rafmacalaba/data-use-mentions-extended huggingface dataset:
the outcome of interest for parcel **_p_** in household h, **_T_** is an indicator for LTR participation, **_X_** is a set of parcel characteristics, **_Z_** household characteristics, and  an error term. If land registration were voluntary, we would be concerned about endogeneity of outcomes such as investment and land market activity. However, tenure regularization was compulsory and covered all private land in the cell. In other words, plots were registered up to a cell‟s administrative boundaries, but not beyond the cell border. The spatial discontinuity generated by this allocation rule thus can be used to allow identification of program effects by comparing individuals who live within a band on either side of the border following Magruder (2011). This approach extends work comparing enterprises on either side of a state border in the US to look at the effects of minimum wage legislation (Card and Krueger 1994) to explore effects of centralized bargaining agreements on employment using geographic and labor force data. For the approach to be valid, communities on either side of the border have to be identical and cell-level effects that could drive our results need to be absent. To assess the validity of this assumption, we note that key policies with

> 8 Outer and inner bandwidths were 125 m in Nyamugali, 400 m in Kabushinge, 350 m in Biguhu, and 1250 m in Mwoga.

11
br>...|2.63|...|...|
|Kenya<br>10.42|14.23|...|...|
|Rwanda<br>4.84|7.69|...|...|
|Tanzania<br>12.43|10.82|...|9.65|
|Uganda<br>12.96|12.49|...|10.25|
|_ADSL monthly service charge_||||
|Burundi<br>...|...|...|...|
|Kenya<br>...|220.44|37.51|37.51|
|Rwanda<br>...|...|86.38|86.38|
|Tanzania<br>...|38.87|29.74|29.74|
|Uganda<br>...|300.00|300.00|300.00|
|Source: AICD.||||

As evident in East Africa, prices for _Monthly postpaid fixed basket_ Internet services were lower than in the rest of Burundi ... 2.63 ... the community thanks to connections with the Kenya 10.42 14.23 ... international cables. Prices for long distance Rwanda 4.84 7.69 ... (interregional and international) were among Tanzania 12.43 10.82 ... the highest in the region, however, because Uganda 12.96 12.49 ... monopolies control several international long- _ADSL monthly service charge_ distance services. Even though Ethiopia has a Burundi ... ... ... Kenya ... 220.44 37.51 monopoly in access to the international data Rwanda ... ... 86.38 gateways, government
J. L. and Kogan, J. (2003) _Just‐in‐Case Inventories : A Cross‐Country Analysis_ . 3012.

- Gurara, D. and Tessema, D. (2018) ‘Losing to Blackouts : Evidence from Firm Level Data’, _IMF Working Paper_ , 18(159). doi: 10.1177/00220345740530061001.

- Harriet, T., Poku, K. and Emmanuel, A. K. (2013) ‘An Assessment of Traffic Congestion and Its Effect on Productivity in Urban Ghana’, _International Journal of Business and Social Science_ , 4(3).

- Iimi, A. (2011) ‘Effects of Improving Infrastructure Quality on Business Costs: Evidence from Firm‐Level Data in Eastern Europe and Central Asia’, _The Developing Economies_ , 49(2), pp. 121–147. doi: 10.1111/j.1746‐1049.2011.00126.x.

- Iimi, A., Humphrey, R. M. and Melibaeva, S. (2015) _Firm Inventory Behavior in East Africa_ . The World Bank (Policy Research Working Papers). doi: 10.1596/1813‐9450‐7280.

- Islam, A. (2018) _The Burden of Water Shortages on Informal Firms_ . 8457.

- Islam, A. and Hyland, M. (2018) _The Drivers and Impacts of Water Infrastructure Reliability A Global Analysis of Manufacturing Firms_ . Available at: http://www.worldbank.org/research. (Accessed: 5 February 2019).

- Jin, J. and Rafferty, P. (2017) ‘Does congestion negatively affect income growth and employment growth? Empirical evidence from US metropolitan regions’, _Transport Policy_ , 55, pp. 1–8. doi: https://doi.org/10.1016/j.tranpol.2016.12.003.

