alarcon et al. 2006; authors' calculations.

# **iv. Transport Costs and Infrastructure**

A key attribute of Uganda’s economy is its limited infrastructure and high transport costs, which are among the highest in the world (Buys 2006). In multiple sectors, including food, these costs provide both implicit protection for domestic producers and implicit taxation on exports (Milner et al. 2000; Rudaheranwa 2006). Poor roads are responsible for much of the high costs. The 2005 National Transport Plan reported only 5 percent of the country’s roads as paved, and only approximately 40 percent of those in good condition (Uganda Ministry of Works, Housing, and Communications and TAHAL Engineers 2005).

62
Table 4: Effects of innovation on employment (IV), by Region and Income Categories

||All Countries||by Re|gion|||by Income||
|---|---|---|---|---|---|---|---|---|
|||Africa|South Asia|ECA|MENA|Low|Middle|High|
|Process innovation only|-0.001|-0.000|0.004|0.007|0.034|-0.024|0.022|0.022|
||(0.01)|(0.06)|(0.04)|(0.02)|(0.03)|(0.03)|(0.02)|(0.02)|
|Sales growth d.t. new products|0.938***|1.726***|0.945***|0.803***|1.030***|1.013***|1.056***|0.797***|
||(0.07)|(0.52)|(0.20)|(0.08)|(0.12)|(0.25)|(0.10)|(0.10)|
|Size 20 to 99|0.006|-0.043|0.007|0.021*|-0.013|-0.022|0.012|0.013|
||(0.01)|(0.04)|(0.01)|(0.01)|(0.02)|(0.02)|(0.01)|(0.01)|
|Size 100 and over|0.005|-0.077|0.029|-0.008|-0.008|-0.019|0.012|0.011|
||(0.01)|(0.09)|(0.02)|(0.02)|(0.03)|(0.04)|(0.02)|(0.03)|
|Constant|-0.004|0.243*|0.208*|-0.133**|0.229*|0.172**|-0.039|-0.086|
||(0.01)|(0.15)|(0.12)|(0.06)|(0.13)|(0.09)|(0.08)|(0.08)|
|N|14688|1756|3322|6657|2953|3468|6024|5196|
|R-Squared|0.29|0.32|0.19|0.19|0.14|0.39|0.18|0.18|
|First stage F-statistic|404.59|29.35|25.57|268.94|126.34|56.89|136.92|218.77|
|Hansen-Sargen Overid testp-value|0.87|0.93|0.44|0.58|0.84|0.64|0.86|0.49|

Note: All regressions include country-industry and size dummies, constrained to sum to zero. Dependent variable is net labor growth (minus growth in sales of old product). Instrumental variables are indicator whether new product is _completely new to the firm_ and whether firms _invests in R&D_ .
# Social cohesion, economic security, and forced displacement in the long-run: Evidence from rural Colombia<sup>*</sup>

Juan F. Tellez<sup>†</sup> Laia Balcells<sup>‡</sup> JEL codes: O15, R23, D74, H56 Keywords: IDPs, social cohesion, civil war, displacement

> *This paper was commissioned by the World Bank Social Sustainability and Inclusion Global Practice as part of the activity “Preventing Social Conflict and Promoting Social Cohesion in Forced Displacement Contexts.” The activity is task managed by Audrey Sacks and Susan Wong with assistance from Stephen Winkler. This work is part of the program “Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership”. The program is funded by UK aid from the United Kingdom’s Foreign, Commonwealth and Development Office (FCDO), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. This work does not necessarily reflect the views of FCDO, the WBG or UNHCR. We would like to thank Ana Maria Ibañez for making data on the _Fondo de Tierras_ available and one anonymous reviewer for their helpful comments.

> †Assistant Professor of Political Science, University of California, Davis. Contact: jftellez@ucdavis.edu

> ‡Provost’s Distinguished Associate Professor of Government, Georgetown University. Contact: laia.balcells@georgetown.edu
ALLOCATION AND MANAGEMENT OF PUBLIC SPENDINO

# Monltoring **and Managing Plans and Investment Programmes**

34. Monitoring of development projects **Is** rightly a matter of concern. Thls **is** appropriate both to keep track of projects' implementation and to discover whether they have the effects intended. But the factors that are most Important In the monitoring of a plan/investment programme as a whole are not necessarily the same as for the individual projects of which it **is** comprised. Different types of information may be required and by different sets of people, and for the programme as a whole there **Is** a premium on standardised project information that can be aggregated. Very often elementary Information that might be regarded almost as trivial by someone responsible for the M&E of a particular project would actually be extremely useful **if** consistently available for all projects. When it comes to the aggregate monitoring of **a** PIP, there **is** a clear order **of** priority for the Information that **Is** needed, and sophisticated but partial information is much less useful than basic but comprehensive data.

35. The first requirement **Is** Information that should be, but often **is** not, available from government accounting records concerning actual expenditures by Individual projects and the programme as a whole. Simple breakdowns by implementing agency, sector, source of finance etc. are crucial, as is their prompt availability. Of course financial information of this sort may conceal as much as It reveals. Physical progress may not be commensurate with expenditure, a project whose financial implementation **Is** going according to plan may nevertheless be totally misconceived and so forth. Financial monitoring Is not enough, but It Is a vital starting point and first priority.

36. As far as Investment programmes are concerned, a second elementary requirement is that it must be possible to relate expenditure information to a financial plan for each project. The development and maintenance of standardised project profiles for all approved projects is an Invaluable technique (see Box 6). Initially, at least, monitoring data should follow the classifications used In project administration, even If these do not correspond precisely to economic or other relevant categories.

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