rafmacalaba/datause-encoder-data huggingface dataset:
**Table 5.** Effect of the IST intervention at 9 and 24 months follow-up on educational achievement (spelling and <u>arithmetic) outcomes for younger (class 1) and older (class 5) children.</u>

|<br>**Outcome; N (%)**|<br> <br>**(50**|<br>**Control**<br>**schools)**|<br>**Int**<br>**(51**|<br>**ervention**<br>**schools)**|**Mean difference**<sup>**a**</sup><br>**(95% CI)**|**p-**<br>**value**|**Cluster-**<br>**size;**<br>**range**<br>**(mean)**|
|---|---|---|---|---|---|---|---|
|**9 months follow-up**||**Mean (SD)**<sup>**b**</sup>||**Mean (SD)**<sup>**b**</sup>||||
|**CLASS 1**|N=1210||N=1281|||||
|**(median age:8, range: 5-15)**||||||||
|**Spelling (score:0-20)**<sup>**c**</sup>||||||||
|Unadjusted|1068|11.70 (4.59)|1162|10.47 (4.57)|-1.23 (-2.21,-0.24)|0.015|8-27 (22.1)|
|Adjusted|1060|11.69 (4.59)|1133|10.49 (4.58)|-0.67 (-1.26,-0.08)|0.026|8-27 (21.7)|
|**Arithmetic(score:0-20)**<sup>**d**</sup>||||||||
|Unadjusted|1071|4.21 (3.13)|1162|4.04 (3.26)|-0.17 (-0.60, 0.26)|0.433|8-27 (22.1)|
|Adjusted|1069|4.21 (3.12)|1143|4.07 (3.28)|-0.21 (-0.54, 0.12)|0.214|8-27 (21.9)|
|**CLASS 5**|N=1283||N=1365|||||
|**(median age:12, range: 8-18)**||||||||
|**Spelling (score: 0-75)**<sup>**e**</sup>||||||||
|Unadjusted|1169|31.34 (12.61)|1223|28.73 (12.36)|-2.73 (-5.26,-0.19)|0.035|8-30 (23.7)|
|Adjusted|1154|31.37 (12.60)|1214|28.76 (12.34)|-0.31 (-1.26,0.63)|0.515|8-30 (23.4)|
|**Arithmetic(score:0-30)**<sup>**f**</sup>||||||||
|Unadjusted|1180|31.15 (5.49)|1229|30.72 (5.17)|-0.49 (-1.40, 0.42)|0.294|8-30 (23.9)|
|Adjusted|1173|31.14 (5.50)|1210|30.73 (5.17)|0.13 (-0.41, 0.68)|0.629|8-30 (23.6)|
|**24 months follow-up**||||||||
|**CLASS 1**|N=1201||N=1269|||||
|**(median age:8, range: 5-15)**||||||||
|**Spelling (score:0-20)**<sup>**c**</sup>||||||||
|Unadjusted|961|12.03 (3.05)|1062|11.04 (3.49)|-0.97 (-1.54,-0.40)|0.001|8-26 (20.0)|
|Adjusted|954|12.02 (3.05)|1036|11.04 (3.50)|-0.65 (-1.11,-0.20)|0.005|8-25 (19.7)|
|**Arithmetic(score:0-30)**<sup>**g**</sup>||||||||
|Unadjusted|962|5.97 (3.05)|1061|5.38 (2.97)|-0.59 (-1.08, -0.10)|0.018|8-26 (20.0)|
|Adjusted|960|5.97 (3.04)|1042|5.40 (2.97)|-0.60 (-1.02, -0.19)|0.005|8-25 (19.9)|
|**CLASS 5**|N=1267||N=1350|||||

**(median age:12, range: 9-18)**

24
# REFENCES

- Barro. Robert J., "The Ricardian Approach to Budget Defieits," Journal of Economic Perspectives, Vol.3, Number 2, Spring, 1989, 37-54.

- Havnes, S. and Stone, J., "Specification of Supply Behavior in International Trade," **Review of Economicjs and Statistics-,** 65, 1983b, 626-631.

- Khan, Mohsin S., and Knud, Z. Ross., "Cyclical and Secular Income Elasticities of the Demand for Imports," **The Review of Economics and** Statistics, August, 1975, 357-361.

