rafmacalaba/datause-encoder-data huggingface dataset:
Figure 1: DVAR of Processing Exports (2000-2007), with 95% (Bootstrapped) Con...dence Intervals <!-- Start of picture text --> 2000 2001 2002 2003 2004 2005 2006 2007<br>Year<br>Measured DVAR 95 c.i. (upper bound)<br>95 c.i. (lower bound)<br>.6<br>.55<br>.5<br>DVAR<br>.45<br>.4<br><!-- End of picture text --> Table 1: Issues and Assumptions or Solutions

||Issues|Assumptions or Solutions|
|---|---|---|
|1|Domestic content in imported materials.|Negligible according to KWW (2012).|
|2|Imported content in domestic materials.|Lower DVAR by 1.5% - 5.7%.|
|3|Firms import capital equipment.|Remove equipment from ...rm imports.|
|4|Firms buy imported materials from ...rms.|Drop excessive exporters.|
|5|Firms sell imported materials to other ...rms.|Drop excessive importers.|
|6|Multi-industry ...rms hinder the calculation|Restrict the sample to single-industry|
||of industry DVAR.|...rms.|

<u>Table 2: Domestic Value Added Ratio</u>

|||Processing (P)||Ordinary (O)|Aggregate (A)|
|---|---|---|---|---|---|
|Year|DVAR (Filter 1)|DVAR (Filter 2)|DVAR (Filter 3)|DVAR (Filter 1)|DVAR (Filter 3)|
|2000|0.487|0.475|0.459|0.924|0.650|
|2001|0.495|0.488|0.468|0.915|0.652|
|2002|0.517|0.505|0.488|0.918|0.668|
|2003|0.502|0.494|0.478|0.914|0.661|
|2004|0.539|0.531|0.507|0.900|0.674|
|2005|0.579|0.571|0.544|0.893|0.695|
|2006|0.565|0.558|0.520|0.904|0.697|
|2007|0.599|0.587|0.548|0.900|0.701|

Notes: Filter 1: Include exporters that have material > imports, exports >= imports. Filter 2: Include exporters that satisfy Filter 1 and DVAR < 50<sup>th</sup> Pct(DVARO). Filter 3: Include exporters that satisfy Filter 1 and DVAR < 25<sup>th</sup> Pct(DVARO). DVARA= Processing_Shr�DVARP+(1-Processing_Shr)�DVARO 44
# Behavioral Insights in Infrastructure Sectors: A Survey

George Joseph, Sophie Ayling, Pepita Miquel-Florensa, Hernán D. Bejarano, Alejandra Quevedo Cardona

## **1. Introduction and Background**

Infrastructure is a key driver of economic growth, whether it be in transport (Cantu, 2017), energy (Sahin et al., 2014), or water and sanitation (Frone & Frone, 2014). In 2019, the total infrastructure investment globally stood at USD 2.7 trillion (Global Infrastructure Hub, 2019), while private investment constituted USD 96.7 billion (3.5% of the total) across 409 projects (World Bank, 2020). Nonetheless, though these sound like substantial sums, there is a pronounced infrastructural gap in developing countries. For instance, the estimated cost to meet the SDG for drinking water and sanitation services is $1.7 trillion, three times more than has been invested in the sector to date (Nagpal et al., 2018). Moreover, approximately one billion people still lack access to electricity globally (Fay et al., 2021); 2.2 billion lack access to safe drinking water and 4.2 billion do not have safely managed sanitation services (WHO & UNICEF, 2019). Governments are called upon to extend, improve and better maintain existing infrastructure, but real investment in infrastructure sectors taken together does not match estimated needs. Real investments in Africa, South Asia, and Latin America are 3.5, 4.7, and 3.2 percent, respectively, while needs are estimated at 9.2, 7.5 and 4.5 percent of GDP (Rozenberg & Fay, 2019).

Beyond the acute need for increasing infrastructure investments and addressing the financing gap, infrastructure sectors are plagued with inefficiencies stemming from the suboptimal use of available inputs, limited exploitation of scale economies in service provision and underutilization 2
23

