rafmacalaba/data-use-mentions-extended huggingface dataset:
# **4. CONCLUSION**

Despite the impact of the crisis, LiTS2 data indicate that satisfaction with public service delivery has risen over time in most countries in the region. Prevalence of unofficial payments is quite low: when respondents were asked how often it is necessary for people to make unofficial payments/gifts when using public services, a large majority reported that such payments are never needed. Nevertheless, the data show that the level of satisfaction with public service delivery in most transition countries tends to be lower than prevailing levels in western European comparator countries. Conversely, the perceived frequency of unofficial payments is higher than in comparator countries.

We find that satisfaction data on peoples‘ experiences and perceptions of service delivery quality can usefully complement more objective measures of service delivery (e.g. from facilities surveys) to help policy makers and development practitioners track progress of country programs and strategies. The data provide insights on the linkages between utilization, satisfaction and the prevalence of unofficial services, as well as the availability of grievance redress mechanisms. An important limitation is, however, that comparisons across countries or individuals must be interpreted with caution, as people may not share the same basis for comparison or have information about the level of quality and service they should be expecting. Still, perceptions data can serve as a ―call to action‖ for governments if the data reveal outcomes that fall short of expectations (or conversely indicate low expectations). It can also serve a diagnostic function: while there is increased recognition that the quality of governance and service delivery affects service delivery outcomes, the empirical evidence on those links remains limited.

How can governments in transition countries further increase citizens‘ satisfaction with service delivery? The insights gained from the LiTS2 provide clues as to how this might be achieved. First, LiTS2 data show utilization of public facilities in transition countries to be generally higher than in the western comparators; this suggests that further analysis may shed more light on the potential for efficiency and quality improvements, and the extent
where teachers display cognitive gaps. The specificity of this information enables personalized development programs to be designed based on teachers’ actual needs. Program administrators and participating training providers could use this information to develop a more pertinent training curriculum that addresses the knowledge gaps of teachers across different school districts.

Furthermore, combining available information on student and teacher assessments would allow policy makers in Ecuador to identify the worst-performing schools. Given that resources to provide in-service training opportunities are limited, the government could use this information to 19
Bishkek, the country’s capital.<sup>27</sup> These records contain crucial information for the design of our sampling procedure. The data identify the address of each of the households in the district as well as the transformer through which each of them is served.

Within the district for which we have utility data, seven villages (comprising 248 eligible transformers)<sup>28</sup> were chosen due to their accessibility from Bishkek during the winter months.<sup>29</sup> Given that the area surrounding the capital tends to be better off than those in the country’s other regions, the district in which the study occurs is not representative of the entire country. In order to improve external validity, the study sample was restricted to the 124 eligible transformers with below median monthly household electricity consumption in the year prior to the intervention. Such households and transformers are more representative of the country as a whole. To complete the sampling process, 20 percent of the households from each transformer were randomly selected to participate in the study.<sup>30</sup> Because the number of households per transformer is heterogeneous, the exact number sampled in each transformer also varies.

# **3.2 Experimental saturation design**

The randomized saturation design varies household exposure to the CFL technology to test for evidence of a household energy efficiency effect, spillovers in take-up, and a technological externality due to aggregate impacts. Transformers are first randomized to differing treatment saturations and then households within those transformers are randomized either to receive CFLs or to control status, according to the saturation previously assigned.

Figure 1 depicts how treatment status was randomly assigned in two stages. In the first stage, the 124 sampled eligible transformers were randomized into three groups: control, lower treatment saturation, and higher treatment saturation. Due to funding constraints, households in 14 control transformers were not surveyed. This resulted in only 110 eligible transformers being finally included in the study, with 25
the RBI was formed as the central bank and high priority was given to increasing credit to rural areas and small businesses. In 1955, the government took over the largest bank, the Imperial Bank of India, to form SBI. The State Bank of India Act in 1959 directed SBI to take over regional banks that were associated with local governments and make them subsidiaries of SBI, which were later named “associates.” SBI is now the largest commercial banking organization in the country – and one of the largest in the world. SBI and its seven regional associates have a substantial rural branching footprint – of about 14,000 branches of these banks, 74% are located in rural and semiurban areas (India Banking Yearbook 2003).