- Krugman, Paul and Lance Taylor., "Contractionary Effects of Devaluation," **Journal of International Econumics,** August, 1978, 445-456.

- Murray, T. and Ginmam, P., "An Empirical Examination of the Traditional **Aggregate** Import **Demand** Model," Review of **Economics and** Statistics, 58, 1976, 75-80.

- Niehans, Jurg., **International Monetz.'y** Economics, Baltimore, MD: The Johns Hospkins University Press, 1984.

- Pindvck, Robert S., and Rubinfeld, Daniel L., Econcmetric Mbdels **and** Economic Forecasts, Second Edition, New York: McGrawHill Book Company, 1981.

- Stiglitz, Joseph E., "Money, Credit, and Business Fluctuations," The **Ecroomic** Recond, December 1988, 307-322.

- Stiglitz, Joseph E., and Weiss, Andrew., "Credit Rational in Markets with Imperfect Information," American Economic Review, Vol.71, No.3, June, 1981, 393-409.

- Tanzi, Vito., "Fiscal Policy, Growth, and the Design of Stabilization **Programs," Fiscal** Policy, Stabilization, and Growth in **Develoopin** Countries, Mario I. Blejer and Ke-yourng Chu (ed.), IMF., **September,** 1989, 13-49.

- Tanzi, Vito., "Fiscal Policy Responses to Exogenous Shocks in Developing Countries," **American Economic Review,** Vol.76, No.2, May, 1986, 88-91.

- Tobin, James. "Monetary Policy and the Economy the Transmission Mechanism," **Southern Ec3c30ic** Journal, **January** 1978.

- Warner, Dennis., and Kreinin, E. Mordechai., "Determinants of International Trade Flows," **The Review of Economics and** Statistics, Vol.L)XV, Number 1, February, 1983, 96-104.

24
> 46 <mark>Guangyong Zou, A Modified Poisson Regression Approach to Prospective Studies with Binary Data,</mark> _<mark>A</mark> merican Journal of Epidemiology_ <mark>, Volume 159, Issue 7, 1 April 2004, Pages 702– 706, h</mark> ttps://doi.org/10.1093/aje/kwh090

> 47 Predicted probabilities are calculated at the means of all other predictors.

> 48 Thirty percent of children under-5 are either low birth weight, have mothers with a low BMI or have mothers with short stature. Under-5s exposed to any one of these risk factors have 1.6 times the risk of stunting compared to children who are not exposed, controlling for age, sex, socio-economic factors and other postnatal stunting determinants. Children with any two of these risk factors have 1.7 times the risk of stunting, and children with all three of these risk factors have 1.9 times the risk of stunting, controlling for other factors.

> 49 The contribution of a risk factor to a disease or a death is quantified using the population attributable fraction (PAF). PAF is the proportional reduction in population disease or mortality that would occur if exposure to a risk factor were reduced to an alternative ideal exposure scenario (From WHO: https://www.who.int/healthinfo/global_burden_disease/metrics_paf/en/).

65
GDP in 2019 (to -3.1 percent of GDP). The improvement in the CA over the 2018-2019 period is primarily driven by the projected credit contraction, weaker domestic growth, real exchange rate depreciation and higher macroeconomic uncertainty. The CAD is then projected to expand by a 0.4 percent of GDP in 2020 (to -3.5 percent of GDP) and by additional 0.2 percent of GDP in 2021 and 2022, yet remaining below the 2008-2017 average in 2022. The projected weak worsening of the CAB over 2020-2022 can be mainly attributed to the real GDP growth recovery, projected real exchange rate appreciation, slow increase in credit activity, higher accumulated levels of net foreign liabilities and the fall in inflation uncertainty.

**Figure 27: Projected evolution of the current account in the baseline scenario**

<!-- Start of picture text --> 2%<br>1%<br>0%<br>-1%<br>-2%<br>-3%<br>-4%<br>-5% WEO forecast<br>Model forecast<br>-6%<br>2017 2018 2019 2020 2021 2022<br>Projected Current Account Balance (Percent of GDP)<br><!-- End of picture text --> Source: Authors’ calculations.

