rafmacalaba/datause-encoder-data huggingface dataset:
if we change the definition from population-based to administrative. All KHDS locations were matched with their census ward-level classification, which distinguish between urban, mixed and rural areas. All urban and mixed areas are classified as towns (or cities if in Mwanza, Dar or Kampala); all rural areas as rural. The KHDS CAPI application had a series of conditional drop-down menus where the interviewer chose the region, district, ward and village in which the household was located. These were pre-populated with the exact census locations and codes, so that the matching exercise is perfect. The census classification is based on 2002 data and the average migrant respondent in our survey moved in 2003.

The consumption data originate from extensive food and non-food consumption modules in the survey, carefully designed to maintain comparability across survey rounds and controlling for seasonality. The consumption aggregate includes home produced and purchased food and non-food expenditure. The non-food component includes a range of non-food purchases, as well as utilities, expenditure on clothing/personal items, transfers out, and health expenditures. Funeral expenses and health expenses prior to the death of an ill person were excluded. Conservatively, rent is also excluded from the aggregate to avoid large differences in prices for similar quality housing being the driver of any measured urban-rural disparities. The aggregates are temporally and spatially deflated using data from the price questionnaires included in each survey round. As household size may differ between urban and rural households, consumption is expressed in per adult equivalent units rather than per capita. The poverty line is set at 326,474.2 Tanzanian shillings (TSh),<sup>5</sup> calibrated to yield for our sample of respondents who remained in Kagera the same poverty rate as the 2007 National Household Budget Survey estimate for rural areas (37.6 percent).

Unless otherwise indicated the analysis below is conducted at the individual level.

Decomposition analysis by Christiaensen et al. (2019), illustrated again in Table 2, shows that moves to cities are more lucrative for those who make them, but that many more migrants end up in secondary towns. Furthermore, while there was similarly clear stochastic dominance between future town migrants and future city migrants by 2010, there was no stochastic dominance during the early 1990s at baseline (Figure 2), suggesting that town and city migrants were observationally equivalent (at least by consumption per capita). For

> 5At the time of the survey, one US dollar was worth around TSh 1,450.

6
monetary and psychological, of the move. Based on data from the 2001 Population and Housing Census of Albania, we construct a variable measuring the share of the male population between the ages of 20 and 39 for each of the 374 communes/municipalities and use it as an additional instrument. A lower percentage of this variable is associated with higher levels of migration in the commune/municipality, thus indicating the presence of a larger migration network abroad. Furthermore, the local availability of male workers can be assumed to be exogenous to agricultural labor decisions due to the extremely thin agricultural labor markets in rural Albania. Very few farm households (7.6%) hire labor in agriculture, and this was the pattern even prior to the unfolding of massive out-migration.

Lastly, we also use as instrument the minimum distance between the household and the two border crossings with Greece (Kakavije and Kapshtice). Distance can be assumed to discourage migration by raising transaction costs. Similarly to the language variable, it is possible for the distance instrument to also capture affinity with the customs and culture over the border and lower information costs. For this reason, the language and distance instruments we end up using in our regressions are the two – within a pool of possible distance variables<sup>4</sup> – that best identify migration.

# **_c) Variable Description_**

In our analysis the dependent and explanatory variables described in the reminder of this section are used.

# _Dependent Variables:_

<u>Agricultural Labor:</u> Household agricultural labor is measured as the total number of hours spent working in agriculture by all current members, both in total and per capita terms. The sign of the impact of migration on both total and per-capita allocation is ambiguous. On the one hand, migration has a direct impact on total availability of family labor. Although this drop could (in part) be compensated by those household members left behind by working more hours, the opposite would also occur due to higher demand for leisure following improvements in income. Furthermore, this impact may very well differentiate across gender lines, as women may be left with additional on-farm

> 4 Distances to the port cities of Durres and Vlora, main exit points to Italy were also considered as possible instruments.  However, the instrument, whether taken alone or in combination with the distances to border crossings, did not pass the overidentification tests and thus were excluded.

