rafmacalaba/datause-encoder-data huggingface dataset:
We also find that close to half of the total fall in consumption among urban households was due to a decline of nearly 80% in expenditures in durables, including items such as a stoves or refrigerators. Urban households also cut back on education-related expenses (around 13%) although –as will be discussed below—we do not observe that this led to an increase in dropout rates.

A regular argument in the literature on household risks and welfare is that the negative effects of shocks are typically larger among the poor. We looked at whether this was also the case in the context of Agatha. In the absence of household and/or individual panel data, we relied on estimates of municipal poverty rates in 2006 drawing from a poverty map jointly produced by the World Bank and INE. The subgroup analysis using different levels of poverty incidence did not support the notion that poverty at baseline is a strong predictor of the direction and magnitude of the effects.

# _Household income and adult labor supply_

In trying to assess whether the fall in household consumption is itself the result of a persistent negative income shock triggered by the storm, we examine the effects of Agatha on household income per capita. In addition to collecting detailed consumption data, _Encovi_ captured rich information on labor and non-labor income for all individuals in the household both before and after Agatha.

Similar to the analysis on consumption, we ran standard fixed-effects model specifications of the D-D estimator using the binary definition of the treatment. The results –summarized in Table 5– indicate that Agatha is associated with a reduction of household income per capita on the order of 10%, equivalent to 44.4 quetzales per capita per month. Effects estimated separately for urban and rural areas are also negative but not statistically significant. Unpacking the effects by income components reveals that the fall in total income is largely driven by a drop in income from labor, particularly from salaried jobs in urban areas, corresponding to 27% of the baseline median (third column of Table 5). Indeed, lower wage income among affected urban households helps explain nearly 70% of the decline in their household income per capita. The results also point to a fall in non-wage labor income in urban 14
Other data used in our analysis include commodity export price shocks, conflict, population and income per capita.

The commodity export price shocks data is a comprehensive database of country-specific commodity price shocks created by Gruss and Kebhaji (2019). A commodity export price shock for each country is the combination of changes in international prices of up to 45 commodities and the country’s exposure to these commodities’ exports (as share of GDP). The weights of each commodity can be fixed weights (based on average exports over GDP over several decades) or time-varying weights (which can account for time variation in the mix of commodities traded and the overall importance of commodities in economic activity). We used fixed weights in our regressions, although using time-varying weights yields consistent results.

Conflict data are from the UCDP/PRIO Armed Conflict Version 21.1 Dataset. This is a dataset that tracks all country-year pairs when conflict occurred, having at least 25 deaths in a given year, from 1946 to 2020. The dataset covers 4 types of conflict: extra-systemic (any conflict between a state a non-state entity outside the territory of the state in question), interstate (any conflict between two states), intrastate (any conflict between a government and non-government entity on the same territory of the government) and finally internationalized intrastate (any intrastate conflict that also includes foreign government intervention). For our paper, we have focused on internal conflict, that is captured through intrastate and internationalized intrastate conflict. A dummy variable is created to identify the country-year pairs when either of these types of conflict occur.

Population and GDP per capita data are from the World Bank’s World Development Indicators (WDI) Dataset. They are a proxy for country size and the level of economic development in a country, respectively. GDP growth rates used to measure growth volatility and indicate years of economic booms are also obtained from the WDI Dataset.

Table 2 presents summary statistics for all the variables of interest. In addition, summary statistics of longer-horizon forecasts are also included. It is interesting to note that the longer the horizon of forecasts, the larger forecast error and absolute forecast error. For example, the average GEP’s same-year one-year ahead and two-year ahead forecasts error are 1.3, 1.8 and 2.6 percentage points respectively. Even when we restrict them to have the same sample (in Appendix Table A1), the same results hold. This finding is consistent with those in Ho and Mauro (2016).

