rafmacalaba/datause-encoder-data huggingface dataset:
education and literacy status of the household head does not hold consistent results across country. In Ethiopia, literacy of the household head reduces measurement bias (in acreage terms), but years of education has the opposite effect. In fact, the education of the household head significantly increases the relative bias in Ethiopia (but is not statistically significant in Nigeria or Tanzania). One can speculate that, controlling for literacy, education may be associated with increasing opportunity cost of time or decreasing involvement in agriculture, which could make the respondent less attentive or knowledgeable regarding the measurement or reporting of plot area.

Finally, one key decision point when collecting self-reported area data is whether to allow respondents to use non-standard units, or to force them (or enumerators) to convert responses from traditional to standard units at the moment of the interview. In Table 11, we compare deviations between self-reported and GPS data separately splitting the sample between observations where respondents used traditional and standard units in the Ethiopia and Nigeria sample. When land area is collected using non-standard units, as opposed to forcing respondents or enumerators to perform a conversion to standard units at the time of the interview, data from self-report appears to approximate the preferred GPS measures much better. This finding supports the idea that it is best to allow non-standard units to be used at interview time, while organizing complementary collection of adequate conversion factors to translate all the data in a common metric at the data processing stage.

# **VI. Conclusions**

Several important findings emerge forcefully from this analysis, which translate into clear implications for future survey design and implementation. The first result is that our experimental data confirm what we already knew about the presence of large, systematic measurement error in farmers’ self-reported estimates of land area, and on its direction, correlates and determinants (which include land area itself, introducing potentially large biases at the data analysis stage). While not a novel finding, this is a useful reminder of the urgency to find alternative measures that are both accurate and usable in the context of large scale household surveys. GPS measurement is the obvious candidate.

Much of the focus of the paper has, therefore, been on assessing the fitness for purpose of GPS measures. In this respect an important finding of the study is that on average GPS measures return very accurate estimates of plot size, even for very small plots, and even for reasonably small samples. We also do not detect any evidence that GPS systematically under-reports land size, as is the case in earlier studies. That should suffice to make GPS an attractive method for land area data collection for most household survey 26
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employment. The hypothesis of public employment being used as a redistribution tool is not supported by these findings, while the role of public employment as an insurance to mitigate exposure to undiversifiable external risk finds better support, though differently across groups of countries.

It is possible that the use of public employment varies along the business cycle, expanding during booms, but being rigid during recessions. We tested the hypothesis of asymmetric or ratchet effects in the response of public employment to changes in GDP per capita, with a dummy variable equal to 1 when GDP per capita growth is positive, included by itself and interacted with GDP per capita. We found no significative difference in the response of public employment to changes in GDP along the cycle and hence reject the ratchet effects hypothesis.<sup>11</sup>

# **vi. Actual public employment compared with its predicted level**

Based on the preferred specification in terms of goodness of fit and statistical significance (column 2 in Table 9), we predict the public employment levels to compare with actual public employment.<sup>12</sup> The comparisons, grouped by geographic region and averaging over all the years available,<sup>13</sup> show clear differences across regions (Figure 8). EAP shows public employment lower than the predicted by the model, while MENA shows the opposite. In Latin America and the Caribbean, most countries have lower public employment than the predicted levels, except for Argentina, Brazil, Mexico, Suriname, Trinidad and Tobago, and the República Bolivariana de Venezuela, supporting the IDB’s conclusion that the wage bill in LAC is driven by large public sector wage premiums (IDB, 2018). ECA has higher employment than predicted by the model, while AFR has mixed results, with Botswana and South Africa showing significantly higher public employment levels.

We examine the potential role of political ideology driving these deviations, under the hypothesis that left-leaning governments would have larger public employment levels. Hence, we correlate the deviations with a variable that captures the political ideology of the government in office (left, center of right) obtained from the Database of Political Institutions 2017 from the Inter-American Development Bank. The deviations from the predicted level do not appear to be driven by political ideology, as suggested by

> 11 The model is estimated at an annual frequency (not five-year averages) to examine the public employment along the business cycle. Results are not presented to save space but are available from the authors upon request. 12 To increase the number of countries, in cases where general government employment data are not available, we substitute it with public sector employment.