10
erences in factor abundance, this measure would be akin to a revealed measure of factor intensity. If countries that are relatively abundant in a factor tend to concentrate a larger fraction of their exports in goods that are relatively intensive in the use of that factor, a revealed measure of factor intensity of commodity _h_ for factor _F_ , _φh_ ( _F_ ) , can be built from the within-country composition of trade, as where Φ _i_ ( _F_ ) is country _i_ 's relative abundance of factor _F_ , and _Xi,W_<sup>_h_arethetotalexportshares</sup> described above. Three measures of factor intensity are built using this procedure, capturing skilled labor intensity, capital intensity, and resource intensity. Following Romalis (2004), I also use GDP per capita as a measure of overall factor abundance. Also following Romalis (2004), the country level factor abundances Φ _i_ ( _F_ ) are taken from Hall and Jones (1999) and from the World Bank (2008) but instead of expressing factor intensities relative to the US, they are expressed relative to the world average. The reason is that while Romalis (2004) tries to explain bilateral exports to the US, this paper focuses on a country's trade with the rest of the world. Notice that, when applied to the output per capita of dierent countries instead of their factor endowments, this measure

> 6Romalis (2004) measures factor intensity directly from US Census of Manufactures (1992), but this would seriously constrain the set of goods that can be considered.

10
The residuals are checked for spatial autocorrelation, and significant residual clustering was still found. Spatial autocorrelation is not easily addressed in count data with standard regression implementations, hence the results are simply from a mis-specified model. For this reason, the models that relax distributional assumptions (10-12) should provide improved indications of significance, with Model 10 being proffered. The main results of the paper presented in section 4 take spatial processes explicitly into account and are in turn preferred over Model 12.

A few results using the count data echo the main findings. In particular, the slope of population density is negative, indicating that the more populous areas in the Netherlands 16
, S. (2021). Place-based interventions at scale: The direct and spillover effects of policing and city services on crime. Journal of the European Economic Association, 19(4), 2022-2051.</mark>

- <mark>Bruhn, M., & McKenzie, D. (2009). In pursuit of balance: Randomization in practice in development field experiments.</mark> _<mark>American economic journal: applied economics</mark>_ <mark>,</mark> _<mark>1</mark>_ <mark>(4), 200-232.</mark>

- <mark>Borrowman, M., & Klasen, S. (2020). Drivers of gendered sectoral and occupational segregation in developing countries. Feminist Economics, 26(2), 62-94.</mark>

- Deshmukh, N., Desai, S., Pramanik, S., & Tiwari, D. (2020). Improving measurement of women’s work participation: Sensitivity of work participation rates to questionnaire design. _NCAER National Data Innovation Centre Measurement Brief No 2020-1_ .

- <mark>Dillon, A., Bardasi, E., Beegle, K., & Serneels, P. (2012). Explaining variation in child labor statistics.</mark> _<mark>Journal of Development Economics</mark>_ <mark>,</mark> _<mark>98</mark>_ <mark>(1), 136-147.</mark>

- <mark>Discenza, A., Gaddis, I., Palacios-Lopez, A., Walsh, K. (2021). Measuring Women and Men’s Work: Main Findings from a Joint ILO and World Bank Study in Sri Lanka. Washington DC: World Bank.</mark>