10
from the resource fund. Then a scaling-up path of public investment is specified as a second-order delay function, where _gnss_ is the scaling-up investment target expressed as percentage deviation from the initial steady state, _k_ 1 _>_ 0 represents the speed of adjustment of public investment to the new level, and _k_ 2 _≥ k_ 1 represents the degree of investment frontloading.<sup>9</sup> In particular, if _k_ 1 = _k_ 2 = 0, public investment stays at its original steady-state level, i.e., _gt_ = _g ∀t_ .

- **Permanent income hypothesis (PIH)** . This approach is arrived at by letting _k_ 1 _, k_ 2 _→∞_ and setting _gnss_ according to the PIH annuity as shown in Section 5. Then public investment jumps to the new steady-state level immediately.

# **5 Policy scenarios: application to Kenya**

A series of commercial oil explorations in Northern Kenya have recently boosted prospects for Kenya’s upstream oil industry. Discovered reserves estimated at 600 million barrels were announced in February 2014, and follow up explorations and appraisals have further de-risked the discovered resources. In addition, several companies have acquired blocks and are drilling (or planning to drill) both onshore and offshore. It will take several years before Kenya’s oil and gas reserves have been assessed and the current slump in oil prices does not accelerate this process; nevertheless, the authorities are already considering the policy and development implications of this discovery.

In global terms, Kenya’s discovered resources are relatively small. Kenya’s 600 million barrel stock puts Kenya at 47th position worldwide in terms of oil reserves, just ahead of Uzbekistan. This quantity constitutes a small fraction of the reserves in resource rich African countries like Libya, Nigeria, Angola and Algeria, both in terms of absolute and per capita amounts. For comparison, Saudi Arabia produced about 11.5 million barrels

> 9Differentiating (52) it can be shown that for _k_ 2 _> k_ 1, _gt_ reaches a maximum at time _tmax_ = _k_ 2 _−_ <u>1</u> _k_ 1<sup>log( 2</sup> _k_<sup>_k_</sup> 1<sup><u>2</u>)andthat</sup><sup>_tmax_isadecreasingfunctionof</sup><sup>_k_</sup> _k_<sup><u>2</u></sup> 1<sup>.Thusas</sup><sup>_k_2increasestheinvestmentpathbe-</sup> comes more front-loaded. In principle _k_ 1 and _k_ 2 can be chosen to be optimal in relation to a policymaker’s objectives. This aspect is left for future research.

15
# **TABLE A20:  Latinobarometro (LBO)**

## **A20: Latinobarometro**

http://www.latinobarometro.org Latinobarometro is a public opinion survey representing the opinions, attitudes, behaviour and values of citizens of the countries in which it is conducted. The survey began being applied regularly in 8 countries of the region in 1995, and in 17 countries beginning in 1996. Latinobarometro conducts an annual survey, using representative samples and an identical questionnaire in each country. It asks questions in in the following areas: Economy and International Trade, Integration and Regional Trading Blocks, Democracy, Politics and Institutions, Social Policies, Civic Culture, Social Capital and Social Fraud, the Environment, and Current Issues.

In the table below we list the variables included in each of the governance indicators. We use data from the 1996, 1998, 2000, and 2002-2005 surveys.

|**Table A20: Latinobaromet**|**ro(18 Latin American**|**coun**|**tries)**|||||
|---|---|---|---|---|---|---|---|
||2005|2004|2003|2002|2000|1998|1996|
|**Voice and Accountability**||||||||
|Satisfaction with democracy|X|X|X|X|..|X|X|
|Trust in Parliament|X|X|X|..|..|X|..|
|**Political Stability**||||||||
|Country terrorist threat|..|..|..|X|..|..|..|
|**Government Effectiveness**||||||||
|Trust in Government|X|X|..|X|..|..|..|
|**Regulatory Quality**||||||||
|NA|..|..|..|..|..|..|..|
|**Rule of Law**||||||||
|Trust in Judiciary|X|X|X|X|X|X|X|
|Trust in Police|X|X|..|..|X|X|X|
|Have you been a victim of crime?|X|X|X|X|..|..|..|
|**Control of Corruption**||||||||
|Frequency of corruption|X|X|X|X|X|X|..|

