rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 4.5 4.1 4.2 4.5<br>4.0 3.5 3.8 4.0<br>3.5 3.0 3.5<br>3.0 2.4 2.5 2.6 3.0 2.6 2.8 2.8<br>2.5 2.5 2.2 2.2<br>2.0 1.9 2.0 1.7 1.9 1.9<br>1.5 1.5<br>1.0 1.0 0.8<br>0.5 0.5<br>0.0 0.0<br>Slovak Republic OECD MICs EU15Czech Rep.BulgariaHungaryRomania Poland Slovak RepublicRomania OECD EU15Czech Rep. MICsHungaryBulgaria Poland<br><!-- End of picture text --> _Source:_ see Figure 3. _Note_ : Other MICs are Brazil, Mexico, Turkey. Values refer to 2006 for Bulgaria and Romania, 2004 for Brazil, and 2003 for all other countries. For full data set see appendix I.

# _2.2.1 Public ownership_

The aggregate indicator for public ownership covers size and scope of public enterprise sector, as well as direct control over business enterprises. Given that there is much variation among EU15 or OECD countries for these indicators, we show the full set of comparators (Figure 18). The figure suggests the size and scope of the public sector in Bulgaria is fairly standard (although within the upper tier), while state control of public enterprises remains more extensive.

23
# **The Future of Work in Agriculture -**

# **Some Reflections**<sup>*****</sup>

Luc Christiaensen, Zachariah Rutledge, and J. Edward Taylor

## **JEL classification:** J43, J61, O13, Q16

**Keywords:** agricultural labor, digital technology, migration, agricultural value chain development, agricultural education and extension

> *  Luc Christiaensen (lchristiaensen@worldbank.org), World Bank; Zachariah Rutledge (zjrutledge@ucdavis.edu) University of California Davis, J. Edward  Taylor (taylor@primal.ucdavis.edu) University of California Davis. This paper builds on the World Bank / IFAD / UC Davis workshop on The Future of Work in Agriculture held at The World Bank, Washington DC, March 19-20, 2019. The papers are available at <u>https://farmlabor.ucdavis.edu/future-work-agriculture-conference. We thank all of the workshop participants for</u> their contributions and the insightful discussions. The views presented here do not necessarily represent those of our respective institutions.
**Table 13: Cost of meeting universal access objectives (US$)**

||**Total cost of**<br>**meeting**<br>**objective**|**No. of years delay**<br>**with**<br>**existing**<br>**resources**|**Annual**<br>**universal**<br>5 years|**cost**<br>**of**<br>**access obje**<br>10 years|**meeting**<br>**ctive in**<br>15 years|
|---|---|---|---|---|---|
|Water|87.4|4<sup>*</sup>|17.7|9.0|6.1|
|Sewerage|429.3|20<sup>*</sup>|86.1|43.2|28.9|
|Gas|383.2|11|76.9|38.6|25.8|
|**Total**|**899.9**||**180.7**|**90.8**|**60.8**|

_Note_ : Based on a hypothetical national fund that collects 3% of sector turnover 6.10 For the case of natural gas, resources currently available through the Patagonian gas fund would be sufficient to meet the universal access objective within an 11-year period. In practice, there are a couple of factors that could affect this estimate. First, this cost estimate only considers the cost of household connections and interior conversions, but does not consider the potential need to extend or expand the main distribution network. Second, the cost may be somewhat lower given that it is not feasible to install the service in houses that are very precariously built, which limits the extent of service expansion.

6.11 In the case of water and sewerage, the resources needed to meet universal access are very high in relation to what might be collected through a hypothetical national fund based on 3% of sector turnover, so that it would take as long as 24 years to meet this objective. On the other hand, if only 50% of connection costs were subsidized (that is US$84.7 per connection), the resources of they hypothetical fund would go a lot further, allowing the objective to be reached within a 12 year period.

6.12 Turning to public transport, it is estimated that the cost of assigning a US$0.67 subsidy per day (which is approximately equal to the cost of a return commuting journey) to each head of household that qualifies according to the multi-dimensional poverty proxy, would be US$207 million per year. This is equivalent to 150% of the cost of the current supply-side subsidy to bus, train and metro operators. However, if this policy were limited to households facing extreme poverty (which is to say the bottom quintile of the income distribution), the associated cost would be broadly similar to that of the current supply-side subsidy.

