rafmacalaba/data-use-mentions-extended huggingface dataset:
- 40 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity

Pandey, B; Joshi, PK; Seto, KC (2013). Monitoring urbanization dynamics in India using DMSP/OLS night time lights and SPOT-VGT data. _International Journal of Applied Earth Observation and Geoinformation_ , 23: 49–61. https://doi.org/10.1016/j.jag.2012.11.005

- Partridge, MD; Rickman, DS; Kamar, A; Olfert, MR (2008). The geographic diversity of U.S. nonmetropolitan growth dynamics: A geographically weighted regression approach. _Land Economics,_ 84: 241–266

- Parr, JB (1979). Regional economic change and regional spatial structure: some interrelationships, _Environment and Planning A,_ 11(7): 825 – 837.

- Pesaresi, Martino; Ehrilch, Daniele; Florczyk, Aneta J.; Freire, Sergio; Julea, Andreea; Kemper, Thomas; Soille, Pierre; Syrris, Vasileios (2015):  GHS built-up grid, derived from Landsat, multitemporal (1975, 1990, 2000, 2014). European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/jrc-ghsl-ghs_built_ldsmt_globe_r2015b

- Pesaresi, M; Ehrlich, D; Ferri, S; Florczyk, A.J; Freire, S; Halkia, M; Syrris, V (2016). Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014; JRC Technical Report EUR 27741 EN. Ispra (VA), Italy. https://doi.org/10.2788/253582

- Pinkovskiy, M (2013). Economic Discontinuities at Borders: Evidence from Satellite Data on Lights at Night. Working paper, Massachusetts Institute of Technology, Boston MA.

- Quigley,
# **4 Mechanisms**

So what are the potential mechanisms via which the threshold scheme did not yield higher survival rates than the linear group payment scheme? In view of the theory presented in section 2.1 and in Appendix A, we hypothesize two. First, survival rates were measured at endline, and the survey questions aimed at gauging treatment impacts on the intermediate measures of cooperation analyzed above asked for an endline evaluation of the process of cooperation. The reported outcomes may, however, hide important differences in the dynamics of cooperation over time – consistent with our theory’s prediction that the higher thresholds are less likely to be equilibria. Second, coordination in the threshold group payment schemes becomes more difficult when there is uncertainty regarding the probability of accidentally crossing the target threshold. In the following two subsections we aim to test the relevance of each of the two.

## **4.1 The dynamics of cooperation**

At endline, the intermediate measures of cooperation were higher in the threshold group payment treatment, while the ultimate measures of cooperation were lower. These opposing outcomes may be reconciled if cooperation improved over time, but that this improvement materialized too late in the process. To probe this possible mechanism, we asked participants in the endline survey how they evaluated the (dynamics of the) amount of cooperation in their maintenance group; answers are summarized in Table 3. Overall, we do not find that the distributions in answers differ significantly between the two treatments ( _p_ = 0.275 according to a _χ_<sup>2</sup> test). About 69% of the subjects in the linear group payment scheme reported that the cooperation was intensive from the start and that it remained very good over the entire nine-month period; about 2% of them stated that there was hardly any cooperation. In the threshold group payment scheme these percentages were, respectively, 65% and 1.5%. However, we also find that, if anything, cooperation was more likely
to field a single visit and collect information either specific to an agricultural season or with a 12month recall. In turn, there is worry that the existing survey methods may elicit unreliable and incomplete information on harvests cutting across seasons, taking place over extended periods, and varying by crop variety.

This paper presents the results of a household survey experiment that was implemented in top cassava-producing districts in Malawi and that randomly assigned sampled households to one of four approaches to cassava production measurement over 12 months, namely (1) daily diarykeeping at the household-level for 12-months, with semi-weekly in-person supervision visits (D1) – the traditional gold standard; (2) daily diary-keeping at the household-level for 12 months, with semi-weekly supervisory mobile phone calls (D2); (3) a two-visit recall-based data collection at the plot-level for a 6-month reference period, carried out in two visits 6 months apart during a 12-

> 34 The MoAIWD APES-based cassava statistics are taken at face value most recently by Kanyamuka et al. (2018) who study the cassava value chain in Malawi and provide recommendations for its improvement. However, the concerns regarding the gross overestimation in cassava production and yields reported by the MoAIWD were in fact raised nearly two decades ago by Russel Freeman in a report that was prepared in 2000 for the International Monetary Fund. 35 Similar discrepancies between crop cutting and FAOSTAT-based estimates are noted by Gourlay et al. (2017) in the case of maize in Uganda.

19
Two of the SIDS—Suriname and Antigua and Barbuda—are assessed as having unsustainable public debt. Both countries have accumulated substantial external debt arrears (they are in default). Restoration of debt sustainability requires agreements with creditors to restructure the debt and reduce the debt burden; Suriname is currently negotiating with its creditors to do this. For these two countries, therefore, some form of substantial debt alleviation is imperative.