70
change in bilateral FDI frictions, we also change uniformly the vector of bilateral trade frictions based on our estimate from column (2) of Table 1. Relative to the scenario where only the bilateral FDI frictions are changed (i.e., the results presented in columns (2) and (3) of Table 6), we see qualitatively a very similar picture and quantitatively an increase in both, inward and outward FDI. Specically, on average, trade liberalization has contributed to 0.7 percentage points (or about 25%) increase in inward FDI and about 15 percentage points in outward FDI. These estimates reveal that trade liberalization via DTAs is an important channel to stimulate FDI, thus complementing the results from Anderson et al. (2019), who show that FDI liberalization is important for trade.

Panel B of Table 6 reports estimates that are obtained based on the estimates on the continuous depth variables from Section 4.1. The estimates in columns (6) and (7) rely on the estimates from column (4) of Table 2. Allowing for the continuous depth leads overall quantitatively and qualitatively similar results, with heterogeneous changes across countries. Finally, the estimates in columns (8) and (9) of Table 6 are obtained when, in addition to the change in bilateral FDI frictions, we also change the vector of bilateral trade frictions based on our estimate from column (3) of Table 1. Similar to the uniform changes, the additional allowance for changes of bilateral trade frictions leads to larger eects for inward and outward FDI.

To sum up, according to our analysis, the DTAs that were in force in 2011 have contributed to about 3% of inward FDI in the world and about 70% of outward FDI. The latter is heavily driven by the fact that some countries have large stocks of FDI used in many countries in the world, multiplying the eect of any change in outward FDI of those countries due to changes in frictions.

40
The standard global version of the assessment comprises 93 items grouped in 14 subtasks (activities) chosen after analyzing a large cross-country dataset consisting of 24,533 children in 23 countries and identifying items with robust psychometric properties across contexts (Pushparatnam et al., 2021). In addition to the standard AIM-ECD subtasks items, during the contextualization process for Pakistan, a single subtask testing English letters was included so that children could be assessed on both their Urdu and English letter knowledge. **Figure 2** shows the AIM-ECD DA subtasks and the developmental domains to which each subtask belongs (World Bank, 2022).

While the AIM-ECD was not designed for specific use in Pakistan, the constructs it assesses are highly relevant according to both provincial and national sources. _Appendix A: Crosswalk between Student Learning Objectives and AIM-ECD Subtasks_ reviews the alignment of the assessment with the Single National Curriculum and finds good coverage (National Curriculum Council, 2020). _Appendix B: Crosswalk between Punjab Early Learning and Development Standards and AIM-ECD_ reviews the contents of Punjab-specific standards and the AIM-ECD assessment, again finding good matches for many constructs deemed important by the provincial education authorities (Quaid-e-Azam Academy for Educational Development & UNICEF, n.d.).

_Figure 2. AIM-ECD DA Subtasks and Domain Assignments_ <!-- Start of picture text --> AIM-ECD Overall<br>English Letter<br>(Early Learning and Identification<br>Development)<br>Early Early Executive Social English Letter<br>Numeracy Numeracy Functioning Emotional Identification<br>Head, Toes,<br>Listening Receptive Perspective<br>Knees,<br>Comprehensio Spatial Shoulders Taking/Empat<br>n Story Vocabulary Task hy<br>Urdu Letter Producing a Forward Digit<br>Identification Set Span<br>Initial Sound Simple Backwards<br>Addition and<br>Discrimination Subtraction Digit Span<br>Number<br>Name Writing Identification<br>Number<br>Comparison<br>Shape<br>Identification<br><!-- End of picture text --> The Teach ECE tool was developed to measure both structural and process aspects of classroom quality (World Bank, 2021). Structural aspects of quality refer to static aspects of quality that exist in the classroom, for example the quality and availability of learning materials, adequacy of the classroom environment, teacher-to-student ratio, and qualifications of the ECE 7
- 36 -

# FOOTNOTES

# See Bruno (1986) and Liviatan (1986a)

2 S The spread with respect to nominal (ex post) dollar linked loans for domestic activity was considerably smaller. The latter interest rate remained about 1-1.5 percentage points above inflation in monthly terms. Even this represents a large spread, especially if we take into account that the expected nominal rate on these loans exceeded the actual one because the same thing applies to devaluation (especially in 1987). Interest rates on loans to exporters were kept close to international levels.

