rafmacalaba/datause-encoder-data huggingface dataset:
National-level studies of education and econom-ic growth have established that mass educational expansion -- mainly at the primary level but, to a lesser extent, at secondary level -- has a significant positive impact on economic growth (Meyer et al., 1979; Wheeler, 1980; Walters, 1981; Benavot, 1989). There is also evidence that "qualitadive" features of national school systems (e.g., pupil expenditures, textbook provisions, teacher training) have modest economic effects, especially in the developing world (Heyneman and Siev-White, 1985; Fuller and Heyneman, 1989). Debate currently continues as to specific array of polidcal, social and historical conditions under which the economic impact of mass education is most likely to be pronounced (e.g., Fuller et al., 1986; Hage et al., 1988).

The present study, by contrast, focuses on a new line of inquiry. It explores, from a crossnational perspecdve, the relationship between official curricular policies and long-term economic growth. Specifically, it seeks to determine whether, and to what degree, national variations in curricular content and structure -- as distinct from enrollment growth or qualitative provisions -- have a significant impact on economic development. Comparative educational research typically analyses the content of national school systems according to a three-tiered classification scheme: the official, intended curriculum; the implemented curriculum; and the achieved curriculum (Garden, 1987). This study exclusively focuses on the economic consequences of the first tier -- the formally proscribed curriculum set forth by national educational authorities -- and only for primary education. Based on a sample of over 60 nations (including 43 less-developed counties), it examines the impact of official emphases in eight different primary-level subject areas (as measured by average annual hours of instruction) on long-term economic growth between 1960 and 1985. Mathematics and science education, two subject areas which many believe to be especially pertinent to the economic prosperity of nations, are highlighted throughout the paper.

The analyses and findings reported in this study are significant for severl reasons. First, they provide a new perspective from which to evaluate the merits of national policies such as lengthening the school term (e.g., Karweit, 1985) or requiring more instruction in academic as opposed to vocational or "value-oriented" subjects (e.g., religion, moral education, civics). Second, they entail a preliminary test of the widely assumed, though still unsubstantiated, claim that an emphasis on l
|P000771|1996|1998||8 Closed|Ethiopia|
|---|---|---|---|---|---|
|P000771|1996|1999||10 Closed|Ethiopia|
|P000771|1996|2000|6/28/2000|13 Closed|Ethiopia|
|P000771|1996|2001|4/30/2001|15 Closed|Ethiopia|
|P000771|1996|2002|5/16/2002|17 Closed|Ethiopia|
|P000771|1996|2003|3/26/2003|19 Closed|Ethiopia|
|P000771|1996|2004|3/31/2004|21 Closed|Ethiopia|
|P000771|1996|2005|5/31/2005|24 Closed|Ethiopia|
|P000782|1988|1988||0 Closed|Gabon|
|P000782|1988|1989||4 Closed|Gabon|
|P000782|1988|1990||6 Closed|Gabon|
|P000783|1994|1994||1 Closed|Gabon|
|P000783|1994|1995||3 Closed|Gabon|
|P000784|1990|1990||3 Closed|Gabon|
|P000784|1990|1991||4 Closed|Gabon|
|P000784|1990|1992||7 Closed|Gabon|
|P000784|1990|1993||9 Closed|Gabon|
|P000784|1990|1994||10 Closed|Gabon|
|P000786|1993|1993||5 Closed|Gabon|
|P000786|1993|1994||7 Closed|Gabon|
|P000786|1993|1995||8 Closed|Gabon|
|P000786|1993|1996||11 Closed|Gabon|
|P000786|1993|1997||12 Closed|Gabon|
|P000786|1993|1998||14 Closed|Gabon|
|P000786|1993|1999||16 Closed|Gabon|
|P000786|1993|2000|6/15/2000|19 Closed|Gabon|
|P000786|1993|2001|6/27/2001|22 Closed|Gabon|
|P000786|1993|2002|6/28/2002|25 Closed|Gabon|
|P000787|1989|1989||1 Closed|Gabon|
|P000787|1989|1990||3 Closed|Gabon|
|P000787|1989|1991||5 Closed|Gabon|
|P000787|1989|1992||8 Closed|Gabon|
|P000787|1989|1993||11 Closed|Gabon|
|P000787|1989|1994||14 Closed|Gabon|
|P000787|1989|1995||15 Closed|Gabon|
|P000804|1979|1985||0 Closed|Gambia,The|
|P000804|1979|1986||0 Closed|Gambia,The|
|P000805|1982|1985||0 Closed|Gambia,The|
|P000805|1982|1986||0 Closed|Gambia,The|
|P000805|1982|1987||0 Closed|Gambia,The|
|P000805|1982|1988||0 Closed|Gambia,The|
|P000806|1982|1985||0 Closed|Gambia,The|
|P000806|1982|1986||0 Closed|Gambia,The|
|P000806|1982|1987||0 Closed|Gambia,The|
|P000806|1982|1988||0 Closed|Gambia,The|
|P000806|1982|1989||6 Closed|Gambia,The|
|P000806|1982|1990||9 Closed|Gambia,The|
# **3.3 International Trade**

