rafmacalaba/datause-encoder-data huggingface dataset:
economic competitiveness. The decline of educational excellence in the United States, so the argument goes, mirrors its declining share of international markets, its diminishing capacity for manufacturing high quality goods and its rising trade deficits. If educational standards were raised by teaching more "basics" (read: academic courses) and by assigning more hours of homework, then achievement scores would rise, worker productivity would increase, scientific and technological inventiveness would revive, and the United States would eventually reaffirn its competitive edge in the world economy (among many such citations, see those discussed in Gross and Gross, 1985; Doyle and Levine, 1985).

In addition, studies of cross-national variations in educational achievement have often been used to partially explain past economic growth rates or foreshadow future ones. The findings from two major comparative research projects are particularly relevant in this regard. The first involves the well-publicized findings of recent lEA studies of academic achievement in the areas of mathematics and science (McKnight et al., 1987; Rosier, 1987; IEA, 1988; Travers and Westbury, 1910); the second relates to the cross-cultural research of H. Stevenson and J. Stigler comparing the academic achievement of Japanese, Chinese and U.S. elementary students (Stigler, Lee, Lucker and Stevenson, 1982; Stevenson, Lee and Stigler, 1986). These two projects, taken together, tend to show average or below average scores of U.S. and some Western European pupils but significantly higher achievernents among Japanese and other Asian students on standardized mathematics and science tests.

Based on the (mostly) cross-sectional analyses performed in these studies, researchers have concluded that national variations in academic achievement axe very much related to the organization and structure of the national school curriculum. In addition, the basic descriptive findings of these studies have lent support to rhetorical arguments tying national curricular content to economic performance. Thus, without any solid empirical support, it has become received wisdom that the composition and structure of national curricula, by their influence on student achievement levels, have important effects on the quality and productivity of work force and, in the long run, on a nation's competitive position in the world economy.

How valid and logically consistent are such claims? To begin, when the comparison is limited to Japan and the United States, these arguments have some face validity. Japan has a much longer 3
private sector. The quality of (political) institutions is controlled for with the help of the Polity IV database, which offers a score variable (polity2) that combines two other score variables (Democ and Autoc) and quantifies the nature of the political system in a country. The Democ and Autoc scores include information on competitiveness and openness of executive recruitment, constraints on chief executive, regulation and competitiveness of participation. The polity2 measure is defined as Democ minus Autoc and ranges from +10 to‐10 with ‐10 implying a strongly autocratic regime and +10 a strongly democratic regime. We also control for the existence of war in a given country during the period under study. The cross‐country dataset contains data on all 80 countries for which all variables in Table 1 are available.<sup>9</sup> Table 2: BMA results

||PIP/PM(PSD)|PIP/PM(PSD)|PIP/PM(PSD)|
|---|---|---|---|
|_e0_|_0.999/0.854(0.102) _|_0.999/0.850(0.102)_|_0.999/0.842(0.095) _|
|_y0_|_0.683/4.722(3.891) _|_0.694/4.834(3.889)_|_0.768/5.732(3.923) _|
|_life0_|_0.504/0.307(0.361) _|0.493/0.298(0.359)|_0.514/0.315(0.365) _|
|_vol_|0.242/‐0.23(0.540)|0.239/‐0.23(0.536)|0.249/‐0.24(0.542)|
|_polity_|0.119/0.012(0.078)|0.118/0.011(0.077)|0.120/0.014(0.078)|
|_pavroad_|0.117/‐0.00(0.016)|0.117/‐0.00(0.016)|0.111/‐0.00(0.015)|
|_gini_|0.273/‐0.06(0.126)|0.272/‐0.06(0.126)|0.256/‐0.05(0.117)|
|_cred_|0.215/‐0.01(0.044)|0.206/‐0.01(0.043)|0.315/‐0.03(0.059)|
|_war_|0.128/0.246(1.204)|0.126/0.232(1.182)|0.164/0.489(1.583)|
|_t_<sup>_d_</sup>|0.287/‐3.27(6.344)|‐|‐|
|_c_<br>_d_|‐|0.173/‐1.53(4.715)|‐|
|_g_<br>_d_|‐|‐|_0.822/‐51.9(31.16)_|
|_g_‐prior|BIC|BIC|BIC|
|Prior model size|5|5|5|
|Obs.|80|80|80|
|Number of models|1024|1024|1024|

PIP stands for ``Posterior inclusion probability", PM is ``Posterior Mean" and PSD is ``Posterior Standard Deviation". Figures in italics correspond to entries with PIP > 0.5.

