rafmacalaba/datause-encoder-data huggingface dataset:
17 Staniland, P., A. Naseemullah, and A. Butt. 2018. “Pakistan’s Military Elite.” _Journal of Strategic Studies_ (August 7): 1–30.

Sütalan, Z. 2006. “Globalization and the Political Economy of Reform in Jordan.” Master’s thesis, Graduate School of Social Sciences of Middle East Technical University, Ankara, Turkey.

Swaidan, Z., and M. Nica. 2002. “The 1991 Gulf War and Jordan’s Economy.” _MERIA Journal_ 6 (2). http://www.mafhoum.com/press4/114E11.htm.

Troquer, Y. L., and R. H. al Oudat. 1999. “From Kuwait to Jordan: The Palestinians’ Third Exodus.” _Journal of Palestine Studies_ 28 (3): 37–51.

United Nations Disaster Relief Organization (UNDRO). 1990. “The Iraq/Kuwait Crisis: International Assistance to Displaced People Through Jordan.” Case Report, United Nations, Geneva, Switzerland.

U.S. Department of State. 1995. “Background Notes: Jordan.” Bureau of Public Affairs, Washington, DC.

van Hear, N. 1992. “Consequences of the Forced Mass Repatriation of Migrant Communities: Recent Cases from West Africa and the Middle East Options.” United Nations Research Institute for Social Development, DP 38, United Nations, Geneva, Switzerland.

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Zureik, E. 1994. “Palestinian Refugees and Peace.” _Journal of Palestine Studies_ 24 (1): 5–17.
17 of monetary, trade, and fiscal policy. Thus, finance does not simply follow growth; financial development predicts long-run growth.

While improving on past work, there are methodological and interpretation problems with the KL analyses. As noted in the Introduction, the proxy measures for financial development, DEPTH and the alternative measures, do not directly measure the ability of the financial system to (i) overcome information asymmetries and funnel credit to worthy firms, (ii) monitor managers effectively and exert corporate governance efficiently, (iii) provide risk management services, or (iv) facilitate exchange and the pooling of savings. This lowers the confidence one has in interpreting the results as establishing a link running from financial development to economic growth. Also, while KL show that finance predicts growth, they do not deal formally with the issue of causality. Finally, KL only focus on one segment of the financial system, banks. They do not incorporate measures of other components of national financial systems.

# **2.2. Cross-country studies: Stock markets and banks**

Following Atje and Jovanovic (1993), Levine and Zervos (1998, henceforth LZ) add measures of stock market and banking development to cross-country studies of growth. Thus, they simultaneously examine two components of the financial system: banks and equity markets. This provides information on the independent impact of stock markets and banks on economic growth. Thus, these analyses help policymakers set reform priorities and influence debates on the comparative importance of different segments of the financial sector (Demirguc-Kunt and Levine, 2001).

LZ construct numerous measures of stock market development to assess the relationship between stock market development and economic growth, capital accumulation, and
loss was offset by 0.3 job gains in services sector one year later. This suggests the offsetting equilibrium effects took effect much earlier in the services sector by absorbing laid-off workers in the tradable sector. Two-year lags of Bartik and the instrumented change in tradable jobs also have similar impacts on retail and restaurants and services sector. Columns [4] to [6] reveals that one tradable job loss is offset by 0.1 new job in retail and restaurants, 0.03 job in construction and 0.18 job in services sector two years later.

