rafmacalaba/data-use-mentions-extended huggingface dataset:
smaller in rural areas. The latter may be due to large hidden underemployment in rural areas as has been shown elsewhere (Alderman et al, 1993).

The production figures by broad sectors show that increases in GDP share has been corresponded by larger number of people employed. This indicates that labor productivity may have not been increasing and possibly declining. This, among other things, definitely has implications for the profitability of most economic activities and their absorptive capacity in the long-run and require further analysis.

Between 1987-92 the survey figures indicate that there has been reverse migration with large number of people moving from urban to rural areas. Apart from few growth poles, such as Greater Accra, Ashanti and Western regions, all other urban centers have been displaying out migration. In terms of reasons most migration has been for family reasons; employment related migration has been on the increase. This indicates that labor mobility has been increasing, but it is difficult to establish how many of them were moving with lucrative employment.

Literacy rates have been showing an interesting picture. Female literacy rates have been lower than males and this seem to have had an impact on employment opportunities in the informal sector, making it a male dominated sector. The proportion of female graduates is on the increase although the rates of return are still low. Most of the 47
Equation 1 is the probability of temporary migration. Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models the logarithm of hourly wages upon return, and excludes returnees from all western countries. For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use the diplomatic exchange, which is the diplomatic representation of each country of destination of Egyptian migrants in Egypt at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange and is equal one if there is evidence of diplomatic exchange at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality on the logarithm of hourly wages, unconditional on return migration. The predicted logarithm of hourly wages for non-migrants, illegal migrants and legal migrants are reported in the last row.

53
The impacts of inheritance will naturally reflect how the ―dynastic family‖ allocates its resources, given the market and institutional environment. In principle at least, the extended family has the ability to attain any desired distribution of consumption, independently of the formal assignment of ownership rights. Indeed, it is an open question in this setting whether there is any net impact on the inheritor of a land bequest within the family.

Our results suggest that other mechanisms for the inter-generational transmission of inequality—notably related to parental education and children‘s schooling—are in fact more important than land inheritance in explaining interpersonal economic welfare and economic activities. Even in very poor settings, parental background can influence the schooling, expectations and life chances generally of children in ways that matter to the realized living standards of adults. There is evidence consistent with this view in the literature. Estudillo et al. (2001) emphasize the combination of both land inheritance and schooling in the intergenerational transmission of wealth in the rural Philippines. Lesorogol et al. (2011) find that the current wealth of Kenyan pastoralists is correlated with parental wealth and formal education but not with livestock inheritance. Ferreira and Gignoux (2010) find that family background characteristics are an important source of unequal opportunities in Latin America. Dumas and Lambert (2011) find that parental education has a strong effect on child schooling in Senegal. Some degree of intergenerational correlation in occupational choices can be expected, for which we find supportive evidence, though with a (possibly surprising) degree of mobility.

We begin in the following section by discussing the multiple roles of land ownership in this setting. After that we describe our data for Senegal and the methods of analysis, followed by our empirical results.

# **2. Land inheritance in an underdeveloped rural economy**

Land ownership has long played a role in policy making and thinking about economic development. Taxes and transfers are often conditioned on the amount of land owned. Famously, the American political economist of the late 19th century Henry
UCT vs. CCT|0.550|0.665|0.842|0.052|0.650|0.120|0.001|0.406|
|p-value Treatment|0.297|0.910|0.784|0.101|0.306|0.287|0.001|0.484|
|Mean in Control Group|0.897|0.212|0.029|0.000|0.906|0.000|0.000|0.000|
|Sample Size|2,002|2,048|2,045|2,049|2,049|1,271|776|774|

_Notes:_ Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them representative of the target population in the study EAs. Opportunity cost of time is calculated by taking the minimum daily wage the respondent would take for one year of work in her village. Detail on the construction of the super-indices can be found in Appendix A and Appendix B. The change in subjective wellbeing asks the respondent where she sees herself on a 10-step ladder comparing five years ago to today, where zero represents the worst possible life she could have and 10 represents the best possible life she could have. Baseline values of the following variables are included as controls in the regression analyses: age indicators, stratum indicators, household asset index, highest grade attended, and an indicator for never had sex. We restrict the sample to respondents who were surveyed during the latest household survey conducted two years after the program (Round 4). Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.
Katmandu achieve a progressive distribution of subsidy benefits (Ω is 1.54), as do the subsidies in Bangalore (Ω is 2.14). Nonetheless, errors of exclusion of the subsidies are high: 72 percent of poor households do not receive this subsidy in Katmandu, and 61 percent do not receive it in Bangalore. The error of exclusion for the public tap subsidies is thus higher than that of the IBT in place in these cities (see Table 1), reflecting the fact that more poor households in the city have private taps than use public taps.