All other SIDS are assessed as having sustainable public debt. Two countries, São Tomé and Príncipe and Grenada, are assessed as being in debt distress because they have unresolved debt arrears to external creditors. However, these arrears are unresolved for legal and technical reasons rather than a lack of fiscal resources to repay them, and the current debt metrics of both countries are not markedly worse than those of many SIDS which are not in debt distress. The baseline projections of public debt trajectories in the DSAs for both countries indicate that the key sustainability metrics decline over the medium to long term and can be brought back below the applicable sustainability thresholds. Hence it is possible that both countries can resolve their public debt problems without debt relief.

Fourteen SIDS are assessed as being at high risk of debt distress. These countries differ very widely in respect of their current public debt metrics. Some have high levels of public debt, some moderate levels, and some low levels. Out of the 12, for which present value data are available in the DSA, only five currently breach the applicable sustainability threshold for the Public and Publicly Guaranteed (PPG) debt-to-GDP and only two breach the sustainability threshold for external PPG debt to GDP.

There are three main reasons given in the DSAs for these countries to be assessed as at high risk of debt distress (for some countries more than one reason is cited):

- i) For three countries, current high levels of public debt do not decline fast enough in the baseline projections leading to prolonged breaches of debt sustainability thresholds;

- ii) For four countries, public debt ratios
# **2.1. Afghan refugees in Iran**

Iran is one of the leading refugee host countries in the world. The United Nations High Commissioner for Refugees (UNHCR) reports that 800,000 refugees live in Iran, of which 780,000 are Afghans. These are Afghans with a valid residence card (Amayesh card) whose stay is regularized as refugees. Besides, 275,000 Afghans live in Iran with passport and visa (UNHCR, 2021). However, the Afghan population living in Iran is much larger. Many Afghans live without any valid document to state their legal residence (undocumented Afghans). According to Iran’s 2016 census, more than 1.58 million Afghans live in Iran (2% of the population).

For decades, Iran has been the destination of seasonal workers, merchants, and pilgrims from Afghanistan. From the 1960s onwards, young Afghans migrated to Iran to seek temporary employment or study in Islamic seminaries. Nevertheless, the mass displacement of Afghans in Iran started 40 years ago. There were four significant phases of forced displacement of Afghans in the past four decades (Abraham, 2013). The military coup by the People’s Democratic Party of Afghanistan (PDPA), followed by Soviet occupation to assist the party against the Mujaheddin rebels in the early 1980s, created massive instability in Afghanistan. The spread of violence initiated the first phase of displacement and the two neighboring countries, Pakistan and Iran, were the major destinations. Even after the withdrawal of Soviet forces, the instability continued due to the internal power struggle and created another phase of displacement. The Taliban rise in the mid-1990s and the conflicts following the United States war in 2001 led to the third and fourth phases of Afghans’ forced migration.

The first phase of Afghans displacement resulted from the transformative vision of the Islamic revolution in Iran. With an emphasis on Islamic brotherhood, Iran followed an open-door policy toward Afghan refugees after the 1979 revolution. In the 1980s and early 1990s, the
our analysis, household wealth quintiles are constructed using a principal component analysis (Filmer & Pritchett, 1999) that accounts for housing conditions, ownership of durable goods, land, etc. Information on the type of water source and sanitation facility used by the household is not included in the calculation since these are considered separate variables in our analysis. The top three quintiles are combined to form the wealth variable, which indicates whether a household is in the top 60 percent of the population in terms of wealth. The analysis takes into account whether water and sanitation facilities are improved or not.<sup>16</sup>

> 13Through theoretical and simulation results in a wide range of scenarios, matched samples can result in substantial bias and variance reduction even if compared with random samples of the same size (Rubin & Thomas, 1992, 1996). 14 The baseline for children in the 2007 DHS is the 2000 DHS, the baseline for children in the 2011 DHS is the 2004 DHS, and the baseline for children in the 2014 survey is the 2007 DHS.

> 15  Of the 6,399 children in our sample, we matched 1,626 children from the 2007 DHS, 2,508 children from the 2011 DHS, and 2,265 children from the 2014 DHS. Figure 1 in the annex shows the geographic distribution of household clusters available before and after geomatching.