- Kajitani, Y. and Tatano, H. (2014) ‘Estimation
0.66<br>(0.006)|0.674<br>(0.006)|0.648<br>(0.007)|0.501|0.605|0.468|
|Absent at the endline exam|0.266<br>(0.003)|0.282<br>(0.005)|0.267<br>(0.006)|0.241<br>(0.006)|0.614|0.169|0.3203|
|Observations|18,281|7,105|5,962|5,214||||

_Notes_ : This table reports the balance test for various student level variables captured in baseline survey. Means, standard deviations and p-value of differences is reported by GS (Goal-Setting), GS + R (Goal-Setting + Recognition) and Control group. Standard errors are clustered at the level of school.
obtain the information for the desired number of geographic locations, one has to file multiple requests in succession. The resulting data points are comparable across terms and geographic locations only within one request.

To achieve the desired comparability of Google search data points across different requests – and following Abay et al. (2020) – we constructed our data set as follows. First, we extracted the Google search data for every subnational region in our sample _and_ California jointly for a given search term and query period. In this case, the data on search terms from a given subnational region and California define one request. At the second stage, within each request, we normalized the time series from the target subnational region by the maximum value of the same search term index observed in California during the query period. Having repeated the above steps with every search term, we obtained a data set containing a list of search term indices comparable across different subnational regions. We extracted the information on the following search terms: food bank, assistance, online education, prayer, Bible, and Quran.

Finally, we extracted information on food prices from FAO’s Daily Food Price Monitor. The raw data set contains information on percentage changes in food prices in 180 countries relative to the baseline period (February 14, 2020). We have information on prices for the following food staples: apple, banana, bread, cheese, eggs, lettuce, meat, chicken, onions, oranges, potato, milk, and rice.

When aggregating data by regions or income levels, we weight each observation by the population in a given subnational region, normalized by the total country-level population in 2018. Thus, every country in our sample is given the same weight, whereas higher weights are given to more

> 3 https://www.google.com/covid19/mobility/ 4 This preprocessing takes place at Google.

5
WPS3592

# **Which Inequality Matters?**

# **Growth Evidence Based on Small Area Welfare Estimates in Uganda**

## **Youdi Schipper and Johannes G. Hoogeveen**<sup>*****</sup>

## **Abstract**

Existing empirical studies on the relation between inequality and growth have been criticized for their focus on income inequality and their use of cross-country data sets. This paper uses two sets of small area welfare estimates – often referred to as poverty maps – to estimate a model of rural per capita expenditure growth for Uganda between 1992 and 1999. We estimate the growth effects of expenditure and education inequality while controlling for other factors such as initial levels of expenditure and human capital, family characteristics and unobserved spatial heterogeneity. We correct standard errors to ref lect the uncertainty due to the fact that we use estimates rather than observations. We find that per capita expenditure growth in rural Uganda is affected positively by the level of education as well as by the degree of education inequality. Expenditure inequality does not have a significant impact on growth.

## World Bank Policy Research Working Paper 3592, May 2005

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about developm ent issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org ._

> *Schipper is with the Vrije Universiteit Amsterdam and consultant to the World Bank. Hoogeveen is with the World Bank. Please send correspondence to both yschipper@feweb.vu.nl and jhoogeveen@worldbank.org
Figure 1: Aggregate Headcount Poverty Rates – GHS (National Poverty Line) a) <!-- Start of picture text --> Aggregate Poverty Rates - GHS (National Poverty Line)<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>child 0-4 child 5-9 child 10-14 child 15-17 adult 18-59 adult 60above adult child<br>Wave 1 Wave 2 Wave 3<br><!-- End of picture text --> b)

# **AGGREGATE POVERTY RATES -GHS (NATIONAL POVERTY LINE)**

<!-- Start of picture text --> adult child child north child south child rural child urban<br>W A V E 1 W A VE 2 W A V E 3<br>65%<br>53% 51% 54% 54% 59%<br>41% 43% 46%<br>29% 23% 20% 31% 27% 21% 33% 20%<br>14%<br><!-- End of picture text --> Source: Authors’ estimations based on GHS Wave 1-3. Note: Individuals with missing age were dropped from the analysis.