25
|Communication|1.5|1.6|1.6|1.7|1.7|1.7|1.8|1.8|2.1|2.4|
|---|---|---|---|---|---|---|---|---|---|---|
|Recreation and Culture|1.2|1.4|1.5|1.5|1.5|1.6|1.6|1.6|1.6|1.4|
|Education|0.7|1.1|1.3|1.7|1.9|2.5|3.1|3.8|4.5|7.3|
|Restaurants and Hotels|1.6|1.9|1.9|1.9|2.0|2.9|2.2|2.3|2.6|3.3|
|Miscellaneous|5.2|5.4|5.4|5.4|5.6|5.5|5.5|5.8|5.9|6.5|
||100|100|100|100|100|100|100|100|100|100|

Source: Authors own calculation from PBS data. Note: D=Decile The evolution of inflation experienced by the top and bottom deciles between November 2018 and May 2022 is shown in Figure 4. The figure suggests that poor families experience higher inflation overall.

**Figure 4: Inflation Inequality across Income Deciles**

<!-- Start of picture text --> 18<br>16<br>14<br>12<br>10<br>8<br>6<br
Given the difference in educational systems throughout the world, the implementation of similar policies to stimulate the commercialization of public research should be approached with caution. Other scholarship has highlighted technology field differences, recognizing that licensing is not effective to the same degree in all fields and arguing that the Bayh-Dole Act did in fact lead to shifts in university patenting towards fields where licensing is an effective mechanism to transfer knowledge and generate licensing revenue (Scott 2004). Academic study on the impact of the Bayh-Dole Act suggests that technology field differences, the research focus and institutional structures of universities, and the extent of collaboration between public and private actors all influence the extent to which universities can monetize patent assets via licensing. Policy makers in the developing world must appreciate these subtleties when considering similar approaches.

_Figure 5. Middle-income States: Licensing and Royalties Balance of Payments, 1980-2009_ Middle-income states: licensing & royalties balance of payments, 1980-2009 <!-- Start of picture text --> $1<br>$0<br>($1)<br>($1) Botswana<br>($2) Poland<br>($2) South Africa<br>($3)<br>Brazil<br>($3)<br>South Korea<br>($4)<br>Russian<br>($4)<br>Federation<br>($5)<br>Billions<br><!-- End of picture text --> Source: International Monetary Fund, Balance of Payments Statistics Yearbook and data files (Source: International Monetary Fund, Balance of Payments Statistics Yearbook and data files) Figures 5 and 6 show that Balance of Payments for IP royalties in middle-income nations have moderately decreased over time and low-income countries have experienced more rapid decline. 1995 marks a clear turning point for most countries in the sample. While the high-income countries began experiencing a positive increase in Balance of Payments, middle- and lowincome countries moved
or when birder observations occur within protected areas (Figures S3 and S4), or AP changing the composition of birders toward those who are more skilled or more likely to report observing greater numbers of birds (Figure S5). Our additional replication of a flexible method of controlling for surveyor effort lends further credence to these results (Figure S6). We find similar year-by-year changes when we estimate the effect of AP management on the number of bird species observed, though the average effect is slightly negative in this case (Figure S7 and Table S4). The marked rise in bird abundances following transference to AP management reinforces the notion that AP management improves wildlife outcomes.

# **2.2 Tourism**

We now turn our attention to the effect of AP management on tourism. There are several reasons why AP might boost tourism. The increased wildlife populations under AP management could attract more tourists, or AP’s potentially superior ability to market its parks internationally compared to government-managed parks could increase visitation. Due to the lack of comprehensive data on actual tourist visits to parks across Africa, we rely on the following proxies as the best available measures of tourism.

We first utilize data from the widely-used citizen science platform iNaturalist to approximate tourism visits [60], following prior research leveraging photographs of wildlife posted to social media platforms like Flickr to measure tourism [61, 62]. Users of iNaturalist upload geolocated and timestamped photos of flora and fauna, providing information regarding the location and timing of park visits (SI A.3). It is important to note, however, that we cannot use iNaturalist data as a measure of wildlife outcomes due to the absence of information on surveyor effort.

