rafmacalaba/datause-encoder-data huggingface dataset:
consumption spending ( _G_ ) on real output ( _Y_ ), the initial level of real government spending, linear and squared inflation (  and <sup>2</sup> ) and a deterministic time trend ( _t_ ), 2 ln( _Gi_ , _t_ )   _i_  _t_   _i_ ,0 ln _Yi_ , _t_   _i_ 1, ln _Gi_ , _t_  1  1  _i_ , _t_  2  _i_ , _t_  _i_ , _t_ To prevent reverse causality from government spending to growth we instrument output growth with lagged values of output growth and current and lagged values of oil prices. We consider the standard deviation of the residual of this regression, Vol(  i,t) as the estimate of the volatility of discretionary fiscal policy. Note that data on government expenditure was obtained from WDI while inflation and the world price of oil were taken from the IMF’s International Financial Statistics (IFS).<sup>7</sup> We also construct a measure of _monetary policy volatility_ in the same fashion as the measure of fiscal policy volatility described above in spite of the difficulties to implement this measure across countries —as outlined by Fatas and Mihov (2006). We use data on the monetary base from IFS and from national sources whenever the data was unavailable from the IMF.

_Exchange Rate Regimes_ : In order to determine the exchange rate regime adopted by a country we use the classification of Reinhart and Rogoff (2004). Here, we consider three binary variables. The dummy for _fixed regimes_ takes the value of 1 if the country has either a hard peg or de facto pegs —that is a fixed exchange rate regime (and 0 otherwise). On the other hand, the dummy for _intermediate regimes_ takes the value of 1 if the country has a de facto crawling peg or band (i.e. intermediate regimes). Finally, the _flexible exchange rate regime_ is the base category and it is excluded from our regression analysis. Note that the ReinhartRogoff database contains data up to 2001. From 2002 we have used the IMF’s new exchange rate regime classification from the Annual Report on Exchange Arrangements and Exchange Restrictions that follows the natural classification stipulated by Reinhart and Rogoff (2004).

_Inflation:_ The monthly data on CPI collected for the calculation of the real effective exchange rate is used to compute annual _inflation_ measures. For the purpose of our regression analysis we compute the 5-year period average of the annual inflation in our monthly database.

_Vulnerabilities_ : We construct our indicator of _output concentration_ using the 9-sector classification from the 1-digit level ISIC code on economic activity, which comprises the following activities: (i) Agriculture, Hunting, Forestry, and Fishing; (ii) Mining and Quarrying; (iii) Manufacturing; (iv) Electricity, Gas, and Water; (v) Construction; (vi) 13
7 consistent between studies (in particular the studies for India sometimes employ different reference standards). In calculating numbers of deaths averted, studies usually assume a particular death rate for severely malnourished children, and hence base effectiveness results on the reduction in numbers of severely malnourished children. There are ethical problems in collecting these data (severely malnourished children, once identified by health personnel, are usually referred for treatment). Thus these data are somewhat imprecise.

Having outlined some of the methodological and data problems, the next three sections go on to discuss in turn unit costs, cost structure, and cost-effectiveness of selected nutrition interventions.

# X1. Unit costs.

Unit cost data are more readily available than cost-effectiveness data, since it is relatively easier to calculate the number of beneficiaries of a project (or the volume of food delivered), than programn impact. However it is not very meaningful to compare unit costs across different programs. For example the cost of a radio message containing nutrition education information may be $1-5 per person, and a feeding program might cost $70-90 per person per year per '000 calories delivered per day, but these cannot meaningfully be compared in this form. Unit cost information is more useful when comparing different interventions of the same type, but even this has to be done with care. Is a $5 radio message simply more costly for the same outcome as a $1 one, or might it be more

# effective?

For feeding programs one important standardization which can be undertaken is to take account of the size of the ration and the number of feeding days. (Throughout this paper we use cost per beneficiary measures for unit costs of feeding programs, rather than the less preferable cost per unit of food delivered.) Mateus (1989), Beaton and Ghassemi (1979) and Ghassemi (1989) performed an enormous service in compiling unit costs of feeding programs, but stopped short of standardizing
- Redding, Stephen J. and Turner, Matthew A. (2015). Chapter 20 - transportation costs and the spatial organization of economic activity. In _Handbook of Regional and Urban Economics_ (eds Gilles Duranton, J. Vernon Henderson and William C. –

- Strange), Handbook of Regional and Urban Economics, volume 5, pp. 1339 1398. Elsevier.

- Song, Shunfeng (1996). Some tests of alternative accessibility measures: A population density approach. _Land Economics_ , **72,** 474–482.

- Storeygard, Adam (2016). Farther on down the road: transport costs, trade and urban growth in Sub-Saharan Africa. _The Review of Economic Studies_ , **83,** number 3, 1263–1295.