Table 5.4: Breakdown by sector

|||Chang<br>Reduced form<br>|e in number o<br>|f non-tradable<br>|jobs<br>IV<br>||
|---|---|---|---|---|---|---|
|VARIABLES|R & R|Construction|Services|R & R|Construction|Services|
||[1]|[2]|[3]|[4]|[5]|[6]|
|Bartik|0.189***|0.217***|0.534***||||
||[0.019]|[0.020]|[0.059]||||
|Bartik (t-1)|0.052***|0.069***|-0.242***||||
||[0.018]|[0.020]|[0.066]||||
|Bartik (t-2)|-0.083***|-0.006|-0.152**||||
||[0.018]|[0.017]|[0.065]||||
|△tradable jobs||||0.227***|0.261***|0.603***|
|||||[0.012]|[0.013]|[0.039]|
|△tradable jobs (t-1)||||0.036***|0.060***|-0.33***|
|||||[0.012]|[0.013]|[0.039]|
|△tradable jobs (t-2)||||-0.109***|-0.030**|-0.18***|
|||||[0.012]|[0.013]|[0.037]|
|Constant|Y|Y|Y|Y|Y|Y|
|Time fixed effect|Y|Y|Y|Y|Y|Y|
|County fixed effect|Y|Y|Y|Y|Y|Y|
|Observations|42,850|42,850|42,850|42,850|42,850|42,850|
|R-squared|0.042|0.065|0.026||||
|Number of county|3,086|3,086|3,086|3,086|3,086|3,086|

Robust standard errors in brackets, *** p<0.01, ** p<0.05, * p<0.1 20
share of votes obtained in each election by left-wing parties, center parties (the residual of neither left-wing nor right-wing parties), and right-wing parties.

# **B. B Presidential elections**

Our analysis of presidential elections focuses on the first-round elections that took place between 2000 and 2022. The 2022 presidential elections took place May 29; the results are publicly available on Colombia’s electoral agency web page<sup>23</sup> but are not compiled in a single document, so we scraped the web page to gather all the information. As in the mayoral elections, we classified all candidates according to their apparent political ideology, following Fergusson et al. (2020).

To ensure consistency in classifying presidential candidates, we provide Table A.5, which outlines the criteria used. Candidates are categorized as left, center, or right based on and Candidates as self-identification, policy platforms, party ideology. classified Right met at least two conservative criteria (8 candidates), those classified as Left met at least two left-leaning criteria (7 candidates), and those classified as Center met fewer than two criteria (40 candidates).

23https://resultados.registraduria.gov.co/presidente/0/co 61
They could also be driven more by consumption than by investment demand, a hypothesis that is difficult to test with the available data. In states with large shares of agriculture in output, rainfall and floods also affect growth.

The immediate impact of the global crisis was an absolute decline in labor-intensive exports in the coastal states, leading to significant loss of jobs. The cyclical downturn in domestic private investment further accentuated the impact. Drops in incomes in the coastal states dampened domestic demand and retarded output growth in the landlocked states, to a lesser degree and with a time lag. Although agricultural growth decelerated during 2009 and 2010, rising food prices improved the terms of trade for farmers, muting the impact on farm incomes.

**Table 10 Real Annual Changes in Gross State Domestic Product and Own Revenue in Major Coastal and Landlocked States in India, 2007–10 (Percent)**

||_Gro_|_ss state do_<br>_(GS_|_mestic pr_<br>_DP)_|_oduct_||_Own reve_|_nues_|
|---|---|---|---|---|---|---|---|
|_States_|_2007_|_2008_|_2009_|_2010_|_2008_|_2009_|_2010_|
|Major coastal|10.0|6.5|8.1|6.8|9.3|-0.8|16.2|
|Major landlocked|7.1|8.0|7.5|9.0|-1.8|13.3|13.6|
|All<sup>a</sup>|9.3|7.0|8.1|7.7|5.0|4.1|14.5|

_Sources:_ (i) Central Statistical Organization; (ii) Reserve Bank of India; (iii) Individual state budgets and finance accounts.

a. GSDP growth rate is different from national GDP growth rate in table 1 because defense and central public administration are included there and excluded here.

The movement in states’ own revenues reflected the difference in impact in coastal and landlocked states. In the coastal states, economic growth deceleration in 2008 resulted in the deceleration of own revenue growth in 2009. In the landlocked states, growth accelerated in 2008, and revenues rose in 2009. In 2008 and in 2009, states’ own revenue moved in opposite directions for these two groups of states (table 10).