We estimate that AP management significantly increases the probability of positive iNaturalist visits by 21.5 percentage points, or by 47% relative to the mean among control areas (Figure 3, Row 3 and Figure S8). “Positive visits” refers to the presence of any iNaturalist observations in a given protected area-year; this
by the Gini index, did not vary. In contrast, exercising the theoretical option to link people’s perception and an underlying household level measure of absolute polarization, enabled us to show that perception is indeed significantly linked to welfare distribution.

The potential applications of this new method can be implemented beyond Morocco. According to some preliminary analysis that has been undertaken, other countries in the MENA region show evidence of growing polarization, and as in Morocco their citizens seem to be quite unhappy about their socioeconomic status. A broad regional analysis in the spirit of the present paper might indeed 27
two reasons. First, consumption smoothing through borrowing or own saving works well for transitory income shocks (Aiyagari (1994), Heaton and Lucas (1996), and Levine and Zame (2002)), but not when income shocks are highly persistent or permanent (Constantinides and Duffie (1996) and Krebs (2003a and 2004)). Thus, highly persistent income shocks have a large effect on consumption volatility and welfare, whereas the effect of transitory shocks is relatively small. Second, the transitory term in our econometric specification of the income process will absorb the measurement error in individual income, and therefore allows us to arrive at a better estimate of the true amount of individual income volatility. For these reasons, we eventually focus on persistent shocks and their relation to trade policy.

# _II.1. Data_

In Mexico, the National Urban Employment Survey (ENEU) conducts extensive _quarterly_ household interviews in the 16 major metropolitan areas and is available from 1987 (we use data from 1987-1998 in our study). The ENEU is structured so as to track a fifth of each sample across a five quarter period. The sample is selected to be geographically and socioeconomically representative. The treatment of sample design, collection and data cleaning is careful. The survey questionnaire is extensive in scope and covers all standard elements such as participation in the labor market, earnings etc.<sup>13</sup> We use information on labor market participants between the ages of 16 and 65. Individual panels were constructed by matching workers by their position in an identified household, level of education (years of schooling), age and sex. Questions referring to labor income refer to income earned in the previous quarter. Workers earnings include their overall earnings from fixed salary payments, hourly or daily wages, piece-meal work, commissions, tips and any entrepreneurial earnings (earned by the self-employed). Taken together, we have 44

> 13The actual surveys and documentation of methodology are available on request.

8
||||[-0.292]|
|**Many (mor**|**e than 2.1) TLU**|||
|Hazard|0.039|0.039|-0.010|
||[0.908]|[0.906]|[-0.192]|
|Hazard<sup>2</sup>||-0.010|-0.008|
|||[-0.235]|[-0.215]|
|Hazard<sup>3</sup>|||0.032|
||||[1.044]|
|**Landless ho**|**useholds**|||
|Hazard|**-0.212*****|**-0.214*****|0.112|
||**[-3.393]**|**[-3.553]**|[0.453]|
|Hazard<sup>2</sup>||0.018|0.023|
|||[0.265]|[0.352]|
|Hazard<sup>3</sup>|||-0.186|
||||[-1.391]|
|Obs.|4,593|4,593|4,593|
|R-sq|0.132|0.133|0.137|

_Source:_ World Bank.

_Note:_ NDVI = Normalized Difference Vegetation Index. Results are from a weighted regression such that each household is assigned its survey-specific population weight. Countries and years included are Niger (2018) and Nigeria (2011, 2013, 2016). Dependent variable is the log of per capita (or adult equivalent) consumption. Other variables included and not shown are education
deviation increase in wealth is associated with a price increase of CDF 123 over the price paid by poorer women in the same facility.