# **Table 6. Bank Regulation and Political Institutions in 2011, Survey IV**

|`--------------------`<br>`--------------------`|`------------`<br>`Private`<br>`Monitorin`<br>`Index`<br>`------------`|`--`<br> <br>`g`<br>`--`|`----------`<br>`Official`<br>`Sup.`<br>`Power`<br>`Index`<br>`----------`|`-----------`<br>`Entry`<br>`Req.`<br>`Index`<br>`-----------`|`-----------------`<br> <br>`Activity`<br>`Restrictions`<br>`Index`<br>`-----------------`|`--------------------`<br>`Government`<br>`Owned`<br>`Banks`<br>`--------------------`|
|---|---|---|---|---|---|---|
|`Voice &`|`0.479*`||`-1.077**`|`-0.0706`|`-0.513*`|`-3.906`|
|`Accountability`<br>`--------------------`|`(0.271)`<br>`------------`|<br>`--`|`(0.445)`<br>`----------`|`(0.0747)`<br>`-----------`|`(0.273)`<br>`-----------------`|`(4.784)`<br>`--------------------`|
|<br>`Observations`<br>`--------------------`|<br>`54`<br>`------------`|<br> <br>`--`|<br>`60`<br>`----------`|<br>`62`<br>`-----------`|<br>`60`<br>`-----------------`|<br>`48`<br>`--------------------`|

``` Notes: This table presents five regressions, where each column reports the findings from an instrumental variables regression.
``` ``` The dependent variables, measured in 2011, are:Private Monitoring Index measures the degree to which regulatory policies encourage and facilitate private sector monitoring of banks; Official Supervisory Power Index measures the extent to which official supervisory agencies have the authority to take specific actions to prevent and correct problems; Entry Restrictions Index measures the number of specific legal requirements for obtaining a banking license; and Restrictions on Activities Index measures regulatory restriction on the degree to which banks can engage in securities, insurance, and real estate activities plus restrictions on banks owning and controlling nonfinancial firms. Government Owned Banks equals the percentage of the banking system’s assets held by banks that are 50% or more government owned.
``` ``` The explanatory variable of focus is Voice & Accountability, which measures the degree to which average citizens have voice in the political process and the degree of accountability on the part of politicians (Kaufmann, Kraay, and Zoido-Lobaton, 1999).
``` ``` The unreported, though included in the second-stage instrumental variables, arethe legal origin of each country (that is, we include three dummy variables for Common, French civil, and German civil law countries respectively).
``` ``` The excluded instrumental variables are the latitude of the capital, the percentage of years since 1776 that the country has been independent, the fraction of the population that is Catholic, the fraction of the population that is Muslim, and the fraction of the population that is not Catholic, Muslim, or Protestant.
Heteroskedasticity-consistent standard errors are in parentheses under the estimated coefficients. To designate statistically significant benchmarks, *** indicates significance at the 0.01 level, ** at the 0.05 level, and * at the 0.10 level.
```
**Table 4.7. Food and nonfood consumption, maize livelihood zone (soil moisture)**

||Total consu|mption, all c<br>zone|ountries in|Total cons<br>disagg|umption, co<br>regated info|untries with<br>rmation|Fo|od consumpti|on|Non|food consum|ption|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Landed h|ouseholds||||||||||||
|Hazard|**0.055*****|**0.059*****|**0.119*****|**0.058*****|**0.060*****|**0.131*****|**0.081*****|**0.082*****|**0.158*****|**0.037***|**0.041****|**0.101*****|
||**[3.852]**|**[4.097]**|**[4.792]**|**[3.731]**|**[3.900]**|**[4.681]**|**[4.754]**|**[4.680]**|**[5.581]**|**[1.903]**|**[2.168]**|**[2.751]**|
|Hazard<sup>2</sup>||**0.022***|0.011||**0.024***|0.012||0.011|-0.002||**0.030***|0.019|
|||**[1.831]**|[0.839]||**[1.753]**|[0.809]||[0.729]|[-0.152]||**[1.758]**|[1.009]|
|Hazard<sup>3</sup>|||**-0.028*****|||**-0.032*****|||**-0.035*****|||**-0.027***|
||||**[-2.954]**|||**[-3.041]**|||**[-3.740]**|||**[-1.902]**|
|Landless|households||||||||||||
|Hazard|**0.043****|**0.038***|0.032|**0.060*****|**0.053***|0.063|**0.093*****|**0.100*****|**0.112****|0.028|0.009|0.029|
||**[2.239]**|**[1.844]**|[0.875]|**[2.593]**|**[1.910]**|[1.161]|**[3.942]**|**[3.610]**|**[2.121]**|[0.950]|[0.255]|[0.407]|
|Hazard<sup>2</sup>||0.002|0.004||0.005|0.002||0.022|0.018||-0.013|-0.019|
|||[0.101]|[0.181]||[0.195]|[0.071]||[0.739]|[0.546]||[-0.384]|[-0.515]|
|Hazard<sup>3</sup>|||0.007|||-0.002|||-0.003|||-0.008|
||||[0.399]|||[-0.067]|||[-0.125]|||[-0.269]|
|Obs.|50,266|50,266|50,266|31,671|31,671|31,671|31,671|31,671|31,671|31,671|31,671|31,671|
|R<sup>2</sup>|0.362|0.363|0.364|0.363|0.363|0.365|0.281|0.281|0.284|0.387|0.388|0.389|

_Source:_ World Bank.

