rafmacalaba/data-use-mentions-extended huggingface dataset:
2010) exceeds that in the remainder of Maseru (2.98 in 2000 and 5.73 in 2010), pointing towards limited representativeness of these areas for the intervention.<sup>12</sup> While interpreted imagery is available only for 2016 (i.e., post-intervention), it supports this argument by pointing towards a much higher number of structures or built-up area in the wards assigned to the treatment group (75 vs. 31 structures and 7,045 m<sup>2</sup> vs. 2,132 m<sup>2</sup> of built up area, respectively).

# **3.2 DID approach using household survey data and structures extracted from satellite imagery**

To provide data for a DID impact evaluation in the four wards (MMC 1-3 and 27) selected as treatment and control as described above, a baseline survey on a sample of 1,904 households was conducted in 2013 as described in more detail by Maredia et al. (2016), followed by an end-line survey in 2019, almost 6 years after the baseline. Both surveys used a household and female instrument and the endline survey instrument was identical to that used in the baseline except for minor modifications to improve response rates, capture changes in household composition and landownership, and provide detail on participation in the systematic registration program.

Baseline survey data as reported in data appendix table 1 indeed point towards significant differences in observed attributes between the groups. Propensity score matching (Rosenbaum and Rubin 1983)

> 12 Levels of systematic registration were similar between treatment and the rest of the intervention area and with an average of 0.5 plot registered systematically in the control, data suggest that contamination remained limited.

10
as well as country-level data for all the OECD countries. Our data allow us to build up a panel data set for sophisticated econometrics regressions and estimations. We use OLS, Fixed Effects Model, and Random Effects Models to estimate the impact of various determinants on the property tax revenue. Our econometric analysis allows us to measure the impact of key economic and demographic factors that influence property tax revenue.

According to our research results (see Tables 5 and 6), we find that every 1 percent increase in GDP and population will increase the property tax revenue by around 0.13% and 0.76% on

> 1 OECD (2010), for example, ranks the order of efficiency (or least economic distortion, growth-impeding) tax types: property tax, VAT, personal income tax and corporate income tax.

> 2 Bahl (1999) reflects the criticality of property taxes in its direct linkage to efficiency in service delivery at the local government level.

2
and endogenous mark-up by firms to explain cross country differences in prices of identical products (Simonovska, 2015; Manova and Zhang, 2012), and the pass-through of trade shocks such as exchange rate shocks on import and retail prices (Li, 2019; Burstein and Gopinath, 2014). In this literature, our paper is most closely related to Stroebel and Vavra (2019), who establish a causal response of retail prices to fluctuations in house prices, attributing the response to lower price sensitivity of homeowners who become wealthier. There are two key differences of our work with Stroebel and Vavra (2019): First, we link the household shopping time behavior and resulting price sensitivity to changing labor incomes induced by MDP entry. Second, we introduce a theoretical model of firms, retailers, and consumers with non-homothetic preferences that can explain the relationship between rising retail prices and rising local wages. Our main contribution is to bring this mechanism of higher wages affecting consumer shopping time behavior and hence local inflation from the primarily macro literature into the urban literature.

Thus, the third literature we contribute to is the urban literature, primarily focused on studying the effects of local economic development on economic inequality. The urban literature, although concerned about differences in costs of living across space, has mostly focused on inflation caused by rising house prices and transportation costs.<sup>1</sup> A recent review of the literature by Proost and Thisse (2019) makes this point: _In short, aside from housing costs, the impact of city size on the cost of living remains an open question._ A few recent work attempt to understand how new neighborhood construction affects retail prices using barcode-level retail price data from Montevideo, Uruguay (Borraz et al., 2021); the effects of income specific tastes across goods on regional prices indexes (Handbury, 2021), and the relationship between income and nutritional inequality using barcode-level price data from the US (Allcott et al., 2019). To our knowledge, we are
and researchers have promoted standardized methods to determine comparable cross-country poverty estimates by harmonizing a spatially-deflated welfare aggregate and by estimating a unique poverty line. At the regional level, $2.5 and $4 per person per day at 2005 PPP values have become the poverty lines widely used in LAC. Using these lines, 12 percent of the population in the region qualified as extremely poor in 2013, while 24 percent qualified as moderately poor during the same year.