> 16 According to the Joint Monitoring Programme (JMP), an improved sanitation facility is defined as “one that hygienically separates human excreta from human contact.” The JMP considers the following categories as improved sanitation: flush to piped sewer system, flush to septic tank, flush to pit (latrine), flush to unknown place, ventilated improved pit latrine, pit latrine with slab, and composting toilet. Shared sanitation is excluded from the improved sanitation category. The following categories are considered as improved water: piped into dwelling; piped into compound, yard, or plot; piped to neighbor; public tap/standpipe; bottled water
We now discuss the results of the stochastic dominance tests performed on the CDF of the volatility of manufactured and commodity import prices shown in Figure 3. For the one-sided test, the statistic is 0.034. It is smaller than the 1.073 critical value for the 10% level of significance.<sup>8</sup> Thus we cannot reject the null hypothesis that the CDF of manufactured goods is smaller or equal than that of commodities. The CDF of manufactured goods weakly dominates that of commodities. For the two-sided test, the corrected combined p-value is 0, so we can reject the null hypothesis that the two distributions are equal at 1% significance level. Overall, the results of the stochastic dominance test suggest that the CDF of the standard deviations of prices of manufactured goods strictly stochastically dominates that of commodity prices.

# **IV. ROBUSTNESS**

This section tests the robustness of our surprising finding that prices of commodities are less volatile than those of manufactured goods. This finding could be misleading for at least four reasons. First, some products tend to disappear from the sample. If most product exits are observed within the group of manufactured goods, then it is possible that the observed volatility of manufactures might be biased upward, driven by product destruction rather than by within-product price fluctuations. Second, the trade data on unit values comes from ratios of reported values over reported quantities. Hence it is worth examining the volatility of quantities. Third, the key distinguishing feature of commodities is their relative lack of product differentiation over time, and this characteristic might not be neatly identified in the

> 8 Critical values of the one-sided test are 1.073, 1.2239, and 1.5174 for the 10%, 5%, and 1% levels of significance respectively (Barrett and Donald 2003, page 78).

11
also give an "adjusted" estimate in which a control variable was added for the first difference of the log of the ratio of the consumer price index to the national income deflator (i.e., the difference in the rate of inflation implied by the two deflators).<sup>44</sup> This was included to allow for possible bias in estimating the growth elasticity due to the difference in the deflator used for the national accounts data and that used for the poverty lines.

For the 1957-2012 period as a whole, the national poverty measures responded significantly to growth in all three measures. The IV estimates to address survey measurement errors (also likely to be negatively correlated between the dependent variable and the explanatory variable) are slightly lower than the corresponding OLS estimates. The (absolute) elasticities are higher if one uses the NSS estimate of mean consumption, rather than the national accounts estimate. The elasticities are lowest for per capita income. There are a few possible reasons. Inter-temporal consumption smoothing may make poverty (in terms of consumption) less responsive in the short-term to income growth than to consumption growth. Imperfect matching of the time periods between the NSS and the NAS could also be playing a role in attenuating the elasticities using NAS growth rates. A further reason for lower (absolute) elasticities with respect to NAS consumption or income has to do with the increasing divergence between NSS and NAS growth rates of mean consumption; significantly faster growth in NAS means relative to NSS means is reflected in the lower elasticities with respect to the former.

When we split the period into two at 1991, we find appreciably higher (absolute) elasticities of poverty indices with respect to the survey mean in the post-1991 period; the difference in the estimated elasticities over the two periods is statistically significant.<sup>45</sup> The pattern of higher post-1991 elasticities is similar for all poverty measures, for higher and lower poverty lines and for the OLS as well as the IV estimates (Table 3)
land titling; and (3) reinforce women’s rights through a presumption that any property acquired in marriage is jointly owned by both spouses.

The LAA Act established the LAA as a one-stop shop for land registration that would provide reliable information on land ownership; expedite registration and transfer of leases; reduce registration fees; and deliver services efficiently. A fully digital register and cadaster, designed to be fully interoperable with systems held by other agencies, such as municipalities’ building registers, was established. By 2013, 18 regulations to regulate systematic land titling, planning, surveying, registry operation, and dispute resolution had been drafted and most had been adopted.

A program of systematic initial registration was designed to ensure the security of land tenure by issuing legal documents to 55,000 property owners in largely informal settlements in Maseru, a city with 300,000 inhabitants. The program was piloted in 2011 with the intent of covering all intervention areas in 2012, but contractual issues delayed the roll-out to 2013.

# **3. Data, Descriptive Evidence, and Approach**

To assess how policy reform and systematic initial registration of land rights in 2011–13 affected parcels registered, women’s co-ownership, the functioning of land and credit markets, and investment linked to land, we divided the area of interest into almost 2,000 250m x 250m blocks that serve as the basic units of analysis. Digitized administrative data for registered transactions and gridded information on built area was then aggregated to block level. Descriptive data show that 2010 policy reforms coincide with a downward shift in transaction costs; an uptick in registered sales and mortgage transactions; and a massive increase in female co-ownership. We discuss econometric strategies to examine short- and medium-term impacts and the differing contribution of 2006 and 2010 legal reforms on gender outcomes.

# **3.1 Data**

To obtain a complete set of textual and spatial data before and after the intervention, we digitized paper records from registry books for all transactions registered before LAA migrated
discuss potential mechanisms in Section 5, and we present a range of robustness checks in section 6. Section 7 provides concluding remarks.