Why do facilities charge different prices for the same quality of consultation? One reason might be that health providers are pro‐social and charge wealthier users higher fees to subsidize the provision of care to poorer users. Alternatively, this behavior might represent price discrimination for profit maximization. Given the autonomy in setting prices and the ability to distinguish women by their socioeconomic status, health providers can charge according to the willingness to pay by the distinct groups. One way to distinguish between the two options would be to compare the marginal cost of providing a consultation with the price charged to the poor. Under cross‐subsidization, the price for the poor will be less than the marginal cost. Unfortunately, the data do not allow for reliably estimating the marginal cost. However, the household survey data provide the composition of wealth in the different health zones. If the goal of the providers is fully or partially covering the costs of serving the poor by charging higher prices to wealthier women, the additional amount needed to be charged from each wealthier woman will decrease as the ratio of wealthy to poor increases. As a result, the higher is the number of wealthier women per poor woman in the health zone, we would expect a flatter wealth‐price gradient once controlling for the quality of consultations.

Table 7 presents the results of testing for heterogeneity in the price‐wealth relationship by the ratio of wealthy (top quintile) to poor (bottom quintile) households. When restricting the sample to health zones in which the number of women in the bottom quintile is greater than the number of women in the top quintile, we do not find a statistically significant price‐wealth relationship when we incorporate health zone dummies (column 1) or facility dummies (column 2). However, when the analysis is restricted to the sample of health zones where there are more women in the highest wealth quintile than in
**|
|**CAFTA bilateral liberalization**|||
|**Nicaragua and US liberalize**|**0.91**<br>**1.05**|**0.55**|
|**Nicaragua liberalizes only**|0.69<br>0.88|0.18|
|**US liberalizes only**|0.22<br>0.16|0.37|

Table 14 highlights that trade liberalization is positive overall, however it tends to favor urban households more than rural ones. It also shows that calculating income changes for households headed by self-employed farmers with equation (1) creates a downward bias. Given the uncertainty linked to the imputation method implied by equation (1), we report poverty results when the income changes are estimated using the combined method of equations (1) and (2).<sup>24</sup> These results are also clearly summarized in Figure 3: a gain of around 2% is the most likely change in income for a random household in the full liberalization scenario, and a gain close to 1% has the highest probability of occurring in the CAFTA case, which also shows less dispersion around this average gain.

> 24 The figures in Table 14 are not fully consistent with those obtained from the CGE model. In fact even by modifying the SAM in accordance with the household survey, some discrepancies between the macro and micro databases remain. Besides, as already outlined, despite the segmentation in the factor markets of the CGE model, intra segment factors’ mobility is allowed, and substitution across goods in consumption is permitted, whereas this is not case for the micro data. At the micro level, individuals never switch jobs and households’ consumption patterns do not change: the parameters θ ’s are fixed.

36
online appendix Weight. As discussed in Appendix A.1 units of quantity may vary. We use the import value and weight to construct a mask price per unit of weight for transaction _r_ made at the firm-border post combination _f_ in month _t_ .

We winsorize border prices at the 5th and 95th percentile.

# **A.3 Additional appendix data sources**

**VAT** Firms with turnover equal to or in excess of RwF 20 million ($20,000) annual turnover (or the RwF 5 million ($5,000), for three consecutive quarters) are required to file VAT. Among those filing VAT, small firms may file quarterly, while large are required to declare by the 15th of each month. Firms who file VAT are required to report their sales using an EBM device. EBM II data is subsequently used to verify VAT declarations.

A VAT declaration reports the total (taxable and non-taxable) sales of a firm. Since this includes both EBM I and EBM II, VAT is a more comprehensive measure of turnover. Since a VAT liability is determined by taxable outputs and inputs and the statutory tax rate (18%), a VAT declaration requires a firm to report total tax paid on inputs, only.<sup>A5</sup> VAT does not require declarations of non-taxable input, as well as inputs from non-VAT registered vendors. Consequently, EBM is often a more complete measure of a firm’s aggregate input.

In Appendix E.1, we use VAT turnover data to test the exogeneity of mask manufacturing exposure. In Appendix D, we use VAT turnover data to to measure firm A5The tax code in Rwanda also provides for exemptions and a 0% rate for some products. See the VAT law ( `https://www.rra.gov.rw/index.php?id=36` ) for more detail.

A10