# **_Performance of connection subsidies_**

One overriding weakness of subsidies for consumption is that they exclude households that are not currently using the subsidized service. With the exception of the service-level targeted subsidies, this means that unconnected households are left out of the pool of subsidy beneficiaries. This “access handicap” severely compromises the targeting performance of consumption subsidies offered on standard connections. As only _unconnected_ households are the potential beneficiaries of connection subsidies, the problem that plagues consumption subsidies can work in favor of the targeting performance of connection subsidies.

Few cases of existing connection subsidies have been studied in sufficient detail to apply the conceptual framework used in this study. We therefore simulated connection subsidies using data on coverage levels among the poor and non-poor in all cases for which sufficient information was available. The first simulations were of universal connection subsidies and assumed that all unconnected households were both offered and accepted subsidized connections. The error of exclusion for these universal connection subsidies is solely a function of the connection rate in each country: more poor households are excluded in high coverage cases than in low-coverage cases. The distribution of benefits from these universal connection subsidies is nearly always progressive (Komives et al 2005). The only exceptions are Azerbaijan and Belarus, where non-poor households are as likely as poor households to be unconnected.

This assessment of connection subsidies presumes that all potential or targeted beneficiaries could and would eventually be connected (or at least that households in all income
70. Models 1a and 1b show that there is a statistically significant and large impact of e-procurement on winning rebates, defined as the percent discount achieved compared to the initial estimate. Winning rebates are 7.4-8.0 percentage points higher in e-procurement than in manual tenders (7.0 percent in e- procurement versus negative 0.4 percent in manual as per Model 1a; and 8.9 percent versus 0.9 percent, as per Model 1b). These results are robust to removing different ranges of outlier winning rebate values (see Appendix 9.11).

71. In Model 2a, e-procurement has a small, negative and statistically significant impact on cost overruns, while the effect in Model 2b is small, positive and statistically insignificant.30 In Model 3a, e- procurement has a negative, and statistically significant impact on time overrun, that is e-procurement decreases the probability of contract extension. However, the effect is positive and significant in model 3b. Taken together, Models 2 and 3 point at no clear pattern of the impact of a bidding and award stage-focused e-procurement system on contract implementation. To further explore such patterns, appendix 9.12 systematically tests whether abnormally low bids (i.e. high winning rebates) lead to higher time overruns (used as a proxy for cost overruns as the cost overrun sample was unreliably small). If e-procurement makes this relationship steeper due to its heavy focus on effective competition at the bidding stage, it may offset some of the positive effects we see across the board. Reassuringly, while the most excessively low bids (i.e. very high winning rebates) are weakly associated with more contract modifications, e-procurement did not increase the size of this effect.

> 30 Please note that sample size drops for models with within bi-annual period matching (models 2b and 3b) because contract implementation data was only collected for 1 financial year for the e-procurement sample.

25
In this simulation experiment a census data set of _N_ = 20 _,_ 000 observations is created, where observations are allocated among 40 areas ( _a_ = 1 _, . . . , A_ ). Within each area, observations are uniformly spread over 10 clusters ( _c_ = 1 _, . . . , Ca_ ). Each cluster, _c_ , consists of _Nac_ = 50 observations, and each cluster is labeled from 1 to 10. The assumed model contains both cluster and area effects. Cluster effects are simulated as _ηac iid∼ N_ �0 _,_ 0 _._ 12�, area effects as _ηa iid∼ N_ �0 _,_ 0 _._ 052� and household specific residuals as _eachiid∼N_ �0 _,_ 0 _._ 52�, where _h_ = 1 _, . . . , Nac_ ; _c_ = 1 _, . . . , Ca_ ; _a_ = 1 _, . . . , A._ Covariates are simulated as follows:<sup>11</sup>

1. _x_ 1 is a binary variable, taking value 1 when a random uniform number between 0 and 1, at the household level, is less than or equal to 0 _._ 3 + 0 _._ 5 40<sup>_<u>a</u>_+ 0</sup><sup>_._2</sup> 10<sup>_<u>c</u>_.</sup>

2. _x_ 2 is a binary variable, taking value 1 when a random uniform number between 0 and 1, at the household level, is less than or equal to 0 _._ 2.

3. _x_ 3 is a binary variable, taking value 1 when a random uniform number between 0 and 1, at the household level, is less than or equal to 0 _._ 1 + 0 _._ 2 40<sup>
- Koppmair, S, M Kassie, and M Qaim. 2017. “The Influence of Farm Input Subsidies on the Adoption of Natural Resource Management Technologies” 61 (4): 556. https://doi.org/10.1111/14678489.12220.