49
- [50] Ferraro, P. J., Sanchirico, J. N. & Smith, M. D. Causal inference in coupled human and natural systems. _Proceedings of the National Academy of Sciences_ **116** , 5311–5318 (2019).

- [51] African Parks. A Chartered Course: African Parks Annual Report 2020 (2021). URL https://www.africanparks.org/sites/default/files/uploads/resources/ 2021-06/AFRICAN%20PARKS%20-%202020%20Annual%20Report%20-% 20Web%20Ready%20-%20LowRes ~~3~~ .pdf.

- [52] Denny, S., Englander, G. & Hunnicutt, P. v1.0.1 Data and Code for “Private management of African protected areas improves wildlife and tourism outcomes but with security concerns in conflict regions”’. figshare. Dataset. (2024). URL https://doi.org/10.6084/m9.figshare.25560351.

- [53] Geldmann, J., Manica, A., Burgess, N. D., Coad, L. & Balmford, A. A globallevel assessment of the effectiveness of protected areas at resisting anthropogenic pressures. _Proceedings of the National Academy of Sciences_ **116** , 23209–23215 (2019).

- [54] Meng, Z. _et al._ Post-2020 biodiversity framework challenged by cropland expansion in protected areas. _Nature Sustainability_ 1–11 (2023).

- [55] Reynaert, M., Souza-Rodrigues, E. & van Benthem, A. A. The environmental impacts of protected area policy. _Regional Science and Urban Economics_ 103968 (2023).

- [56] Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W. & Wager, S. Synthetic difference-in-differences. _American Economic Review_ **111** , 4088–4118 (2021).

- [57] Monitoring the Illegal Killing of Elephants. Numbers of carcasses found for each site and year (2023). Downloaded on Jan 16, 2023. Available at https://cites.org/sites/default/files/MIKE/data/Numbers ~~C~~ arcasses eachMIKESite year ~~2~~ 022-03-31.xlsx.

- [58] Cornell Lab of Ornithology. eBird Basic Dataset. Version: EBD ~~r~~ elFeb-2023 (2023). Downloaded on April 10, 2023.

- [59] Liang, Y. _et al._ Conservation cobenefits from air pollution regulation: Evidence from birds. _Proceedings of the National Academy of Sciences_ **117** , 30900–30906 (2020).

- [60] iNaturalist. Dataset (2023). Downloaded on March 1, 2023. Available from https://www.inaturalist.org.

- [61] Wood, S. A., Guerry, A. D., Silver, J. M. & Lacayo, M. Using social media to quantify nature-based tourism and recreation. _Scientific reports_ **3** , 2976 (2013).

- [62] Echeverri, A. _et al._ Biodiversity and infrastructure interact to drive tourism to and within Costa Rica. _Proceedings of the National Academy of Sciences_ **119** , e2107662119 (2022).

- [63] Yeh, C. _et al._ Using publicly available satellite imagery and deep learning to understand economic well-being in Africa. _Nature Communications_ **11** , 2583 (2020).

- [64] Kikuta, K. The Environmental Costs of Civil War: A Synthetic Comparison of the Congolese Forests with and without the Great War of Africa. _The Journal of Politics_ **82** , 1243–1255 (2020).

20
improvements in the contractibility of components increase FDI: creating incentives for the suppliers of headquarter services through vertical integration becomes a relatively more important problem when PTA disciplines improve the contractibility of components. To the contrary, in the transactions cost approach, vertical integration is an efficient response to any type of contracting difficulties. Therefore, PTA provisions that improve the contractibility of headquarter services and/or components are predicted to always lower FDI and increase outsourcing. The empirical analysis that follows, therefore, provides an indirect test of the two theories.

# **3. Data description and methodology**

We begin our empirical analysis by describing the data on the depth of trade agreements and on vertical FDI.

