rafmacalaba/datause-encoder-data huggingface dataset:
51 Income tax, have no such motive. Also, In Irrigation districts, Inputs on land leased from ejidatarios are recorded as Incurred by private farmers while the outputs, as we have seen, are credited to the ejidatarlobs (Yates,1981b:747).

113. These points are not entirely convincing. First, Independently of the taxevasion Issue, ejidatarlos may well be as evasive as private farmers In answering questions about their Income, put to them by persons clearly associated with the government: In the case of rental or sharecrop deals between ejidatarlos, both parties may choose to understate the output from land Involved in these deals (because the deals are Illegal), leading to an understatement of total ejldo output; the evidence (e.g. Mummert,1987) suggests that deals between ejldatarlos may well be as widespread as deals between ejidatarios and private farmers.

114. Second, In cases where ejldatarlos rent-out their land to private farmers, Is It always true that "outputs ...are credited to ejidatarlos"?; Yates reasoning may be neat but reality Is often more mossy. However, there Is one particularly significant point that may be derived from his discussion: In the context of widespread clandestine leasing of ejido parcels (coupled with sharecropping), the formal (de Jura) definition of the size of each tenure sector bears little relation to the de facto control over resources exercised by ejidatarios and private farmers. in other words, apart from being outdated (failing to shed any light on trends since 1970), the census data provide no firm basis for reaching conclusons about the relative efficiency of ejldos and private farms.

(b) Case study evidence. The Laguna cotton-producing region of Coahulla state was the site of many pre-1970 case studies. Owing to the relatively generous resource endowment of collective ejidos In this zone, It provided a "best-case scenario' for evaluating the economic efficiency of this sector. Since the Mexican
both. However, diaries did not have any significant effect on revenue-expenses or reported profits, nor did they improve the correlation between the two measures.

Finally we considered the issue of deliberate underreporting. Both the self-reports of firms about firms like theirs, and direct observations of transactions suggest that firms underreport revenues by around 30 percent. It seems reasonable to believe that a similar degree of underreporting also occurs with profits.

We draw two main conclusions from this exercise. The first is that asking firm owners directly for their profits (including using business revenues used to pay household expenses) seems to give a more reasonable measure than asking for all the ingredients in terms of detailed revenue and expenses. As with our opening analogy, sometimes you should just ask for the sausage and not ask how it is made. However, the level of underreporting we observe suggests that one should still be a cautious consumer.

Finally, we note that carefully asking more detailed questions about revenue, expenses, mark-ups, and household uses can also provide an accurate measure of profits, and in many applications, one is also interested in these sub-components. Furthermore, it may be that aggregating detailed individual questions is more accurate than asking about the aggregate for measuring other elements of a firm’s production function, such as capital stock.<sup>10</sup> Therefore, there is still a case for detailed household enterprise modules in multipurpose surveys. Our results just show that directly eliciting profits can provide useful information when this is the prime object of interest.

> 10 Deaton and Grosh (2000) also suggest that asking more detailed questions (up to a point) leads to more accurate consumption aggregates. Several reasons for this are somewhat analogous to our discussions, such as the need to adjust for durable expenditure, and for consumption of non-market items. One difference between firm profits and consumption is that consumption smoothing can allow a good picture of consumption to be gathered in as short as one to two weeks of detailed information, whereas longer periods are needed to provide an accurate representation of microenterprise profitability. Our experiment with books suggests owners are unlikely to maintain account books over a long period of time, so in practice one must either rely on recall of a large number of sales and expense items over a long period, or a picture of the microenterprise collapsed into a single profit measure.

22
A major disruption to family life can have serious consequences for children. A particularly traumatic event is the father's permanent departure from the household. There are at least three different channels through which this event can affect children’s human capital accumulation, particularly their school and work participation rates (for further discussion of the following points, see Case, Paxson, and Ableidinger [2004] and Gertler, Levine, and Ames [2004]). First, this event is likely to involve substantial income loss that may particularly affect school choices coupled with credit and insurance market failures. Second, the balance of decision-making power in the household may change, with the preferences of the remaining adults gaining increased importance, which may have important consequences for children. Third, the loss of a parent may have significant emotional and psychological consequences for children. The importance of the first and third channels was highlighted in a World Bank Development Outreach report (Bell, Bruhns, and Gersbach 2006): If parents sicken and die while their children are still young, then all the means needed to raise the children so that they can become productive and capable citizens will be greatly reduced. The affected families’ lifetime income will shrink, and hence also the means to finance the children’s education, whether in the form of school fees or taxes. On a parent’s death, moreover, the children will lose the love, knowledge and guidance which complement formal education.