Comparing the postcrisis years (2009 and 2010) with the precrisis period (2003–07), annual growth in states’ own revenue decelerated only slightly, from 10.2 percent to 9.2 percent. This aggregate picture hides significant variation, however: own revenue growth declined by more than 5 percentage points in some coastal states (Andhra Pradesh, Odisha, Tamil Nadu, 28
- A. The Structure of Benefit-Cost Analyses of Ex Ante Risk Mitigation Projects

Because natural disasters are probabilistic events, the appropriate framework for analyzing the efficiency of loss mitigation projects is probabilistic benefit-cost analysis. Equation (1), adapted from Smyth et al. (2004a) describes the present value of expected losses in the event of an earthquake, where _T_ * : horizon _d_ : discount rate _f_ ( _a_ , _T_ ) : pdf of quake severity a in year _T Di_ : damage level _i_ _CiD_ : cost of damage level _i_ .

To illustrate, Smyth et al. (2004a) consider four damage levels associated with damage to an apartment building in Turkey in the event of an earthquake (see Table 6, adapted from Smyth et al., 2004a). Slight damage results in property loss equal to 1% of the replacement value of the building ($250,000 USD), or $2,500; moderate damage in loss equal to 10% of replacement cost ($25,000); major damage results in the loss of the building ($250,000); and total collapse in the loss of the building and NL lives, valued at V dollar per life.

**<u>Table 6 Levels of Earthquake Damage and Associated Costs</u>**

|**Damage level**<br>_i_<br>_D_|**Cost**<br>_D_<br>_i_<br>_C_|
|---|---|
|1<br>_D_<br>: slight damage|_D_<br>_C_1<br>)<br>=<br>(<br>) (<br>_V_<br>_S_<br>_s_<br>×<br>+<br>×<br>0<br>1|
|2<br>_D_ : moderate damage|_D_<br>_C_2 =<br>(<br>) (<br>_V_<br>_S_<br>_s_<br>×<br>+<br>×<br>0<br>2<br>)|
|3<br>_D_ : major damage|_D_<br>_C_3 =<br>(<br>) (<br>_V_<br>_S_<br>_s_<br>×<br>+<br>×<br>0<br>3<br>)|
|4<br>_D_ : total collapse|_D_<br>_C_4 =<br>(<br>) (<br>_V_<br>_N_<br>_S_<br>_s_<br>_L_ <sup>×</sup><br>+<br>×<br>4<br>)|

Source: Smyth et al. (2004a) The expected benefits of a building strengthening project are the difference between expected loss in (1) and the expected loss conditional on the project, which would result D in a reduction in the {Ci }. Expected net benefits are the difference between expected benefits and the present value of the costs of the project itself. Smyth et al. (2004a) compute the net benefits of three possible strengthening projects for apartment buildings in Turkey. Two such measures (partial and full retrofitting) are assumed to reduce deaths NL to zero.

9
the ATI program available in the treatment municipalities regardless of whether households actually participate (by choice or not) in the training sessions offered by the ATI program. The ITE is of particular interest to policy makers since it captures the operational efficiency of the program, such as inefficiencies in the delivery of the program, as well as any potential general equilibrium or spillover effects of the program. Below we detail our estimation method.

The typical matching estimator pairs each municipality in the treatment group to an observationally similar municipality (or several) in the control group and compares the differences in their outcomes as the effect of the program (Rosenbaum and Rubin, 1983). In practice, using outcomes post- intervention, this would be estimated from: where Y1i represents the average of outcome Y within the treatment municipality _i_ and Yoj the average of outcome Y within the control municipality _j_ which was paired to _i_ . W(i, j) are weights which depend on the metric used for the distance between treatment and matched to treatment municipalities.<sup>9</sup> One of the maintained assumptions for the validity of this ITE estimate of the impact of ATI based on the matching estimator is the conditional independence assumption (CIA) assumption which assumes the absence of the role of any unobserved variables such as political affiliation of mayors etc. into the selection into the treatment group (i.e. selection based on observables). In fact the choice of the seven treatment municipalities was decided by GGM and CJM in charge of the program implementation and was based on the willingness of the municipality government to engage on the supply-side of the project and the existence of a functioning participatory budgeting process.