## **3.1. Depth and composition of PTAs**

Preferential Trade Agreements are usually thought of as bilateral or multilateral agreements that aim at the reduction in tariffs. Recently, the economic literature started to examine in more detail the composition of trade agreements, allowing us to distinguish between shallow and deep agreements. Shallow agreements are those agreements that guarantee reciprocal decreases in tariffs. Following Horn, Mavroidis, and Sapir (2010) and WTO (2011), we define deep agreements as those agreements covering multiple provisions that go beyond tariff liberalization.<sup>12</sup> The WTO constructed a data set on the content of preferential trade agreements by mapping a total of 52 disciplines across 100 PTAs signed between 1958 and 2011. The agreements included in the data set cover more than 90 percent of world trade.<sup>13</sup> Due to availability of FDI data, we focus our analysis on three countries, Germany, Japan, and the United States. These countries are the main origin of most foreign investments in the world and represent the hubs of production networks.<sup>14</sup> We therefore work with a sub-sample

> 12 Horn et al., (2010) identify up to 52 provisions in US and EU agreements.

> 13 The database has been assembled by the Economic Research division of WTO for the World Trade Report 2011, available at http://www.wto.org/english/res\_e/publications\_e/wtr11\_dataset\_e.htm

> 14 In addition, as reported in Lanz and Miroudot (2011), Germany, Japan and the US are among the most represented countries in ORBIS in terms of the number of parent companies and sales of vertical foreign affiliates.

13
33 The second major difference is that the roles of consumption and investment are drastically reversed in Denmark. Here, the increase in the _share_ of investment is almost thirteen times as large as the corresponding consumption figure.

Figure **15.** Denmark: Index of Consumer Confidence. Source:European Economy Supplement B.

Finally, thie best indicator of how confident were Danish agents about the stabilization is given by the Index of Consumer Confidence. This index clearly reflects a change in consumer's mood after stabilization was announced. As Figure 16 shows, Denmark's index rose sharply in 1983.

What explains the Danish boom? A combination of factors. On the one !land, optimistic expectations of future output trends led to a boom in aggregate demand were permanent income considerations played an important role. Also, optimistic views about _the pace_ of the disinflation seem to have helped in reducing the real wage and keeping the real interest rate under control. These had favorable effects on supply, allowing for an output expansion that granted, at least partially, the initially optimistic expectations. Though Demnmark did eventually suffer from current account problems, the
India’s villages now receive some education, moreover, so that this form of _f_ ( _et_ ) can also be thought of as applying over the relevant range up to a full education. Turning to competition among siblings, rather little is known about its effects on human capital formation, so we adopt the agnostic position that _ψ_ = 1 _∀_ **N** _t_ . With these choices, (2) specializes to _2. Preferences_ . In the macroeconomics literature, especially the empirical kind, the logarithm tends to hold sway. There is usually, however, an aggregate consumption good. For present purposes, therefore, form the Cobb-Douglas aggregate _x_<sup>_a_</sup> 1 _t_<sup>_·x_1</sup> 2<sup>_−_</sup> _t_<sup>_a_</sup> (0 _< a <_ 1), which is homogeneous of degree one in **x** _t_ . Applying the logarithm to this index of consumption, we obtain _·_ There is much less guidance to be had about _φ_ ( ). In their study of Kenya over the historical period 1950-1990, Bell, Bruhns and Gersbach (2006) employed the logarithmic form for _u_ , but the data resisted their attempts to impose this on _φ_ . More curvature was needed, and successful calibration was achieved with the iso-elastic form – whereby the value of _η_ lay in the range 0.35 0.65, with a clustering around 0.5. This form will be adopted here, too. The associated values of _η_ will provide a useful point of departure.

# **3.2 Parameters and the values of exogenous variables**

We need some starting values, namely, for period _t_ = 1. In view of India’s demographic history and the prevailing state of affairs in rural areas, let **N** 1 = (3 _,_ 2 _,_ 0 _._ 75), with _n_ 1 = 3 _._ 5. There is much illiteracy among the old, but less among their children, who are today’s parents. Rising productivity over the past generation also suggests that _λ_<sup>2</sup> 1 is substantially larger than _λ_<sup>2</sup> 1<sup>.Hence,let</sup><sup>**_λ_**1= (1</sup><sup>_._7</sup><sup>_,_1</sup><sup>_._2).</sup> Given the numerous parameters, and corresponding degree of under-identification, there is no call for great precision everywhere. Let _γ_ = 0 _._ 65 and the social norms demand _β_ = 0 _._ 6 and _ρ_ = 0 _._ 8. Households are still rather poor, so their taste for good 13
# **Supplementary Online Appendix**