Some countries, particularly in Africa, have established policies to provide education and healthcare support to children who have lost one or both parents. These policies may have been implemented in response to the increase in HIV-associated mortality, which has resulted in millions of children losing parents to AIDS. However, a 2
|Dependent variable:|Export sta|tus in(t+1)|||||
|---|---|---|---|---|---|---|
||Apex|’s Treated (Exp|orters)|A|pex’s Treated|(total)|
|||Treated*Exporte|rs||Treated||
||(1)|(2)|(3)|(4)|(5)|(6)|
|Exposure to Apex|0.00030***<br>(0.0001)|0.00025***<br>(0.0001)|0.000201**<br>(0.00010)|-0.00005<br>(0.0000)|-0.00001<br>(0.00004)|0.000043***<br>(0.00001)|
|lfrmssize||0.0025***|0.00244***||0.0025***|0.0025***|
|||(0.0003)|(0.00034)||(0.0003)|(0.00034)|
|frmsage||-0.00079|-0.00080||-0.00082|-0.00083|
|||(0.0009)|(0.00088)||(0.0009)|(0.00088)|
|frmsage2||0.000045***|0.00005||0.00005***|0.000046***|
|||(0.00001)|(0.00001)||(0.00001)|(0.00001)|
|lwage||0.00007|0.00003||0.00007|0.00003|
|||(0.0001)|(0.00013)||(0.0001)|(0.00013)|
|lschooling||-0.00057|-0.00066||-0.00057|-0.00067|
|||(0.0006)|(0.00059)||(0.0006)|(0.00059)|
|shexpsrnet (t)||-0.00105***<br>(0.0002)|-<br>-||-0.00106***<br>(0.0002)|-<br>-|
|shexpsrnet (t+1)||-|0.00325***||-|0.00326***|
|||-|(0.00024)||-|(0.00023)|
|ManagerexpL1||0.01905|0.01775||0.01908|0.01777|
|||(0.0339)|(0.03375)||(0.0339)|(0.03375)|
|ManagerexpL2||0.04358|0.04202||0.04361|0.04204|
|||(0.0366)|(0.03653)||(0.0366)|(0.03653)|
|ManagerexpL3||0.0681***<br>(0.0251)|0.06752***<br>(0.02500)||0.0681***<br>(0.0251)|0.06752***<br>(0.02500)|
|Import||0.0203***|0.0201***||0.02031***|0.0201***|
|||(0.0035)|(0.00353)||(0.0035)|(0.00353)|
|layer||0.0005**|0.00052**||0.00052**|0.00052**|
|||(0.0003)|(0.00026)||(0.0003)|(0.00026)|
|Fixed efects|.|.|.|.|.|.|
|Firms|YES|YES|YES|YES|YES|YES|
|Year*Sector|YES|YES|YES|YES|YES|YES|
|R-squared|0.0037|0.0073|0.0104|0.0037|0.0072|0.0104|
|Number of id|266,769|266,155|266,155|266,769|266,155|266,155|
|Observations|644,674|642,927|642,927|644,674|642,927|642,927|

Note: Linear Probability Model (LPM) using panel fixed effects using the alternative definition of firms’ size categories. Treatment variable: Number of treated firms in the same region and same sector in period (t).

Table 10: Impact of Apex treatment on untreated firms in period (t+1) - spillover effect exporting firms in the same sector-region at period _t+1_ (shexpsrnet _t+1_ ). The magnitude of the effect is low, which is consistent with the low dynamic of new exporters in Brazil. Also, the effect is significant only at 10% with additional covariates (equivalent to specification in column 2) when binary treatment status is considered. Therefore, the interpretation of the empirical results on spillover effect should be undertaken cautiously.

