rafmacalaba/data-use-mentions-extended huggingface dataset:
**Table 3. Probability of Reporting Trade Integration Data and Firm Size**

||(1)|(2)|(3)|
|---|---|---|---|
||FDI Reported|For. Input<br>Reported|Export. Int.<br>Reported|
|log emp.|0.0207|0.143***|0.0234|
||(0.0429)|(0.0187)|(0.0380)|
|Constant|1.929***|-0.379***|2.485***|
||(0.318)|(0.0855)|(0.261)|
|Observations|56,911|56,911|56,911|

Clustered standard errors by country in parentheses *** p<0.01, ** p<0.05, * p<0.1 26
in existing data, there are mechanisms through which this information gap can be filled in the short to medium term until such systems are developed and implemented. This paper is one attempt to fill this information gap. It presents a simulation exercise using an adapted version of the ECE Accelerator Costing and Simulation Model<sup>1</sup> to outline the ECE costs, financing gap, and human resources and infrastructure needed to achieve government goals and SDG 4.2 targets. The aim is to facilitate evidence-based planning and decision-making for the ECE subsector in the upcoming years. The model also considers alternative scenarios for meeting targets by 2035. The discussion section considers system capacities and additional support mechanisms required for these endeavors.

This paper draws on administrative data sources, the population census, and publicly available data on government spending to consider what a core package of ECE services might cost in Pakistan. It considers goals such as those articulated in SDG 4.2 as well as prospective goals being considered by the government.<sup>2</sup> This paper presents cost estimates for three packages: (i) a business-as-usual package, (ii) a core service delivery package, and (iii) an augmented service delivery package. Various approaches to service delivery are costed including community construction and vouchers.

> 1 The development of this tool was supported by UNICEF in collaboration with others. The original tool is available at https://www.ece-accelerator.org/resources/early-childhood-accelerator-simulation-model.

> 2 The analyses presented in this paper were motivated by a government request to understand the costs of expanding ECE provision across Pakistan.

2
this method is that the surveys typically represent only the phone-using population. In addition, this mode of data collection allows asking only a short set of questions.

Using “big data” in the MENA region—such as administrative records, geospatial information, and social networks—can help to measure well-being without requiring burdensome face-to-face interactions. This can be particularly useful when collecting traditional data through sample surveys is not possible. However, the main disadvantage of “big data” approaches is that researchers cannot control the population covered, and the data can overlook populations of interest or introduce welfare estimation biases. Restrictions on data access and confidentiality related to administrative records can be further serious constraints. However, mixing “big data” approaches with traditional surveys has significant potential to improve poverty measurement.

In summary, the MENA region is facing substantial problems related to the availability and timeliness of HBSs necessary for regular welfare measurement. Priority for the region should be to regularly collect consumption survey data and to improve access to, and the quality of, existing surveys. Where conducting more HBSs is not feasible—especially in violence and conflict affected situations—using alternative nonconsumption surveys and imputation techniques, and/or conducting phone surveys, may be considered as a second-best solution. Using “big data” opens possibilities to measure well-being, but researchers should use them cautiously and in conjunction with traditional surveys, where possible.

28
0.042**|-0.042***|-0.048***|-0.050***|
||(0.015)|(0.012)|(0.009)|(0.007)|
|Spousal unemployment|0.016|0.005|0.008|0.012|
||(0.027)|(0.022)|(0.020)|(0.018)|
|Observations|75356|75356|75356|74669|
|Year FE||Y|Y|Y|
|Country FE|||Y|Y|
|X controls||||Y|

_Source:_ Authors’ analysis based on data from the Demographic and Health Surveys (DHS).