> 9 Only one control municipality satisfying the common support region was chosen to pair with each treated municipality. Weights are proportional to the number of times a control municipality is used to form a match in the treatment group (see Abadie and Imbens, 2002).

13
Table 1. Balance Test of Reservoir Water’s Price Level and the Characteristics of the Water User <u>Groups</u>

|||Reservoir wat|erprice level|||
|---|---|---|---|---|---|
|_WUG characteristics_|Area-based<br>pricing<br>(MC=0)<br>(n=7)|Low<br>volumetric<br>price<br>(n=20)|Medium<br>volumetric<br>price<br>(n=20)|High<br>volumetric<br>price<br>(n=20)|_F_-statistics<sup>a</sup>|
|Group size||||||
|No. of parcels|106|112|105|124|0.67<br>[0.54]|
|Homogeneity||||||
|Gini coefficient of parcel size|0.23|0.28|0.27|0.23|0.64<br>[0.59]|
|Infrastructure condition||||||
|Simplicity of water flow direction|0.11|0.12|0.15|0.12|0.66<br>[0.57]|
|Earth canal length|1.33|0.48|1.31|1.10|1.06<br>[0.37]|
|Exit options||||||
|No. of ponds per member (pre-reform)|0.63|0.56|0.68|0.90|1.45<br>[0.23]|
|No. of pumps per member (pre-reform)|0.61|0.91|0.89|0.83|0.68<br>[0.57]|
|Surrounding parcel characteristics||||||
|No. of surrounding parcels|2.79|2.76|2.74|2.71|0.02<br>[0.99]|
|No. of surrounding parcels squared|8.32|8.17|8.47|7.98|0.04<br>[0.98]|
|Years knowing the owners of surrounding<br>parcels|38.00|32.73|34.84|35.60|0.55<br>[0.65]|
|Proportion of surrounding parcels whose owners<br>send their children to the same school|0.51|0.39|0.33|0.35|0.41<br>[0.74]|
|Management skills and water demand||||||
|Average schooling years of working member|8.36|8.97|9.23|8.59|1.54<br>[0.21]|
|Parcel size of paddy|0.15|0.14|0.13|0.13|0.43<br>[0.73]|
|Sandy/high seepage soil dummy|0.07|0.17|0.09|0.03|1.76<br>[0.16]|

_Note_ : _p_ -values in brackets. a) H0: Means of four price-level groups are all equal _Source:_ Authors’ analysis based on data sources discussed in the text.

23
Lesk, C, Pedram R, and Ramankutty N. "Influence of extreme weather disasters on global crop production." _Nature_ 529.7584 (2016): 84.

Lobell, D. B., & Asseng, S. (2017). Comparing estimates of climate change impacts from process-based and statistical crop models. _Environmental Research Letters_ , _12_ (1), 015001.

<mark>Maccini, S., & Yang, D. (2009). Under the weather: Health, schooling, and economic consequences of early-life rainfall.</mark> _<mark>American Economic Review</mark>_ <mark>,</mark> _<mark>99</mark>_ <mark>(3), 1006-26.</mark> Menon, Arathy, et al. "Consistent increase in Indian monsoon rainfall and its variability across CMIP-5 models." _Earth System Dynamics_ 4 (2013): 287-300.

Rowhani, Pedram, David B. Lobell, Marc Linderman, and Navin Ramankutty. 2011. "Climate variability and crop production in Tanzania." _Agricultural and Forest Meteorology_ 151(4): 449-460.

Sadoff C W, Hall JW, Grey D, Aerts JCH, Ait Kadi M, Brown C, Cox A, Dadson, S, Garrick D, McCornick P, Ringler C, Rosegrant, M, Whitting D, and Wiberg D (2016) Securing Water Sustaining Growth, University of Oxford, UK Willmott, C. J., and K. Matsuura. 2001. “Terrestrial Air Temperature and Precipitation: Monthly and Annual ~ Time Series (1900–2014).” <u>http://climate.geog.udel.edu/ climate/html_pages/download.html</u> World Bank 2006 Ethiopia: Managing Water Resources to Maximize Sustainable Growth; Water Resources Assistance Strategy, Agricultural and Rural Development Dept, Washington D.C.