Next, we examine whether robots are associated to stronger engagement in GVC by looking at the shares of exported revenue and imported input expenditure. Whether robots translates into greater competitiveness in international markets is important given the relevance of export markets to fuel the sustained growth of manufacturing sectors.

The results in Table 8 suggest that the increase in turnover spurred by automation is matched by a more than proportionate increase in plants’ exports so that robots’ adoption raises the export share of plants’ sales (column 1). This is consistent with the idea that robots’ adoption improves productivity and product quality enabling plants to compete more effectively in international markets.

The increase in the export share is also accompanied by a similar increase in the import share of inputs induced by robots’ adoption (column 2). This is consistent with the higher proportion of imported inputs used for exports than for domestic sale (Amiti and Konings, 2007). These results suggest that robots’ adoption in a developing country increases the integration of plants into GVCs, a finding that to the best of our knowledge has not been documented before.

Table 8: Plant level engagement in international trade.

||(1)<br>Export share|(2)<br>Import share|
|---|---|---|
|ETR _×_ secondary|0.004***|0.004***|
||(0.000)|(0.001)|
|Observations|53,517|53,517|
|R-squared|0.852|0.792|
|Plant FE|yes|yes|
|Industry-year FE|yes|yes|
|Other technologies|yes|yes|

The table presents OLS estimates of the relationship between plants’ exposure to robots, the share of exported output and the share of imported inputs. Secondary is the 2006 share of plants’ employment with secondary education. Other technologies are capture by an index of innovation activities in 2006, interacted with year fixed effects. Standard errors are clustered at the 2-digit industry- and year-level. The coefficients with<sup>_⋆⋆⋆_</sup> are significant at the 1% level, with<sup>_⋆⋆_</sup> are significant at the 5% level, and with<sup>_⋆_</sup> are significant at the 10% level.

# **3.4 Robustness**

In this section we test the robustness of the plant level results to changing the key identification assumption in (1). In particular we replace the share of secondary education 24
Overall, the differences in questionnaire design do not significantly affect the poverty predictors required for Models 1, 2, 8 and 9. However, differences in food and non-food consumption aggregates collected with the short questionnaires can have a significant impact on the predictive accuracy of Models 3 and 4, highlighting the importance of questionnaire design for poverty imputation and survey research in general.

# **3. Analytical framework**

The analytical framework features the poverty imputation method developed in Dang _et al._ (2017), which builds on the survey-to-census poverty mapping method in Elbers _et al._ (2023).<sup>10</sup> The method has been applied to fill poverty data gaps for general household populations in various low- and middle-income countries from different regions, including India, Jordan, Tunisia, Viet Nam, and Sub-Saharan African countries (Beegle _et al._ , 2016; Dang and Lanjouw, 2023), as well as to estimate poverty within refugee sub-populations (Dang and Verme, 2023). Most recently, the method is employed in Dang _et al._ (forthcoming) to analyze data from 14 multi-topic household surveys from Ethiopia, Malawi, Nigeria, Tanzania, and Viet Nam. We briefly describe the method below.