## _Notes:_

* p<0.10, ** p<0.05, *** p<0.01. Standard errors are robust to arbitrary forms of heteroscedasticity and are clustered at the country level. The dependent variable in each regression is a binary dummy variable taking the value one if the individual female respondent was a victim of violence, defined as physical (panel A), sexual (panel B) or either (panel C) in the last twelve months. X is a vector of controls for age and education of respondent, education of her spouse, and urban location. Relative to Table 1, female and male unemployment rate shocks are replaced with an indicator for the individual unemployment status of the woman and her partner. Information on employment status of partner is available in only 18 of our 32 countries.
). Conflict debt is the discounted history of conflict years where a conflict year is defined as by more than 0.08 fatalities per 1,000 population. Country conflict debt is computed in the same manner at the country level. The regressions weighted by population are on the cross-sectional data of sub-country regions between 1992-2018 whenever sub-regional poverty rates are available.

In Table 4 we use both the UCDP and the ACLED data to look into the relationship between conflict history and the poverty rate at the regional level but within the continent of Africa. As before we use population weights to reduce measurement error. Columns (1) and (2) show results for UCDP and columns (3) and (4) show results for ACLED. All results are for the African continent only and are remarkably robust between the two conflict data sets. Most importantly, the results are now robust to country fixed effects in columns (2) and (4), i.e. they are significant within countries. This provides some evidence for a local mechanism being at play. It is again remarkable how the coefficient on conflict debt is robust across tables with magnitudes being very similar in Tables 1, 3 and 4.

> 6 In Appendix Table A5 we show that this is also robust to dropping countries for which we have only aggregate country observations.

> 7 According to the UNHCR, some 41.3 million people were internally displaced due to armed conflict at the end of 2018.

16
including price and quantity regulation, taxes, subsidies, assignment of property rights or simply by taking over a firm or a sector.<sup>4</sup> Evidence, however, suggests that governments are as likely to fail as markets. Multiple goals in politics and the lack of capacity in design and enforcement of policy tools are among the reasons, as discussed. Undeniably, however, the risk of failure is also closely linked to the ways civil servants and politicians responsible for fixing market failures in the sector are disciplined with checks and balances and democracy. Infrastructure development—particularly in Africa—is often much slower than demanded by populations and expected by donors; see the result of the recent AICD study,<sup>5</sup> for instance, or Estache and Wodon (2005) – while Keefer and Khemani (2003) point at how democratization has failed poor voters in many developing countries. Infrastructure pricing policies in Latin America have tended to be much more regressive than claimed by public and private providers (Foster, Estache, and Wodon, 2002) and subsidies in the sector have often failed to reach those who need it most and helped instead those who need it least (Komives et al. 2005 and 2007 or Bacon and Ley, 2010). Private agendas behind sector-specific governance put the effect of policy choices and huge investments at risk. Even if difficult in practice to distinguish between benevolent and non-benevolent distortions in politics, the latter category will also have to be addressed.

Indeed, with huge budget allocations, public good characteristics, the essential value of the services, complex contracts, and opportunities to hide corruption, the infrastructure sectors are among the more exposed sectors to biases at the political level.<sup>6</sup> These facts make it quite likely that distortions in politics can be part of the explanation of performance failures. This is why researchers and practitioners alike argue that _governance failure_ in the sector should be central to any assessment of the sources of

> 4
<!-- Start of picture text --> Figure 1<br>CDFs of Community Average Child Characteristics in 1997 and Program Duration in 2000<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>-3 -2 -1 0 1 0 .2 .4 .6 .8 1<br>Average height-for-age in community, 1997 Proportion of stunted children in community, 1997<br>Rural Urban Rural Urban<br>A. Community Average Height for Age B. Community Average Proportion Stunted<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>10 11 12 13 14 0 10 20 30 40<br>Community average household per capita expenditure, 1997 Length of Program (in months), 1998-2000<br>Rural Urban Rural Urban<br>C. Community Average ln(PCE) D. Program Duration<br>Sources: For A, B and C, IFLS (1997); for D, Community Infrastructure and Facility Survey, IFLS (2000).<br>Cumulative Probability Cumulative Probability<br>Cumulative Probability Cumulative Probability<br><!-- End of picture text --> 26
creates jobs? Small versus large versus young. _Review of Economics and Statistics_ , _95_ (2), 347‐361.