11
# Figure 5 Welfare changes with varying levels of transfer, targetting on each household

<!-- Start of picture text --> % change in welfare<br>120<br>100<br>4 0 . ....................... - . . . . .. . . . . . . ... . .. . . .. . .<br>2 80- 0 . .... ..... ..... .................................<br>0 20 40 60 80 100<br>Transfers as % of benchmark gross Income<br>45 line - MEXP --*- MSAV -93- MVIVA<br>x MADP -+- MFOR -A MIND E MINAC<br><!-- End of picture text -->
- S ̈arndal, C.-E., B. Swensson, and J. Wretman (2003) _Model Assisted Survey Sampling_ (Springer)

- Schreiner, M. (2014a) ‘How do the poverty scorecard and the PAT differ?’ _mimeo_

- (2014b) ‘The process of poverty-scoring analysis.’ _mimeo_

- Sitter, R. (1997) ‘Variance estimation for the regression estimator in two-phase sampling.’ _Journal of the American Statistical Association_ 92, 780–787

- Stifel, D., and L. Christiaensen (2007) ‘Tracking poverty over time in the absence of comparable consumption data.’ _World Bank Economic Review_ 21(2), 317–341

- Tamhane, A.C. (1978) ‘Inference based on regression estimator in double sampling.’ _Biometrika_ 65(2), 419–427

- Tarozzi, A. (2007) ‘Calculating comparable statistics from incomparable surveys, with an application to poverty in India.’ _Journal of Business & Economic Statistics_ 25(3), 314–336

- (2011) ‘Can census data alone signal heterogeneity in the estimation of poverty maps?’ _Journal of Development Economics_ 95(2), 170–185

- Tarozzi, A., and A. Deaton (2009) ‘Using census and survey data to estimate poverty and inequality for small areas.’ _Review of Economics and Statistics_ 91(4), 773–792

- van Veelen, Matthijs, and Roy van der Weide (2008) ‘A note on different approaches to index number theory.’ _American Economic Review_ 98(4), 1722–1730

- Yoshida, N., R. Munoz, A. Skinner, C. Kyung-eun Lee, M. Brataj, W. Durbin, and D. Sharma (2015) ‘Swift data collection guidelines version 2.’ mimeo, The World Bank

# **A Proofs**

**Proof of Theorem 6** Letting _k_ = _K_ (or _r_ = 1) and _θ_<sup>ˆ</sup><sup>_I_</sup> = _θ_<sup>ˆ</sup> in the proof of Theorem 10, we obtain the proof of Theorem 6. □

35
statutory towns. More than one third of the increase in the urban population over this period came from Census towns.

Even counting Census towns as urban may result in an under-estimation of India’s degree of urbanization. Most countries rely on population size to distinguish between rural and urban areas, usually setting the “tilt-over” threshold in the range of 2,000 to 5,000 inhabitants. Relative to international practice, the criteria used to identify Census towns in India may be too stringent. If only population size was used to identify urban areas in India, and villages with more than 5,000 inhabitants were considered urban, the share of the rural population would decrease by close to 15 percentage points (Li and Rama, forthcoming).

While the Census may lag behind reality in capturing India’s rapid urbanization, the NSS lags further behind the Census in reclassifying rural areas as urban. The latest round of the NSS on employment and unemployment, conducted in 2011-12, used the Census 2001 as its rural sampling frame. This might have resulted in a considerable underestimation of the share of the urban population, given the unprecedented growth in Census towns between 2001 and 2011. Part of the apparent decline in rural female LFPR could thus be a composition effect, reflecting urban-type outcomes in officially rural areas.

A few studies have paid attention to demand-side factors to explain the observed decline in female labor force participation. The implicit hypothesis in most cases is that the number and type of jobs available matters. Klassen et al. (2013) focus on female LFPR in urban areas and find that the decline is explained by a combination of rising household incomes and declining white collar jobs, especially for educated women. Rodgers (2012) examines rural Bihar and finds that the decline in female LFPR can be attributed to limited job opportunities for women outside agriculture. Kannan et al. (2012) and Chand et al. (2014) suggest that the decline in female LFPR could be due to poor agricultural performance and the diversification of jobs in rural areas.

The number and type of jobs available also affect the measurement of both work and labor force participation. According to Hirway (2012), a sizable part of female employment is related to home-based and subsidiary work, which are not adequately captured by the NSS. Female unemployment may be underestimated as well. Under the NSS Usual Status and Principal Status definitions, a person is not considered part of the labor force if he or she has not been looking for a job for at least six months during the survey year. This is an excessively stringent criterion, as suggested by two other indicators in the NSS survey. A substantial number of people who were registered with a placement agency, or who worked or sought worker under MNREGA are indeed counted by the NSS as being out of the labor force. If their number is taken into account, female labor force participation would be about 3 percentage points higher in rural areas, and up to 5 percentage points higher in urban areas (table 1).