Building on a household utility budget model (Deaton and Muellbauer, 1980), (log) household consumption per capita ( ) is typically estimated using the following reduced-form linear model jj for survey _j_ , for _j_ = 1, 2<sup>11</sup> yy

> 10 Previous studies offer various refinements of certain features of the poverty mapping technique, such as imposing a parametric probit functional form on the error term (Tarozzi, 2007) or offering a different formula to estimate the standard errors (Mathiassen, 2009). Dang _et al._ (2017) offer a well-defined framework for survey-to-survey imputation with simpler variance formulas and formulas for standardization of variables from surveys with different sampling designs (e.g., imputing from a household consumption survey into a LFS).

> 11 The subscript for households is omitted for less cluttered notation in the subsequent discussion.

13
# **_6.2 Economic and Financial Development_**

The data also show a positive and significant relationship between economic and financial development and entrepreneurship. The log of GDP per capita and domestic credit to the private sector (as a percentage of GDP) are both positively and significantly correlated with entry rates (Figure 7) and business density (not shown). This suggests that greater business opportunities and better access to finance are related to a more robust private sector.

Disentangling the direction of causality – whether positive economic growth is a determinant for the creation (i.e. registration) of new businesses or whether greater entrepreneurship leads to economic growth and innovation – in an important area of future research.

# **Figure 7: Entry Rates and GDP Per Capita and Private Credit to GDP, by country, Average 2003-2005**

<!-- Start of picture text --> Entry rate Entry rate(percent)<br>20% 20<br>15% 15<br>10% 10<br>5% 5<br>0%<br>0<br>0 10000 20000 30000 40000 50000 0 50 100 150 200 250 300<br>GDP per capita Private credit /GDP<br><!-- End of picture text --> Source: World Bank Group Entrepreneurship Database (2007) and World Bank (2005).

# **_6.3 Relationship with the Informal Sector_**

Total firm registrations are significantly higher in countries with a smaller informal sector (Figure 8). This suggests a substitution effect and a larger informal sector in countries with higher entry barriers. The data also show a significant relationship between the entry rate and the informal sector.

Together, these results suggest that an increase in total and newly registered firms might indicate a decrease in the size of the informal sector. Indeed, a 30 percentage point increase in business density and a 10 percentage point increase in entry rates are commensurate with a 10 percentage point decline in the informal sector (as a share of GDP). We do not include this variable in the multivariate analysis because of its very large (about 80%) and significant relationship with GDP per capita.

20
2013; Verduzco-Gallo, Ecker, & Pauw, 2014). Even with a low maize prices and higher incomes that can support wider food purchasing, there is still a lack of variety in consumption diets (Ecker & Qaim, 2011; Matita et al., 2023). Similarly, production patterns have not changed much with incentives for growing nutrient-rich crops such as legumes (Ortega et al., 2016; Waldman et al., 2017). Addressing hunger often takes precedence over diversifying diets which is linked to micronutrient deficiencies and ensuing health effects (Nuss & Tanumihardjo, 2010; Poole et al., 2021; Verduzco-Gallo et al., 2014). For instance, 37 percent of children under the age of five are stunted, only 25 percent meets the minimum dietary requirements, and female obesity is on the rise, a contributing factor to non-communicable diseases (NSO & ICF, 2017). Depending on context and the measure used, the evidence regarding the effects of AISP on food and nutrition security has also been mixed. While some studies (Chakrabarti et al., 2024; Harou, 2018; Mason et al., 2020; Matita et al., 2022; Mwale et al., 2022; Snapp & Fisher, 2015; Tione et al., 2022) show positive effects, others (Hemming et al., 2018; Karamba, 2013; Ragasa & Mazunda, 2018; Jacob Ricker-Gilbert & Jayne, 2017; Walls et al., 2018) report weak and inconsistent results. Increasingly, more marginal lands are being cleared for the cultivation of maize and soil health is at risk because the subsidies are not tied to any initiative to improve the environment (Holden, 2018). Thus, the effectiveness of standalone AISP is in question. This study therefore examines whether integrating input subsides with other interventions, such as irrigation schemes and social transfers, can result in improved food and nutrition security indicators (dietary diversity, food consumption scores, coping strategy index).

