rafmacalaba/datause-encoder-data huggingface dataset:
# **I.  Introduction**

Public policies in developing countries are often articulated in terms of poverty reduction objectives. In Morocco, a long-standing approach has been to provide assistance in the form of social transfers, food subsidies, and public investments. Recent years have seen a growing appreciation that a significant fraction of such public expenditures has gone disproportionately to the non-poor segments of society. For example, calculations by the Moroccan Planning Commission (Haut-Commissariat au Plan, HCP) indicate that the top quintile in the consumption distribution has received more than 40% of total food subsidies and more than half of the government’s subsidies to secondary and tertiary education.

In 2005 the Government of Morocco launched the “Initiative Nationale Pour Le Développement Humain (INDH)” (National Initiative for Human Development). This broad strategy for combating poverty in Morocco aims explicitly to correct for social distortions brought about by mis-targeting of public resources. To this end there has been considerable attention paid to the scope for, and potential benefits from, targeting of poor communities. However, despite their intuitive appeal, transfer schemes targeting poor communities are difficult to design. Data on incomes or consumption expenditures tend to derive from sample surveys that are not large enough in size to permit estimates of poverty at a highly disaggregated level. Absent detailed information on local-level poverty, policymakers have often sought to use proxies. When such proxy indicators are used for targeting rather than direct estimates of poverty, mis-targeting can result due to problems with the proxy welfare index at the community level.<sup>2</sup>

> 2 For further discussion of the latter problem see Hentschel, Lanjouw, Lanjouw and Poggi (2000).

2
any case a good deal of the output of food farmers is non-market. The level of income of this group varies mostly with the level of production. However, "uncertainty surrounds the robustness and reliability of the national accounts estimates of agricultural output of Ghana. Government crop production statistics give a more positive account of agricultural production during the ERP years" (World Bank 1995b, p 40). Food crop production, measured in terms of calorie equivalents of starchy and cereal crops seem to have increased 60 per cent between 1987-9 and 1991-2 (ibid, Figure 3.2). In sum, the agricultural production and price data are consistent with the decline in rural poverty suggested by the GLSS data sets, but seem to suggest that the latter might have exaggerated the decline.

The World Bank Report (1995a) has, however, drawn attention to another source of income growth and poverty reduction which might hold the key to the clarification of the factors involved. The GLSS data show that for the rural households as a whole the proportion of income accruing from non-farm self-employment increased from 14.8 percent in 1987-8 to 21.5 percent in 1991-2 (ibid, table 3.3, p.28). The growth in nonfarm activities during the recovery of the economy-involving increase both in trade and non-food commodities-could indeed have been a powerful source of poverty reduction.

# **6.4.4 Absolute Levels of Poverty in the Rural and Urban Sectors and Labor Migration**

While the work reported in the Extended Poverty Studies (World bank, 1995b) show a decline in poverty in the rural areas, and an increase in the capital city, it still maintains that in 1992 the absolute level of poverty was much higher in the rural areas. The poverty ratio was reported to be 23.0 percent in Accra, 27.7 percent in other urban areas, and varied from 28.6 percent to 38.3 percent in the different rural regions.

**Table 6.9: Net Migration Flows (percent)**

||**(1)**Up<br>|to 1970<br> <br>|(2) Up<br>|to 1987<br> <br>|(3) 1982<br>|-1987<br>|
|---|---|---|---|---|---|---|
|REGION|Destination|<br>Origin|Destination|<br>Origin|Destination|Origin|
|Western|21.1||74.0||39.0||
|Central||22.4||11.0||6.8|
|Accra|46.5|||_15.0_||57.6|
|Eastern||17.0||3.0|27.1||
|Volta||28.9||31.0||15.3|
|Ashanti|12.3||13.0||13.6||
|Northern||31.8|13.0||20.3||
|& Upper|||||||
|TOTAL|100.0|100.0|100.0|100.0|100.0|100.0|