Harrison, A., & Rodríguez‐Clare, A. (2010). Trade, foreign investment, and industrial policy for developing countries. In _Handbook of development economics_ (Vol. 5, pp. 4039‐4214). Elsevier.

Hausmann, R., & Rodrik, D. (2003). Economic development as self‐discovery. _Journal of development Economics_ , _72_ (2), 603‐633.

Hillberry, R. and Hummels, D. (2008). Trade responses to geographic frictions: A decomposition using micro‐data. _European Economic Review_ , 52(3), pp.527‐550.

Hsieh, C. T., & Klenow, P. J. (2009). Misallocation and manufacturing TFP in China and India. _The Quarterly journal of economics_ , _124_ (4), 1403‐1448.

Javorcik, B. S. (2004). Does foreign direct investment increase the productivity of domestic firms? In search of spillovers through backward linkages. _The American Economic Review_ , _94_ (3), 605‐627.

OECD (2010), _High‐Growth Enterprises: What Governments Can Do to Make a Difference_ , OECD Publishing, Paris. DOI: http://dx.doi.org/10.1787/9789264048782‐en.

OECD (2016), "High‐growth enterprises rate", in Entrepreneurship at a Glance 2016, OECD Publishing, Paris, <u>http://dx.doi.org/10.1787/entrepreneur_aag‐2016‐23‐en.</u> Rodrik, D. (2005). Growth strategies. _Handbook of economic growth_ , _1_ , 967‐1014.

Rosenthal, Stuart and William Strange (2004). “Evidence of the Nature and Sources of Agglomeration Economies.” _Handbook of Regional and Urban Economics_ Vol. 4.
provide very complete statistics on unemployment since, in particular, they include groups of persons who are not covered in unemployment statistics by other methods.

- Social Insurance Statistics which come from compulsory unemployment insurance schemes, which as a rule have broad industrial coverage and relate to wage earners and salaried employees, or sometimes to wage eamers only. Unemployment figures are then computed by comparing the number of people drawing insurance benefits with the total number of insured persons covered by schemes. The problem with this type of approach is that it is difficult to ascertain whether the people covered by the insurance scheme are representative of the country or whether they cover enough people. Therefore the data will give a distorted picture of unemployment in the former case or simply underestimate unemployment in the second.

- Employment office statistics generally refer to the number of applicants for work on the registers at the end of each month. They may include people without a job, but also people on strike, or temporarily unable to work and persons engaged on unemployment relief projects. While the statistics, in principle, do not include persons already in employment, some series also include persons in employment who are seeking a change of job or extra work and are therefore registered at employment offices. The value of these statistics varies widely. Where, for example, employment offices function in close connection with unemployment insurance, and registration is a qualifying condition for the receipt of unemployment benefits, they are comparable in reliability to compulsory unemployment insurance statistics. On the other hand, where registration is entirely voluntary and especially where the employment offices function only in the more populous regions of a country, or are not widely patronized by employees seeking work or by employers seeking workers, the data are generally very incomplete and do not give a reliable indication of the extent of unemployment. The scope of employment office statistics is therefore very difficult to ascertain and, in general, these statistics are not comparable from country to country.

- Official estimates, which are provided by national authorities and are usually based on
# **1. Background**

Jharkhand is one of the poorest states in India. A nationwide survey of the situation of agricultural households (SASH) in 2019 found that farmers in Jharkhand have the lowest average farm incomes in the country (SASH, 2021). The average farmer in Jharkhand earned only Rs. 8,000 in 2018-19 from all crops, compared to the national average farm income of Rs. 37,000. The average total income of agricultural households in Jharkhand is the lowest in India (Rs. 3,993/month, compared to the national average of Rs. 8,337/month) and the share of crop income in the total household income is also the lowest (17%) after Jammu & Kashmir and Kerala ( _ibid_ ). The most recent NSSO Consumption Expenditure Survey, carried out in 2011-12, found that more than 45 percent of farmer households in Jharkhand lived in poverty – the highest in India. Income from agriculture in Jharkhand is also highly vulnerable to droughts, dry spells, and other weather shocks. In 201819 for example, 75 percent of all paddy growers and 96 percent of farmers growing black matpe ( _urad_ ) in Jharkhand reported losses due to droughts or inadequate rains.