|S1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45|
|---|
|S1.1 Harmonized Household Data Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .45|
|S1.2 Harmonized 12-Month Survey Panels from Labor Force Surveys . . . . . . . . . . . . . . .49|
|S1.3 Administrative Matched Employer-Employee Data Sets . . . . . . . . . . . . . . . . . . . .50|
|S2 Detailed Country Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52|
|S2.1 Using Harmonized Household Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52|
|S2.2 Using Administrative Matched Employer-Employee Data . . . . . . . . . . . . . . . . . . .64|
|S3 Additional Figures with Regional Averages . . . . . . . . . . . . . . . . . . . . . . . . . . . 66|
|S4 Supplemental Empirical Results on Formal Status . . . . . . . . . . . . . . . . . . . . . . .68|
|S5 Wage Distribution among Formal Workers in Household Surveys and Social Security|
|Records . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .69|

44
We also use several outcome variables to measure public goods provision by the GP, allowing us to check the robustness of our results.

We find that female GP presidents are much less knowledgeable than are male presidents regarding the GP president position and the Panchayat system in general. Additionally, female presidents have significantly less contact with higher-level government officials vis-à-vis their male counterparts. This is important because contact and coordination with higher-level officials is necessary for certain village public goods. The results of our analysis strongly suggest that female presidents provide their GPs with fewer schools and roads, two public goods that require more connection with higher-level officials in the panchayat system. To the best of our knowledge, these results are new in the literature. We also find weak evidence that women provide fewer household toilet connections, household drinking water connections, and streetlights. For all other public goods, there is generally no statistically significant difference between provision by male and female presidents. Given the low level of public goods and infrastructure in these GPs, we consider more public goods provision to be indicative of “better” performance as a GP president.

In the next section, we briefly review the history of local governance in India, specifically in Tamil Nadu. We describe our field survey in Section II and our empirical strategy in Section III. In Section IV, we analyze the performance of female presidents and estimate the effect of president gender on village public goods provision. In the final two sections, we discuss our findings and offer some concluding thoughts.

# **I. Setting**

Self-governing village communities in India seem to have existed since two to three thousand years ago (Mathew 2000b). Village governance weakened during British rule in India 6
and stable orphan rates (Group A) the average number of children is 4.9 for non-orphans, but is 4.7, 4.6 and 4.2 for paternal, maternal and double orphans respectively. In countries with rapidly growing orphan rates (Group C) the average number of children remains at 3.7 and 3.8 for nonorphans and orphans, respectively.

When children live with their grandparents, there are typically fewer children in the household than when children live with a parent. For example in the countries with high and stable rates (Group B) the average number of children is 3.3 for non-orphans and orphans living with a grandparent. Some countries have a different pattern, most notably Namibia where maternal orphans living with a grandparent typically live in households with one child more than maternal orphans living with their father (4.2 versus 3.2 children).

Conversely, when orphans live with an “other relative” and especially if they live with a non-relative, the households often have more children as compared to living with a grandparent or a surviving parent. The difference can be quite large: for example maternal orphans in Niger who live with a non-relative live in a household with an average of 6.7 children, while those who live with a grandparent live in a household with an average of 4.4 children. Or double orphans living with a non-relative in Namibia live in households with an average of 5.6 children, while those who live with a grandparent live in a household with 3.9 children on average. These differences are consistent with orphans residing with caregiving relatives or non-relatives who have their own children as well as the fostered children. To some extent, the disadvantage that might exist for orphans cared for by grandparents as opposed to younger relatives might be offset by the smaller number of children in the household.

# **V. Conclusions**

The well-being and development of a child are closely tied to the household in which she or he resides. While most children in Africa live with one or both parents, this traditional arrangement can be impacted when one parent is deceased; by definition this is true for children with both parents deceased. Several multi-country studies which examine human capital outcomes for orphans compared to non-orphans have shown a large degree of heterogeneity across countries. A handful of longitudinal analyses from mid-to-high prevalence countries (Kenya, South Africa and Tanzania, among others) have shown that the negative impact of 19
and intensity are larger when catastrophe is defined at a lower threshold. As thresholds increase, the MPG increases in all countries. It is therefore clear that most of the increase in the MPG is due to a modest decline in the mean gap relative to the headcount as the threshold is raised. The ‘catastrophic’ effect of health costs manifests itself more as an increase in poverty incidence than a deepening of poverty among those who are already poor.