The results of the BMA exercise are presented in Table 2. We report the posterior inclusion probability of each variable (PIP), which is computed as the sum of the posterior probability of the models including that variable, together with the mean of the posterior distribution of the parameter attached to the variable (PM) and its standard deviation (PSD). The PIP can be interpreted as the probability that a given variable belongs to the true model, and we will classify explanatory variables as robust if after observing the data the probability that the variable belongs to the model increases with respect to the prior inclusion probability of the variable. For the BMA results in Table 2, we imposed a diffuse prior over the model space, so

> 9 The countries included in the cross-sectional sample are (in abbreviation form): AUS, AUT, BDI, BEL, BFA, BGD, BOL, BRA, BWA, CAF, CAN, CHE, CHN, CIV, CMR, COL, CRI, DNK, DOM, DZA, ECU, EGY, ESP, FIN, FRA, GBR, GHA, GMB, GRC, GTM, HND, IDN, IND, IRL, IRN, ISR, ITA, JAM, JPN, KEN, KOR, LKA, LSO, MAR, MEX, MLI, MRT, MWI, MYS, NER, NGA, NIC, NLD, NOR, NPL, NZL, PAK, PAN, PER, PHL, PNG, PRT, PRY, RWA, SEN, SGP, SLE, SLV, SWE, SWZ, THA, TTO, TUN, TUR, UGA, URY, USA, VEN, ZMB and ZWE.
|China Bengbu Integrated Environment Improvement Project|ShunongHu|
|---|---|
|China Bengbu Integrated Environment Improvement Project|ShunongHu|
|China Bengbu Integrated Environment Improvement Project|ShunongHu|
|China Bengbu Integrated Environment Improvement Project|ShunongHu|
|China Bengbu Integrated Environment Improvement Project|ShunongHu|
|Jiangsu Water and Wastewater|KhairyAl-Jamal|
|Jiangsu Water and Wastewater|KhairyAl-Jamal|
|Jiangsu Water and Wastewater|KhairyAl-Jamal|
|Jiangsu Water and Wastewater|KhairyAl-Jamal|
|Jiangsu Water and Wastewater|KhairyAl-Jamal|
|Jiangsu Water and Wastewater|KhairyAl-Jamal|
|Jiangsu Water and Wastewater|KhairyAl-Jamal|
|Jiangsu Water and Wastewater|KhairyAl-Jamal|
|Jiangsu Water and Wastewater|KhairyAl-Jamal|
|Jiangsu Water and Wastewater|KhairyAl-Jamal|
|Fourth PovertyReduction StrategyCredit|William Experton|
|Fourth PovertyReduction StrategyCredit|William Experton|
|Fourth PovertyReduction StrategyCredit|William Experton|
|Programmatic Development PolicyGrant 2|RoyS. Canagarajah|
|Tonga Transport Sector Consolidation Project|Julie Babinard|
|Tonga Transport Sector Consolidation Project|Julie Babinard|
|Tonga Transport Sector Consolidation Project|Julie Babinard|
|Tonga Transport Sector Consolidation Project|Julie Babinard|
|Tonga Transport Sector Consolidation Project|Julie Babinard|
|Tonga Transport Sector Consolidation Project|Julie Babinard|
|Tonga Transport Sector Consolidation Project|Julie Babinard|
|Tonga Transport Sector Consolidation Project|Julie Babinard|
|Tonga Transport Sector Consolidation Project|Julie Babinard|
|Tonga Transport Sector Consolidation Project|Julie Babinard|
|Tonga Transport Sector Consolidation Project|Julie Babinard|
|Tonga Transport Sector Consolidation Project|Julie Babinard|
|Tonga Transport Sector Consolidation Project|Julie Babinard|
|Tonga Transport Sector Consolidation Project|Julie Babinard|
|Tonga Transport Sector Consolidation Project|Julie Babinard|
|Tonga Transport Sector Consolidation Project|Julie Babinard|
|Tonga Transport Sector Consolidation Project|Julie Babinard|
|Punjab-Irrigation Sector Development PolicyLoan|Manuel Contijoch Escontria|
|Punjab-Irrigation Sector Development PolicyLoan|Manuel Contijoch Escontria|
|Punjab-Irrigation Sector Development PolicyLoan|Manuel Contijoch Escontria|
|La Guajira Water & Sanitation Infrastructure and Service Managem|eAntonio Manuel Rodriguez Serrano|
|La Guajira Water & Sanitation Infrastructure and Service Managem|eAntonio Manuel Rodriguez Serrano|
|La Guajira Water & Sanitation Infrastructure and Service Managem|eAntonio Manuel Rodriguez Serrano|
|La Guajira Water & Sanitation Infrastructure and Service Managem|eAntonio Manuel Rodriguez Serrano|
|La Guajira Water & Sanitation Infrastructure and Service Managem|eAntonio Manuel Rodriguez Serrano|
|La Guajira Water & Sanitation Infrastructure and Service Managem|eAntonio Manuel Rodriguez Serrano|
|La Guajira Water & Sanitation Infrastructure and Service Managem|eAntonio Manuel Rodriguez Serrano|
|La Guajira Water & Sanitation Infrastructure and Service Managem|eAntonio Manuel Rodriguez Serrano|
# **APPENDIX D: ONLINE SURVEY OF CLINICS**