# **6 Conclusion**

The number of EPAs has increased sharply in the last decades, particularly in developing and emerging economies. Usually, these agencies focus on providing support to domestic firms in order to deal with incomplete information to access foreign markets and realize gains from trade.

This paper shows evidence of positive impact of EPA services on the export propensity of 26
<!-- Start of picture text --> Secondary Sector Realized FDI Growth<br>7. !<br>x2oO<br>Qo} ----,---7-------+4 ---}---]-------------<br>°& 7 :<br>a<br>2020q2 2020q3 202094 202191 2021q2 2021q3 202194 2022q1 2022q2 202293<br>Quarter<br><!-- End of picture text --> <!-- Start of picture text --> Tertiary Sector Realized FDI Growth<br>wo i t<br>ced3oO }<br>g<br>Lo=<br>a’<br>2020q2 2020q3 202094 2021q1 2021q2 202193 202194 2022q1 2022q2 202293<br>Quarter<br><!-- End of picture text -->
experience of efforts to bolster social sustainability and outline the implications for development theory, policy, and practice. The final section concludes. This paper draws on a World Bank publication, _<u>Social Sustainability in Development: Meeting the Challenges of the 21st Century</u>_ <u>. The narrative has been adjusted to better contextualize the evolution of social</u> sustainability in the broader development agenda, including a comparative analysis between social, environmental and economic sustainability in the next section that was not included in the book.

# **II.** **<mark>What is social sustainability? Why is it distinctive?</mark>**

<mark>Origins of the concept</mark> Current efforts to articulate an agenda for socially sustainable development build on a long history. This history began to accelerate during the 1970s and 1980s, when increased global attention on exclusion and poverty elevated the role of social issues in development. For instance, the concept of vulnerable or excluded populations entered into political discourse during this period, amid increasing evidence that economic growth, urbanization, and development often excluded, displaced, or negatively impacted certain groups, necessitating more targeted assistance.<sup>ii</sup> Concerns about the link between poverty and the environment also emerged around this time, leading to an emphasis on the concept of sustainability; with the UN Stockholm Conference on the Human Environment in 1972 being the first to highlight the link between human development and environmental sustainability. By the late 1980s, the concept of sustainable development was further defined by the UN’s Brundtland Commission as 6
**<u>Table 5: Comparison of the Estimates of Female Labor Force (millions)</u>**

|Panel I: Female|Himanshu (|2011)|Abraham(2|013)|Kannan and Re<br>(2012)|veendran<br>|
|---|---|---|---|---|---|---|
|Labor Force<br>(millions)|Male|Female|Male|Female|Male|Female|
|1999-00|280.9|125.9|279.5|125.4|||
|2004-05|316.5|152.6|314.7|151.9|315.94|150.84|
|2009-10|339.2|130.6|339.0|130.2|340.46|129.9|
|2011-12|||351.3|132.4|||
|Panel II: Change in Fe|male Labor Force|(millions)|||||
|99-00 to 04-05|35.6|26.7|35.2|26.5|||
|04-05 to 09-10|22.7|-22.0|24.3|-21.7|24.52|-20.94|
|09-10 to 11-12|||12.3|2.2|||

Source: Estimates adapted from Himanshu (2011), Abraham (2013) and Kannan and Raveendran (2012).

In terms of gender, these studies show that the size of the male labor force increased by 35.2 million workers during 1999-2000 to 2004-05, while the female labor force increased by 26.5 million during the same period (Abraham, 2013). Yet, during the later period of 2004-05 to 200910, when the size of the male labor force increased further by about 24.3 million, the size of the female labor force dropped by around 21.7 million. During the entire decade spanning 1999-2000 to 2009-10, when male participation increased by 59.7 million, female participation increased by only 5 million. In the following period of 2009-10 to 2011-12, there was only a marginal addition of 2 million female workers, while the male labor force increased by 12.3 million.