> 13 Figure 16 shows the comparison using only the last available 5-year window.

19
|(1=yes)|||||(0.011)|(0.007)|(0.011)|
|---|---|---|---|---|---|---|---|
|Improved seeds used (1=yes)|0.57|0.64|0.59|0.53|0.04<br>(0.06)|0.11**<br>(0.05)|0.07<br>(0.05)|
|Plants crops usually in lines|0.45|0.52|0.52|0.39|-0.00|0.13*|0.13*|
|(1=yes)|||||(0.09)|(0.07)|(0.07)|
|Lime applied (1=yes)|0.008|0.009|0.009|0.007|0.000<br>(0.007)|0.002<br>(0.005)|0.003<br>(0.005)|
|DAP applied (1=yes)|0.70|0.76|0.74|0.66|0.02<br>(0.05)|0.09*<br>(0.05)|0.07<br>(0.05)|
|Used irrigation (1=yes)|0.19|0.17|0.19|0.20|-0.03|-0.03|-0.00|
||||||(0.05)|(0.04)|(0.04)|
|Last season was bad (1=yes)|0.48|0.46|0.45|0.51|0.02<br>(0.08)|-0.04<br>(0.07)|-0.06<br>(0.07)|
|No. of times talked to|5.53|5.76|6.42|5.07|-0.66|0.69|1.35|
|extension agent in last year|[10.97]|[11.06]|[14.35]|[9.20]|(1.23)|(0.90)|(1.06)|
|Attended agric. training|0.27|0.30|0.34|0.23|-0.03|0.08**|0.11***|
|in last year (1=yes)|||||(0.05)|(0.04)|(0.04)|
|**Panel C: Community-level cha**|**racteristics**|||||||
|Mean annual temperature 2017|20.56|20.45|20.40|20.68|0.05|-0.23|-0.28|
|(°C)|[4.10]|[4.21]|[4.24]|[4.00]|(1.00)|(0.82)|(0.83)|
|Mean annual rainfall 2017<br>(mm)|1108.84|1140.53|1140.11|1083.12|0.42|57.40|56.98|
||[396.23]|[381.88]|[380.26]|[406.66]|(90.84)|(77.59)|(77.13)|
|Distance to Woreda capital<br>(km)|14.62|13.66|15.52|14.65|-1.86|-0.99|0.87|
||[15.42]|[16.31]|[13.69]|[15.69]|(3.67)|(3.27)|(2.85)|
|N|2,382|539|532|1,311|1,071|1,850|1,843|

_Source:_ Authors’ analyses based on baseline survey data.

_Note:_ HH stands for household. Basic asset score comprises the following: HH has modern roof, improved stove, modern lighting, toilet facility. TLU stands for tropical livestock unit. Calculation of food insecurity score based on self-experienced events of food insecurity, based on Household Food Insecurity Access Scale (HFIAS). FTC stands for farmer training center. Main crops are maize, wheat, teff, barley, sorghum. Temperature and rainfall assessed at endline. For means, standard deviations in brackets; for mean comparisons, robust standard errors in parentheses, clustered at the mws level. Significance levels: *** p<0.01, ** p<0.05, * p<0.1.