47
<mark>parties may know about each other, but one party has more information than the other. For example, a lender will be unwilling to extend a loan to a small-scale farmer because there is no easy way to ascertain creditworthiness. Such information problems, in particular information asymmetries, have been widely studied, including by George Akerlof, Michael Spence, and Joseph Stiglitz who jointly received the 2001 Nobel Prize for work in this area. Today, technology helps the disabled use text, voice and video to communicate, people can more easily work from home—even for clients half-way around the world—and new data sources such as mobile phone records help extend credit even to those lacking access to formal banking services. In the “market for lemons” (Akerlof 1970), websites that document a vehicle’s history now reduce the information gap between seller and buyer. More generally, from the perspective of the individuals now able to participate in all of these markets, the result is greater inclusion. From the perspective of a seller or service provider, this expands the market.</mark> <mark>The middle part of Figure 1 is closest to the original Coasian concepts. It covers transactions that already took place, but that now have become cheaper, faster or more convenient thanks to new technologies. By automating or facilitating some processes, such as communicating with buyers or suppliers, it makes other factors more productive. Most importantly, easier coordination improves capital utilization as in the sharing or renting of tractors, and labor productivity, for example, through access to critical information via mobile phones or internet. Human capital augmenting technology has always been at the core of productivity improvements and therefore of increasing welfare, from the simplest Stone Age tools to the industrial revolution, and the digital revolution is just the latest stage in this process (e.g., Autor 2015). This increases efficiency in the economy.</mark> <mark>On the right of Figure 1, finally, transaction costs fall to such a level that they are essentially negligible at the margin. Processes can be fully automated on at least one side of the transaction as in e-commerce applications or in matchmaking in the on-demand economy for transport or hospitality services. If the service is to provide a digital product, (re)production costs are also essentially zero as in electronic news or music. The new companies and services in this area are most directly associated with the new economy, which is characterized by a high degree of innovation. Their cost structure of high initial investments to build an internet platform but very low costs of individual transactions gives rise to scale economies both on the supply side</mark> 6
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 10. Benchmarking ICT indicators**

||Unit||Resource-rich<br>countries<br>**Liberia**|Low-income<br>countries|
|---|---|---|---|---|
|GSM coverage|% population||66.9<br>**32.0**|42.4|
|International bandwidth|Mbps/capita||4.1<br>**0.1**|3.0|
|Internet|subscribers/100 people||0.1<br>**1.0**|0.1|
|Landline|subscribers/100 people||19.3<br>**0.1**|7.5|
|Mobile phone|subscribers/100 people||11.4<br>**34.0**|6.4|
||**Liberia***||Countries without access to<br>submarine cables<br>Other|developing regions|
|Price of monthly mobile basket||**5.21**|11.12|9.9|
|Price of monthly fixed-line basket|||13.58|—|
|Price of 20-hour Internet package||**140.00**|67.95|11.0|
|Price of a 3-minute call to the United States|||2.59|2.0|
|Price of inter-Africa calls, mean|||0.72|n.a.|

Source: Minges and others 2009, derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data. — = data not available. n.a. = not applicable.

# **Challenges**

Liberia is not currently connected to the SAT3 submarine cable that skirts the coast of West Africa or to the backbone networks of neighboring countries with submarine cable access. As a result, internet access is only available through small scale VSAT systems. Service costs $140 per month, which is about double the average for other African countries lacking access to a submarine cable. Compared to other developing regions, the cost is about ten times as high.

Given the imminent arrival of several new submarine cables along the West African coast, Liberia is likely to develop a connection in the near future and is considering various options. The experience of other African countries suggests that achieving cable access can substantially reduce the costs of internet and international connectivity. Nevertheless, the extent to which these cost savings are passed on to consumers depends critically on the regulatory environment. In other words, countries that preserve a monopoly on access to the international gateway will experience a more modest price reduction than those that ensure competition between multiple international gateway providers.