The variation in catastrophic health payments across Balkan countries is also illustrated graphically in Figure 1 which shows, for each country, the share of health expenses or out-of-pocket payments (OOP) by cumulative percentage of population, ranked by decreasing payment fraction.

**Figure 1: Catastrophic payments as share of total expenditure**

|1<br>Albania<br>Bosnia<br>Montenegro<br>Serbia<br>Kosovo||
|---|---|
|.2<br>.4<br>.6<br>.8|oop as % of tot. expenditure|
|0<br>0<br>.2<br>.4<br>.6<br>.8<br>1<br>cum % of pop, ranked by decreasing oop share||

The horizontal axis in Figure 1 shows the cumulative share of the sample, ordered by the health expenditure ratio, beginning with individuals with the smallest ratio, while the vertical axis shows the out-of-pocket expenditure as a proportion of total expenditure (and represents any possible threshold level). The incidence and intensity is larger in Kosovo and Albania, followed by Serbia, then Bosnia and Herzegovina and Montenegro, where the impact is the smallest. Indeed, if the threshold is set at 10% of the pre-payment income, for instance, the Figure 1 (and Table 8) show that in Kosovo the headcount of people spending more than the threshold for health care is around 26% of the sample, in Albania around 21%, in Serbia 12%, in Bosnia and Herzegovina 3% and in Montenegro around 1% of the population. Moreover, the area under the payment share curve, but above any threshold level, is the intensity or mean catastrophic gap, which is largest in Kosovo and Albania and smallest in Bosnia and Herzegovina and Montenegro for any threshold level<sup>15</sup> .

Yet, even in countries with fairly low average catastrophic expenditure shares, the distribution of those expenditures can be quite uneven within the country, with segments of the population devoting large shares of their consumption expenditure to health care. For example, while Montenegro seems to bear the least burden of out-of-pocket payments and many people seem to incur little or no expenditure; a few sick individuals have very high expenditure on health care. This can be seen in Table 9 where, for

> 15 See World Bank _Quantitative Techniques for Health Equity Analysis—Technical Note # 18._

18
involved consultations with statistical offices across several countries, evaluation of questionnaires that have previously been used to survey informal businesses, and application of the extensive experience of the team that implements the World Bank Enterprise Surveys (WBES) of formal firms. This trial-and-error learning process led to the following principles when designing the appropriate questionnaire.

Informal businesses should not be interviewed using questionnaires designed for registered businesses. The primary reason for this is that terminology and definitions used in such questionnaires reflect experiences of legal, registered entities, and may not be suited for informal businesses. Accounting and technical terms commonly used in formal business surveys, such as 'equity', 'income statement', or capacity utilization' must be avoided. It is important to recognize that informal businesses operate in ways that are fundamentally different from formal businesses. Therefore, not only the terms used in the questionnaire, but also concepts need to be adapted to ensure that pertinent measures are being collected. For example, when measuring labor inputs, it is important to capture non-remunerated labor or support from family, so survey questions must be worded accordingly and/or added.

The interviewees of the Informal Sector Enterprise Surveys tend to have lower levels of education than the typical respondent of a formal business survey, and some may even be unable to read or write. Consequently, direct yes/no questions are preferable, and commonly used aids for interviewing, such as 'show cards', should be avoided. The language used as well as the question structure should be simple, straightforward, and able to be read quickly. This means that questions must be formulated in ways that require little or no explanation. Questions that require great cognitive effort, such as those involving counterfactuals or vignettes scenarios, are avoided.

To maximize the effectiveness of the questionnaire, questions requiring mathematical computations or complex numerical responses should be minimized, and those requiring responses in percentages should be avoided. Importantly, since many informal businesses may not keep any written accounts of their activities, it is not advisable to have questions that use accounting terms or timeframes, such as ‘fiscal year’. Questions that use annual time horizons are also not advisable as many informal businesses may have a short horizon. Instead, to minimize recall bias that is likely strong in the absence of written records, shorter periods of reference such as last month, are preferable. Shorter time periods, however, bring the need to introduce measures of seasonality.

7