In collaboration with the Provincial Management Unit of the program (UGPS), in May 2015 we conducted a short online survey (using Survey Monkey<sup>®</sup> ) in those clinics that participated in the pilot. The survey aims to measure the absolute and relative importance of seven different prenatal care procedures including initiating prenatal care prior to week 13 of pregnancy. The absolute scores range from 1 to 5, with 5 being the highest score in terms of importance, and an additional option of zero indicating that the procedure is not appropriate for a pregnant woman. Hence, the absolute score ranges from 0 to 5 points. The relative ranking aimed to sort the seven practices from 1 to 7, with 1 being the highest ranking. In practice however, the survey instrument allowed the respondent to repeat numbers.

The survey was sent out to by email to clinics directors (or the next person in rank). We were unable to obtain current email addresses for 8 out of the 36 clinics. Another 4 clinics confirmed having received the email but refused to answer it. Out of the 24 clinics that did respond to the survey, 21 fully completed it while 3 only partially completed it. Out of the 21 clinics with complete responses, 13 belong to the treatment group and 8 to the control group. Appendix Table D1 shows that there are no significant differences in baseline characteristics between clinics that responded to the survey and clinics that did not respond. In addition, we account for survey non‐response using Inverse Probability Weighting based on the logistic regression reported in Table D2 (Wooldridge 2007). We report results for both IPW and non‐IPW regressions.

Figures 8 and 9 do not suggest any difference in the absolute score and relative ranking of the procedures between treatment and control clinics. To test for the significance of the differences between the two groups, we run an OLS regression of the absolute score and the relative ranking against a binary indicator for treatment. To account for the small sample size we also compute the _p_ ‐ value for the differences in means permuting our data and using a random sample of 10,000 permutations. The results are shown in Tables D3 and D4.

53
at least 12 months, were operating outside of agriculture, were not formally registered companies, and that had fewer than 50 employees. A communication campaign in 2013 attracted 3,220 firms that met this eligibility criteria, and a sample of 1,500 were then selected for the experiment.

# **A. Experimental Sample and Design**

The experimental sample consists of 789 female-operated businesses and 711 male-operated businesses. In contrast to studies which focus on youth running new enterprises, and on ultra-poor individuals operating subsistence businesses, the entrepreneurs in this study are older (median age of 42 for women and 38 for men) and are running established firms (median firm age of 11 years for women and 8 years for men). 82% of men and 65% of women had at least one other worker in their firm, with a mean of 3.8 workers for men and 1.9 workers for women. Mean monthly profits are US$246 for men and US$173 for women.<sup>1</sup> 71% of the women are in commerce, with the typical activities being selling food and clothing. Hairdressing and sewing are the other main activities. In contrast, only 22% of the men are in commerce, with the majority spread across a range of different manufacturing activities, as well as in construction and repair services.