This paper provides inputs to guide the update of regional extreme and moderate poverty lines for measuring poverty in LAC as a whole, and it is the first attempt to explicitly propose and document these inputs in the region. The recently released 2011 PPPs represent an excellent opportunity to estimate regional extreme and moderate poverty lines in LAC. In order to achieve this objective, we collected the most comprehensive and up-to-date available information on extreme and moderate official poverty lines combined with the standardized microdata available under the SEDLAC project. Unlike previous global estimates, we do not find a strong relationship between per capita income and the value of the poverty line. Therefore, we did not exclude any countries from regional estimates in LAC.

This paper sets regional extreme and moderate poverty lines ranging from $2.5 to $3.2 and from $5 to $6.8 per person per day, respectively. Depending on the regional poverty line selected, we find that approximately 8.0 percent to 12.0 percent of LAC’s population qualified as extremely poor in 2013, while approximately 25.0 percent to 33.0 percent of the population qualified as moderately poor.

We believe that the approach described in this paper, together with the results and underlying data, will serve as valuable inputs for guiding the regional debate on poverty measurement in LAC. Additional research going forward includes updating the middle-income line currently set at $10 per person per day at 2005 PPPs, which reflects a level of income at which the probability of falling back into poverty (based on $4
associated with individual transactions, which can give rise to serious privacy concerns.

The representativeness of the data is also a major issue, as online job-search platforms are subject to selfselection biases and other forms of sampling error. Carnevale et al. (2014) argued that online job-portal data are vulnerable to systematic errors arising from how employers and job seekers use them. For example, online job advertisements tend to be skewed toward upper-level and professional positions, while lowerlevel and unskilled positions are often filled through other channels. Babajob was selected for this study due in part to the fact that its postings tend to include more entry-level and informal jobs than other jobsearch portals. In addition, while barriers to internet access can restrict the type of job seekers who apply for online positions, Babajob also provides phone-based access to job seekers, eliminating the necessity of 26
Matter for Women? Evidence from Urban India. IGC Working Paper, November, S-35306-INC-1.

- Frölich, Markus. 2004. "Finite-sample properties of propensity-score matching and weighting estimators." _Review of Economics and Statistics_ 86 (1):77-90.

- Ghani, Ejaz, William R Kerr, and Stephen D O'Connell. 2014. "Political reservations and women's entrepreneurship in India." _Journal of Development Economics_ 108:138-153.

- Gillespie, Stuart. 2004. "Scaling up community-driven development: A synthesis of experience." _International Food Policy Research Institute, Food Consumption and Nutrition Division, FCND Discussion Papers_ (181).

- Godtland, Erin M, Elisabeth Sadoulet, Alain de Janvry, Rinku Murgai, and Oscar Ortiz. 2004. "The impact of farmer field schools on knowledge and productivity: A study of potato farmers in the Peruvian Andes." _Economic development and cultural change_ 53 (1):63-92.

- Government of India. 2011a. Census of India 2011. New Delhi, India. Government of India. 2011b. Key Indicators of Employment and Unemployment in India, 2009-10. New Delhi: National Statistical Organization, Ministry of Statistics & Programme Implemetation, Government of India.

- Gradín, Carlos, Coral del Rio, and Olga Cantó. 2010. "Gender wage discrimination and poverty in the EU." _Feminist Economics_ 16 (2):73-109.

- Haushofer, Johannes, and Jeremy Shapiro. 2016. "The short-term impact of unconditional cash transfers to the poor: Experimental evidence from Kenya." _The Quarterly Journal of Economics_ 131 (4):1973-2042.

- Haushofer, Johannes, and Jeremy Shapiro. 2018. "The long-term impact of unconditional cash transfers: Experimental evidence from Kenya."

- Heckman, James J, Robert J LaLonde, and Jeffrey A Smith. 1999. "The economics and econometrics of active labor market programs." In _Handbook of labor economics_ , 1865-2097. Elsevier.