- Tuttle, Benjamin T, Anderson, Sharolyn J, Sutton, Paul C, Elvidge, Christopher D and Baugh, Kim (2013). It used to be dark here. _Photogrammetric Engineering & Remote Sensing_ , **79,** number 3, 287–297.

- UN-OCHA (2011). Barrier update: Special focus. _UN-OCHA report_ .

- Wachs, Martin and Kumagai, T Gordon (1973). Physical accessibility as a social indicator. _Socio-Economic Planning Sciences_ , **7,** number 5, 437–456.

- Wu, Jiansheng, He, Shengbin, Peng, Jian, Li, Weifeng and Zhong, Xiaohong (2013). Intercalibration of DMSP-OLS night-time light data by the invariant region method. _International Journal of Remote Sensing_ , **34,** number 20, 7356–7368.

42
Table D.1: Summary Statistics for Baseline Variables

|Variable|# obs|Mean|Std<br>dev.|10<sup>th</sup><br>pctile|90<sup>th</sup><br>pctile|p:balance|
|---|---|---|---|---|---|---|
|_Panel A: Demographic Measures_|||||||
|Age|6891|23.6|3.3|19.8|28.3|0.583|
|Male|6891|0.382|0.486|||0.267|
|University degree / diploma|6891|0.167|0.373|||0.889|
|Any other post-secondary qualifcation|6891|0.212|0.409|||0.642|
|Completed secondary education only|6891|0.610|0.488|||0.794|
|_Panel B: Assessment Results_|||||||
|Numeracy score|6891|0.052|0.988|-1.187|1.411|0.523|
|Communication score|6891|0.050|0.992|-1.093|1.694|0.206|
|Concept formation score|6891|0.047|0.991|-1.516|1.260|0.764|
|Grit score|6891|0.031|0.992|-1.313|1.279|0.089|
|Other scores|6891|-0.002|1.070|-1.305|1.318|0.859|
|_Panel C: Labor Market Measures_|||||||
|Employed|6891|0.378|0.485|||0.468|
|Earnings|2116|565|740|100|1400|0.083|
|Ever worked|6877|0.704|0.457|||0.418|
|Ever held a long-term job|6877|0.090|0.286|||0.696|
|_Panel D: Job Search Measures_|||||||
|Searched|6891|0.968|0.175|||0.058|
|Applications submitted<sup>a</sup>|6815|9.9|18.6|2.0|20.0|0.809|
|Search cost|6147|242|1520|30|400|0.276|
|Search hours|6699|17.0|20.8|2.0|48.0|0.231|
|Ofers received<sup>a</sup>|6810|1.20|7.20|0.00|2.00|0.280|
|_Panel E: Belief Measures_|||||||
|Planned applications<sup>a</sup>|6840|48.9|1629.9|4.0|36.0|0.252|
|Correct about all assessment results|6891|0.082|0.274|||0.960|
|Incorrect about all assessment results|6891|0.290|0.454|||0.961|
|Overconfdent about all assessment results|6891|0.219|0.413|||0.732|
|Underconfdent about all assessment results|6891|0.010|0.100|||0.783|

Table shows summary statistics for selected baseline variables. Percentiles are omitted for binary variables. All monetary figures are reported in South Africa Rands. 1 Rand _≈_ USD 0.167 in purchasing power parity terms. In this table, intensive-margin labor market measures (e.g. earnings) are set to missing for non-workers, and intensive-margin search measures (e.g. search cost) are set to missing for non-searchers. All assessment results are standardized to have mean zero and standard deviation one in the control group. Missing values reflect item non-response, mostly due to respondents reporting that they don’t know the answer. All period-specific outcomes use a 7-day recall/forecast period unless marked with<sup>a</sup> (30-day recall/forecast period). The final column reports the p-value for testing equality of means of the baseline variables across all treatment groups, using heteroskedasticity-robust standard errors clustered by treatment date.

49
Poi.icy RESIEARC(I-I WORKIN; PAI'hiR 1410

# Summary findings

Spatafora and Warner investigate the impact on economic growthi and development of long-run movements in the external tcrms of tradc, with special reference to the experience of 18 oil-exporting countries between 1973 and 1989.

They argue that this sample approximates a controlled cxperiment for examining the impact of unanticipated- but permanent shocks to the terms of trade. They analyze the sample econometrically using panel data techniques.

They find that permanent ternis-of-trade shocks have a strongly significant positive effect on investment, which they justify theoretically on the grounds that countries in the sample import much of their capital equipment.

The shocks al;o have a significant positive effect on consumption. Government consumption responds almost twicc as strongly as private consumption.

The shocks have no effect on savings and adversely affect the trade and current account balances.