**Table 11. High international call charges driven both by technology and market power**

|US$|**Percent of**<br>**cases**|**Call within**<br>**region**|**Call to U.S.**|**Internet**<br>**dial-up**|**Internet**<br>**ADSL**|
|---|---|---|---|---|---|
|Without submarine cable|67|1.34|0.86|68|283|
|With submarine cable|33|0.57|0.48|47|111|
|monopoly on international gateway|16|0.70|0.72|47|120|
|competitive international gateway|16|0.48|0.23|47|98|

Source: Minges and others 2009.

By the end of 2009, an estimated 32 percent of the population in Liberia will be within reach of a GSM signal. While this represents a great improvement over the recent past, it still falls well short of the GSM footprint found elsewhere in the region. Across Sub-Saharan Africa more than 60 percent of the 23
Table I.14. Robustness Analysis for the Job Satisfaction Outcome

|_Panel A. Polynomial of order zer_|_o_||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|1[PEP_i_ = 1]|0.061|-0.036|0.091|0.069|-0.040|0.087|0.061|-0.036|0.091|0.069|-0.040|0.087|
||(0.155)|(0.166)|(0.159)|(0.157)|(0.153)|(0.155)|(0.155)|(0.166)|(0.159)|(0.157)|(0.153)|(0.155)|
|Obs. left|258|231|248|250|228|246|258|231|248|250|228|246|
|Obs. right|600|473|534|652|665|637|600|473|534|652|665|637|
|Mean values (Non-RAMV)|0.324|0.324|0.324|0.324|0.324|0.324|0.324|0.324|0.324|0.324|0.324|0.324|
|_Panel B: Polynomial of order one_|||||||||||||
||(13)|(14)|(15)|(16)|(17)|(18)|(19)|(20)|(21)|(22)|(23)|(24)|
|1[PEP_i_ = 1]|0.119|0.218|0.052|0.180|0.248|0.183|0.058|0.080|0.097|0.142|0.115|0.182|
||(0.248)|(0.268)|(0.307)|(0.195)|(0.198)|(0.211)|(0.277)|(0.348)|(0.334)|(0.212)|(0.219)|(0.219)|
|Obs. left|255|226|196|255|226|196|255|226|196|255|226|196|
|Obs. right|574|464|399|1068|1082|1040|574|464|399|1068|1082|1040|
|Mean values (Non-RAMV)|0.324|0.324|0.324|0.324|0.324|0.324|0.324|0.324|0.324|0.324|0.324|0.324|
|_Panel C: Polynomial of order two_|||||||||||||
||(25)|(26)|(27)|(28)|(29)|(30)|(31)|(32)|(33)|(34)|(35)|(36)|
|1[PEP_i_ = 1]|-0.048|-0.107|-0.060|0.060|0.045|0.076|-0.138|-0.098|0.097|-0.068|-0.094|-0.028|
||(0.351)|(0.385)|(0.381)|(0.287)|(0.288)|(0.293)|(0.340)|(0.411)|(0.334)|(0.316)|(0.329)|(0.322)|
|Obs. left|258|226|248|258|226|248|258|226|196|258|226|248|
|Obs. right|604|464|534|984|940|993|604|464|399|984|940|993|
|Mean values (Non-RAMV)|0.324|0.324|0.324|0.324|0.324|0.324|0.324|0.324|0.324|0.324|0.324|0.324|
|Bandwidth type|mserd|mserd|mserd|msetwo|msetwo|msetwo|cerrd|cerrd|cerrd|certwo|certwo|certwo|
|Kernel|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|

_Notes:_ All columns include department (Antioquia, Atl ́antico, Bogot ́a, and Norte de Santander) and sampling-city fixed effects. Individual controls include: age, gender, and years of education before migration. Labor history in Venezuela controls include: ever worked [=1], type of job, had a written contract [=1], and gap between last job and migration. Household controls in Venezuela include: number of children; household size; if had energy, water, and sewage [=1]; owner of dwelling [=1]; and had smartphone [=1]. Migration decisions controls include: had family/friends in Colombia before migrating [=1], knew of job opportunities before migrating [=1], if the head migrated for health reasons [=1], and time of settlement in Colombia. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.
same cluster) and a household specific error. The common error is referred to as location error. The household specific error will also be referred to as an idiosyncratic error. Empirical results from a wide range of countries indicate that spatial correlation is indeed significant, and that the approach put forward by ELL works quite well.