Poor access to irrigation is a major reason why agriculture is a high-risk, low-return endeavor in Jharkhand. An analysis carried out in 2016 found that 18 districts in Jharkhand were among the most irrigation deprived in India, and that high energy costs rather than a physical scarcity of water was the main reason for poor access to irrigation in the state (Shah et al., 2016). According to the most recent Minor Irrigation Census, 94 percent of small irrigation structures in Jharkhand were powered by diesel or muscle in 2013-14, and both were significantly more expensive than grid electricity as a source of energy for irrigation. While 65 percent of farmers in the state irrigated at least a part of their land in the _Rabi_ (winter) season, only 19 percent of the cropped area was
For tables A17 to A20, in all regressions, the IV is generated using the mean border distance. Regressions (1) to (4) show results with the regressor (Log of refugees at 50km) constructed using distance to closest cluster. For regressions (5) to (8), the refugee index is generated using the square of the distance while for regressions (9) to (12), the square root of the distance to closest cluster is used. These represent the decay categories of the refugee index. Year and cluster fixed effects are included in regressions (1) to (3), (5) to (7) & (9) to (11). For each of the 3 decay categories of the regressor (i.e. the refugee index), the first regression is without other controls; the second regression has age, agesq & sex(male==1) controlled for; and the third regression has the more endogenous controls added (i.e. HH size, initial marital status, education, occupation, other HH income sources & ownership of land). In Regressions (4), (8) & (12), household fixed effects replace the cluster fixed effects and all the other controls are included, but the time invariant ones drop off (only age, agesq & male remain). Sampling weights are considered and Standard errors clustered at the cluster level in all regressions. Robust standard errors in parentheses. Significance level is: *** p<0.01, ** p<0.05, * p<0.1.

# **Table A17: Second stage: Log Refugees at 50km with decay function (i.e. index generated using 1)distance to closest cluster, 2)square of distance to closest cluster** **<u>and 3) square root of distance to closest cluster) instrumented by distance-based IV.</u>**

|||
aggregate using arguably more “correct” data, this would not be possible in other contexts in which census data is missing or out of date. For this reason, we drop any AGEBs with an estimated population of zero. Only 0.6 percent of AGEBs are dropped.

> 10 For some policy contexts, it may be desirable to give greater weight to more populous municipalities when evaluating the accuracy of estimates, but in this analysis, we limit consideration to the case where each municipal estimate is given equal weight when evaluating the performance of the estimates as a whole.

11 We construct the sampling weight as the inverse of the probability of selection, using the formula for sampling with replacement, as an approximation for the actual probability of sampling based on our sampling strategy. In P s particular, the probability of selection is approximated as i = 1 −�1 − P PPt ss ~~�~~ NN , where popsma = population of AGEB a, poptotal = total national population and ππ s PP ttsstt 9 ss<sup>), the number of individuals in the sample.</sup> NN= m mm(40, P PP
countries. To have a sense of the fraction of a country’s interaction with the world economy that the sample of firms represents, we calculate for each country-year survey the ratio between the aggregate exports over all sampled firms and the aggregate exports for the country as recorded in the UN Comtrade database. On average, this fraction is 19%, but it ranges widely across countries, which is important to keep in mind if one wants to generalize from the WBES results.

The second source of information that we exploit is the multi-region input-output table (MRIO) which is part of the Eora database. In contrast with two widely-used comparable global IO tables, the WIOD (maintained by researchers at the University of Groningen) and the TiVA database (of the OECD), the Eora database includes information on all countries in the world, including all individual sub-Saharan African economies.<sup>3</sup> Naturally, the more disaggregate country dimension comes at the cost of greater reliance on proportionality assumptions and imputations, but the advantage is that the MRIO includes bilateral input flows between all African country-sector pairs. As a result, it provides an unprecedented wealth of information on the regional production network in Africa and its connections with the rest of the world economy.