- Heinrich, Carolyn, Alessandro Maffioli, and Gonzalo Vazquez
- 11 -

25. The summary statistics are shown in ( **Table 2** ). The average monthly salary is about MGA 276,000 or US$78. On a simple average comparison, commuters were paid 20 percent more than noncommuters ( **Figure 8** ). Using geo-referenced household location data, three variables measuring transport connectivity are constructed: (i) distance to the nearest Taxi-be route ( _TAXIBE_ ), (ii) distance to the nearest suburban bus route ( _SUBBUS_ ), and (iii) estimated travel time to the CBD ( _MINICBD_ ). In our sample, it takes on average 30 minutes for households to go to the CBD, with a wide variation from nearly zero minutes to 80 minutes ( **Figure 9** ). Also see **Figure 3** above.

26. With respect to public transportation, many households have good access to it: The mean distance to the nearest taxi-be and suburban bus route is about 530 meters and 480 meters, respectively. Note that the distances are measured from the bus routes, not bus stops. There are no strictly designated bus stops in the Malagasy bus systems. People know where many buses stop, however, in practice, people can get on anywhere along the routes. That is why the main national roads to the center of the city, such as RN1, RN2, RN3, RN4 and RN7, are too congested currently: Buses stop and go no matter where, causing massive traffic congestion.

27. Because of our sampling strategy, several suburban areas were surveyed, in which taxibe, which operates mainly in the center of the city, may not be available. Thus, the maximum distance from the taxi-be route is longer than that from the suburban route. Suburban bus services connect most remote areas, however, density and frequency of services are low ( **Figure 10** ). As the result, the poor tend to have limited connectivity to the CBD.
This version: 15 Feb 2024 65 PePI shows significant perception bias, i.e., consumers tend to remember price increases more. The gap between PePI and CPI is also large during 2018, the year preceding our qualitative survey.

Lastly, in the absence of the CPI series for low-income households at the state level, we are unable to assess comparative price levels because. Nonetheless, our analysis of standard CPI data indicates considerable regional variation. Average prices have risen much more rapidly over the years in Selangor than in the more rural states such as Sabah and Terengganu (Figure B5).

In sum, regardless of the overall and low-income group specific figures, price increases on most goods and services have been low and stable. This is also true of core inflation, i.e., excluding items with administered prices and items with more volatile prices such as food commodities (World Bank, 2023). If anything, the standard CPI data show a moderating rate of inflation in recent years. However, there is a significant difference in the average increase in prices across locations as evident from trends in CPI data for Selangor, Sabah, and Terengganu. Such differences may have contributed to differences in purchasing power and higher perceived cost of living.

65
, however, that regulation tends to stay constant over long periods of time.

Table 1 shows simple correlations between the seven regulation indices. The strongly positive correlations among all but the fiscal burden and labor indices suggest that regulation policy comes in “packages.” Judging from these correlations, we can distinguish three regulation categories: fiscal, labor, and product-market regulations, where

> 6 We refer the interested reader to Loayza, Oviedo and Servén (2005a) for details on the construction and components the business regulation indices.

3
MINIMUM WAGES IN CAMBODIA 24 <!-- Start of picture text --> HH with garment worker HH without garment worker<br>.1 .2 .3 .4 .5 .6 .7 .8 .9 .1 .2 .3 .4 .5 .6 .7 .8 .9<br>Quantiles of wage distribution<br>Point estimates 95% CI<br>.2<br>.15<br>.1<br>.05<br>0<br>-.05<br>Quantile treatment effects<br>-.1<br>-.15<br><!-- End of picture text --> Figure 8. Quantile treatment effect of minimum wage hikes on hourly wage rate _Source_ : Author’s calculations from Cambodia SES 2007-2015.

_Note_ : The figure plots the estimate of _α_ 2 + _β_ 2 (left panel), and _α_ 2 (right panel) from from quantile treatment effect estimation of Equation (6) for total wage earnings at the household level. Quantiles are indicated by the horizontal axis. Sample restricted to those households with some wage income. Sample further restricted to households surveyed within 8 quarters of any of the minimum wage hikes. In addition to controls mentioned in the notes for Table 1, the estimates also control for garment sector specific time trend for each location, and garment sector specific global recession shock.

slightly increases by 0.14 (4 percent) and the hours worked by all its members fall by 3.6 percent (columns (3) and (4), Table 3). For household without a garment sector worker, there are no changes in either the number of jobs, or on hours worked by its members.