In summary, prior studies report that between 2004-05 and 2009-10, there was a drop in FLFP between the range of 20.94 million (Kannan and Raveendran, 2012) to 22 million (Himanshu, 2011). Abraham (2013) suggests a drop of 19.7 million between 2004-05 and 2011-12. Our estimates in Table 4 closely follow the figures provided by other studies, particularly by Abraham (2013). When focusing on the working age group population of 15 years and above, we find that there was a drop in FLFP by approximately 19.16 million workers between 2004-05 and 2011-12.

10
<!-- Start of picture text --> Figure 2a: Distribution of firms in 2008 Figure 2b: Distribution of firms in 2009<br>0 100000 200000 300000 400000 0 100000 200000 300000 400000<br>Gross Revenue from Income Tax return Gross Revenue from Income Tax return<br>Figure 3a: Distribution of firms around GEL Figure 3b: Distribution of firms around GEL<br>100,000 in 2008 100,000 in 2009<br>50000 100000 150000 50000 100000 150000<br>Gross Revenue from Income Tax return Gross Revenue from Income Tax return<br>Figure 4a: Distribution of firms around GEL 30,000 Figure 4b: Distribution of firms around GEL 30,000<br>in 2008 in 2009<br>10000 20000 30000 40000 50000 10000 20000 30000 40000 50000<br>Gross Revenue from Income Tax return Gross Revenue from Income Tax return<br>50000<br>40000<br>40000<br>30000<br>30000<br>20000<br>Number of firms Number of firms 20000<br>10000<br>10000<br>0 0<br>600 800<br>600<br>400<br>400<br>Number of firms Number of firms<br>200<br>200<br>0 0<br>2000<br>1500<br>1500<br>1000<br>1000<br>Number of firms Number of firms<br>500<br>500<br>0 0<br><!-- End of picture text --> 9
13 Hardly. The gainers from government expenditure were those who benefit from basic public infrastructure such as better roads, more regular electricity and water supply, better telephone communications, as well as more expenditure in the social sectors. This would be a mixed bunch, consisting of the poor and not so poor.

A word about the removal of subsidies, or rather, the introduction of user charges, which was another feature of the ERP. Who gains and who loses from a subsidy? Obviously, those who manage to have access to the subsidy gain, and the losers are those on whom the incidence of taxation to raise the money for this subsidy falls. The point about access to the subsidy, whether it be for fertliser or for drugs, is important. A general subsidy will reach the poor provided there is sufficient budget to back up the access for all those who demand it. When the budget isn't there, access will be rationed, and who gets a look in depends on the rationing mechanism that is adopted, implicitly or explictly. We can be fairly certain that the very poor will not get a look in. They do not have the contacts or whatever is needed to ensure access to that subsidized bag of fertiliser or those prescription drugs. The rich will always be alright-they can use their contacts or bribe their way into the rationing queue. Then there will be some groups of people such as the lower rungs of civil servants who may get access because of institutional features such as free drugs for the emnployees of the health ministry, etc. These are the groups who will lose as a result of the removal of the subsidy-they are not the very rich, but they are not very poor either. And they are organised; but we will come to the political economy presently. The macroeconomc data show that by 1983 there must have been widespread rationing of the so called
and negative commodity price shocks, using data from Niger.

# **4.3 Sensitivity Analysis**

In this section, we consider changes in some key policy parameters to assess the robustness of the simulated results under the sustainable investing approach _−_ which is taken as benchmark. We focus on the fiscal responses following these changes under an oil production shock. Specially, we look at the fiscal responses by considering different values of the parameter governing the efficiency of public investment and different values of the parameter capturing productivity of public capital.

## **4.3.1 Absorption capacity constraints**

The parameter capturing the efficiency of public investment is a fundamental factor in our framework. In practice, this policy parameter captures all the weaknesses in public-sector management and administration responsible for the failure to translate available resources into effective public investment. Figure 4 presents the macroeconomic responses given an oil production shock for two values of _ε_<sup>_G_</sup> ( _ε_<sup>_G_</sup> = 0 _._ 25 and _ε_<sup>_G_</sup> = 0 _._ 95) around our benchmark value set to 0.6. Figure 4 shows that with much more public investment, households enjoy more consumption under the sustainable investing approach. An interesting result is that without absorption constraints ( _ε_<sup>_G_</sup> _→_ 1), tradable and non-tradable sector outputs decline less under an oil production shock, mitigating the negative impact of a potential Dutch disease effect.