_Note:_ Results are from a weighted regression such that each household is assigned its survey-specific population weight. Countries and years included are Ethiopia (2011, 2016), Malawi (2010, 2016, 2019), and Mozambique (2014). Dependent variable is the log of per capita (or adult equivalent) consumption. Other variables included and not shown are education of household head, gender of household head, whether the household location is remote, agroecological zone indicators, whether the household was surveyed during the lean season, survey year, country, country x survey year, and administrative fixed effects for district (Ethiopia. Lesotho, Malawi) and province (Mozambique, Zambia, Zimbabwe). Robust t-statistics are shown in parenthesis. Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent.

26
ratings - to be published on the OECD’s website<sup>24</sup> . Corporate claims would continue to attract a minimum 8% capital charge. Considering the implementation of Basel II in a developing country banking system, sovereign ratings are frequently less important as banks tend not to lend too much of their portfolio abroad and in that case the SSA is then similar to Basel I across the board 8% minimum requirement.

However, even under SSA’s basic approach to credit risk there are some differences to Basel I. First, there is a difference in lending to a bank’s own sovereign – or holding bonds of the sovereign in an investment account. If the loan is funded and lent in the sovereign’s own currency, then this may attract a zero capital charge minimum but, if not, it would attract the charge relevant to the (Export Credit Agency) rating of the sovereign<sup>25</sup> . Second, the altered sovereign ratings feed into different ratings for banks<sup>26</sup> . Third, Basel II SSA would allow residential mortgages to have a minimum 35% risk weight (under Basel I this was 50%), and fourth a 75% minimum the retail exposures and fifth a 150% weight for loans in arrears if provisions are less than 20% (previously this was not specified and hence 100% with the presumption that provisioning would increase)<sup>27</sup> . Six, there is a subtle change for short term lending to banks. Under Basel I, shorter-term loans (initially under 12 months and then reduced to 6 months) to banks attracted a 20% charge. Under SSA, the normal procedure would be that bank loans to other banks had a capital requirement that corresponded to one rating worse than the sovereign. However, if the loans were under 3 months, funded and lent in the owncurrency of the country, and the country used the zero risk weight for lending to the sovereign, then a more generous treatment might apply. However, for a developing country with a non-investment grade rating this would certainly be higher than the previous 20% treatment.

Perhaps most importantly, SSA would imply the introduction of a capital requirement (and associated supervisory monitoring and enforcement) for

> 24 The advantage of this is that it replaces the old “OECD rule” under which sovereign members of the OECD attracted a zero capital charge whereas non-OECD members attracted 8%.

> 25 Again, this refers to the replacement of the Basel I “OECD rule”.  However, this was rarely applied to a bank’s own sovereign and hence there was something of a vacuum.  It is not clear whether developing countries will apply Basel II as written when it comes to bank’s own sovereign.  In practice, there is a growing list of developing countries that do already apply capital charges to lending to their own sovereign and that go beyond Basel II.  This is an area where further consideration is certainly required.

> 26 Under SSA loans to other banks attract a capital requirement that corresponds to one rating “bucket” worse than the sovereign itself.

> 27 To obtain the capital requirement the risk weight must be multiplied by the basic capital requirement – the minimum in Basel II as in Basel I is 8%.

17
Table 1: Summary statistics, Grade 12 exam

||1998|2002<br>2006|2010|
|---|---|---|---|
|Panel|A: fema|le test takers||
|total|210|382<br>624|820|
|region||||
|2|113|146<br>239|305|
|3-4|42|162<br>202|242|
|5-6|55|74<br>183|273|
|Panel|B: male|test takers||
|total|749|=846—<br>1,130|1,371|
|region||||
|2|282|251<br>486|532|
|3-4|210|356<br>272|323|
|5-6|257|239<br>372|516|
|Panel|C: fema|le share of test|takers|
|total|0.22|0.31<br>0.36|0.37|
|region||||
|2|0.29|0.37<br>0.33|0.36|
|3-4<br>|O17<br>|0.31<br>0.43<br><br>|0.43<br>|
|5-6|0.18|0.24<br>0.33|0.35|
|Panel|D: Engl|ish and Math s|core, female|
|total|-0.46|-0.24<br>-0.28|-0.23|
|region||||
|2|-0.33|-0.01<br>-0.15|-0.04|
|3-4|-0.64|-0.44<br>-0.40|-0.36|
|5-6|-0.60|-0.27<br>-0.33|-0.32|
|Panel|E: Engl|ish and Math s|core, male|
|total|-0.21|-0.14<br>-0.18|-0.09|
|region||||
|2|-0.01|0.11<br>-0.04|0.01|
|3-4|-0.51|-0.37<br>-0.30|-0.23|
|5-6|-0.18|-0.05<br>-0.26|-0.11|