A violation of either of the two key assumptions will affect the precision of the SAEs. Therefore, each time the method is used, it is important that the user tests the validity of these assumptions, as this may vary from country to country. Specifically, if one decides to ignore spatial correlation, while it is in fact present, one runs the risk of significantly underestimating the standard errors, and hence overestimating precision.

# **IV. Estimates of Expenditure Poverty and Inequality**

# _IV.1 Selection of explanatory variables_

The first step in the poverty mapping exercise is to select the explanatory variables in the regression model with either expenditure or income as the dependent variable. These variables should meet the following criteria:

- Available in both the household survey and the census.

- Household survey and census are comparable (both questionnaires accommodate the same variable definition, and both data sets show similar summary statistics).

- Sufficiently correlated with household expenditure or income.

After carefully screening the questionnaires and examining the data (comparing summary statistics) of candidate common variables from the 2006 VHLSS and the 2006 RAFC, we selected 27 household variables which will be used as the explanatory variables in the models for expenditure and income.

We also constructed commune level data that was merged with the household level data. For selected household level variables from the 2006 ARFC we derived commune mean values, which were merged with the VHLSS at the commune level. For example, we construct the percentage of ethnic minorities of communes, the average household size of communes, etc. Note that these variables are comparable by construction. They are referred to as the `mean variables of communes’.

The commune (and district level) variables were complemented with GIS variables from third data sources. The list of all the explanatory variables is presented in Table A.1 in the Appendix.

8
of the “default” owners of the land. Women fare a little (but not much) better in ‘use’ rights, with just over 40% of households reporting that at least one woman in the household must agree before the land can be sold, transferred or rented out.<sup>5</sup> 2. **Titled with male ownership** : The Land Act becomes more salient when formalization has taken place and titles have been issued in the male spouse’s name only, granting women ownership status when they invest in or maintain the land in question as well as giving them rights to block the sale or mortgage of land. However, the Land Act conflicts with older, more established pieces of legislation like the 1971 Law of Marriage act which stipulates that property assigned solely to one spouse cannot be claimed by the other later on.<sup>6</sup> , so it remains unclear whether or not women’s rights are actually binding in a _de jure_ sense. Although the move from informal to formal sole male ownership does not necessarily weaken women’s claims to lands (and might, under some circumstances, improve it), evidence to date implies that households tend to cement the status quo during formalization: in the Kinondoni property registrar approximately 70-75% of all land registered with a residential license is done so with a single male name.<sup>7</sup> Similarly, when households in our study sampled were asked who would be included on a full title if they applied for one, only 25% mentioned a women as one of the owners (Table 1).

3. **Titled with co-ownership** : Things become more clear when women are granted co-ownership of the land as part of the titling process. Here the Land Act is quite explicit: co-owners (or occupiers-in-common as they are known) have veto powers over all forms of land dispensation<sup>8</sup> , and because the women has been named as an owner of the property, there is no longer any conflict with the Law of Marriage Act. What is less clear is whether or not co-titling improves the _de facto_ state of women’s ownership relative to that of male-titling or informality, a question this experiment ultimately aims to answer.

# **3 Experimental design and data collection**

The setting for the main experiment is Kinondoni, one of the three municipalities constituting Dar es Salaam. We focus on two adjacent communities: Mburahati Barafu and Kigogo Kati are unplanned, informal settlements with markedly low levels of access to infrastructure and public utilities, even by the relatively low benchmark set by other communities in the municipality. Both of these _mitaa_ also appear to have noticeably lower

> 5To avoid priming, households were not asked directly about female ownership. Instead, they were asked to list all members of the household that were default owners, must be consulted before a sale, or would be included on a CRO.

