rafmacalaba/data-use-mentions-extended huggingface dataset:
note that often policies that show promising results when implemented as pilots at small scale turn out to be disappointing when implemented at scale. They point to the need for continuous evaluation and openness to course corrections as projects are scaled up to avoid such disappointing outcomes. The next group of notes turns to the question of _“how to evaluate policies.”_ One of the most striking trends in economics – and particularly in development economics – in the past decade or two has been the increase in the seriousness with which the profession has taken the identification of causal effects of policies on outcomes. As Xavier Giné and Hanan Jacoby write, this has most visibly come through the mainstreaming of randomized controlled trials as a tool to evaluate policy interventions. This has been accompanied by increasingly creative use of natural experiments to identify causal effects in circumstances where randomization is not possible or desirable, as well as by more carefully combining economic theory with empirical evidence to shed light on underlying mechanisms. The scope for insightful evaluation of policy outcomes has also broadened remarkably over the past decade as economists have been able to draw on nontraditional data sources, ranging from satellite imagery to cell phone records. Robert Cull, Dean Jolliffe, and Vijayendra Rao provide examples of how these new data types, combined with new tools such as machine learning and natural language processing algorithms, have shed new light on old questions.

The last set of four notes covers an assortment of policy domains where recent research has provided new insights on the effects of policies. Sergio Schmukler, Michael Toman, and Adam Wagstaff draw on examples as disparate as early childhood development, financial crises, and environmental degradation to illustrate the general principle that early policy interventions at large enough scale can be highly cost‐ effective. They also discuss why, despite these high returns, opportunities for early intervention often are missed. Susmita Dasgupta and Michael Toman describe the accumulating evidence that investments to improve resilience to climate change can have big payoffs in terms of development outcomes. Leora Klapper discusses how the combination of policy
.2|207.2|

Sources: Indonesia; Department of Health, Bureau of Planning; Central Bureau of Statistics, Census; National Family Planning, Coordinating Board; SUSENAS (National Household Survey, February 1984)

## IV. EmRirical Results

17. Applying the conceptual framework in Figure one to cross province data for Indonesia reveals that observed pairs of IMR and TFR for individual provinces are the result of the intersection of the two simultaneous
measure is the political competition index produced by the Polity5 project (Center for Systemic <u>Peace 2020). This categorical index measures the degree of political competition in a given</u> country, independently of the political regime, and ranges from a value of 1 (meaning that political competition is suppressed) to a value of 10 (meaning that political competition is institutionalized and electoral). We use this measure as an additional test of the Accountability Hypothesis under the assumption that regimes where political competition is higher tend to be more accountable and responsive to citizens’ demands.

In testing Governance 2, we again turn to the WGI, which also provides a measure of government effectiveness (GE) across countries. To quote the WGI website, “Government effectiveness captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies.” (World <u>Bank 2022b). GE is a composite index of indicators of the quality, coverage, and citizen</u> satisfaction with public goods and services (roads, public transport, electricity, health and education services, water and sanitation, bureaucratic quality). Similarly to VA, the GE index is normalized to have mean zero and a standard deviation of unity.

For the Technology Hypothesis, we draw on data from the International Telecommunications Union (ITU) that focuses on the use of ICT.<sup>26</sup> We focus on two technologies, namely mobile phones and the internet. For the former, we use the number of mobile phone subscriptions per capita. For the latter, we use the percentage of the population using the internet. Both these measures are only available from the year 2000 and onwards.

While we have about 2,500 observations of the expenditure on SP by country and year, the covariates used to test the hypotheses laid out
# et al., 2019 for Uganda).

Against this background, the first contribution of our paper is to provide empirical evidence from an alternative smallholder farming system in Mali on non-classical measurement errors (NCME) in self-reported (SR) crop yields vis-`a-vis their crop cutting (CC) counterparts on the same plots and demonstrate the consequences of NCME in SR yields for the estimated relationships between crop yields and agricultural inputs besides land, specifically seeds and labor. The literature’s heavy focus on land is understandable given that it is a primary factor of production. And our analysis fully corroborates the findings of Desiere and Jolliffe (2018), Gourlay et al. (2019), and Abay et al. (2019). However, with rising population densities and low levels of agricultural productivity, especially in Sub-Saharan Africa, more attention is being paid to the effects of measurement errors on the understanding of agricultural intensification (Abay et al., 2021) and of the extent of misallocation of inputs across farms (Gollin and Udry, 2021). In this context, understanding how crop yields relate to the use of other agricultural inputs is of great policy relevance.