The 1,500 firms were stratified by gender and sector, and grouped into triplets according to baseline profits. Within each triplet, firms were then randomly assigned to a control group (N=500), traditional business training group (N=500), and personal initiative training group (N=500).

# **B. The Intervention: Personal Initiative Training**

Both business training programs were implemented in April 2014 in the form of 36 hours of inperson classroom training spread over four weeks, followed by a trainer visiting the business for three hours, once per month, for each of the next four months to assist with implementing the concepts learned. Both programs had a similar delivery cost of approximately $750 per invited business, and charged firms a highly subsidized fee of $10. Take-up rates were equal in the two training programs, at 84 percent.

The traditional business training used the Business Edge program developed by the International Finance Corporation and adapted to the Togo context. It focused on teaching standard business

> 1 We convert all financial outcomes into real September 2021 U.S. dollars using an exchange rate of 550.5 CFA per USD and the Togo Consumer Price Index collected by INSEED (https://inseed.tg/).

4
x year cell; considering the large density of point measures in each governorate, this procedure minimizes the risk of assigning drought shocks to governorates that had only a negligible portion of territory exposed to drought shocks and are scarcely populated.

We considered SPEIs at 12 months. Specifically, an accumulation period of 12 months is more appropriate to account for interseasonal precipitation patterns over a medium duration timescale (Svoboda et al., 2012). We classified as treated governorates in which the average 12-month SPEI value assumed a value equal or lower than -1 s.d. at least one time over the period 2000-2019. According to the classification provided by the World Bank and reported in Appendix Table A1, we build a discrete and ordered treatment with 4 categories, where the untreated governorate never experienced an average SPEI lower than -1. This threshold identifies droughts classified as at least moderate.

Figure 3 shows the national average (the black line) and the minimum value (the red line) of the 12-month SPEI observed over the period 2000-2019. The national average SPEI ranges around -0; when we consider the minimum SPEI, we detect several episodes of moderate drought (SPEI ≤ 1 s.d.), with most of them falling in the second half of the period. Figure 4 shows the distribution of the minimum 12-month SPEI averaged over the period of analysis (2000-2019) across Tunisian governorates. The driest conditions are mostly located in the Northern and Eastern areas, including several coastal governorates. Table 1 shows summary statistics for all of the socioeconomic and climatic variables described above.

# **4. Empirical strategy**

We estimate dynamic treatment effects assuming that each governorate can be exposed to drought shocks of different intensity and at different points in time _t_ . Moreover, the treatment can switch on and gg off, implying that after the first drought shock, a governorate could experience years with standard weather conditions or additional drought shocks, from severe to extremely severe. In this discrete nonabsorbing difference-in-differences design, is the period- _t_ SPEI value in the governorate and gg,tt gg,tt is the observed labor market outcome. To estimate the treatment effect in this setting, we follow the DD gg YY approach by De Chaisemartin and D’Haultfoeuille (2020) that proposes the following estimator : gg,ll δδ gg,ll gg,FFgg+ll gg, gg,FFgg+ll δδ = EE(YY �DD �−YY (00)|DD)

7
# **Figure 1: Data Collection and Quality Assurance**

<!-- Start of picture text --> START<br>INBO SENDS OUT DATA PROCESSOR<br>QUESTIONNAIRES SENDS OUT<br>QUESTIONNAIRES<br>INBO NON INBO<br>MEMBERS RESPONDENTS<br>QUESTIONNAIRES<br>FILLED OUT ON<br>WEBSITE OR BY HAND<br>ARE<br>NO QUESTIONNAIRES NO<br>CORRECTLY &<br>COMPLETELY<br>FILLED<br>YES<br>DATA<br>PROCESSOR<br>POPULATES<br>DATABASE<br>END<br><!-- End of picture text --> 18
**<u>Table 5: Comparing production and yields between estates and smallholders</u>**