7
<!-- Start of picture text --> 16,000 15,184 + 1950<br>14,000 950<br>12,000 850<br>Fertilizer Subsidies<br>10,000 (Billion Rp, LHS) 750<br>650<br>8,000<br>790<br>6,261<br>4,000 3,16<br>2,527 350<br>2,000 1,171 31] InternationalPrices Urea 250<br>0 242 343 USS, RHS 150<br>2004 2005 2006 2007 2008<br><!-- End of picture text -->
In this paper, we use detailed firm level survey data on 2400 firms in China to investigate which of the two views are consistent with the operation of the informal sector in China. Is the informal sector associated with high growth and profit reinvestment and serve as a substitute to the formal financial system or does the informal sector primarily serve the lower end of the market? To answer this question, we first investigate whether Chinese firms’ financing patterns are different when compared to those of similar firms in other countries that have been the focus of the prior literature. Next, we explore how the financing patterns, both formal and informal, vary across different types of firms across cities and regions. We then study how bank finance and financing from informal sources are associated with firm sales growth, productivity growth and profit reinvestment.

We address these issues using a new data source, the Investment Climate Survey<sup>6</sup> , a major firm level survey conducted in China in 2003 and led by the World Bank. The survey has information on financing choices for firms across 18 different cities. One of the strengths of the survey is its coverage of small and medium enterprises. Hence, in addition to information on commercial financing sources such as bank finance, the survey also includes information on sources of financing that are associated with small firm finance such as trade credit and finance from informal sources such as a money lender or an informal bank or other financing sources.

We find that 20% of firm financing in our sample is sourced from banks, which is comparable to the use of bank financing in other developing countries such as India, Indonesia, Brazil, Bangladesh, Nigeria and the Russian Federation. However, the

> 6 Other studies using the investment climate survey data on China include Cull and Xu (2005), Dollar et al. (2004).

4
**Figure 2. 2  Number of cashed checks per person in Colombia**

<!-- Start of picture text --> 4<br>3.0<br>3<br>3 2.4<br>2.0<br>2 1.8<br>1.6<br>2<br>1<br>1<br>0<br>1999 2000 2001 2002 2003<br>No. of cashed checks<br>Source: Superintendencia Bancaria<br>No. of cashed checks per capita<br><!-- End of picture text --> 7. **Proposed tax refunds will only be available to people with bank accounts.** In an economy with a low savings rate such as Colombia, tax returns contribute to the savings capacity of low income people. This is supported by evidence from income tax returns in the United States. Recently introduced legislation in Colombia intends to return 2% of the 10% sales tax charged on purchases with debit and credit cards as a tax rebate<sup>28</sup> . The purpose of this law is to foster payments with cards, which makes evading VAT more difficult. The amount will be refunded through financial institutions by crediting the bank account of the purchaser. The tax refund is therefore only available to buyers who have a bank account. While the measure encourages the use of lower cost non-cash transactions among the banked, it precludes people without a bank account from access to an additional source of savings.

8. **Under the current regulation, no obligation exists for banks to open accounts for low income clients.** In view of their higher risk and cost of serving such clients, banks are often reluctant to provide such services. Recognizing the importance of access to basic financial services, other countries have introduced measures to make basic account services available to all clients with limited costs. These vary from government guidelines on basic account services to disclosurebased models such as the Community Reinvestment Act in the US.

> 28 Ley 863 de 2003, Articulo 850-1.

33
Policy Research Working Paper 9288

# **Abstract**

This paper studies the impact of a computer-assisted learning program on learning outcomes among high school students in The Gambia. The program uses innovative technologies and teaching approach to facilitate the teaching of mathematics and science. Since the pilot schools were not randomly chosen, the study first used administrative and survey data, including a written test, to build a credible counterfactual of comparable groups of control students. It used these data to conduct a pre-analysis plan prior to students taking the high-stakes certification exam. The study later used the certification exam data on the same students to replicate the results. The findings show that the program led to a 0.59 standard deviation gains in mathematics scores and an increase of 15 percentage points (a threefold increase) in the share of students who obtained credit in mathematics and English, a criterion for college admission in The Gambia. The impact is concentrated among high-achieving students at the baseline, irrespective of their gender or socioeconomic background.