To do so, however, it is necessary to ensure that the relationships are correctly assessed and are anchored in high quality survey data. Recognizing the technical and resource constraints that are faced by survey implementing agencies in contexts where data and yield improvements are needed the most, we present and validate an innovative approach to agricultural survey data collection and analysis in a way that requires the implementation of crop cutting only in a sub-sample of households/plots and that leverages machine learning techniques to derive predicted objective measures of crop yields for the sub-sample that is not subject to crop cutting. This constitutes the second contribution of our paper to the literature. In recent years, machine learning (ML) techniques have made their way into economics research and have proved their utility. Athey (2018), as part of an assessment of the early contributions of ML to
# **3 Discussion**

Using large-scale spatially resolved wealth and pollution data we find that ambient air pollution is, on average, positively correlated with wealth in LMICs around the world. This appears to be driven by urbanization, which is highly positively correlated with both wealth and ambient air pollution in most LMICs. However, there is substantial heterogeneity in this result which appears driven partly by the the weaker correlation between urbanization and ambient air pollution in countries where the primary source of ambient air pollution is non-anthropogenic.

Within cities the theoretically anticipated relationship between pollution and wealth is ambiguous. It will be driven by patterns of residential sorting that are likely influenced by cultural, geographic, and historic differences. As a result, one would expect city-level results to vary widely across different cities in different countries. We find this to be the case. Sorting across different terrains, particularly elevation, appears to be important in places where cities are built on varied geographies. Concentration of economic activity also appears to explain some of the relationship between wealth and pollution levels. Cities with more economic concentration have a stronger and more positive relationships between wealth and pollution. This may be due to the relatively wealthy wishing to live closer to areas of concentrated economic opportunity – which may also be the most polluted areas. This is analogous to the mechanism that appears to drive the correlations between pollution and wealth at a country level – living in close proximity to economic opportunity can come at the cost of living closer to emissions sources.

There are several limitations to our results. First, we do not account for indoor air pollution, which is negatively correlated with urbanization<sup>32</sup> because rural households are more likely to cook with pollution-generating fuels. Relatively higher concentrations of indoor air pollution in rural areas suggests that the total burden of air pollution may be less positively correlated with wealth than the ambient concentrations examined here. In the extreme case, sufficiently high levels of indoor concentrations in rural areas could completely offset the positive correlation of ambient concentrations
Using data from Columbia, Mexico and Morocco from the 1980s and early 1990s, Clerides (1998) conclude that the evidence supports the self-selection hypothesis, while providing little support for the learning by exporting hypothesis.  Bernard and Jensen (1999) and Liu _et al._ (1999) find similar results for the United States and Taiwan, China.  Aw _et al_ . (2000), using data from the 1980s and early 1990s, find some evidence to support the learning-byexporting hypothesis for some industries in Taiwan, China but no evidence to support it for Korea. Other studies, however, do find evidence consistent with learning by exporting.  Kraay (1999) and Fajnzylber (2004) finds evidence or learning among Chinese and Brazilian enterprises respectively.  Finally, using data from Indonesia, the Philippines, Thailand, Malaysia and Korea, Hallward-Driemeier _et al._ (2002) find that exporters take concrete steps to improve productivity before they enter export markets  (e.g., training employees and using foreign technology).  They interpret this as suggesting that firms try to pass the threshold to enter such markets.

> 4 Bigsten _et al_ . (2004) also find evidence consistent with the self-selectivity hypothesis.

3
can be engaged in several forms of work in a short reference period. The 20<sup>th</sup> ICLS in 2018 revised the International Classification of Status in Employment (ICSE‐18) aligning this classification with the narrowed definition of employment. Workers are classified in 10 categories. These 10 categories can be aggregated according to the authority the worker can exercise ( _independent workers_ vis‐à‐vis _dependent workers)_ and the type of economic risk the worker is exposed to _(workers employed for profit_ vis‐à‐vis _workers employed for pay)._ The new International Classification of Status at Work (ICSaW) extends ICSE‐18 by including all types of work activities such as own‐use production, volunteer work or unpaid traineeships and, thus, aligns with the new definition of work (ILO, 2018a).

The establishment of a definition of work has implications for the trichotomy of _employment, unemployment_ and _persons out of the labor force_ (also called inactivity), concepts that were ‐ and still are ‐ at the heart of labor market statistics. While greatly expanding the scope of labor market statistics by defining work, the 19<sup>th</sup> ICLS narrowed at the same time the definition of employment to _work for pay or profit_ (19th ICLS, §27). In contrast to the previous definition, individuals engaging exclusively in producing goods for own final use, such as subsistence farmers, are no longer classified as ‘employed’.<sup>7</sup>

> 5 ILO’s decent work indicators are another set of relevant labor market indicators (ILO, 2013a). The 71 decent work indicators, including 21 legal framework indicators, cover the four pillars of decent work (full and productive employment; rights at work; social protection; promotion of social rights). We do not review these indicators here.

> 6 See ILO’s recent guidebook for a detailed overview (ILO, 2018c).