29
# Table II: Cross-Country Regressions

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dependent|FOFM|FOFM|FOFM|ln_L_|ln_L_|ln_L_|
|ln_L_|-0.096<sup>_∗∗∗_</sup><br>(0.0055)|-0.092<sup>_∗∗∗_</sup><br>(0.0042)|-0.096<sup>_∗∗∗_</sup><br>(0.0053)||||
|�<br>Rule of Law|-0.157<sup>_∗∗∗_</sup>|-0.163<sup>_∗∗∗_</sup>|-0.207<sup>_∗∗∗_</sup>|-0.056|0.092|0.180|
||(0.0185)|(0.0301)|(0.0415)|(0.0614)|(0.1240)|(0.1512)|
|�<br>Rule of Law_×_ln_L_|||0.013<sup>_∗_</sup><br>(0.0072)||||
|FOFM||||-0.392<sup>_∗∗∗_</sup><br>(0.0736)|-0.370<sup>_∗∗∗_</sup><br>(0.0667)|-0.394<sup>_∗∗∗_</sup><br>(0.0607)|
|FOFM_×_<br>�<br>Rule of Law||||||-0.192<sup>_∗∗_</sup><br>(0.0780)|
|Controls||X|X||X|X|
|Clustering|Country|Country|Country|Country|Country|Country|
|No. Clusters|21|21|21|21|21|21|
|_R_<sup>2</sup>|0.20|0.20|0.20|0.12|0.15|0.16|
|_N_|271249|265509|265509|271249|265509|265509|

� Note: Controls include log GDP per capita, log firm age and 4 digit SIC dummies. Rule of Law is the rule of law index, standardized to have mean zero and unit standard deviation.

28
Table 3: Impact of random assignment to the reserved space on reported harassment, overall and by presence of men

||||Dependent<br>|variable:<br>||||
|---|---|---|---|---|---|---|---|
|Any har|assment|Physical|harassment|Verbal h|arassment|Sta|ring|
|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|_Panel A: Overall impact of _|_randomiz_|_ed assignm_|_ent_|||||
|Assigned to reserved space<br>-0.025|-0.025|-0.013|-0.013|-0.009|-0.009|-0.007|-0.007|
|(0.013)|(0.013)|(0.005)|(0.005)|(0.008)|(0.008)|(0.012)|(0.012)|
|High crowding|0.005||-0.013||0.002||-0.002|
||(0.029)||(0.014)||(0.011)||(0.028)|
|Few men in reserved space|-0.004||0.002||0.005||-0.011|
||(0.018)||(0.008)||(0.008)||(0.016)|
|Constant<br>0.168|0.169|0.031|0.031|0.058|0.055|0.124|0.129|
|(0.006)|(0.012)|(0.002)|(0.005)|(0.004)|(0.006)|(0.006)|(0.010)|
|_Mean depende_|_nt variabl_|_e_||||||
|Assigned to public space<br>0.1|76|0|.026|0.|066|0.|128|
|(0.0|13)|(0|.004)|(0.|009)|(0.|013)|
|_Panel B: Impact of randomized assignmen_<br>ˆ|_t by prese_|_nce of me_|_n in reserve_|_d space_||||
|_βH_1: Assigned to reserved space _×_ Few men in reserved space<br>0.139|0.139|0.018|0.017|0.052|0.052|0.109|0.108|
|(0.012)|(0.012)|(0.004)|(0.005)|(0.006)|(0.006)|(0.011)|(0.011)|
|ˆ_βH_2: Assigned to public space _×_ Few men in reserved space<br>0.167|0.167|0.035|0.034|0.061|0.061|0.121|0.121|
|(0.012)|(0.012)|(0.005)|(0.005)|(0.006)|(0.006)|(0.011)|(0.011)|
|ˆ_βH_3: Assigned to reserved space _×_ Many men in reserved space<br>0.146|0.146|0.019|0.020|0.047|0.047|0.125|0.125|
|(0.013)|(0.013)|(0.006)|(0.006)|(0.007)|(0.007)|(0.011)|(0.012)|
|ˆ_βH_4: Assigned to public space _×_ Many men in reserved space<br>0.168|0.168|0.027|0.028|0.056|0.056|0.126|0.126|
|(0.014)|(0.014)|(0.005)|(0.005)|(0.008)|(0.008)|(0.011)|(0.011)|
|_Mean depende_|_nt variabl_|_e_||||||
|Assigned to public space _×_ Few men in reserved space<br>0.1|41|0|.025|0.|053|0.|095|
|(0.0|23)|(0|.006)|(0.|014)|(0.|02)|
|Assigned to public space _×_ Many men in reserved space<br>0.2|09|0|.027|0.|078|0.|161|
|(0.0|24)|(0|.007)|(0.|019)|(0.|021)|
|Observations<br>3695|3695|3695|3695|3695|3695|3695|3695|
|Riders<br>259|259|259|259|259|259|259|259|
|Control for high crowding<br>No|Yes|No|Yes|No|Yes|No|Yes|
|Rider fxed efect<br>Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|_Post-estimate tests for h_<br>Impact on harassment when few men in reserved space: reserved space - p|_eterogene_<br>ublic spa|_ous efects_<br>ce||||||
|ˆ_βH_ - ˆ_βH_<br>-0.028|-0.028|-0.017|-0.017|-0.009|-0.009|-0.012|-0.012|
|1 2<br>P-value<br>0.087|0.087|0.004|0.004|0.366|0.367|0.454|0.453|
|Impact on harassment when many men in reserved space: reserved space -|public s|pace||||||
|ˆ_βH_3 - ˆ_βH_4<br>-0.022|-0.022|-0.008|-0.008|-0.008|-0.008|-0.002|-0.002|
|<br>P-value<br>0.259|0.260|0.264|0.259|0.501|0.502|0.924|0.923|