## **4.3.2 Productivity of public capital**

In addition to the absorptive capacity, another important policy parameter for savings and investment decisions is the productivity of public capital _−_ also viewed as the return to public capital. In this subsection, we consider the impact of a higher and a lower return to public capital ( _α_<sup>_G_</sup> = 0 _._ 05 and _α_<sup>_G_</sup> = 0 _._ 5) around the baseline value set to 0.2. The results illustrated in Figure 5 show that the supply-side responses of capital and labor are key determinants of the output response to public investment. Panel _A_ of Figure 5 shows that under a higher return to public capital, households enjoy much more consumption, public sector records higher 37
# **_Table 16: Descriptive statistics (male)_**

||**Total**|**sample**|**Control**|**M**|**ovie**|**M**|**FB**|**Movie**|**+ MFB**|**Pure**|**control**|**Means**|**by gender**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Variable**|**N**|**Mean**|**Mean**|**Mean**|**P-value**|**Mean**|**P-value**|**P-value**|**P-value**|**Mean**|**P-value**|**Male**|**Female**|
|_Personal characteristics_||||||||||||||
|Age of respondent|672|36.79|35.97|35.53|0.715|37.34|0.231|37.23|0.296|38.35|**0.054***|36.79|38.16|
|Married|683|0.72|0.73|0.68|0.315|0.77|0.446|0.69|0.438|0.75|0.817|0.72|0.89|
|Widowed|683|0.00|0.00|0.01|0.349|0.00||0.00||0.02|0.129|0.00|0.03|
|Single|683|0.27|0.27|0.31|0.376|0.23|0.446|0.31|0.438|0.23|0.585|0.27|0.08|
|Muslim|682|0.40|0.38|0.45|0.250|0.41|0.617|0.35|0.648|0.40|0.734|0.40|0.34|
|Christian|682|0.59|0.61|0.55|0.307|0.57|0.469|0.63|0.732|0.60|0.828|0.59|0.66|
|Can speak English|679|0.77|0.80|0.74|0.318|0.81|0.810|0.77|0.600|0.75|0.375|0.77|0.68|
|<br>Igbo|682|0.28|0.26|0.23|0.638|0.27|0.824|0.31|0.297|0.33|0.210|0.28|0.17|
|Yoruba|682|0.67|0.69|0.71|0.662|0.72|0.628|0.60|0.129|0.63|0.367|0.67|0.78|
|Other ethnicitiy|682|0.05|0.05|0.05|0.991|0.01|**0.072***|0.08|0.315|0.03|0.490|0.05|0.05|
|_Education_||||||||||||||
|No completed school education|683|0.06|0.02|0.05|0.187|0.06|0.101|0.08|**0.041****|0.09|**0.024****|0.06|0.08|
|Primary school education|683|0.20|0.21|0.21|0.980|0.22|0.901|0.17|0.435|0.17|0.451|0.20|0.23|
|High school diploma|683|0.56|0.59|0.55|0.456|0.51|0.165|0.60|0.860|0.56|0.587|0.56|0.48|
|Diploma|683|0.09|0.10|0.11|0.822|0.10|0.998|0.06|0.186|0.07|0.418|0.09|0.11|
|Graduate school|683|0.09|0.08|0.07|0.939|0.11|0.299|0.09|0.652|0.11|0.316|0.09|0.10|
|_Household characteristics_||||||||||||||
|Household (HH) size|678|4.29|4.15|4.23|0.713|4.26|0.620|4.38|0.369|4.45|0.242|4.29|4.63|
|<br>Number of children below 12 in HH|667|1.27|1.39|1.09|**0.040****|1.24|0.331|1.20|0.238|1.50|0.557|1.27|1.35|
|Number of dependents in HH|675|2.82|2.79|2.75|0.870|2.78|0.961|2.74|0.849|3.12|0.249|2.82|2.28|
|Number of dependents outside the HH|641|1.88|2.25|1.86|0.259|1.41|**0.015****|2.06|0.598|1.82|0.258|1.88|1.41|
|_Business characteristics_||||||||||||||
|Months in operation|678|99.58|106.76|105.08|0.885|91.69|0.175|100.78|0.607|92.53|0.245|99.58|96.50|
|Has a savings account|682|0.64|0.70|0.62|0.176|0.61|0.144|0.64|0.347|0.62|0.189|0.64|0.54|
|Keeps written financial records|678|0.38|0.38|0.32|0.342|0.42|0.506|0.36|0.796|0.40|0.693|0.38|0.37|
|Operating inside main market|676|0.14|0.16|0.12|0.344|0.14|0.674|0.17|0.845|0.14|0.643|0.14|0.30|
|Number of employees|680|1.86|2.10|1.80|0.309|1.65|0.135|1.67|0.162|2.15|0.901|1.86|1.27|
|Business experience inyears|683|11.37|10.70|10.57|0.883|11.42|0.481|11.48|0.448|13.04|**0.033****|11.37|10.49|
|*** p<0.01, ** p<0.05, * p<0.1||||||||||||||