Figure 2 a): Rural- Urban Headcount Poverty Rates – GHS (National Poverty Line) Figure 2 b): North - South Headcount Poverty Rates – GHS (National Poverty Line) <!-- Start of picture text --> Rural-Urban Poverty Rates - GHS North-South Poverty Rates - GHS<br>(National Poverty Line) (National Poverty Line)<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%
Meat_<br>_3. Milk_<br>_4. Apple_|
||_5. Egg_<br>_6. chocolate_|
|**Region dummies**|_Region dummies_|

_Note: Variables that were identified as relevant in any model are marked in italics._ We trained separate models for both urban and rural areas to ensure they effectively capture household welfare in each setting. Moreover, considering the high seasonality in welfare levels, the model leverages the third quarter of the IE-LFS 2019/20 since it matches the data collection schedule of the AWMS R3. It is reasonable that the income-generating process varies by location. For example, correlation between wall materials and household expenditure differs between urban and rural households. Additionally, poverty in Afghanistan has a strong seasonal character, with welfare deteriorating in winter when agricultural income-generating opportunities are fewer, and less food is available in local markets. However, during 2019/20, the spring season coincided with the onset of the COVID-19 crisis, which put pressure on urban households. This cyclical variation in poverty rates implies that a quarter-specific model provides the best predictive performance. Considering the AWMS R3 was collected between April and June 2023, the preferred model specification uses a model trained on the respective IE-LFS 2019/20 quarter.

7
Customers can make person-to-person transfers of mobile money credit to the accounts of other mobile money users on the same network. They can also use their mobile money credit to pay bills and to buy phone airtime. Withdrawals (conversion to cash) can be made at the offices of the network‘s mobile money agents. It is also possible for a mobile money customer to make a transfer to someone who is not registered with the same network. In this case, when notice of the transfer is received in the form of an SMS text message, the recipient can receive the cash at a mobile money agent.

After a slow start in 2007, mobile money usage has since grown rapidly. As of June 2011 the value of person-to-person transactions alone exceeded 30 percent of GDP (see Figure 2).<sup>2</sup> Due to the rapid growth of mobile money usage, the portrait of mobile money users is a moving target. Figure 3 summarizes various sources on total numbers of mobile money users through June 2011. The number of mobile money subscribers as of June 2011 was over 17 million, representing approximately 3 out of every 4 Kenyans age 15 and older.<sup>3</sup>

# **5 Data**

We analyze mobile savings data from a survey conducted by the Financial Sector Deepening – Kenya (FSD-Kenya) organization during the period October to November 2010. Data were collected for 6,083 individuals. These observations were then categorized into M-PESA users and non-users, and a more extensive questionnaire was administered to M-PESA users, comprising a subsample of 2,692 observations. An extra 103 observations were collected by the snowballing method to boost the sample of M-KESHO users. These observations were dropped from the analysis presented here. Individual probability weights are used to make the sample nationally representative.

The survey was not conducted in North Eastern province due to insecurity in the region. Mobile savings data collected include general financial product usage, savings behavior, mobile phone

> 2 These figures do
section: Line B, which is the infrastructure project that I’m studying; line C, a feeder line, similar to line B, that was to connect northwestern locations in the State of Mexico with the center of Mexico City, but was never built; and Line 12, which is the latest subway line, opened in 2012.

I estimate the same difference-in-differences specification from equations 3.3 and 3.2. The only difference is that the treatment variable corresponds to a dummy variable indicating whether the centroid of the census tract is within some buffer zone of line B (i.e., 1500 meters), and similarly, the control group consists of locations within some buffer zone of line C and/or line 12. I run these regressions for four different buffers: 1500, 2000, 2500, and 3000 meters.

Figure A4 in the Online Appendix depicts the point estimates for the log of the ratio between formal and informal workers from equation 3.2. I find a similar pattern to the previous results. The log of the ratio between formal and informal workers increases by approximately 10% when I compare treated locations with census tracts close to the other two lines. As shown, in the graph, this finding is robust to the use of different buffer zones and is very stable.

> 23I also find similar results restricting the sample to census tracts with a centroid that is farther than 600 meters from one of the new stations. I also show that the results are similar if the dummy variable is constructed using a walking range of 20 minutes (Figure A11)

> 24One reason that may explain the effects in 1999 is that the Government announced the construction of the line in 1994.