_Note:_ Unit of observation is one ride. Sample includes randomized assignment of space rides for riders who started such rides. Rides with no corresponding platform audits were dropped. Observations weighted by the inverse of the number of rides taken by the individual rider. Standard errors in parentheses, clustered at rider level.
||<br>|**Table 6**|**: Lever**<br>**Ge**|**age for**<br>**rmany**<br>(|**Nine A**<br>**and th**<br>%,<br>means|**sian C**<br>**e US**<br>)<br>|**ountrie**<br>|**s,**<br>|||
|---|---|---|---|---|---|---|---|---|---|---|
|Country|J1988|1989|1990|1991|1992|1993|1994|1 1995|J1996|1T1988-96|
|Hong Kong|1.832|2.311|1.783|2.047|1.835|1.758|2.273|1.980|1.559|<br>1.902|
|Indonesia|n.a.|n.a.|n.a.|1.943|2.097|2.054|1.661|2.115|1.878|<br>1.951|
|Japan|2.994|2.843|2.871|2.029|2.042|2.057|2.193|2.367|2.374|2.302|
|Korea|2.820|2.644|3.105|3.221|3.373|3.636|3.530|3.776|3.545|3.467|
|Malaysia|0.727|0.810|1.010|0.610|0.627|0.704|0.991|1.103|1.176|<br>0.908|
|Philippines|n.a.|n.a.|n.a.|0.830|1.186|1.175|1.148|1.150|1.285|1.129|
|Singapore|0.765|0.922|0.939|0.887|0.856|1.102|0.862|1.037|1.049|<br>0.936|
|Taiwan|n.a.|n.a.|n.a.|0.679|0.883|0.866|0.894|0.796|0.802|0.820|
|Thailand|1.602|1.905|2.159|2.010|1.837|1.914|2.126|2.224|2.361|2.008|
|US|0.798|0.848|0.904|0.972|1.059|1.051|1.066|1.099|1.125|<br>1.034|
|Germany|1.535|1.552|1.582|1.594|1.507|1.534|1.512|1.485|1.472|<br>1.514|

Note: Table A5 reports medians, standard deviations, and sample sizes.

Most East Asian countries saw some increase in leverage in the last few years: this was most notable for Japan, Korea, Malaysia and Thailand. Japan had seen some de-leveraging earlier in the decade, possibly as there was some financial retrenchment, in the early 1990s, but lack of equity and corporate sector difficulties may have meant that no new equity was raised and loans were rolled over in the later part of the period. Leverage consequently rose. The rise in leverage in the Philippines is probably the result of its reforms in the mid-1980s, which led to revived corporate and financial sectors and better financing possibilities.