There is a significant positive effect on the output of all main categories of nontradables. But Dutch disease effects arc strikingly absent. Agriculture and manufacturing do not contract in reaction to an oil price increase. Dutch disease effects may be absent in part because of policy-induced output restraints in the oil sector, or because of the "enclave" nature of the oil sector, which does not participate in domestic factor markets.

This paper-a product of the International Economic Analysis and Prospects Division, International Economics Departmentis one in a series of background papers prepared in support of the analyses and scenarios in _Global Economic Prospects 1994._ Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contactJackie Queen, room S8-216, extension 33740 (37 pages). January 1995.

_The Policy Research Working Paper Series disseminates the fidings of work mi progress to encourage the exchange of ideas ahout development isue An objecae of the series is to get the findings out quickly, even if the presentations are kess than fly polished The papers carry the names of t authors and should be used and cited accor&ngly. The findings. inteapretations, and condusions are the authors' oum and should not be attributed to the World Bank. its Exwtive Board of Directors._ or _an of its moneber countries._ Produced by the Policy Research Dissemination Center
**_Table 5: Labor productivity convergence regressions, Tertiary sector_**

||Ter<br>|tiarysecto<br>|r<br>|
|---|---|---|---|
||Total<br>aggregation|Disaggre<br>se|gated by all<br>ctors|
||(1)|(2)|(3)|
|Dependent variable:||||
|Growth of VAper worker over a decade||||
|Log initial VA per worker|0.017|0.005|‐0.022***|
||(0.011)|(0.004)|(0.008)|
|Constant|‐0.130|‐0.023||
||(0.101)|(0.036)||
|Department FE|�|�||
|Industry FE|�|�||
|Observations|24|120|120|
|R‐squared|0.094|0.008|0.686|
|Stardard errors in parenthesis.||||
|*** p<0.01, ** p<0.05, * p<0.1||||

**_Table 6: Labor productivity convergence regressions conditioning on initial poverty level, Tertiary sector_**

||Tertia<br>|rysector<br>|
|---|---|---|
||Total<br>aggregation|Disaggregated<br>by all sectors|
||(1)|(2)|
|Dependent variable:|||
|Growth of VAper worker over a decade|||
|Log initial VA per worker|‐0.002|0.002|
||(0.013)|(0.004)|
|Log initial poverty level|‐0.021**|‐0.024***|
||(0.009)|(0.008)|
|Constant|0.024|‐0.012|
||(0.113)|(0.032)|
|Observations|24|120|
|R‐squared|0.285|0.083|
|Stardard errors in parenthesis.|||
|*** p<0.01, ** p<0.05, * p<0.1|||

# **4.4 Convergence in overall labor productivity**

We now combine the three broad sectors (primary, secondary and tertiary) and examine if regional labor productivity convergence within some industries (manufacturing and mining) has been sufficient for convergence in overall regional labor productivity. As Figure 5 suggests, this is indeed the case: department with lower levels of labor productivity in 2002, on average, experienced faster rates of growth.

16
# Table 9: Decomposition of the multidimensional headcount ratio in Colombia 2008-2012

|**Indicator**|**2008-**|**2010-**|**2008-**|
|---|---|---|---|
||**2010**|**2012**|**2012**|
|**Education**||||
|Change due to category (Percentage points)|-1.124|-1.034|-2.465|
|Percentage contribution of category (%)|27.51|30.24|32.85|
|**Childhood and youth**||||
|Change due to category (Percentage points)|-0.484|-0.663|-1.152|
|Percentage contribution of category (%)|11.85|19.40|15.35|
|**Labour**||||
|Change due to category (Percentage points)|-0.334|-0.100|-0.405|
|Percentage contribution of category (%)|8.18|2.93|5.39|
|**Health**||||
|Change due to category (Percentage points)|-1.418|-1.007|-2.372|
|Percentage contribution of category (%)|34.72|29.46|31.61|
|**Standard of living**||||
|Change due to category (Percentage points)|-0.725|-0.614|-1.111|
|Percentage contribution of category (%)|17.74|17.97|14.80|
|**Total**|-4.09|-3.42|-7.50|

34
Ladd, Jonathan McDonald and Gabriel S. Lenz (2009). “Exploiting a Rare Communication Shift to Document the Persuasive Power of the News Media.” American Journal of Political Science 53:2, 394410 (April).

La Porta, R., Lopez-de-Silanes, F., Pop-Eleches, C. and A. Shleifer, 2004 “Judicial Checks and Balances”, _Journal of Political Economy,_ 112 (2), 445-470.