Zaveri, E., Russ, J., & Damania, R. (2018). Drenched Fields and Parched Farms: Evidence Along the Extensive and Intensive Margins. SSRN Working Paper 10
<!-- Start of picture text --> Algeria: Medium-term Credit<br>and Algeria: Medium-term Credit<br>Medium-term Oil Price and<br>Medium-term Oil Price<br>1964 1974 1984 1994 2004 2014 1964 1974 1984 1994 2004 2014<br>year year<br> Medium-term Credit Cycle Medium-term Oil Rrice Medium-term Credit Cycle Medium-term Oil Rrice<br>UAE: Medium-term Credit<br>and Oman: Medium-term Credit<br>Medium-term Oil Price and<br>Medium-term Oil Price<br>1964 1974 1984 1994 2004 2014 1964 1974 1984 1994 2004 2014<br>year year<br> Medium-term Credit Cycle Medium-term Oil Rrice Medium-term Credit Cycle Medium-term Oil Rrice<br>20<br>10<br>10 5<br>0<br>0<br>Percent Percent -5<br>-10<br>-10<br>-20 -15<br>10<br>10<br>5<br>5<br>0<br>0<br>Percent -5 Percent -5<br>-10<br>-10<br>-15 -15<br><!-- End of picture text --> <!-- Start of picture text --> Qatar: Medium-term Credit<br>and<br>Medium-term Oil Price<br>1964 1974 1984 1994 2004 2014<br>year<br> Medium-term Credit Cycle Medium-term Oil Rrice<br>20<br>10<br>0<br>Percent<br>-10<br>-20<br><!-- End of picture text --> 55
**2**

The experiences of these countries with the trade reforms illustrates that the key ingredients of a successfidl reform strategy include boldness and consistency of policy implementation and complementary macroeconomic and exchatnge rate policies. However, these reforms will be sustainable only if they result In domestic macroeconomic stability and growth, and _if_ the international environment is committed to open and liberal trade policies.

# 2. CHARACTERISIICS OF THE TRADE REFORMS

# A. Prem Trade Reime

Table 1 presents the main characteristics of the trade regimes of these 16 countries at the start of the reforms of the 1980s. Most of these countries had high tariffs, foreign exchange restrictions, quantitative restrictions (QRs) on both imports and exports, export taxes, and multiple exchange rates.

TABLE **1: TRADE IMElb** CHARACTEIRtSTICS **AT THE** START **OF TnE TRADE REFORMS**

||**_Yer_Reform**<br>**Sared**|_HWh_**_Tarif_**|**_ForIn_**<br>**Exchange**<br>Rnom06"|_tdn__**QEs**_an_|_xpetl_**_Tax_**<br>**and/Or**|**MUIdpI**<br>**Ehae**<br>Rd|
|---|---|---|---|---|---|---|
|AWat"in|1988^*||||||
|Bras<br>310ibh|1987<br>**1985**||***s*ee**|*****|**h**||
|**Charo**<br>-a|**1988**<br>|*****<br>**C**|*****||||
|**Colombia**|**1985**||||||
|**Costa Rica**|**196**|*****|||||
|**EBuador**|1989||*||*****||
|**Guatenml**|**1986**|*|*|*|d||
|Honduras|1986||*****|*****|**C**||
|**Jamsica**|1982|**.***|*****|**C**|||
|Mexico|**1985**||**C**|*****|*****||
|**Paguay**|1989||||||
|**PaLr**|1989|*|**C**|*|*|**C**|
|**Tndad &**Tobago|1989|*|||||
|**Uruguay**|1987|**C**|||||
|**Venezauel**|1989|*|||||
|No. of**Countrie**|16|16|IS|13|11|8|

_Soume:_ **World** **_Bank_** (various repots)
-49.

# Organization

The Provident Fund is governed by a Board with 16 members, of which five represent employers' associations (in turn dominated by parastatals, i.e. by the government), five represent employees' associations and six are named by the government.