Sample is Grade 12 student exam records, public schools Regions 2-6, 1998-2012. Test scores are mean 2-scores, standardized based on universe of students taking exam in that year, including Region 1.
Budget savings deriving from the reforms were expected to be partly redistributed in the form of transfers to the population (50 percent), partly used by the government for administration (20 percent), and for improving the efficiency of the energy, transport, and industry sectors (30 percent).

The actual reforms that unfolded in the weeks following December 18, 2010 included major prices increases for all fuel products, electricity, water, transport, and bread. The price of gasoline increased fourfold from the equivalent of US$ 0.10 per liter to US$ 0.40 per liter for quotas<sup>2</sup> and from US$ 0.60 per liter to US$ 0.70 per liter for nonquotas. The price of diesel increased tenfold from US$ 0.06 per gallon to US$ 0.6, and the price of natural gas for domestic consumption increased at least fivefold from 1–1.3 cents per cubic meter to 7 cents per cubic meter. Prices for electricity and water also increased by around 300 percent on average, and the reform did not spare public transport or bread, with prices increasing by more than 200 percent.<sup>3</sup> The price reform was accompanied by a compensatory cash transfer of 445,000 rials per person per month, an amount equivalent to 28 percent of the median household income and 50 percent of the income of a minimum wage worker with a family of four. This transfer was quasiuniversal. About 80 percent of households were made eligible on the eve of the reform, and more households were added later. The cash transfer was administered via bank accounts. The first transfer was deposited in accounts in advance of the price increases in an effort to minimize protest and distrust for reforms.

The reform had a clear impact on prices, which increased during the first half of 2011 across main consumption items, with average increases around 30 percent and peak increases around 100 percent. Consumption of fuel products such as gasoline and liquefied gas decreased by about 10 percent. In January 2012 the government estimated that total savings from the reform amounted to an equivalent of US$ 15 billion. The simulations provided in the Islamic Republic of Iran case-study (Verme and Araar, 2016) show that the compensatory cash transfers provided were excessive to compensate for the reforms and that a large part of the transfers accrued to the nonpoor. Perhaps because of these large effects, the government partly rolled back reforms in March 2012 when the parliament amended the Targeted Subsidies Reform Act.

The main trigger for reforms was the size of subsidies, which by 2010 were estimated at the equivalent of US$ 100 billion, an amount larger than the total oil revenues and over 20 percent of 11
Second, the trade impact of the most ambitious PTAs in cluster # 1 is magnified for agricultural products, a sector where ensuring compatibility of norms and controls is an important challenge.

Table 2: Partial equilibrium impacts of different types of PTAs, PPML estimates

|Dep Var:<br>_Xijt/Xjt_|Manufacturing<br>and Agriculture<br>(1)|Manufacturing<br>only<br>(2)|Agriculture<br>only<br>(3)|
|---|---|---|---|
|PTA<sup>_z_#1</sup><br>_ij,t_|0.519***|0.447***|0.981***|
||(0.065)|(0.066)|(0.112)|
|PTA<sup>_z_#2</sup><br>_ij,t_|0.268***|0.262***|0.174***|
||(0.032)|(0.034)|(0.060)|
|PTA<sup>_z_#3</sup><br>_ij,t_|0.107***|0.011|0.121*|
||(0.035)|(0.036)|(0.072)|
|Opted Out PTA_ij,t_|0.157***|0.108**|-0.086|
||(0.043)|(0.045)|(0.107)|
|INTL BRDR_∗_1990|0.274***|0.233***|0.155**|
||(0.032)|(0.034)|(0.063)|
|INTL BRDR_∗_2000|0.761***|0.616***|0.825***|
||(0.043)|(0.048)|(0.079)|
|INTL BRDR_∗_2010|1.130***|0.963***|1.269***|
||(0.050)|(0.056)|(0.097)|
|Observations|486,623|413,265|388,594|
|R-squared|0.984|0.974|0.997|
|Period|1986-2018|1986-2018|1986-2018|
|Interval|1-year|1-year|1-year|
|FEs|it jt ij|it jt ij|it jt ij|

_Notes_ : Exporter-time ( _it_ ), Importer-time ( _jt_ ) and Exporter-Importer ( _ij_ ) fixed effects. Standard errors in *** ** * parentheses are clustered by country-pair, p _<_ 0.01, p _<_ 0.05, p _<_ 0.1. The excluded category for the international border dummy is the period 1986-1989.