4 In additional, the degree (percentage of compensation) of wage indexation was reduced from 80Z to a range between 70-80S depending on the time which elapsed between consecutive COLA payments.

5 We shall use this notation for Bank of Israel Annual Report 1987 (Hebrew), with similar notation for other years.

6 Within the framework of the tax reform the income tax rate on undistributed corporate profits was reduced from 61 to 45 percent. As for personal income tax, the two highest steps of the tax rates - 50 and - 60 percent were abolished, with the maximum rate being reduced to 48S (however a temporary surcharge **was** imposed on the latter bracket which raised it to 532 till December 1987).

- 7 One form of compensation for the workers was the government's obligation to freeze prices of a number of 'basLce commodities till April 1988.

The base of 13 consists of the money base (base of 42) plus the reserve requirements of the private banks (deposited in the Bank of Israel) against foreign exchange linked deposits of the private sector. Since the latter require a 1002 backing of reserves (again in the form of foreign exchange linked deposits), the shift towards non-linked deposits following disinflation, created excess reserves which were partly sterllized by raising reserve requirement on shekel deposits.

- 9

Total U.S. government aid to Israel in 1984 was about three billion dollars which consisted of 1.8 billion in military aid (half of which **was** a grant) and 1.2 billion dollars as a *civilian (non-defense grant). Note that the change from loans to grants does not affect the net resource transfer in the short run and therefore should not be reflected in the changes in the foreign exchange reserves.

10 Strictly, the stabilization period began in July 1985 but in fact a partial fiscal adjustment took place in the first half of this year.