**While the CAB evolution over the 2018-2022 period is consistent with the IMF’s April 2019 WEO projections of the current account dynamics, the magnitude of the current account deficit contraction differs from the one assumed in WEO.** We must stress that the produced forecast arises from a model that is not optimized for forward projections. The model, however, allows exploring the sensitivity of the projected CAB path to a variety of alternative scenarios.

# **Alternative scenarios**

**Alternatively, we envision five different scenarios for each of five key CA determinants: credit to private sector, real exchange rate, public expenditure, relative GDP growth, and inflation volatility** , **to assess the magnitude of their influence on the current account over 2018-2022.** Each of these variables follows five scenarios as described in Table 5:  the baseline scenario values (in Table 4), plus and minus one standard deviation from the baseline, and plus and minus two standard deviations from the baseline. Combining the five scenarios of each variable and the five determinants will give us a total of 3,125(=5^5) scenarios, while assuming other variables will adopt the pattern of the baseline scenario described in Table 4. We compute the evolution of the projected current account using the baseline model estimates under each of these combined 3,125 scenarios over 2018-2022 and show them in Figure 28.

32
|No. of Workers (logs)|0.534**|0.697***|0.740***|0.901***|0.900***|0.922|2.862**|
|---|---|---|---|---|---|---|---|
||(0.242)|(0.254)|(0.278)|(0.289)|(0.289)|(0.663)|(1.267)|
|GDP per capita (logs)|15.040***|15.198***|17.308***|17.645***|7.628|6.274|6.446|
||(4.897)|(5.046)|(5.080)|(5.455)|(5.472)|(5.550)|(5.507)|
|Herfindahl-Hirschman Index||-6.789**|-5.322|-9.395**|-6.153*|-6.288*|-5.955*|
|||(3.348)|(3.357)|(3.891)|(3.459)|(3.528)|(3.516)|
|Age of Firm (logs)||-0.521|-0.912***|-0.684*|-0.828**|-0.842**|-0.832**|
|||(0.355)|(0.336)|(0.357)|(0.354)|(0.355)|(0.356)|
|Foreign Ownership (proportion)||-0.842|-0.089|0.114|0.178|0.162|0.206|
|||(0.842)|(0.878)|(0.968)|(0.961)|(0.960)|(0.960)|
|Female Ownership Y:1 N:0|||6.177***|5.978***|5.938***|5.919***|5.940***|
||||(0.617)|(0.653)|(0.653)|(0.654)|(0.655)|
|Skills Obstacle (0-4)|||0.581**|0.642**|0.616**|0.611**|0.612**|
||||(0.227)|(0.269)|(0.270)|(0.269)|(0.269)|
|How Much Of An Obstacle:|||-0.038|0.017|-0.013|-0.007|-0.016|
|Transport?|||(0.193)|(0.209)|(0.209)|(0.209)|(0.209)|
|Firm Purchased Fixed Assets Y:1|||-0.918*|-1.053*|-1.023*|-1.002*|-0.977*|
|N:0|||(0.524)|(0.557)|(0.556)|(0.557)|(0.558)|
|Power Outages (days)||||-0.166**|-0.150**|-0.153**|-0.153**|
|||||(0.070)|(0.067)|(0.067)|(0.068)|
|Courts Obstacle (cell average)||||-0.134|-0.592|-0.510|-0.547|
|||||(0.767)|(0.739)|(0.736)|(0.738)|
|Crime Losses Y:1 N:0||||-0.603|-0.665|-0.677|-0.699|
|||||(0.669)|(0.670)|(0.673)|(0.675)|
|Firm Inspected Y:1 N:0||||-0.767|-0.800|-0.801|-0.772|
|||||(0.602)|(0.602)|(0.603)|(0.603)|
|How Much Of An Obstacle: Tax||||-0.228|-0.257|-0.250|-0.265|
|Rates||||||||
|||||(0.230)|(0.229)|(0.228)|(0.228)|

80
coefficient. In (A106), the variables _CFtcoal_ , _CFtoil_ , _CFtgas_ denote the quantity of fuel consumed in million tons or million m<sup>3</sup> . Here, _CF_<sup>_f_</sup> is calculated as the sum of commodity output ( _QC_ ) and imports ( _M)_ less exports ( _X_ ), multiplied by the fuel conversion coefficient (  _meanf_ ) to convert the constant yuan of fuel consumed into the quantity of fuel consumed (in million tons or million m<sup>3</sup> ).