40
# **Delivering Education: A Pragmatic Framework for Improving Education in Low-Income Countries**

Tahir Andrabi (Pomona College, CERP) Jishnu Das (The World Bank, Washington DC and Center for Policy Research, New Delhi) Asim Ijaz Khwaja<sup>1</sup> (Harvard University, CERP, BREAD, NBER) <u>JEL Classifications: I21, I25, I28, L13 and L15</u>

> 1 Email addresses: jdas1@worldbank.org, TRSA4747@pomona.edu, <u>akhwaja@hks.harvard.edu. This paper was produced for</u> the Handbook of International Education and is based on a series of conversations with Jeffrey Hammer, Shanta Devarajan, Veena Das, Pauline Dixon and Lant Pritchett. Tara Vishwanath was our earliest supporter and we continue to benefit from her inputs and suggestions. Studies described in the chapter are part of the Learning and Educational Achievements in Pakistan Schools or LEAPS project, which is funded jointly by the Knowledge for Change Program and the Strategic Impact Evaluation Fund at The World Bank and the Templeton Foundation.
results for both choices of reference year. The “true” contribution of a variable will lie somewhere in between, and of course this type of decomposition is an accounting exercise, ignoring the fact that changes in covariates and changes in coefficients are interdependent.

Table 3 shows the decomposition results. In our sample of married women 25–59, the female labor force participation rate declined by 0.6 percentage points between 1987 and 2011. At 1987 coefficients (Eqn. 2), covariate changes account for a 0.3 percentage point decline. The remaining 0.2 percentage point is accounted for by changes in coefficients and unobservables. At 2011 coefficients (Eqn. 3), we find a much larger negative contribution from covariate changes, which is almost entirely offset by a large positive contribution from changing coefficients and unobservables.

**Table** **<u>3. Decomposition of FLFP, 1987–2011</u>**

|Pr (labor force) 1987|0.185|(N=29031)|||
|---|---|---|---|---|
|Pr (labor force) 2011|0.179|(N=27306)|||
|Difference|-0.006||||
||At 1987 co|efficients|At 2011 coe|fficients|
||Contribution|SE|Contribution|SE|
|Own education|0.047***|0.003|0.011***|0.003|
|Log income|-0.030***|0.002|-0.020***|0.003|
|Male salaried emp.|0.001**|0.000|0.000|0.000|
|Education household head|-0.025***|0.002|-0.021***|0.002|
|Caste and religion|0.001***|0.000|0.000|0.001|
|Age|0.001***|0.000|0.000|0.001|
|Children|0.009***|0.001|0.007**|0.003|
|District employment shares|-0.011***|0.003|-0.019***|0.004|
|District graduate share|0.001|0.008|-0.006|0.007|
|State dummies|0.003***|0.001|0.003***|0.001|
|Total covariates|-0.003||-0.045||
|Coefficients & unobservables|-0.002||0.039||

_Note:_ SE = standard error. *** _p_ < .01, ** _p_ < .05.

28