- Mason, NM, and M Smale. 2013. “Impacts of Subsidized Hybrid Seed on Indicators of Economic WellBeing among Smallholder Maize Growers in Zambia” 44 (6): 670. https://doi.org/10.1111/agec.12080.

- Mason, NM, A Wineman, L Kirimi, and D Mather. 2017. “The Effects of Kenya’s Smarter’ Input Subsidy Programme on Smallholder Behaviour and Incomes: Do Different Quasi-Experimental Approaches Lead to the Same Conclusions?” 68 (1): 69. https://doi.org/10.1111/14779552.12159.

- Mel, Suresh de, David McKenzie, and Christopher Woodruff. 2008. “Returns to Capital in

- Microenterprises: Evidence from a Field Experiment.” _The Quarterly Journal of Economics_ 123 (4): 1329–72.

- Osorio, Camilo Gomez, Dwi Endah Abriningrum, Enrique Blanco Armas, and Muhammad Firdaus. 2011. _Who Is Benefiting from Fertilizer Subsidies in Indonesia?_ The World Bank.

- Ragasa, Catherine, and John Mazunda. 2018. “The Impact of Agricultural Extension Services in the Context of a Heavily Subsidized Input System: The Case of Malawi.” _World Development_ 105: 25–47.

- Ricker-Gilbert, J. 2014. “Wage and Employment Effects of Malawi’s Fertilizer Subsidy Program” 45 (3): 353. https://doi.org/10.1111/agec.12069.

- Ricker-Gilbert, Jacob, and Thomas S Jayne. 2017. “Estimating the Enduring Effects of Fertiliser Subsidies on Commercial Fertiliser Demand and Maize Production: Panel Data Evidence from Malawi.” _Journal of Agricultural Economics_ 68 (1): 70–97
often thought. Alexandratos (2008) concluded that China’s and India’s combined average annual increase in grain consumption was smaller in 2002‐08 than in 1995‐2001. Similar findings have been reported by the European Commission (2015), Alexandratos and Bru‐ insma (2012), Baffes and Haniotis (2010), Sarris (2010), the Food and Agriculture Organiza‐ tion of the United Nations (2009), FAO (2008), Lustig (2008). Deaton and Drèze (2008) noted that in India, despite growing incomes, caloric intake has followed a downward trend since the early 1990s.<sup>**10**</sup> The impact of a rise in the _real interest rate_ is found to be negative but small on the prices of individual commodities, ranging from ‐0.02 for maize to ‐0.06 for soybeans, wheat, and palm oil, and the parameter estimate from the panel regression, ‐0.01, is not statistically different from zero (t‐statistic = ‐1.25). A weak relationship between interest rates and com‐ modity prices is a common finding in the empirical literature. Gilbert (1989) used an error‐ correction model to conclude that high interest rates negatively affect the metal price in‐ dex, though with considerable lags. Baffes (1997), who used a reduced form price model for five metals, estimated an effect that was mostly negative but not significantly different from zero elasticities. Frankel and Rose (2010), based on annual data for a number of commodi‐ ties, found little support for the argument that easy monetary policy and low real interest rates are an important source of upward pressure on real commodity prices. Their finding was confirmed by a subsequent paper (Frankel 2014). Some other studies (e.g., Anzuini and others 2010, Akram 2009) found that interest rates had a moderate effect. Baffes and Savescu (2014) found a
asymmetry, as it seemed to be impossible for the patient to keep track of as to which of the 1,090 procedures covered by RSBY was performed on him or her”.

Jain (2021) is the first study from an LMIC that looks at hospital pricing and coding systematically in the context of an insurance scheme. She combines administrative claims data with a large household survey for the Indian state of Rajasthan, which allows her to better understand how hospitals react to changes in administrative prices. Without the household survey, for instance, it would have been impossible to determine how much households are asked to pay out-of-pocket because the practice is illegal and therefore off the books. She finds that providers do not respect administrative prices: 41 percent of patients paid for their treatment even though the care was supposed to be free and the average payments were $35, which is a large sum for poor households and represents a 37 percent increase over the insurance reimbursement rate.

Moreover, hospitals react rapidly to adjustments in reimbursement rates. Jain (2021) finds that with every additional Rs.100 in reimbursements, prices charged to patients decreased—but only by Rs.55. She also uses an event-study to show that when the relative reimbursement rates within a category change (for instance, childbirth with and without an episiotomy), so do the reported procedures. Within a week of a price change, a 1 percent increase in the reimbursement rate induced a 0.4 percent increase in its claim volume. She suggests that this reflects up-coding, whereby health care providers submit codes for more expensive care than actually provided, but a bigger worry, which she does not rule out, is that hospitals changed the treatments that patients received.