14
Gini, which can occur because the Gini is a broader measure of inequality that takes into account the overall consumption distribution.<sup>5</sup>

**Figure 3: Shared Prosperity has improved over time** Average annual growth in per capita consumption (%)

**Figure 4: Consumption inequality (Gini) has remained stable since 2005**

<!-- Start of picture text --> 6.0 5.6 0.40<br>5.2<br>5.0<br>0.30<br>4.0<br>3.0 0.20<br>2.1<br>2.0 1.7<br>1.3<br>0.10<br>0.8<br>1.0<br>0.0 0.00<br>1994 to 2005 2005 to 2010 2010 to 2012 1993‐94 2004‐05 2009‐10 2011‐12<br>Poorest 40% Average Rural Urban<br>0.36 0.37 0.37<br>0.31<br>0.25 0.27 0.27 0.27<br><!-- End of picture text --> _Note:_ Consumption expressed in constant 2004‐05 All India Rural Rupees, corrected for cost‐of‐living differences between states and rural and urban areas using the official poverty lines. _Source:_ Authors’ estimates from respective NSS rounds.

# High degree of mobility, where movements upward dominate

The true extent of mobility is not evident in trends in the shared prosperity indicator which refers to consumption growth of the “anonymous” bottom 40%, computed from the difference in (per capita) consumption of the bottom 40% of two different distributions at two points of time and _not_ of the same group of households over time. In contrast, consumption growth of the “non‐anonymous
’s participation in world exports of manufactures has grown little and its participation in services has fallen (ECLAC 2011). Meanwhile, only Chile and Barbados are in the top 50 of the more than 140 countries in the World Economic Forum’s Global Competitiveness Index; and even so they do not perform well in dynamic sub-indexes like education and innovation<sup>2</sup> . This situation is concerning since the region risks being squeezed by China moving up into higher tech areas where Latin America has excelled (e.g., aeronautics, autos) and by low wage countries emerging as new laborintensive exporters (e.g., in Africa and South Asia).

These outlined handicaps facing Latin America have been overcome by other countries that actively deployed industrial policies. Countries such as Singapore; Taiwan, China; and Ireland began their respective productive transformations with labor intensive assembly operations for export, much like Central America and the Dominican Republic today, primarily geared to creating employment. However, with ambition, forward looking strategies and proactive horizontal, as well as selective, public promotional policies, they diversified and upgraded into

> 2 The OECD Program for International Student Assessment (PISA) scores for Latin American participants reveals poor student performances.www.oecd.org/edu/pisa/2009

5
’s general ethos is important and widespread in America: One gets rich to change the world, not to found a dynasty.

In this study, we define ‘family firm’ more narrowly to encompass only companies run by heirs of the people previously in charge or by families that are clearly in the process of transferring control to heirs. This definition may seem incomplete in that it misses firms where the current controlling owner ends up founding a dynasty. Bill gates might leave Microsoft to his son, rather than endow universities and concert halls so as to die poor, like Andrew Carnegie. However, we believe that understating the importance of family firms, rather than overstating it, is the more judicious approach for the purposes of this study.

Many small businesses throughout the world are family firms. Small stores, carpentry shops, farms, and restaurants are often family affairs. Casual inspection of any shopping area in virtually any free market country shows that these businesses are important parts of the economy. These businesses are also not the focus of this paper. This is mainly because data on small businesses, unlike data on larger listed companies, are not publicly available. While survey data of various sorts have been compiled for small businesses in some countries, they are, for the most part, not comparable from one country to another. It is to be hoped that this situation might change as more and smaller companies list on stock markets and begin publishing financial data. At present, however, we must pass by these firms.

3
- 2 -

# **I. INTRODUCTION**

1. In recent years, there has been renewed interest in access to jobs in relation to transport connectivity. In the developing world, about 13 percent of youth (ages 15 to 24) are unemployed. In particular in Sub-Saharan Africa, about 14 million working age people are added to the labor market every year (McArthur, 2014). Ensuring sustained access to jobs seems to be a prerequisite for inclusive and robust economic growth. In Africa, urban unemployment is often much higher than the national unemployment rates ( **Table 1** ) (Girsberger and Meango, 2017). In addition, vulnerable employment, such as informal jobs and family workers, is especially high at nearly 70 percent in Sub-Saharan Africa (ILO, 2016).

2. Access to jobs is not a new topic in the literature but has long been discussed in different but relevant contexts, such as urban growth and mobility, traffic congestion, car ownership, modal choice, residential locational choice, environmental sustainability and gender inequality in the labor market. For example, Hymel (2009), using a historic highway development plan as an instrument, shows that traffic congestion weakened subsequent employment growth in U.S. metropolitan areas. Jin and Rafferty (2017) also find a similar result particularly during the 2000s.