> 7 In
) in conjunction with NASA. This database provides worldwide (terrestrial) monthly precipitation information at 0:5�0:5 degree resolution. Dell et al. (2012) use geospatial software to aggregate these rainfall data to the country-year level, weighting by the population distribution within the country.<sup>15</sup> We further use country-level data on the proportion of agricultural land that is irrigated. These data come from the World Development Indicators of the World Bank, and are generally available for the period 2001-2009.

We combine these sets of data to construct an unbalanced panel at the product-countryyear level, covering 70 nations over the period 1988-2006. Table A.1 in the appendix

> 13 The base period is the ...rst year for which tari¤ data are available for the tari¤-setting country.

> 14 The most-favored-nation tari¤ rate resulting from negotiations associated with GATT/WTO membership.

> 15 These data are also used by Jones and Olken (2010).

15
the ethnicity level may introduce biases, in light of research showing that precolonial ethnic institutions had long-lasting effects on development and public goods provision in Africa (Gennaioli & Rainer, 2007; Michalopoulos & Papaioannou, 2013). A separate dummy for each of the more than 800 precolonial ethnic homelands is included to address this concern, constructed using boundaries from Murdock’s (1959) ethnolinguistic map. Each cell is assigned to the ethnic polygon that covers the highest percentage of its surface. Regressions therefore exploit only within-ethnicity variation, and the results cannot be driven by factors varying across ethnicities.

It is also necessary to account for the main missionary purpose, namely conversion of Africans to Christianity. In particular, there is a concern that is likely to approach zero as distance increases. More specifically, the regressors have the form log(1 + distance) to avoid losing the cells at zero distance.

13
more than 4km from the centroid of its assigned market, with the centroid computed recursively from the location of all facilities mapped to the market. The 4km radius was based on data from the baseline, which showed that 73% of patients lived within 4km of the health facility. This algorithm yielded a total of 273 markets of which 30% had one facility, 28% had 2-3 and 42% had 4 or more (Figure S2 in the Supplemental Material shows mapped examples of each type of market). This distribution also implies, as discussed previously, that 79% of facilities are located in markets with 4 or more providers, and 70% of care is sought in such markets.

**Allocation of treatments** : Having defined markets, we used a stratified cluster randomized experimental design to allocate markets to treatments. Clusters are healthcare markets and the cluster size is the number of health facilities per market. We stratify by market size and county for a total of 16 strata.<sup>15</sup> All 273 markets were randomly allocated to one of three arms:<sup>16</sup>

1. The Inspection Only or T1 Arm: 90 markets were assigned to high-intensity inspections with enforcement of warnings and sanctions for non-compliant facilities.

2. The Inspections plus Information or T2 Arm: 96 markets were assigned to the T2 arm, which combines the T1 arm with the public disclosure of inspection results.

3. Control Group: 87 markets were assigned to the “business-as-usual” low-probability inspections arm. Although inspections could have been carried out if there was a serious complaint, in practice, there were no joint inspections in the year of the intervention.

The scorecard system in T2 consisted of 4 letter grades ranging from A (fully compliant, or more than 75% or the maximum score) to D (minimally compliant, or 11%-40%). See Panel A in Figure A1 of the Appendix. After each inspection, the inspector posted the scorecard in a
KAZ|3.37|USA|GBR|47.76|
|USA|AUS|18.18|DEU|GBR|23.38|KAZ|CHN|2.91|USA|DEU|46.83|
|USA|SAU|13.75|FRA|GBR|22.84|CHN|IDN|2.86|CHN|USA|45.53|
|BRA|USA|10.72|CHE|DEU|21.54|VNM|CHN|2.16|USA|IRL|43.08|
|USA|TWN|9.60|GBR|LUX|20.11|KAZ|RUS|1.68|DEU|USA|39.71|
|HKG|USA|9.08|NLD|DEU|19.85|CHN|KAZ|0.49|USA|KOR|36.76|
|Total||371.97|Total||241.69|Total||35.65|Total||522.38|
|Share wo|rld (%)|7.67|Share wor|ld (%)|4.98|Share wor|ld (%)|0.74|Share wo|rld (%)|10.77|

Source: Authors’ calculations using TiVA trade data. Note: Trade is based on exports data. Countries are categorized on the basis of the data model regarding the cross-border data transfers.