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52
# **1. Introduction**

The major economies of the world trading system undertook a variety of approaches to liberalize trade during the 1985 – 2009 period. _Multilateral_ negotiations resulted in the initiation and completion of one round (Uruguay Round, 1986‐1994) which transformed the GATT to the WTO, and WTO members subsequently initiated further liberalization negotiations in 2001 under the (still ongoing) Doha Round. A number of countries liberalized by negotiating and/or expanding access to partners through major _preferential_ trade agreement initiatives: examples include the Canada‐U.S. Free Trade Agreement (CUSFTA) that was signed in 1987 and then extended to include Mexico in 1994 to create NAFTA, and Argentina and Brazil negotiated with other South American countries to form Mercosur in 1991. The European Community expanded from 10 countries to 12 in 1986 to 15 in 1995 to 25 in 2004 to 27 members of the European Union by 2007, and it also formed a customs union with Turkey that went into effect in the mid‐1990s. India responded to its balance of payments crisis of 1991‐1992 by cutting its applied tariffs through a _unilateral_ liberalization. Finally, China underwent fifteen years of _accession_ negotiations to realize WTO membership in 2001, and this locked in a number of its major tariff reductions. Regardless of the trade liberalization path undertaken, a common result is that many of these economies currently have historically low applied import tariffs in place.

While major WTO economies pursued different liberalization routes to reduce and sustain lower applied tariffs over these 25 years, a second theme common to this period is that many increasingly adopted “contingent” or “administered” import protection under policies such as antidumping, safeguards, and countervailing duties – what I refer to jointly as “temporary trade barrier” (TTB) policies. The combined result of these two phenomenon is a new framework for the international trading system: exporters are simultaneously subject to low applied import tariffs on average but they also face the threat of frequently changing – i.e., newly imposed or removed – TTBs. Such an institutional framework ultimately poses many research questions on transmission mechanisms through which government access to and use of TTB policies are economically important.

This paper characterizes this framework by providing an empirically‐based set of facts on the cross‐country use of TTB policies over 1990‐2009, taking stock of newly available, product‐level data organized into the World Bank’s _Temporary Trade Barriers Database_ (Bown, 2010a). I begin by using the data to address a number of basic questions. For which countries and in what episodes are such TTB 2
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Figure 23: Individual Average Months Formally Employed, by Sector - Nontradable Sector Worker Sample 1990-2009 <!-- Start of picture text --> 5<br>4<br>Tradable<br>3<br>2<br>Non-Tradable<br>1<br>0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>-1<br>-2<br><!-- End of picture text --> Each point reflects an individual regression coefficient where the dependent variable is the average number of months formally employed in the relevant sector per year from 1990 to the year listed on the x-axis, calculated using RAIS. For all years, the independent variable is the regional tariff change reflecting tariff changes from 1990-1995 (described in the text), with various controls (described in the text). Positive (negative) estimates imply fewer (more) average months formally employed in the relevant sector for workers initially employed in regions facing larger tariff declines. Vertical bar indicates that liberalization was complete in 1995. Dashed lines show 95 percent confidence intervals. Standard errors adjusted for 112 mesoregion clusters.

62
a closer look at the history of the political economy of the Ivoirian war reveals a somewhat different picture. To some extent education catalyzed the war due to the politically masterminded unequal provision of educational facilities between the North and the South. The education system in the North has been a victim of Ivoirian politics since the early 1990s and the NorthSouth divide following the civil war only exacerbated that ongoing crisis. This makes the task of finding a causal inference of the war on education particularly challenging. In this paper we estimate the causal effect of civil war on years of education for individuals who were exposed to conflict between 2002 and 2006 in their school-going age. We use the Households Living Standards Survey (HLSS) data collected in 2008 (ENV-2008) and the data on local incidences of conflict is taken from the Armed Conflict Location and Event Database (ACLED).

We employed several strategies to identify the true impact of war and quantify its causal effect on education for the school-going age cohort. We use the year of birth and the department of birth to determine an individual‟s exposure to war. The difference-in-difference outcomes indicate that the average years of education for individuals aged 10 to 22 is .94 years fewer compared to the individuals aged 23 to 32 in war-affected regions. The validity of the finding is tested by a number of factors such as internal displacement due to war, heterogeneous selection into victimization both across and within a region and varying intensity of conflict across regions. These are potential sources of bias in the estimated causal effect and furthermore, conflict affects education in several ways. The direct impacts include destruction of infrastructure, displacement and most tragically deaths of students and teachers, problems in harmonization of school calendars across the war-affected regions and closure of schools for an indefinite period. Other effects such as loss of jobs and farm, decrease in income and experiencing violence could also affect the education of children in the same household. To realize the full potential impact of war, as a second strategy we used a set of victimization indicators to identify the true impact of the war.

When education works as a catalyst for the war, the causal inferences on the impact of war suffer from endogeneity problems. Also the activities of NGO-run primary and secondary schools, which stepped in to fill the education gap in the North, might create a downward bias in the impact of war on education. Both of these factors are likely to violate the assumption that the war victims and the comparison group are exchangeable in order to make a causal inference of 25