The combined effects of such interventions are only occasionally evaluated by studies (Matita & Chirwa, 2014; Pace et al., 2017; Shigute, Strupat, Burchi, Alemu, & Bedi, 2019; Thome, Filipski, Kagin, Taylor, & Davis, 2013; Thome, Taylor, & Filipski, 2014). For example, Pace et al. (2018) taking advantage of random assignment of participants to a social cash transfer program (SCTP) in Malawi, assessed the joint effects of the program with input subsidies. They found that participating in both programs can improve household expenditures, livestock, and agricultural productivity. Specifically, among households without labor constraints, the number of meals per day was higher for those who received input subsidies and cash transfers. Similarly, synergies between a community health insurance program and Ethiopia's productivity safety net program were reported by Shigute et al. (2019). The effects on labor supply, the number of livestock holdings, and the utilization of health care facilities were all favorable. According to an impact 2
# **Table 2. Summary Statistics from Diaries:  Shocks, Transfers, Expenditures, and Savings**

|**Panel A. Shocks**|(1)<br>_Daily Average_|(2)<br>_Occurred at Least_<br>_Once over 3 Month_<br>_Sample Period_||
|---|---|---|---|
|Someone in Household Sick (other than respondent)|0.37|0.93||
|Respondent Sick|0.34|0.98||
|Respondent had STI|0.03|0.34||
|Observations|12481|209||
|IDs|192|192||
||(1)|(2)|(3)|
|**Panel B. Transfers & Gifts from Clients(Daily Averages)**|_Sending Out_|_Receiving From_|_Net Flow_|
|Loan and Gift Flows from Family & Friends|32.50|54.57|-22.22|
||(144.36)|(204.60)|(246.07)|
|Gifts received from Regular Clients|94.11<br>(299.01)|||
|Observations|12467|||
|**Panel C. Expenditures(Daily Averages)**|(1)|||
|Total Expenditures|667.62<br>(703.88)|||
|Food Expenditures|183.40<br>(201.76)|||
|Observations|12521|||
|IDs|192|||

Note: Sickness is an indicator variable equal to 1 if household or respondent reported having a cough, fever, malaria, typhoid, diarrhea, cuts or burns, or any other illness. There are more observations than IDs in Column 2 of Panel A because some women were sampled in both rounds.

All monetary values in Kenyan shillings (Ksh). The exchange rate over the data collection period was approximately 70 Ksh / $1 US. Means are reported, with standard deviations in parentheses. Exact number of observations differ for some variables, due to reporting errors.
# **1. Introduction**

Policy-makers in developing East Asia see infrastructure investment as an essential determinant of growth.<sup>1</sup> The two fastest-growing economies in the region, China and Vietnam, are investing around 10 percent of GDP in infrastructure, and even at that rate they are struggling to keep pace with demand for electricity and telephones, and to install major transport networks. Hopes for a significant contribution to growth in the Greater Mekong countries – Laos, Cambodia, Thailand, Vietnam, Myanmar, and China – are centered on plans for greater integration of transport and energy markets. Since its election in late 2004, the new Indonesian government has made infrastructure a national priority, seeking to restore investment to its pre-crisis level of 5-6 percent of GDP.