In order to exploit the staggering amount of information, as discussed below the full MRIO contains more than 24 million input-coefficients, we need to aggregate the table to reduce its dimensionality. We describe the process of collapsing the global IO table in a set of country-

> 3 In the WIOD and TiVA databases, the African countries are grouped together in one or two country-groupings or in the rest-of-the-world aggregate.

3
-> Source: Authors, based on MPF, Substantiation notes of the Government decisions, NBR calculations On the expenditure side, the impact varies between 53 million RON in 2012 and around 106 million RON in 2016. The maximum impact on the expenditure side was registered in 2015, when almost 500,000 employees from the budget sector were beneficiaries of the measure. After a 25% across-the-board wage cut in July 2010, employees from the public sector had 33
gender matters. Female migration is of interest since its influence on labor supply and child outcomes at home might be very different from that of male migration. World Bank (2008a) argues that men and women show important differences in the determinants of their decision to migrate as well as their opportunity cost of migration. Furthermore, the report finds gender differences in the patterns of remittances, budget allocation of remittance income, and hence gender differences in the impact of migration or remittances on household decisions and welfare.

Predictions from economic theory regarding the differential impact by the migrant’s gender on household decisions and outcomes, such as labor supply and education investment, are ambiguous. With migration and remittances, shifts in income sources may affect intra-household decision making, which suggests potential gender dimensions in the impacts on human capital decisions. When a woman moves abroad to work, increased income from remittances of a female migrant may increase her bargaining power and her influence over investment choices in the household. However, the physical absence from the household is expected to create disconnects and loss of control over the decisions and activities at home. Family disruption can have negative consequences for children’s welfare, and whether the mother or the father goes away may matter. Since the net impact is a priori ambiguous, it becomes an important empirical question.

This study empirically estimates how female and male migration and remittances in Indonesia affect sending households’ child outcomes and labor supply behavior.<sup>1</sup> The analysis uses nationally representative data of the Indonesia Family Life Survey (IFLS) 2000 and 2007. Estimating the causal impact of migration and remittances is usually challenging since the decision to migrate and to remit is likely to be endogenous. To account for endogeneity, this study applies an instrumental variable method using historical migration networks as instruments for migration and remittance receipts.<sup>2</sup>

> 1 The impact on poverty is explored in a parallel paper.  Adams and Cuecuecha (2010) find that receiving remittances reduces the probability
source (i.e. the respective website). After all the preparatory steps, the data collection starts, using robust web scraping and parsing algorithms which are replicable and scalable allowing for continuous data collection at a comparatively low cost (assuming source data format and structure remain the same). The last stage is the data evaluation. Even the best source annotation and data collection effort can result in data scraping or parsing errors. Evaluating the collected data is therefore critical for assuring quality data which is well structured and accurately mirrors the official government data. Once the first version of the collected data is ready, our research team verifies that the data structure corresponds to the GCO legislative data template (variable names and contents are permissible). Moreover, data content is checked on a sample to verify if information is correctly parsed from the web into our database and that there are no obvious errors such as variables are completely missing or stored in the wrong table. We check if the missing rates or rates of non-sensical values (e.g. zeros) for each variable are low. These checks typically reveal data quality and scope errors which then are fed back into improving the data collection and parsing algorithms. We cycle through data collection, checking and updates until data quality is sufficiently high.

**Table B1. Overview of GCO legislative datasets**

|**Country**|**Coverage**<br>**(Years)**|**n (laws)**|**n (total)**|
|---|---|---|---|
|Bulgaria|2006-2022|1,659|3,061|
|Chile|1900-2022|(2,504)|13,971|
|Colombia|1998-2022|1,598|10,657|
|France|2012-2022|473|1,700|
|Hungary|1998-2022|4,365|5,743|
|Jordan*|