Other studies provide systematic evidence on differences in quality of care by insurance status, at least for outpatient care. One set of studies finds that when patients get health insurance, their satisfaction remains the same or worsens (Bauhoff, Hotchkiss, and Smith 2011 and Robyn et al. 2013).
The data for each AD user country are contained in a Microsoft Excel 2003 Workbook file ~ (named as in table 2.1 and posted at <u>http://www.brandeis.edu/ cbown/global_ad/</u> ), within which are typically four spreadsheets of data: 1) a master spreadsheet with basic antidumping case investigation information on dates and outcomes; 2) a spreadsheet containing information on the Harmonized System (HS) products under investigation; 3) a spreadsheet containing information on domestic firms involved in the antidumping petition; and 4) a spreadsheet containing information on foreign firms named in the antidumping petition.

We have also attempted to remove “empty” cells in the spreadsheets, as these affect the ability to easily transform the spreadsheets into machine-readable ASCII files for merging into other data sets. Thus a cell entry for missing data (i.e., data that we are confident exists, but which we were unable to find) was filled in with an “MI.” On the other hand, cell entries that were empty because there was no relevant data (e.g., because it never reached that phase of the antidumping investigation) are filled in with a “.” In some instances, this admittedly required judgment. For example, consider the failure to find any information regarding consideration of the revocation of an antidumping measure – if no information was found, we typically assumed that the cell entry was not relevant (i.e., “.”) as opposed to the antidumping measure had been removed and the data was simply missing (i.e., “MI”).

The rest of section 2 details how each of these four basic spreadsheets is formatted for consistency across the AD-user countries.

# **2.1 The “Master” Spreadsheet for Each Country – AD-CTY-Master**

Each country has a master spreadsheet presenting a common set of information. The AD-CTY-Master<sup>11</sup> spreadsheet has the
that is, average annual light growth (a measure of overall economic growth), population sizes (a measure of migration changes), and Mean Logarithmic Deviation (MLD) and Theil index as measures of spatial inequality.

The results in Table 6a and 6b indicate similar statistically insignificant effects as those in Tables 5a and 5b, respectively, even when we used average and real prices as controls; this also suggests the important role of prices in affecting our outcome variables. The same results hold for the coefficients of expectation effects. The results also hold for the coefficients of mineral prices, except for a positive and significant World Bank’s real price coefficient on welfare estimates.

Table 6c shows the post-accession impacts on districts with mineral endowments using the different outcome variables. The results indicate insignificant effects on overall economic growth, migration, and spatial inequality alternative measures regardless of the mineral prices used as controls. The findings confirm the robustness of those in Table 5a. Similar to Table 6c, Table 6d reports the post-accession impacts on districts with active mining activities. Again, the results show insignificant effects on overall economic growth, migration, and spatial inequality alternative measures, regardless of the source of mineral prices data. The results thereby reaffirm the robustness of those in Table 5b.

In order to identify the heterogeneous local economic effects of post-accession, as described in section 3, we exploited the exogenous spatial variation from the scale (small or large) of mining operations, the value (high or low) of minerals extracted, and the nature (extractive or transformative) of mining activities. The main goal is to estimate and evaluate the treatment effects across these difference exogenous variations. Therefore, we specified and estimated the following model: where ������ represents a dummy for an active large or small mining scale operations, a dummy for active high or low value minerals extracted, and a dummy for extractive or transformative mining activities. The coefficient of interest here is ��, which captures a vector of the treatment
30 critically on the assumed stability of this relationship over time. But such an assumption is difficult to verify, and has rarely been tested. Until the performance of these models in predicting changes in poverty is scrutinized with actual data, one must remain cautious with the application of such techniques in practice.

This paper provides a first step at filling this void, drawing on data from two surveys with highly comparable expenditure data, further complemented with two case study applications, thus covering a wide range of different settings, periods of great structural change, and quite divergent poverty trajectories. An adapted version of the SAE technique described in Elbers, Lanjouw and Lanjouw (2002, 2003) is implemented. Consumption prediction models using consumption subcomponents and different combinations of non-consumption assets are tested, in effect using specifications that are plausible given the kind of data that are commonly available in most living standards surveys, light welfare monitoring surveys, and even in the many regularly conducted demographic and health surveys.

Our poverty predictions were found to be broadly successful in two country settings that offer an opportunity to compare predicted poverty against observed poverty. In Vietnam, a variety of models, comprising both consumption sub-components (non-rice food, non-food) as well as non-consumption assets and household characteristics (particularly the full asset model) worked very well. This success is striking in light of the very deep structural transformation that Vietnam went through between 1992/3 and 1997/8 — transformations that would lead one to expect that parameter stability would fail to hold. In the rural Chinese provinces of Inner Mongolia and Gansu, the prediction