The current emphasis on infrastructure draws its inspiration from East Asia’s economic history, including the experience of countries such as Japan, South Korea, Malaysia and Taiwan, China, which also made large investments in infrastructure. East Asia’s accumulation of infrastructure stocks has outpaced infrastructure investment in other regions (Table 1). And East Asia’s economic growth has outpaced the growth of other world regions. Between 1975 and 2005, East Asia’s GDP increased ten-fold; South Asia’s GDP increased five-fold; and all other regions’ economies grew by factors of between two and three.<sup>2</sup> For most policy-makers this is no coincidence.

**Table 1: Growth of GDP and Infrastructure Stocks**

|**19**|**95 levels as mul**<br>|**tiples of 1975 levels**|||
|---|---|---|---|---|
||GDP|Electricity|Roads|Telecoms|
|East Asia|4.8|5.9|2.9|15.5|
|South Asia|2.6|4.4|2.5|8.2|
|Middle East & North Africa|1.8|6.1|2.1|7.2|
|Latin America & Caribbean|1.8|3.0|1.9|5.1|
|OECD|1.8|1.6|1.4|2.2|
|Pacific|1.7|2.0||4.3|
|Sub-Saharan Africa|1.4|2.6|1.7|3.9|
|Eastern Europe|1.0|1.6|1.2|6.9|

GDP – PPP constant 2000 international $; Electricity - MW of generating capacity; Roads – km of paved road; Telecoms – number of main lines. See Annex 1 for construction. Sources: World Development Indicators and Canning (1998)

> 1 See ADB, IBRD, WB, JICA, (2005). 2

> Difference in GDP (PPP) in constant 2000 dollars between 1975 and 2005.

2
Table 2: Structural Model Estimation of the Impact of Women’s Decision-making Power on Budget Share

||Food|Fuel and<br>lighting|Transportation|Food and<br>clothing|Medical<br>care|Education|
|---|---|---|---|---|---|---|
|Theta|-6.442***|-0.551***|1.897***|-0.369***|-0.566***|2.059***|
||[0.217]|[0.063]|[0.088]|[0.041]|[0.087]|[0.084]|
|Theta(Theta * log|0.403***|0.038***|-0.122***|0.022***|0.034***|-0.126***|
|of per capita|||||||
|expenditure)|[0.015]|[0.004]|[0.006]|[0.003]|[0.006]|[0.006]|
|(1-Theta)(1-<br>Theta) * log of<br>per capita|-0.448***|-0.035***|0.131***|-0.027***|-0.040***|0.143***|
|expenditure<br>Ln (# of peple in|[0.015]|[0.004]|[0.006]|[0.003]|[0.006]|[0.006]|
|HH)|-0.026***|-0.018***|0.004**|0.003***|0.002|0.020***|
||[0.004]|[0.001]|[0.002]|[0.001]|[0.002]|[0.002]|
|Observations|7,919|7,919|7,919|7,919|7,919|7,919|
|||Fuel and||Food and|Medical||
|Wald test|Food|lighting|Transportation|clothing|care|Education|
|Theta=0|934.7c|108.5c|484.5c|129.1c|51.7c|644.1c|
|Theta=0.2|892.7c|129.4c|473.5c|113.2c|46.4c|608.3c|
|Theta=0.4|963.8c|139.2c|467.6c|82.2c|45.6c|743c|
|Theta=0.5|1067.8c|104.6c|485.6c|99.6c|55.1c|859.5c|
|Theta=0.6|1095.6c|84.3c|496.3c|118.5c|60c|872.5c|
|Theta=0.8|1081.8c|75.9c|501c|132.9c|61.6c|835.6c|
|Theta=1|1065.9c|76c|501c|136.8c|61.3c|809.4c|

Note: 1. Absolute value of z statistics in brackets. 2. * significant at 10%; ** significant at 5%; *** significant at 1%. 3. Other variables controlled for but not presented include household size, demographic characteristics of household members, ownership of assets, and regional fixed effect.