11 Two other facts about the fiscal adjustment are worth noting. Comparing 1985-87 with 1980-84 in terms of average ratios of GNP we find that
|Vanuatu<br>Ethiopia|LIC and LMIC<br>LIC and LMIC|||6.8<br>57|6.9<br>64|7.7<br>77|28<br>29|
|---|---|---|---|---|---|---|---|
|<br>Belize|<br>LIC and LMIC|12.2|10.3|.<br>8.7|.<br>7.0|.<br>7.5|<br>30|
|Hungary|HIC||||8.9|7.3|31|
|Korea, Rep.|HIC|4.7|4.4|6.4|10.3|7.3|32|
|<br>Cambodia|LIC and LMIC||||6.3|7.2|33|
|Portugal|HIC|13.5|14.2|16.9|9.8|7.1|34|
|Senegal|LIC and LMIC||5.1|5.3|5.8|6.6|35|
|Australia<br>|HIC<br>|6.6<br>|6.0<br>|5.9<br>|6.2<br>|6.5<br>|36<br>|
|Canada|HIC|5.6|6.3|6.2|5.6|6.5|37|
|Estonia|HIC||||5.2|6.4|38|
|Tuvalu|UMIC||||6.5|6.3|39|
|Czech Republic<br>New Zealand|HIC<br>HIC||7.1|5.7|8.1<br>6.1|6.2<br>6.1|40<br>41|
|Dominican Republic|UMIC|10.6|10.4|9.8|7.7|5.8|42|
|Swaziland|LIC and LMIC||10.2|9.8|5.3|5.6|43|
|Netherlands|HIC||5.4|6.2|5.4|5.6|44|
|Cape Verde|LIC and LMIC||0.8|1.4|1.8|5.5|45|
|Belgium|HIC||||5.4|5.5|46|
|France|HIC||6.5|6.8|4.8|5.4|47|
|Italy|HIC|13.8|9.7|7.5|5.6|5.4|48|
|Finland|HIC||6.5|6.2|4.6|5.4|49|
|Israel|HIC||11.2|9.1|8.3|5.3|50|
|Germany|HIC||5.8|6.8|6.3|5.3|51|
|Chile|UMIC|6.3|6.2|8.5|5.9|5.0|52|
|Poland|HIC||||5.5|5.0|53|
|Palau|UMIC|||4.3|5.2|5.0|54|
|Mongolia|LIC and LMIC|||4.3|5.0|4.9|55|
|Slovenia|HIC||||5.5|4.8|56|
|Honduras|LIC and LMIC|4.6|4.3|4.5|4.7|4.6|57|
|Bolivia|LIC and LMIC||5.3|6.0|5.2|4.6|58|
|Yemen, Rep.|LIC and LMIC|||4.5|4.3|4.6|59|
|United States|HIC|4.1|5.0|4.4|4.2|4.5|60|
|China|UMIC||2.2|3.1|4.0|4.4|61|
|Slovak Republic|HIC||||5.0|4.4|62|
|<br>Austria|HIC||6.2|6.6|5.6|4.3|63|
|Armenia|LIC and LMIC||||4.5|3.9|64|
|<br>Thailand|<br>UMIC|6.0|5.2|4.0|<br>3.7|<br>3.9|<br>65|
|Denmark|HIC||4.2|4.4|3.0|3.8|66|
|<br>Iceland|<br>HIC||||<br>3.5|<br>3.8|<br>67|
|Lao PDR|LIC and LMIC|||3.7|5.6|3.7|68|
|Madagascar|LIC and LMIC|||1.0|1.4|3.6|69|
|Gambia The|LIC and LMIC|||||3.5|70|
|,<br>Switzerland|<br>HIC|||3.9|3.4|<br>3.3|<br>71|
|United Kingdom|HIC||3.2|4.2|2.7|3.3|72|
|<br>Samoa|LIC and LMIC||||3.6|3.1|73|
|Japan<br>Kenya|HIC<br>LIC and LMIC|2.1|6.0<br>2.3|4.9<br>2.6|3.3<br>2.6|3.0<br>2.8|74<br>75|
|Georgia|LIC and LMIC||||1.8|2.8|76|
|<br>Peru|<br>UMIC||||<br>4.1|<br>2.8|<br>77|
|Indonesia<br>|LIC and LMIC<br>|1.5<br>|2.1<br>|2.8<br>|3.1<br>|2.7<br>|78<br>|
|Sweden<br>Luxembourg|HIC<br>HIC|5.9|3.7|4.1|2.0<br>2.7|2.6<br>2.5|79<br>80|
|<br>Tajikistan|LIC and LMIC|||2.6|2.5|2.4|81|
|Togo|LIC and LMIC||||2.2|2.4|82|
|<br>Eritrea|LIC and LMIC||||1.8|2.1|83|
|Ghana|LIC and LMIC|||||1.9|84|
|Morocco|LIC and LMIC|2.2|2.3|2.1|2.0|1.9|85|
|Paraguay|LIC and LMIC|3.2|2.5|2.8|1.9|1.9|86|
|<br>South Africa<br>Egypt Arab Rep|UMIC<br>LIC and LMIC|3.7<br>43|3.2<br>34|2.8<br>22|1.5<br>17|1.8<br>18|87<br>88|
|, .<br>Guyana|<br>LIC and LMIC|.<br>0.7|.<br>0.8|.<br>1.0|.<br>1.0|.<br>1.1|<br>89|
|Kyrgyz Republic|LIC and LMIC||||0.9|0.4|90|
|<br>Tanzania|<br>LIC and LMIC|||4.6|<br>6.4|||
|Uganda<br>|LIC and LMIC<br>|2.9|3.7|5.0<br>|7.2|||
|Seychelles|UMIC|||6.2||||

8
technology system, to strengthen links between participants in the national innovation and to contribute to improved productivity, competitiveness and economic growth.