**<u>Table 1. Unemployment rates in West Africa</u>**

|<br>|<br>|<br>Unemployment (% of<br>labor force)<br> <br>|
|---|---|---|
|Country|Capital|Capital<br>National|
|Benin|Lome|6.0<br>0.7|
|Burkina Faso|Ouagadougou|<br>20.1<br>2.7|
|Côte d'Ivoire|Abidjan|15.5<br>3.8|
|Mali
|[1.00]|0.09|(0.35)|1,422|
|Age at menarche|-0.46|(0.30)|[0.55]|13.62|(1.22)|815|
|Number of miscarriages and stillbirths|-0.12|(0.15)|[0.94]|0.46|(1.00)|1,422|
|Number of terminated pregnancies|0.09|(0.12)|[0.94]|0.33|(0.81)|1,422|
|Number of births in hospital|0.37|(0.25)|[0.55]|3.49|(2.24)|1,422|
|Number of babies born preterm|0.05|(0.09)|[1.00]|0.19|(0.59)|1,422|

**_Notes_** :

- The table shows the regression results on pre-specified financial participation outcomes using the 2021 in-person data. Each row is its own dependent variable.

- The outcomes that require definitions are: Marriage quality is a index including spousal education and age gap between respondent and spouse. Index across household decisions is an index across various types of household decisions equalling 1 if the respondent has some decision making power. Age at first marriage is the age at which the respondent was first married. Marriage registered is an indicator equal to 1 if the marriage is registered in a court. Number of marriages reports the number of times the respondent has been married, where polygamous marriages count separately. Number of marriages after first reports the number of time the respondent has been remarried. Wife received prompt dowry is an indicator equal to 1 if the wife received a mahr muqaddam amount greater than zero. Number of pregnancies reports the number of pregnancies experienced by female
The Alkire-Santos MIP is a special case of the theoretical measure proposed in an elegant formulation of the problem by Alkire and Foster (2007). This fact helps in understanding the Alkire-Santos MIP, but does not really get us very far since the theoretical formulation in Alkire and Foster (in common with other papers in this literature) takes virtually all the elements of the measure as given (determined outside the measure), notably the dimensions of poverty, the dimension-specific cutoffs, the weights on deprivations and the minimum number of deprivations needed to be deemed “poor.” As we will see, the devil is in these details.

What dimensions? A key step in implementing any multidimensional measure is to select a set of dimensions. There is, of course, ample scope for debate here. There is a (rather poorlyunderstood) issue about what dimensions are intrinsically, versus instrumentally, important. For example, we can probably all agree that “health” is valued intrinsically, independently of command over commodities. However, it is more contentious that education has such an intrinsic value—as implicitly assumed by the Alkire-Santos MIP—rather than being (very) important to income and (hence) command over commodities (and health too). And even if we agree that education is to be valued intrinsically, it is far from clear that “education poverty” should have the same weight as “health poverty.” The data requirements of the Alkire-Foster index entail that relevant dimensions of poverty must invariably be left out in practice. Consider first the material goods entering the Alkire-Santos MIP. This is a rather narrow set of goods, leaving out a great many things that people do in fact consume. The consumption measure formed from a modern household budget or living-standards survey will aggregate (actual or imputed) expenditures on literarily 100’s of consumption items (1,000 or more items in some surveys). Yet the Alkire-Santos MIP only identifies six factors for “living standards” (as described above). So their measure
|0.18|
|Equatorial<br>Guinea|66.85|1.69|2455.54|11.98|
|Ethiopia|172.52|4.37|96.00|0.47|
|Gabon|78.44|1.99|1518.35|7.41|
|Côte d’Ivoire|22.72|0.58|948.07|4.62|
|Kenya|220.73|5.59|1076.14|5.25|
|Liberia|98.04|2.48|480.18|2.34|
|Madagascar|34.79|0.88|1040.25|5.07|
|Tanzania|1208.23|30.62|1734.26|8.46|
|Uganda|304.69|7.72|1494.15|7.29|
|Zimbabwe|533.16|13.51|165.54|0.81|
|Total|3945.72|100|20500.63|100|

Source: Authors’ computation based on UNCTAD 15