# **2. Data**

# **2.1 Data Sources and Descriptions**

To understand the potential win or loss in demand for various services and potential reallocation of economic activities, we carefully choose Google search terms that can capture demand for major economic activities. The choice of sectors is informed by the relative importance of the sector to employment and GDP.<sup>3</sup> We also focus on services for which representative Google search terms that capture demand for a particular service can be found. For example, the manufacturing sector is a major employer, but finding representative search terms has been difficult, and thus not included in this study. Therefore, we focus on six service categories within the broader services sector: (i) hotels and restaurants, (ii) transport, (iii) tourism, (iv) retail trade, (v) ICT, and (vi) delivery services.

The Google search data provide both historical and near real-time data on search intensity for a given term or topic going back to 2004. Google makes these search data publicly available by transforming them into search intensity or popularity index, which measure the relative popularity of a search term in a specific geographic area and over a specific period. Google applies a two-layer normalization to calculate the popularity index for a given search term. First, it calculates the ratio of the total volume of search for a given search term to all searches in a geographic region and over a given period. Then, the highest ratio is normalized to be 100, and the remaining ratios are scaled relative to this maximum data point to obtain the search index. Because of these normalizations, the index always assumes a number between 0 and 100. This normalization across time facilitates a comparison of search intensity of a given term across time. Besides capturing spatio-temporal variation, the data are available at
The second data source we use in this paper is the Turkey Welfare Monitoring Survey (TWMS) baseline data set. The collection of this dataset was mobilized by the World Bank, UNICEF and the Economic Policy Research Foundation of Turkey, TEPAV, in early 2009 and the survey was fielded in May-June 2009. The aim of the survey was to monitor the welfare of Turkish households during the beginning of the Global Financial Crisis of 20082009 and to measure the ways in which households coped with the economic turndown. The survey was fielded in five major cities: Istanbul, Ankara, Izmir, Kocaeli and Adana, accounting for forty percent of the overall urban population in Turkey. It is representative for the combined five-cities and includes 2,102 households.<sup>4</sup> The TWMS includes a number of different questions on health demand behavior, including retrospective questions on the household‘s utilization of health services during the crisis period October 2008 to May 2009. Combined with information on insurance access included in the survey, it is hence possible to track the health utilization behavior of various types of households through the creation of a quasi-panel.<sup>5</sup> The survey distinguishes between the reduction in curative and preventative care services.

Further, we construct a wealth variable at the household level using the TWMS. The asset index constructed for this purpose is based on housing characteristics and household durable goods following (Filmer and Pritchett 2001). We use the asset index to derive both wealth deciles as well as wealth quintiles of the urban sample.

> 4 The survey can be obtained from www.worldbank.org/turkey  or by emailing the World Bank Turkey Country Office turkeywebfdbk@worldbank.org.

> 5 We consider the responses of households to the following two questions in the baseline data:  (i) ― _Have you had to reduce visits to the doctor between October 2008-May 2009_ ?‖   and (ii) ― _Have you had to reduce the utilization of preventive health services from October 2008-May 2009_ ?
appropriate to compare this loss value to the median cost of compliance to get a sense of its proportion. Our specifications imply that the median cost of compliance is $2, which makes the importer’s loss 100 times larger than the median cost of compliance.<sup>59</sup> Equation (12) implies that if 5 in every 1000 shipment were searched, no-one would comply, whereas if 10 in every 1000 shipment were searched, 50 percent of shipments would be compliant. We parameterize the model in a such a way that which ensures that bins with greater ambiguity will be searched more intensively in equilibrium (see equation (10)). The low levels of search activity in Serbian customs means that _sl_ is generally small, and _p_<sup>_′_</sup> ( _s_<sup>_∗_</sup> _l_<sup>)large.Thismeansthat</sup><sup>_α_cannotbetoolargeifinequality</sup> (14) is to hold. We choose _α_ = 0 _._ 13, a parameterization that implies moderate pessimism on the part of border officials.

Our key calibration target is the allocation of search activity across the seven shipment frequency bins. The structural parameters that determine these outcomes are the bin level ambiguity parameters, _δl_ ’s. To calibrate these parameters, we specify a parsimonious functional relationship between _δl_ and _N_<sup> ̄</sup> _l,t−_ 1, the mean lagged shipment frequency for that bin in the 2016 Serbian data. Our functional representation is where _γ_ is a calibration parameter.<sup>60</sup>

# **Equilibrium solution in the benchmark**

The primary targets in our calibration exercise are the inspection rates that were observed for each bin, which we reported in Table 5 and reproduced in the first row of Table 3, Panel A. In our calibration, each bin has the same share of overall shipments as was reported in Table

> 59For the median