Second, in consumption-based accounting, we also calculate national energy consumption by adding over each industry, using industry specific information about the consumption of coal, coke, liquid _f_<sup></sup> _j_ fuels, etc. We first define consumption coefficients ( ) by taking the data on fuel actually used (in million tons, million m<sup>3</sup> , or billion kWh) from the China Statistical Year book (CSY 2012, Table 7-9 “Consumption of Energy by Sector”) and dividing by the value of energy purchases given in the Input-Output table.

To disambiguate, the fuel conversion coefficient (  _meanf_ ), presented in equation (A101), is computed using the production data at the aggregate level: the _total_ quantity of the commodity output divided by the _f total_ value of the commodity output. In contrast, the consumption coefficient (<sup></sup> _j_<sup>), presented here, is</sup> computed using the consumption data at the industry level: industry _j_ ’s consumption of fuel _f_ (in million tons, million m<sup>3</sup> , or billion kWh) divided by the value of industry _j_ ’s purchases of fuel _f_ (in billion yuan).

Secondary fuels are produced by the Petroleum Refining & Coal Products sector which we group as coke, refined liquids, and other petroleum products. The “other petroleum products,” such as bitumen and lubricants, are assumed to be not combusted (i.e. not contributing to CO2 emissions). Each industry _j_ purchase a different share of coke (coal products) from this sector and we write the value of coke input as a share of the value of Refining & Coal Products in the Use matrix:  _refcoalpr_ co_ , _jUrefine_ , _j_ where _a_<sup>_f_</sup> _s, j_ is the share of fuel _f_ in sector _s_ that industry _j_ purchases, and _Uf, j_ is the value (in billion yuan) of inputs of fuel _f_ for industry _j_ from the Use matrix.

That is, the value of refined liquids is the product of: 1) the share of liquids in total refined petroleum products; 2) the share of _non_ -coal products in the Refining & Coal Products sector that industry _j_ purchases; and 3) the value of Refining & Coal Products purchased by industry _j_ . Similarly, the value of Other Petroleum Products input (on the right) can be interpreted as the product of: 1) the share of _non_ -liquids in total refined petroleum products; 2) the share of _non-_ coal products in the Refining & Coal Products sector that industry _j_ purchases; and 3) the value of Refining & Coal Products purchased by industry _j_ .

The energy consumption coefficients for coke and liquid fuels are thus: ‐68‐
# Figure 2 Nominal Labor Earnings in COte d'Ivoire (Base 1980=100.0)

<!-- Start of picture text --> 200 -<br>Labor costs in<br>the formal sector<br>180 -<br>160 1 -<br>140-<br>140- </L~~~abor earnings in<br>the informal sector<br>120-<br>,;/ / ~~~Minimum<br>100- wage<br>80- - E I l ,<br>1980 1985 1990 1995<br><!-- End of picture text --> 27
provides trade ‡ows for more than 5,000 HS 8-digit products<sup>16</sup> to 253 countries, between 2000-2008<sup>17</sup> . In the following, we consider only exports of agri-food products excluding beverages, animal feed and tobacco. This corresponds to chapters 1 to 21 of the HS classi...cation, and restricts our sample to 845 product lines. Exports ‡ows are reported annually in values (US dollars) and quantities (tons). Among reporting ...rms almost 50% only appear once in the dataset. That is, they export only one product to one destination one year. As such observations are likely to be mis-reports, we exclude them from the analysis. Among these observations we ...nd a large proportion of individuals, for example, "MR OMART FRANCOIS KOUBLANOU", "MR. JOHN AMEFU", or inconsistent exports such as "AIRLINES GHANA LTD" exporting wood logs. Additionally, we exclude from the analysis exports ‡ows from international organizations and embassies, 3% of the observations, since such exports are not driven by pro...t motives and might bias our results<sup>18</sup> . Finally, the data show that 3:5% of export ‡ows are realised by trading companies. Since our analysis focuses on agri-food products, for which changing food safety regulation may impose additional production and or transaction costs, we are concerned about producing ...rms. In a robustness test we drop observations from trading companies and estimate the sensitivity of our results to the exclusion of these observations.