||||**E**|**states by**|**size in ha**||||**Smallho**|**lders by**|**size in ha**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**All**|**<=5 **|**5-10**|**10-50**|**50-100**|**100-500**|**>500**|**All**|**<=1 **|**1-5 **|**5-10**|**>10**|
|**Land use**|||||||||||||
|Area owned|433.9|4.0|8.5|21.6|74.3|272.4|2543.5|0.98|0.42|1.68|7.00|33.83|
|Area operated|67.0|3.5|5.8|10.3|27.4|80.2|294.5|0.64|0.40|1.23|5.60|7.48|
|**Share of area b**|**y crop**||||||||||||
|Tobacco|0.42|0.32|0.16|0.40|0.42|0.48|0.47|0.01|0.01|0.00|0.00|0.00|
|Maize|0.39|0.57|0.67|0.44|0.42|0.31|0.23|0.47|0.48|0.45|0.44|0.42|
|Wheat||||||||0.26|0.26|0.26|0.24|0.26|
|Rice|0.01|0.00|0.00|0.01|0.00|0.02|0.01|0.08|0.08|0.08|0.15|0.10|
|Sorghum|0.00|0.00|0.00|0.00|0.00|0.00|0.01|0.09|0.09|0.10|0.10|0.11|
|Sunflower|0.02|0.00|0.01|0.02|0.03|0.02|0.00|0.00|0.00|0.00|0.00|0.00|
|Groundnut|0.07|0.06|0.13|0.10|0.07|0.03|0.01|0.00|0.00|0.00|0.00|0.00|
|Soybean|0.01|0.06|0.01|0.01|0.01|0.01|0.01|0.00|0.00|0.00|0.00|0.00|
|Coffee|0.01|0.00|0.00|0.00|0.01|0.03|0.02|0.02|0.02|0.02|0.01|0.04|
|Tea|0.04|0.00|0.00|0.00|0.00|0.06|0.17|0.00|0.00|0.00|0.00|0.00|
|Other crops|0.03|0.00|0.02|0.02|0.04|0.04|0.07|0.07|0.06|0.09|0.06|0.07|
|**Yield (kg/ha) b**|**y crop**||||||||||||
|Tobacco|960|854|1,047|905|1,089|1,010|1,000|1,413|1,433|1,313|||
|Maize|1,585|1,313|1,874|1,685|1,286|1,385|1,606|1,630|1,735|1,235|621|1,382|
|Wheat||||||||1,996|2,128|1,596|574|1,188|
|Rice|1,123|||1,310||750|750|1,842|1,986|1,484|571|809|
|Sorghum||||||||1,869|1,967|1,645|503|1,165|
|Sunflower|,3058|||3,692|1,417|2,140||2,474|2,489|2,418|||
|Groundnut|765||440|840|439|783|550|2,576|2,676|2,174|||
|Soybean|869|700|290|592|4,250|820|988|2,133|2,316|1,614||1,285|
|<br>Coffee|1,323||||45|1,598|1,215|2,803|2,902|2,601|241|1,883|
|Tea|648|||||1,537|370|419|444|358|||
|**Sample distribu**|**tion**||||||||||||
|<br>North|<br>0.25|0.25|0.03|0.34|0.30|0.13|0.07|0.16|0.15|0.19|0.13|0.06|
|Center|0.57|0.38|0.72|0.62|0.57|0.49|0.56|0.38|0.35|0.46|0.54|0.70|
|South|0.19|0.38|0.24|0.05|0.13|0.38|0.37|0.46|0.49|0.35|0.33|0.23|
|No. obs|868|8|29|422|96|192|121|17,672|14,408|2,954|156|154|

_Source:_ Own computation from 2006/07 NACAL. Other crops for estates include beans, cotton, sugar cane. Other crops for smallholders include wheat, millet, beans, velvet beans, ground beans, pigeon peas, cow peas, cassava, sweet potato, irish potato, cotton, and sugar cane.