On the other hand, minimum wage hike does not significantly change total household wage earnings. In the 2+ quarters after the hike, total household wage earnings are not significantly different from their pre-hike levels for households
2008). This model was used by Marchetti et al. (2015) using big data covariates to estimate poverty rates and mean income in the ten provinces of the Tuscany region in Italy.

The FH model has been extended to “borrow strength” over time, as well as across areas, by Rao and Yu (1992; 1994). They added AR(1) time effects nested within the area effects in the FH model. You, Rao and Gambino (2003) applied the Rao-Yu model to estimate monthly un-
expected timing of the sub-plot harvest. There were no instructions given to the households on timing of the crop cut. They were, however, requested to harvest the cassava within the sub-plot whenever _they_ were ready and were asked to contact the enumerator and/or the crop cutting assistant beforehand to arrange a time. For districts where households typically harvest their cassava at once, the crop cutting exercise was then done at the time of the main harvest. In districts where cassava is harvested over several weeks or months, the respondents would simply inform the enumerator when they were ready to harvest that piece of land. Together with the enumerator and the crop cutting assistant, the respondents would harvest the 5x5 sub-plot, and weigh the cassava using the same type of weighing scale as distributed to diary households. All crop cutting production was reported in KGs, with details collected on cassava lost prior to harvest and the number of plants harvested.

# **3.3. Production Measurement and Expected Inter-Relationships**

The analysis focuses on household-level annual cassava production computed in kilograms. Since all diary households were provided with a hanging scale, they were expected to record all entries in kilograms on a daily-basis. The production for the diary households is simply the total of the daily records solicited during the 12 months of fieldwork. All diary data was recorded and weighed “fresh”, as respondents weighed the cassava directly after harvesting from the plots. The diary did not collect plot-specific harvest information, but rather information on the total cassava harvest for the household on a specific day.F<sup>23</sup> On the recall front, the respondents reported production at the plot-levelP23F<sup>24</sup> ; could use standard as well as non-standard measurement units; and could have reported the harvest condition as fresh or dry – following the prevailing practices in household and farm surveys and the approach as part of the Malawi IHS. Consequently, the recall respondents reported production for 74 percent of cassava plots in 50-kilogram bags; 19
administrative barriers to investment and customs delays that inhibit Russia participating in international production chains as well as explicit barriers to FDI. Despite these problems faced by foreign investors, there are foreign providers of business services operating in Russia in virtually all of the business services sectors (estimates by our specialist service sector institutes of the shares of the market captured by multinationals are in table 3.4), which shows interest in the Russian market by foreign service providers.<sup>21</sup> And the intensity with which the negotiations have been conducted in the business services sectors is due in most cases to lobbying interests by foreign providers of services. This suggests that multinational service providers have a desire to expand operations in Russia **.** Consequently, it is natural to assume then that a reduction of barriers against multinational providers of services will, at the margin, result in an increase in FDI in those sectors, at least with some time.<sup>22</sup>

**The impact of additional varieties on productivity.** We have discussed the evidence

on the productivity impacts of additional varieties of services in section 1. Here we focus on goods, as we employ the Dixit-Stiglitz mechanism in imperfectly competitive goods sectors as well. As Romer (1994) has argued, product variety is a crucial and often overlooked source of gains to the economy from trade liberalization. In our model, it is the greater availability of varieties that is the engine of productivity growth, but we believe there are other mechanisms as well through which trade may increase productivity.<sup>23</sup> Consequently, we take variety as a metaphor for the various ways increased trade can increase productivity. Winters et al. (2004) summarize the empirical literature by concluding that ―the recent empirical evidence seems to

> 21 See Tarr and Volchkova (2007, table 1) for data on foreign direct investment flows into Russia on an annual basis from 2000-2008.