# 4.2 Additional Data Sources

The data on ...nancial development is taken from the Beck, Demirguc-Kunt, and Levine (2006) database, which contains various indicators of ...nancial development across countries and over time. We use the private credit to GDP as a proxy for country’s ...nancial depth. The variable ranges from 0:21 for Senegal in 2008 to 0:052 for Malawi in 2004. The annual data for GDP per capita is taken from the World Development Indicator report 2006, and is reported in constant 2000 US dollars. Financial development and GDP per capita are correlated at 78% in our sample.

Additionally, we use the level of outstanding short-term credit (TCc) and trade credit insurance (ICc) as reported in the Global Development Finance (GDF) as a share of GDP, as proxies for trade ...nancing. Finally, as an alternative control for the country’s quality of ...nancial systems, we use the Getting Credit Index (EGCc) from the World Bank Doing Business Survey (WBDBS) data for the year 2004<sup>19</sup> . The index ranks countries according to the strength of legal rights and the depth of credit information.

> 16Since HS 8-digit product classi...cations are country speci...c, we ...rst had to harmonise the classi...cations among all countries. Then we match it with the CN 8-digit Eurostat classi...cation, for which the risk index is computed.

> 17Senegal reports data from 2000 till 2008; Mali from 2005 to 2008, Malawi and Ghana from 2004 till 2008; and Tanzania from 2003 till 2009.

> 18Including these exporters, results hold in a similar way. Results are available upon request.

> 19This is the only available year.

13
# Table of Contents

||Page|
|---|---|
|Introduction . . . . . . . . . . . . .|. . . . . . . . . . . .<br>1|
|Data .... . . . . . . . . . . . . . .|. . . . . . . . . .<br>.<br>4|
|Main Findings . . . . . . . . . . . .|. . . . . . . . . . . .<br>5|
|Selectivity Bias and Other Statistica|l Issues . . . . . . . .<br>8|
|Discussion . . . . . . . . . . . . . .|. . . . . . . . . . . .<br>10|

Shoshana Neuman is a lecturer in Economics at .qr Ilan University Israel: Adrian Ziderman, Senior Economist in the Bank, is on leave from Bar Ilan University. The authors acknowledge their thanks to Amyra GrossbardSchechtman, San Diego State University, who suggested to them that research along the lines pursued in this paper would be fruitful.
Depending on the size of the dataset, it may prove difficult to identify all coefficients in (4) due in particular to lack of variation in the exporter-sector or importer-sector dimensions. We therefore propose a simplification in which trade cost elasticities are assumed to be constant across sectors, and country-sector fixed effects are taken to be subsumed by country fixed effects. The resulting estimation equation (5) uses fewer degrees of freedom than (4), but can be expected to provide a reasonable approximation in cases where cross-sectoral variation is not too strong.

_k_ log ( _X ij_ ) = _c_ + δ _i_ + δ _j_ + δ _k_ + β 1 log ( _distij_ )+ β 2 log ( _paved_ ave_ )+ β 3 log ( _paved_ _ min )+ ... _k_ (5) ... + β 4 log ( 1 + _tariff_ )+ β 5 log ( _docs_ )+ β 6 _border_ + β 7 _colony_ + β 8 _language_ + ε _ij_ Our data and sources are set out in detail in Table 8. For bilateral trade, we use the value of 2003 imports by BEC 1-digit sector, taken from the WITS database. Whenever import data are missing, we use export (mirror) data. Trade cost dummies based on geographical and historical factors (contiguity, colonization and common language) are drawn from the CEPII distance database (Mayer and Zignago 2006). Distance is measured using average intercity road distances obtained by computer mapping, as set out above. _Paved_ave_ and _Paved_min_ refer to our measures of average and minimum road quality. Our tariff variable is drawn from effective applied tariffs as recorded in the WITS-TRAINS database.<sup>3</sup> For trade facilitation, we use data

> 3 We use the simple average tariff when aggregating to the BEC 1-digit level. Use of the trade weighted average does not affect our results.