19
# **1b. Despite a more favorable environment, appetite is low**

**Since 2004, the investment environment has become more favorable ...** In 2004, to address its fiscal challenge, the government launched a comprehensive reform program (including the adoption of the VAT), and during 2005-06 carried out a fiscal adjustment, accompanied by an enhanced tax effort. The improved outlook generated a more stable investment environment, potentially attractive for private capital inflows. Moreover, firms - in their fixed investment decisions - are today less influenced by the debt overhang, which constrained them during the late 1990s<sup>10</sup> . Also, the incremental capital output ratio (ICOR) shows that in the post-crisis period investment efficiency has improved.<sup>11</sup> Finally, surveys indicate a greater propensity to invest, as average capacity utilization rates - mainly in the manufacturing sector - have increased to over 80 percent.<sup>12</sup>

**Figure 6. A current account surplus ... Figure 7. ... and rising capital inflows**

<!-- Start of picture text --> 8000<br>Net Capital Inflows<br>10<br>Aquino's assassination<br>6000 8 Coup attempts Asian crisis<br>Marcos leaves<br>6<br>4000<br>Coup attempts<br>4<br>Marcos leaves<br>Economic reforms<br>2000 2<br>0<br>0<br>-2<br>-2000 -4<br>-6<br>Asian crisis<br>-4000<br>Aquino's assassination -8<br>Economic reforms -10<br>-6000<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006<br>19801982198419861988199019921994199619982000200220042006<br>% GDP<br>Current Account Balance (NSA, USM)<br><!-- End of picture text --> _Source:_ Central Bank of the Philippines, 2007.

_Source:_ Central Bank of the Philippines, 2007.

10 Firms with high debt-asset ratios face higher premiums and rarely finance new investments externally. 11 The economy-wide ICOR is measured by the investment in a given period divided by the change in GDP in that period; it is also measured by the ratio of investment to GDP divided by the growth rate of GDP (World Bank, 2006a).

12 This is suggesting private investment should pick up: with remittance-assisted personal consumption growing by more than 5 percent, firms would eventually need to increase investment to fulfill this demand. Moreover, consumer imports comprise less than 4 percent of GDP, whereas personal consumption accounts for 78 percent, indicating that the bulk of the increasing demand can be expected to be supplied by higher local production.

11
current account balance if it is negative. The total investment is financed by the sum of all savings: TOTINV= ∑HSAVh h + GSAV+ ESAV+ EXR⋅FSAV+ WALRAS (37 `)` PQi ⋅QINVi = shareinvi ⋅TOTINV (38) where TOTINV is the total investment, WALRAS is the dummy variable, and shareinvi is the share of commodity i used as an investment in the total investment. The real GDP is calculated using the expenditure approach as follows: GDP= ∑(∑QHi h i,h + QGi+QINVi) + ∑QEi i −∑QMi i (39)

# **2.1.7 CO2 emissions and carbon tax**

CO2 emissions are calculated by multiplying fuel inputs in each production sector and final demand sectors by emission coefficients. CO2 emissions from sector i is calculated as: TCO2 = (∑QHh pp,h + QGpp+QINVpp + ∑QQi pp,i). coefpp (40) Where coefpp is CO2 emissions coefficient of petroleum products expressed in terms of ton of CO2 per unit monetary value of its consumption, carbon tax (CTAX) which is exogenous to the model, is converted to equivalent indirect tax ‘ctr’ as follows: ctrpp = CTAX. coefpp (41) This equivalent indirect tax rate is added to the existing indirect tax rate (indt). **2.2. Data** The main data source used for this study is the social accounting matrix (SAM) of Ethiopia. The SAM was built by the Policy Studies Institute of Ethiopia (PSI) using 2016 input-output tables and national accounts. It is available on request.<sup>6</sup> Other data needed are values for various parameters, such as elasticity of substitution used in CES functional forms used to model the 6 It can be argued that the analysis based on six-year-old data might not represent the economic reality of today in Ethiopia. However, SAMs are developed occasionally (such as at 5-year intervals) even in developed economies. Building a SAM more frequently in developing countries is challenging. Use of a SAM that is 5-10 years old is common practice in CGE modeling for two reasons. First, a SAM represents the structure of an economy, which is unlikely to change in a short period. Second, a CGE approach basically models the behavior of economic agents (consumers, producers). Behavioral change also requires a long period. The key findings of the model are unlikely to change even if a new SAM is built and used. Building a SAM is itself a data and time-intensive exercise.