> 6Section 191(2) of the 1999 Land Act and section 58 of the (1971) Law of Marriage Act.

> 7Authors’ calculations using data from the Kinondoni municipal data.

> 8Section 159(6) of the 1999 Land Act.

8
**34**

applicable if trade restrictions and exchange controls are sufficiently high and comprehensive to distort the official exchange rate.

# **PPP and Data Conversion**

**_52._** Probably the most generally accepted use of PPP is as a conversion factor, given the simplicity of the PPP approach, the ease in calculating relative PPP measures, and the intuitive appeal of PPP as a valid theory. International comparison of real output in different countries requires that the conversion rate reflect relative price levels. The absolute PPP measure **( E, =** Pd/PL founded on the **GDP** price level has been used for the currency conversion of the national accounts data at finely disaggregated levels of expenditure **by** Kravis.'

# What Price Measure to Use in **PPP Computations**

> **_53._** The individual prices used to construct the price level or price index for PPP must be presumed to reflect accurately the prices at vhich transactions occur in a free market.

> In a multicommodity world with relative price changes, differing consumption preferences

> across countries, nontraded goods, transportation costs, and trade barriers, the PPPs are

> likely to be distorted so that they do not reflect true relative purchasing power.

> **' Irving B. Kravis, Alan W. Heston, and Robert Summers, International Comparisons of Real Product and Purchasing Power (Baltimore,MD: Johns Hopkins University, 1978).**
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Whiskers give estimates for the frequency of household characteristics on the horizontal axis in both the poverty exit and the chronic poverty group. Estimates are for the fraction of individuals that live in such a household. We present a range because means have been estimated under both upper and lower bound scenarios (using survey and population weights). In addition, for the upper bound scenario, means have been estimated by weighting each observation with the estimated probability of being in the respective transition category.
Policy Research Working Paper 6288

# **Abstract**

This paper reassesses what works in banking regulation based on the new World Bank survey (Survey IV) of bank regulation and supervision around world. The paper briefly presents new and official survey information on bank regulations in more than 125 countries, makes comparisons with earlier surveys since 1999, and assesses the relationship between changes in bank regulations and banking system performance. The data suggest that many countries made capital regulations more stringent and granted greater discretionary power to official supervisory agencies over the past 12 years, but most countries have not enhanced the ability and incentives of private investors to monitor banks rigorously—and several have weakened such private monitoring incentives. Although it is difficult to draw causal inferences from these data, and while there are material cross-country differences in the evolution of regulatory reforms, existing evidence suggests that many countries are making counterproductive changes to their bank regulations by not enhancing the ability and incentives of private investors to scrutinize banks.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at jbarth@milkeninstitute.org, Gerard.Caprio@williams.edu, and Ross_levine@haas.berkeley.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development 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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# `icio` : Economic Analysis with Inter-Country Input-Output Tables in Stata<sup>_∗_</sup>

Federico Belotti<sup>1</sup> , Alessandro Borin<sup>2</sup> , and Michele Mancini<sup>3</sup> _Keywords_ : global value chains; input-output tables; trade in value-added; Stata. _JEL classification_ : E16, F1, F14, F15.

> _∗_ This paper is a product of the World Bank’s World Development Report 2020 Team, Development Economics, the International Relations and Economics Directorate of the Bank of Italy and the University of Rome Tor Vergata. A pilot version of the `icio` Stata module was used to compute some of the measures of GVC participation used in the analysis of the World Bank’s World Development Report 2020 “Global Value Chain: Trading for Development”. New updates and more information about `icio` can be found here: `http://tradeconomics.com/icio` . To install `icio` , run in Stata: `ssc install icio` . The views expressed in this paper are solely those of the authors and do not involve the responsibility of the Bank of Italy. The usual disclaimer applies.