The 10 largest country-pairs that follow the limited model represent a meagre 1 percent of global digital services trade. The largest bilateral trade relation is between Russia and China, followed by Indonesia-China. Note that the amount of services trade between any of the Top 10 country-pairs is much lower compared to countries sharing the other data models. Not all Top 10 country-pairs following this data model include China. Russia and Kazakhstan share strong digital services trade links as shown in Table 2, but also Russia and
Formal: Cleaner . Formal: Cleaner<br>Informal: Shopper . Infonnal: Shopper<br>Informal: Watchman Informal: Watchman<br>0 50 100 150 200 0 2 4 6 8<br>th.Tg th.Tg<br>Fl|Base Salary Leave, HolidaysBenefits IFormal flInformal<br><!-- End of picture text -->

**Figure 4**

_Source:_ Incomes marked by asterisks are based on the author's informal sector surveys, described in Appendix B. Estimates of other incomes were provided by the Ulaanbaatar City Administration. All incomes are in 3/97 togrogs.

**Figure** 5

> _Source:_ Author's calculations based on the incomes in the preceding figure, and the schedules of rates in the

> _Citizen's Income Tax Law,_ and the _Informal Sector Income Tax Law._

# **-** Summary Ease of Entry is Driving Informality

66. This section has outlined four classes of reasons for the explosion of informal activity in Mongolia since 1990. (i) The crisis of the early and mid 1990s caused the release of large pools of labor which were forced to seek informal employment or none at all; (ii) Migration from rural areas to cities are contributing to the growth of informal labor; (iii) The "market" is reallocating its resources toward areas that were neglected under the old system, namely services such as distribution and transportation; (iv) entry is easier for informals. While all of these factors are contributing to the growth of the informal sector, none of the first three can explain the explosion of informality without the last, ease of entry. Even if large pools of labor were released from formal sector enterprises (they were), and even if migrants to the cities are providing labor (they are), and even if the market is reallocating its resources toward other sectors (it is), the question remains why formal enterprises haven'
but, since its prefiscal inequality is rather low, Egypt ranks lower among the same set of countries in terms of the total impact of fiscal policy on inequality.

The direct and indirect benefits from energy subsidies, however, contribute nearly as much to total poverty reduction (from fiscal policy) as total direct transfers. As the food credit transfer made up nearly 100 percent of total direct transfer expenditures in 2015, the largest poverty reductions are produced by universal or nearly universal transfers, making poverty reduction an expensive proposition. Fiscal policy reduces the poverty headcount ratio and does not impoverish the poor, but it could achieve more if transfers and subsidies were switched from universal to more targeted.

Results of the exercise suggest that recent GoE policy directions have been progressive and led to important impacts on inequality and poverty. The continuous elimination of subsidies that the government launched in 2014, with the corresponding increase in social spending, change the balance of GoE’s spending toward a more equitable distribution. The tenfold expansion of the Takaful and Karama programs between 2015 and 2017 is also an important step in the right direction. Using a means-tested approach, these programs have strong potential to benefit the most vulnerable in the Egyptian population. Finally, the measures to increase administrative efficiency and clean the beneficiary list of the food program to exclude those who are ineligible may have also positively affected the equitable angle of the government spending. Being a quasi-universal program, shifting the resources of the food subsidy program toward those on the bottom of the distribution will surely contribute to a more equity-enhancing distribution of expenditures.

The findings in this exercise should be taken as a solid starting point to promote evidence on the impact of policy levers in Egypt. Going forward, the structure of the exercise could be easily updated to reflect new data as soon as it becomes available. The recent completion of the new HIECS round in October 2018 holds great potential for updating of the results, as long as it can be complemented with administrative data.

37
(2) Measuring the effect of inequality on poverty is slightly more complex because inequality can change in infinite ways. Although intuitively progressive distributional change is likely to reduce poverty<sup>3</sup> , this result cannot be generalized without additional assumptions. For example, consider the (possibly unlikely) case of a transfer from the extremely rich to the very (but not extremely) rich. This would improve inequality levels but would not affect poverty. To make the problem of the impact of inequality changes on poverty tractable, one possibility is to assume that income follows a log-normal distribution.<sup>4</sup> Lopez and Serven (2004) compare the theoretical quintile shares according to a log-normal distribution with their empirical counterparts using data from 794 household surveys and conclude that the log normal approximation fits the empirical data extremely well. In turn, under this assumption it is possible to express the inequality elasticity of poverty φ as the elasticity of poverty with respect to the Gini index _G:_ With these elements in mind, a change in poverty due to a change in policy X can be expressed as: or using (2) and (3) above Equation (5) indicates that the impact that a change in policy X will have on poverty will depend on: (i) the impact that the policy has on growth; (ii) how growth is translated into poverty reduction; (iii) the simultaneous impact that the policy has on inequality (as measured by the Gini coefficient), and finally (iv) how inequality changes are translated into poverty reduction.

In principle, γ and φ could be considered to be independent of the policy in question and depend on the particular income distribution, the initial level of per capita income _Y_ , the poverty line, and the initial level of inequality _G_ .<sup>5</sup> Table 1 presents the theoretical elasticities of headcount poverty to growth and headcount poverty to inequality computed under the assumption that income follows a log