PAIDEC was created to improve cooperation between firms and institutions of higher education by encouraging firms to use available knowledge in research and industrial development projects. Eligible research projects included: applied research, design and development of products and processes and development, and adaptation and improvement of existing technologies. CONACyT granted up to 50 percent of financial support to research projects, with a limit of US$250,000. Resources could be used for raw materials, consultancy services, training, project-related travel, equipment for the project (with a maximum of 15 percent of total resources), salaries of researchers and technicians who participated in project patents, and acquisition of literature and databases.

The fiscal incentives program of CONACyT was directed towards taxpaying firms that have invested in research projects and technology development in order to develop new projects, materials and processes. The main objective of this program was to increase the annual investment and spending of firms on those three items by allowing firms to recover up to 30 percent of their annual investment.

# _STPS_

In 1998, Mexico launched the Integral Quality and Modernization Program (CIMO), which was mainly an in-firm training program. The program was implemented and administered by the STPS. In 2002, the program was decentralized to the states under a new name, Training Support Program (PAC). The program provides subsidies to training and technical assistance to SMEs in order to enhance their productivity and welfare of workers. In 2009, PAC changed once again its design, and was rename the Productivity Support Program (PAP).

**Table 11 CIMO-PAC: Main Results 2001-2006**

|**Year**|**Workers**<br>||**Firms be**|**nefited**||**Subsidies**<br>|
|---|---|---|---|---|---|---|
||**trained**|**Micro**|**Small**|**Medium**|**Total**|**(million dollars)**|
|2001|333,474|47,705|7,911|4,770|60,386|19|
|2002|201,233|27,947|4,634|2,795|35,376|14|
|2003|210,746|26,130|0|0|26,130|10|
|2004|299,656|23,622|6,547|3,577|33,746|10|
|2005|279,725|11,620|12,222|9,928|33,770|11|
|2006|265,041|17,090|10,585|9,244|36,919|11|
|Total|1,589,875|154,114|41,899|30,314|226,327|75|

**Source:** Secretaría del Trabajo y Previsión Social (2006)

From 2001 to 2006, CIMO-PAC trained about 1.6 million workers, benefiting more than 226,000 firms with an estimated annual budget of about US$11 million. The precise number of workers and firms which benefited from the program is not clear since they could apply several times to receive the courses and the subsidies. Table 11 shows one trend worth noting is that PAC sought a decline in their coverage of workers (21 percent), firms (39 percent) as well as resources (29 percent).

10
**Table 6: Average inflation adjusted loan size (Taka)**

||Average loan<br>(Taka)|size|Ratio|
|---|---|---|---|
||2000|2007|(2007/2000)|
|New member|1245|251|0.20|
|Always member|478|375|0.78|
|Drop-out|725|669|0.92|
|Never member|909|721|0.79|
|All Households|896|555|0.62|
|No. of total observations|1528|1528||

**Table A.1: Summary Statistics**

||Mean<br>Standa<br>**InM-PKSF (2006-2007) S**<br>|rd Deviation<br>**urvey (n=793)**<br>|
|---|---|---|
|Moneylender Interest rate|19.10|26.42|
|MFI coverage rate|42.08|22.42|
|% of household with irrigation|62.00|29.94|
|Distance to bank (km)|4.53|3.95|
|Distance to market and facilities (km)|3.31|2.64|
|No. of survey households in the village|204.69|184.38|
|% of households with VGD card|6.43|14.55|
|% of functionallylandless household|80.04|12.50|
|**BIDS-B**|**RAC Panel (2000, 2007) Su**|**rvey (n=1365)**|
|‘New’ Borrowers in 2007|0.07|0.26|
|‘New’ MFI members in 2007|0.28|0.45|
|Log( head's age)|3.74|0.31|
|Heads Education above primary|0.31|0.46|
|No. of Agri Workers|0.79|0.81|
|Agri. Land owned in 2000 (ha)|0.58|1.01|
|Agri land cultivated in 2000 (ha)|0.41|0.71|
|Household size in 2000|5.14|2.27|

35