As in Fontagn ́e & Santoni (2021), we derive a matrix of bilateral frictions on the basis of the estimates from column 1 of Table 2: where ( _Xij,t/X_<sup>�</sup> _ij,t_ ) is the ratio of observed to predicted trade from Equation 3. The “estibrated” _τ_<sup>�</sup> _ij,t_<sup>1</sup><sup>_−σ_</sup> as well as the estimated inward and outward multilateral resistance terms represent the key elements for the baseline and the counterfactual exercise that follows. Note that given _σ_ = 6, the estimated term _τ_<sup>�</sup> _ij,t_<sup>1</sup><sup>_−σ_</sup> should be interpreted as the reciprocal of the bilateral trade friction, denoted by _τij,t_ . Consequently, this term serves as a measure of 12
wealthier students of traditional schools did. When they control for the portion of EDUCO schools in each municipality, they find that EDUCO has no effect on mastery of mathematics but expanded mastery of language by about two subjects, or one standard deviation.<sup>1</sup> When they control for the factors that municipalities used to place EDUCO schools, they find that EDUCO has a positive but insignificant effect on language scores. They also found that EDUCO decreased student absenteeism. Sawada and Ragatz (forthcoming) use similar methodology to find that EDUCO has transferred few administrative processes to local levels but gives local actors greater perceived influence in hiring and firing teachers. Controlling for other factors, teachers in EDUCO spend more hours teaching, more often meet with parents, principals, and other teachers, are absent less, and attend more training sessions than teachers in similar non-EDUCO schools do.

King et al. (1999) evaluate School Autonomy, a Nicaraguan program from the mid1990s that selected public primary and secondary schools to create faculty or parent councils with power to change the curriculum, choose textbooks, independently evaluate students, hire and fire principals, and set monthly fees for students. In other public schools, the Ministry of Education oversaw these activities. They find that program participation significantly increases the portion of decisions made at the school level but did not impact the level of influence felt by principals or teachers.

# 3. Data Sources and Outcome and Control Variables

Our empirical analysis is based on a variety of data sources. These are the annual Mexico School Censuses, locality level data from the 2000 Mexico Population and Housing Census, and administrative data files from the PEC, Oportunidades and CONAFE programs. We combine all school data sources at the school level using unique school

> 1El Salvador’s math and language tests have 30 and 36 questions. Jimenez and Sawada define outcomes according to the number of subjects a student has mastered, where mastery is defined as correctly answering two

6
(SIC) codes in the Economics and Statistics Association of the US Patent and Trademark Office’s 2012 report on intellectual property. Finally, because the impacts of IPAs membership might vary by countries’ comparative development levels, we allow for such effects to vary by discrete income groups, as designated by the World Bank.<sup>9</sup> We use unchanging income-group designations for countries based on 1995 definitions, classifying countries as low-income (LI), lower-middle income (LMI), upper-middle income (UMI), or high income (HI). In all cases we estimate trade effects in samples excluding trade with partners (both before and after the entryinto-force of the PTA) in the treatment IPAs. As discussed below, this approach excludes potential endogeneity between existing trade linkages with those partners and decisions to join such PTAs.

Turning to bilateral trade among all country pairs, we adopt a gravity specification in which we identify specific coefficients on imports and exports of IP-sensitive goods, using the disaggregated sectoral breakdowns. This approach permits estimation of the impacts of membership in an IP-related PTA on sectoral trade with bilateral trade partners outside the agreements.

With this background, our identification relies on three types of variation. First, during our sample some countries entered into IP-related trade agreements, as we define them below, while others did not (note that countries rarely exit PTAs once they have joined). We also distinguish among countries at varying income levels, noting that both their membership decisions and their economic responses to such agreements may vary. Second, as already noted, we distinguish between sectors in terms of their apparent relative usage of intellectual property, computing trade impacts in high-IP and low-IP industries compared to the same sectors in control countries. This distinction is important, for if IP chapters matter for trade, in comparison with just the impacts of membership in a PTA generally, the effects should show up in relatively greater impacts in the high-IP set of industries. The third important element for identification is to control for TRIPS adherence. Most countries in our sample became compliant with TRIPS at some point in the period, which may have happened before or after their joining an IP-related PTA. In order clearly to isolate the IPA effect, therefore, our preferred specification defines

> 9 For detailed lists of industry and country groupings see Maskus and Ridley (2020).

12