- 11 -
The results in Table 3 show that marginal voters, core supporters, and the poor all received a disproportionate share of FONCODES expenditures. The coefficient on the measure of non-marginality is always negative, and is significant at the 5% level when all of the controls are added; the coefficient on the measure of core support is positive and significant at the 5% level in every specification.23 Note that this is a very strong result indeed: the political variables are significant even after dozens of controls, including sophisticated non-linear measures of the poverty of a province, are incorporated into the model. Finally, the coefficient on the FONCODES index is positive and significant at the 1% level, showing that FONCODES spent significantly more on the poor. The sign on the coefficients when the parameters are estimated with Tobit or Powell's censored LAD is the same, and the magnitude of the effects of the political variables and the FONCODES index tends to be larger than the corresponding OLS estimates. The only important difference is that the censored LAD estimates produce larger standard errors, so that the political coefficients are no longer significant when all of the controls are included.

How large are these estimated effects? Table 4 reports the predicted effect of one-standard deviation changes in core support, marginality, and the (log of the) FONCODES index on per capita FONCODES expenditures. Column (ii), which corresponds to column (ii) in Table 3, shows that, _ceteris paribus,_ a one standard deviation increase in the vote for Fujimori resulted in a 55% increase in per capita expenditures, while a one standard deviation increase in marginality resulted in a 13% increase in per capita expenditures. Increases in poverty also have a large imrpact on expenditures: a one standard deviation increase in the (log of the) FONCODES index resulted in a 34% increase in per capita expenditures.

**Table** 4: **The magnitude of political and poverty effects on the distribution of per capita FONCODES expenditures on community-based prcects, December 1991-October** 1993

||W<br>|0~(i)<br>|(iii)<br>|
|---|---|---|---|
|**Percent vote for Fujimori**|43.29|54.83|44.88|
|**Abs. value (% for Fujimori-50)**|-2.65|-12.54|-39.67|
|**In (FONCODES**<br>**index)**|56.08|33.86||

Table 4 suggests that the effect of the two political variables is large indeed, especially as the number of controls increases. But interpretation of the magnitude of the political effects is not 23 If politicians maxiniize the probability of being re-elected, rather ithan the expected number of votes, the positive coefficient on the pro-Fujimori vote could also be a measure of the "partisan reward" for the supporters of the stronger party predicted in the modified Lindbeck-Weibull model (Lindbeck and Weibull, Case). More generally, there is no obvious way to disentangle the effect of "core support" from that of "partisan reward" when the incumbent is also the stronger of the two parties (Mark Thomas, personal communication with the author, March 1998).

21
# **6.2 Legal costs and perception of faster justice**

We complement this evidence from firms’ monthly tax records with primary firm survey data to shed light on the net benefits of faster justice on firms. We proceed in two steps. First, our survey instrument asks firms to evaluate the costs of legal services associated with shorter delays. We present two scenarios of pre-trial delays, using our empirical estimates of the average reform impact. First, the firm is told it should hire a lawyer to resolve a dispute of a median amount. Two types of lawyers are available: one who can reliably complete pre-trial proceedings at the average pre-reform speed (5 months); and one who can reliably complete pre-trial proceedings at the average post-reform speed (3.5 months). We then ask the firm, in an open-ended manner, how much they would be willing to pay each lawyer. The kernel densities corresponding to each response are shown in Figure 10. We find that firms unanimously report being willing to pay more for a faster lawyer, an average of CFA franc 853,522 (approximately USD 1,610), relative to a lawyer performing at pre-reform speed, for which they would pay CFA franc 559,462 (approximately USD 1,056). The mean difference of CFA franc 294,060 (approximately USD 555) represents a 50% increase over willingness to pay for baseline speed, and is significant at the 1% level.

Second, we exploit the fact that some firms only had court cases before the decree was applied, while others had one or more court cases after, to document before-after changes in firms’ perceptions of the justice system around the decree introduction. For robustness, we present results on two samples: all surveyed firms, and a sub-sample of firms that had only one court case (Table 9).<sup>29</sup> We make three central observations. First, firms’ perceived duration and lawyer costs did not change significantly across the decree application (cols 1–4, Table 9). Second, we discern a

> 29Using the former sample, we compare firms that only had court cases before the decree was applied with those that had at least one case after. Using the latter sample, we compare—among firms that only had one case—those whose case was before decree application with those whose case was after. In Table 9, uneven columns report results on the full sample, and even columns report results on the restricted sample.

27