> 22 More systematically, Alessina et al
In addition to the information collected using this youth module, the analysis incorporates data from the latest rounds of nationally representative face-to-face LSMS-ISA surveys conducted in Ethiopia, Malawi, and Nigeria before the emergence of the COVID-19 pandemic. This inclusion allowed us to integrate respondents' demographic characteristics into our analysis.<sup>4</sup>

# **3.2. Sampling Strategy**

The ‘Youth Aspirations and Employment Module’ for High-Frequency Phone Surveys (HFPS) was implemented in a single round of data collection in Ethiopia, Malawi, and Nigeria from April to July 2021. The youth sample was randomly selected from households that had previously participated in HFPS rounds. Within each household, a youth respondent aged between 15-25 years was randomly chosen for the interview, and proxy respondents were not permitted. To ensure that the individual weights were representative of youth aged 15-25 years in their respective countries, household weights were adjusted using sample-based reweighting techniques.<sup>5</sup> A consistent methodology was adopted in sampling, weighting, and implementing the survey across the countries, facilitating cross-country comparisons. In the case of Nigeria, data collection occurred in April 2021. The sample comprises 52% male respondents and 48% female respondents. Among the respondents, 44% fall in the 15 to 18 years age group, 36% are aged between 19 to 22 years, and 20% are in the 23 to 25 years age bracket. Notably, male respondents tend to be younger than their female counterparts. In Malawi and Ethiopia, data collection took place in May and June 2021, and both countries reported a similar distribution of respondents by sex and age.

# **3.3. Statistical Analysis**

A set of logit regressions was used for estimating the probability of personal, household and community factors to have a significant association on the aspirations of youth aspirations for higher education (complete university degree or post-graduate studies), working on STEM-related jobs and for migrating to seek better life opportunities. The core specification
simple framework in which changes in transport costs drive urban growth. The economy under study is one that exports agricultural goods and imports manufactures. Specifically, the periphery city under study exports its agricultural surplus and imports its manufactured goods, both via a core or port city. The key ingredients are tradable goods prices fixed in the core city by international trade but affected in the periphery city by intercity transport costs.

A representative farmer in the periphery has Cobb-Douglas preferences _U_ ( _a, m_ ) = _a_<sup>_α_</sup> _m_<sup>1</sup><sup>_−α_</sup> _,_ 0 _< α <_ 1, over an agricultural good ( _a_ ) and a manufactured good ( _m_ ).<sup>10</sup> The

> 7These manufacturing statistics are based on establishments with more than 10 employees.

> 8Alternatively, it is possible that these hinterland cities are essentially in autarky: because they already have very high transport costs, they are largely insulated from transport cost changes. This is inconsistent with the empirical results below.

> 9To the extent that manufacturing is substantial in some hinterland cities, this would work against the results I find below in empirical work.

> 10The relevant predictions of the model also hold for quasilinear utility of the form _U_ ( _a, m_ ) = _a_ + _m_<sup>_α_</sup> _,_ 0 _< α <_ 1.

5
_Brazilian spatial labor dynamics Page 23_

|Dependent Variable:<br>Delta ln Employment|2SLS Est.|2SLS SE|Spatial GMM<br>Est.<br>(Cutoff = 0.5)|<br>Spatial GMM<br>SE<br>(Cutoff = 0.5)|
|---|---|---|---|---|
|Intercept|-.61966401|.1396479|-.59779578|.19288857|
|ln Wage in 1991|.18148355|.01471158|.1805902|.02061008|
|ln 5 to 15 over 15 to 55 yrs Ratio|.32737947|.03751615|.3284415|.05359506|
|Proportion of Natives in 1991|.26274058|.04047279|.28160825|.06166641|
|Delta ln Market Potential|.23244707|.02541535|.22884513|.04132408|
|ln Teacher qualification in 1991|-.01008945|.0023123|-.01034963|.00348222|
|Homicides in 1991|-23.377317|4.2814245|-26.064766|11.646685|
|Employment in Farming in 1991|-1.0925149|.16331391|-1.160606|.25061706|
|Bank Dummy|-.00883696|.00937983|-.00637937|.01263533|
|ln Population in 1991|-.0271189|.00513829|-.02845369|.00796092|
|Mean Temperature|-.00216998|.00263983|-.00152014|.00396835|
|Total Rainfall|.00002401|.00001217|.00002763|.00001916|
|Rainfall - 1o Princ. Component|.00693925|.00183254|.00660851|.00291956|
|Rainfall - 3o Princ. Component|.01082346|.00425524|.0101329|.00634718|
|Employment rate in 1991|.50765127|.14583623|.55593884|.22256766|
|crit. fn. test of overid