To study the riskiness of the financial structures of East Asian corporates, we next compare their average 1988-96 leverage ratios with the leverage ratios in the other Worldscope countries (Figure 2). Korean and Japanese firms have the highest leverage among all corporates in this group of countries, while companies in Thailand, Indonesia, and Hong Kong also have among the ten highest leverage ratios. At the opposite extreme, Taiwanese firms show relatively low leverage ratios. Firms in the Philippines, Singapore, and Malaysia also have below-average ratios. The pattern across other regions is also interesting. Western European countries typically display high leverage ratios, with Swiss firms having leverage almost as high as Japanese firms. In contrast, corporates in South American countries (Peru, Chile, Argentina, Venezuela, Colombia) have low leverage, reflecting the less deep banking systems of these countries.

9
- _Heavy borrowers_ are countries with cumulative public debt variation greater than 20 percent of GDP from 2012 to 2017. The cumulative variation of public debt for heavy borrowers is, on average, 36.9 percent of GDP and the level of general government gross debt is 77.4 percent of GDP. This group includes the following countries: Angola, Benin, Cabo Verde, Central African Republic, Chad, Congo Rep., Equatorial Guinea, Gabon, Ghana, Mozambique, Namibia, Sierra Leone, South Sudan, Sudan, The Gambia, Togo, Zambia, and Zimbabwe.

**Figure 18. Public Debt in Sub-Saharan Africa, by intensity of debt accumulation**

<!-- Start of picture text --> 80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Light borrowers Moderate borrowers Heavy borrowers<br>2012 Level 2012-17 Change<br>Percent of GDP (weighted average)<br><!-- End of picture text --> _Source: International Monetary Fund, World Economic Outlook Database._

**Figure 18** shows the evolution of public debt stocks over the past five years across the different groups of SSA countries classified by their debt intensity. This figure corroborates the conjecture that there is a great deal of heterogeneity in the pace of public debt accumulation over the past five years. On average, heavy borrowers already had the largest public debt stock among SSA countries but relatively contained at about 40 percent of GDP in 2012. At the same time, the group of heavy borrowers experienced the largest accumulation of public debt from 2012 to 2017. Public debt stocks of heavy borrowers in the region almost doubled by 2017 (i.e. the average level of public debt amounted to 77.4 percent of GDP).

There is variability across countries in both the level and pace of accumulation of public debt within the group of heavy borrowers in Sub-Saharan Africa ( **Figure 19** ). The median cumulative 23
**Table 7: The Effect of AGOA suspension, excluding outliers and oil products**

||Excluding<br>|outliers<br>|Excluding<br>|oil products<br>|
|---|---|---|---|---|
||lnExports|Export dummy|lnExports|Export dummy|
||(1)|(2)|(2)|4|
|Marginal effects|-45%||-40%||
|Suspended×Country×Product|-0.611*|-0.026**|-0.514*|-0.021*|
||(0.348)|(0.013)|(0.293)|(0.011)|
|Product-year FEs|Yes|Yes|Yes|Yes|
|Country year FEs|Yes|Yes|Yes|Yes|
|Country Product FE|Yes|Yes|Yes|Yes|
|Number of observations|566,900|566,900|584,112|584,112|

The marginal effects throughout this paper are calculated as exp(β)−1 if the dependent variable is lnExports. Robust standard errors in parentheses are clustered by country and product. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

# **5. Conclusion**

Understanding the impact of withdrawing access to a non-reciprocal trade preference program is increasingly important amid heightened uncertainty in global trade. Furthermore, AGOA is expected to expire in September 2025, and there has been a marked shift in the US trade policy away from unilateral trade preference programs to more reciprocal bilateral trade arrangements, hence the need to better understand the impact of AGOA suspensions.