This paper is a product of the Office of the Chief Economist, Africa Region and the Education Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at mblimpo@worldbank.org.

_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
Policy Research Working Paper 8569

# **Abstract**

This paper develops and applies a spatial econometric model that links road upgrading to forest clearing and biodiversity loss in the Lao People’s Democratic Republic. The paper uses 500-meter cells to estimate the relationship between the rate of forest clearing in a cell and its distance to the closest point on the nearest road link, the quality of that link, the cell’s legal protection status, transport cost to the nearest urban center, the agricultural opportunity value of the land, and terrain elevation. The parameter estimates are all robust, with the expected signs and very high statistical significance. The paper highlights the results that measure the impact of improved road quality on forest clearing through shorter transport times to market and lower vehicle maintenance costs. The estimated response parameters and a composite biodiversity indicator are used to compute an index of expected biodiversity loss from road upgrading in each 500meter cell. The results identify areas in the Lao People’s Democratic Republic where high expected biodiversity loss may warrant additional protection as road upgrading continues. This analysis will help policy makers in the country to weigh context-specific trade-offs between development and conservation objectives associated with road improvement.

This paper is a product of the Development Research Group, Development Economics and the Environment and Natural Resources Global Practice. 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://www.worldbank.org/research. The authors may be contacted at sdasgupta@worldbank.org.

_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
during the reform process (Field Interviews,<sup>October1999).Forexample,theTaskforcethat</sup> was formed to develop sector strategy and help<sup>draft the policydocuments originallyconsisted</sup> only of people from the public sector (Article<sup>19,1998,p.27).35Further,whenmembersof the</sup> public were invited to submit written submissions<sup>suggestingchangestothe draft policy</sup> document and Bill, few entities other than the<sup>WorldBankandsomegovernmentofficials</sup> actually did (Article 19, 1998, p. 27).36 Consequently,<sup>although private individualsandfirms</sup> might have benefited significantly from reform,<sup>mostwerenotactively lobbyingforitand</sup> pressure from the private sector does not appear<sup>to havebeenoneof theforcesdrivingsector</sup> reform.

In part, the low level of pressure exerted by<sup>private enterprises probablyreflects the</sup> relatively modest role of the private sector in<sup>the non-ruralsector of theeconomy andthe relative</sup> importance of agriculture in Malawi, which accounted<sup>for40 percentof GDPin1998.Before</sup> democratization in 1994, the non-rural sector of<sup>the Malawianeconomywasdominatedby</sup> public enterprises and a few large semi-private<sup>enterprises.3 7</sup> For example, the massive Press Corporation Limited (PCL), which had links to<sup>PresidentBandapriortodemocratization,was</sup> thought to account for as much as 30 percent of<sup>GDPin themid-1990s(Economist Intelligence</sup> Unit, 1996). Although progress with privatization<sup>hasreducedtheinfluenceof state-owned</sup> economy, many enterprises remain publicly<sup>owned andthegovernmentretains significant</sup>

> 35 The task force was comprised of two people<sup>fromMPTC, one fromthepostoffice,onefromTV Malawi, one</sup> from the Privatization Commission, the Law Commissioner,<sup>onefromtheAttomeyGeneral'sChambers,one from</sup> the Ministry of Finance, and one from the Ministry of<sup>Information(Article19,1998,p.27).</sup>

36 In part, the lack of private sector involvement<sup>mayreflecttheimpressionthatsuggestionsfrom non-govemment</sup> entities were generally being ignored. For example,<sup>duringfield interviews(October1999), onenon-govemmental</sup> organization noted that a group of NGOs<sup>submitted commentsand recommendations totheTaskforcerelatedto</sup> reform of radio and television rather than<sup>telecommunications.</sup> However, when their recommendations were ignored, they became frustrated with the process.

37 Forexample, Sahn _et al._ (1990) note that 'Malawi's<sup>industrial sector is characterizedby highconcentrationratios</sup> that suggest both the exacerbation of inefficiencies<sup>anddevelopmentof monopolyrents.Forexample,8 of 21</sup> subsectors at the three digit level ISIC classification<sup>haveless thanthreefirms."</sup> 31