# Indicators of quality

The two main aspects of quality are the risk-adjusted rate of return obtained by members and the quality of services. Regarding the first issue, the law fixes -the composition of the Investment Committee, which has exclusive responsibility for allocating investments, of which five are named by the government and two are persons nominated by the Minister responsible for Labor to represent employers' associations and the other to represent employees' associations<sup>33</sup> . Regarding the outcome, the following table is illustrative.

Table 14 _Investnment Income earned by the Zambia National Provident Fund_ (Million Kwacha of each year. %)

|year|Investment<br>Income|Assets|Rate<br>I<br>of return|nterest Credited<br>to members|Rate of<br>Interest|In'Blation<br>rate (CPI)|Real<br>Return|
|---|---|---|---|---|---|---|---|
|1983|<br>26.317|n.ay.|a. av.|19.618|_5.5 %_|19.5 %|-11.7 %|
|1984|30.967|385.985|8.0 %|22.571|_5.5 %_|20.1 %|-12.2 %|
|1988|<br>8.592|786.168|11.0 %|39.748|_5.5 %_|54.7%|-31.8 %|
|1989|97.162|803.750|12.1 %|37.040|_5.5_No|128.7 %|-53.9 %|

Source: Annual Reports of the Zambia National Provident Fund Board for 82/83. 83184. 87/88 and 88189. CPI Inflation obtained from International Financial Statistics, as the CPI level of the year divided by the CPI level of the previous year. The real return to members was calculated as [(1+ nominal return)/(1+ inflation)] - 1.

The rate of return have been negative in the whole financial system in Zambia, but the Provident Fund has done significantly worse than bank deposits. If the portfolio had included domestic real estate and some foreign 33 Articles 33 and 32 (2) in the Providen Fund Act-
period escaped poverty and were all replaced by households that had previously been non-poor in the first period) and any scenario between these two extremes. Dynamics analysis is crucial for the design of effective and efficient poverty reduction policies, but such analysis requires panel survey data that are usually unavailable (particularly for developing countries).

# _Method Description_

In the absence of actual panel data, Dang _et al._ (2014) and Dang and Lanjouw (2013) recently propose methods to construct synthetic panels from repeated cross sections, which have provided encouraging results in various settings.<sup>23</sup> Their techniques share certain similarities with surveyto-survey imputation methods and include the following steps. First, estimate the household consumption model in Equation (1) using the available cross sections to obtain the predicted coefficients β̂. Second, estimate the correlation coefficient ρ of the error terms μij, using cohortaggregated household consumption between the two surveys. Third, combine β̂ and ρ̂ to obtain estimates of poverty mobility using bivariate probability formulae.

A key difference, however, with survey-to-survey imputation methods is that the _xj_ in Equation (1) should consist of time-invariant characteristics alone. These include such variables as ethnicity, religion, language, place of birth, and parental education, which provide the connectors between different rounds of cross sections with the appropriate age adjustments. For example, the cohorts

> 23 For example, recent applications and further validations of these synthetic panels methods include Ferreira _et al._ (2013), Cruces _et al._ (2015), and Vakis _et al._ (2015) for Latin American countries, Bourguignon, Moreno, and Dang (2018) and Foster and Rothbaum (2015) for Mexico, Balcazar _et al._ (2018) for Colombia, Martinez _et al._ (2013) for the Philippines, Garbero (2014) for Vietnam, Cancho _et al._ (2015) for countries in Europe and Central Asia, Dang and Ianchovichina (forthcoming) for countries in the Middle East and North Africa region, Dang and Dabalen (in press) and Dang, Lanjouw and Swinkels (2017) for Sub-Saharan African countries, and Dang and Lanjouw (2017 and forthcoming) for India, Vietnam, and the United States. Researchers at international organizations including the UNDP and the Asian Development Bank have also applied these methods for analysis of welfare mobility (UNDP, 2016; Jha _et al._ , 2018); see also OECD (2015) for an application by the OECD to study labor transitions in richer countries. See also Gibson (2001) for a related study on how panel data on a subset of individuals can be used to infer chronic poverty for a larger sample.

21