26
is incomplete, covering only large transactions. Firms can still manipulate their taxable income by misreporting small and cash transactions and sales to the nal consumer, or inating costs, deductions and exemptions.<sup>36</sup> Information reporting by state institutions and credit/debit card companies, which also act as withholding agents, is associated with a further reduction in misreporting behavior (panels 3 and 4). For the self-employed, the excess mass among rms subject to state reporting is similar to the excess mass among rms subject only to information reporting by other rms, but the excess mass drops to .52 for rms subject to credit/debit card reporting. For corporations, the excess mass drops from 3.17 among rms subject to information reporting by other rms to 1.44 and 1.35 respectively for rms subject to state reporting and credit/debit card reporting. These drops are again highly statistically signicant.

The heterogeneity of bunching across subsamples is thus consistent with a compliance impact of information reporting, and an even stronger impact of withholding. Our results are also consistent with estimates from the United States, where the Internal Revenue Service reports tax evasion rates of 56%, 8% and 1% respectively on income covered by little information reporting, income covered by substantial information reporting and income subject to withholding (IRS 2012). However, just like the IRS estimates, our estimates do not capture a pure causal eect. Firms subject to information reporting, and especially rms subject to withholding, are on average larger, more urban and probably equipped with more sophisticated management and accounting systems. It is possible that these characteristics, rather than coverage by the tax compliance mechanisms, explain at least part of the heterogeneity in bunching. In addition, the dierent reporting mechanisms apply to dierent types of transactions, rm-to-rm, rm-to-state and card transactions respectively, so that the corresponding informative declarations and the rms covered by them are not strictly comparable.<sup>37</sup>

# 5.2 Event Study

To move a step further towards estimating a causal eect of information reporting, we exploit withinrm variation across time in the coverage by information reporting. Each year, over a thousand rms switch from not being reported to the tax authorities by any third party to being reported by at least one transaction partner. If third-party reporting of transactions to the tax authorities forces rms to declare a larger share of their transactions on their self-assessment declarations, coverage by information reporting should be associated with an increase in reported taxable sales and income.

> 36Bunching is also present in all economic sectors and relatively homogenous across sectors.

> 37For example, managers of rms deciding to sell their output to a state institution may have higher tax morale, or be more committed to public goods provision, than managers transacting only with other private sector rms.

22
Figure 12: Pooled Instances of Rate Changes against Revenue Change (All Above States) <!-- Start of picture text --> All State Pooled<br>100<br>80<br>60<br>40<br>20<br>0<br>-200 -20 0 200 400 600 800 1000 1200<br>-40<br>-60<br>stamp rate growth<br>tax revenue growth<br><!-- End of picture text --> _<u>Regression Statistics</u>_ Multiple R 0.256082 R Square 0.065578 Adjusted R 0.003283 Standard E 24.24569 Observatio 17

# ANOVA

||_df_|_SS_|_MS_|_F_|_ignificance F_||||
|---|---|---|---|---|---|---|---|---|
|Regression|1|618.8332|618.8332|1.0527|0.321148||||
|Residual|15|8817.801|587.8534||||||
|Total|16|9436.634|||||||
||_Coefficientst_|_andard Erro_|<br>_t Stat_|_P-value_|_Lower 95%_|_Upper 95%L_|_ower 95.0%U_|_pper 95.0%_|
|Intercept|18.88986|6.429734|2.937892|0.010181|5.185201|32.59453|5.185201|32.59453|
|X Variable|-0.021837|0.021284|-1.026012|0.321148|-0.067202|0.023528|-0.067202|0.023528|

78
- Song, J., D. J. Price, F. Guvenen, N. Bloom, and T. von Wachter (2019): “Firming Up Inequality,” _Quarterly Journal of Economics_ , 134(1), 1–50.

- Verhoogen, E. (2008): “Trade, Quality Upgrading, and Wage Inequality in the Mexican Manufacturing Sector,” _Quarterly Journal of Economics_ , 123(2), 489–530.

26