This study examines the impact of AGOA suspension on exports from suspended SSA countries to the US. To estimate the effect of AGOA suspension, we use a triple differencein-differences specification that takes advantage of country-product-year variations. We find that AGOA suspension reduces exports of AGOA-eligible products from SSA countries to the US by 39 percent. Compared to the existing literature that examined the impact of withdrawal of GSP (Hakobyan, 2020), the impact is much larger. This is partly 22
# **Understanding child labor beyond poverty**

_The structure of the economy, social norms, and no returns to rural basic education_

## Alexander Krauss<sup>1</sup>

_Keywords_ : Child labor; Poverty; Agriculture; Africa; Child work; Ghana; Mixed methods _JEL codes_ : J23; J24; D13; I31 This article has been published and is available online at http://dx.doi.org/10.1093/cje/bew019 It should be cited as: Krauss, Alexander. 2016. _Understanding child labour beyond the standard economic assumption of monetary poverty_ . Cambridge Journal of Economics, Oxford University Press

> 1 London School of Economics; Institute for New Economic Thinking; University College London. Email: a.krauss@ucl.ac.uk

I am thankful for comments from Lant Pritchett, Eric Edmonds, Corinna Peters, Moritz Meyer, Christian Krekel, Tillmann Heidelk, Josie Stewart, Nicolas Friederici, students during a guest lecture at Johns Hopkins University/SAIS in 2013 and anonymous journal peer reviewers. I am also grateful for funding for the completion of this paper from the Institute for New Economic Thinking.
made no deposit or withdrawal in the past year.<sup>12</sup> In addition, only 29 percent of adults have savings at formal financial institutions and just 17 percent have loans.<sup>13</sup> This data indicate that despite Sri Lanka’s sizeable per capita income and overall level of human capital development, the full potential for financial inclusion remains unfulfilled. If Sri Lanka aims to sustain its recent upper middle-income status, it is more relevant to compare its financial inclusion achievements with those of other countries which have similarly progressed. In this context, we summarize below comparative financial inclusion indicators for Sri Lanka against the South Asian average as well as other middle income and aspirational counterparts.

Table 1: Financial inclusion indicators<sup>14</sup>

||**Sri**<br>**Lanka**|**South**<br>**Asia**|**East**<br>**Asia**|**Upper**<br>**Middle**|**Thailand**|**Malaysia**|
|---|---|---|---|---|---|---|
||**2017**|**2017**|**2017**|**2017**|**2017**|**2017**|
|**GNI Per Capita ($)**|**3850**|**1611**|**6667**|**8176**|**5640**|**9860**|
|**Human Capital Index (0-1)**|**0.58**|**0.46**|**0.61**|**0.58**|**0.6**|**0.62**|
|**Access to Formal Finance**|||||||
|**Financial institution account (% age 15+)**|**74%**|**68%**|**73%**|**73%**|**81%**|**85%**|
|**Usage of Formal Finance**|||||||
|**Saved at a financial institution (% age 15+)**|**29%**|**17%**|**31%**|**27%**|**39%**|**38%**|
|**Borrowed from a financial institution (% age 15+)**|**17%**|**8%**|**21%**|**22%**|**20%**|**23%**|
|**Made or received digital payments in the past year (% age 15+)**|**47%**|**28%**|**58%**|**62%**|**62%**|**70%**|

Source: Little Data Book on Financial Inclusion 2018 The above comparison reveals mixed results. East Asian achievers like Thailand and Malaysia fare better on both access to, as well as usage of, formal finance. While Sri Lanka seems on par or slightly ahead of the East Asian average (as well as the upper middle-income countries’ average) in terms of account ownership, it is lower in terms of usage of credit and digital payments. This indicates that opportunities to scale up usage of transactional services through more efficient

> 12 World Bank Findex 2017.

> 13 Little Data Book on Financial Inclusion 2018.

> 14 The latest Findex available is for 2017, hence data used are from 2017 for comparison purpose.

8