rafmacalaba/datause-encoder-data huggingface dataset:
with a random effect to one that estimates state results directly without a random effect. The latter has the advantage of being much simpler to estimate, particularly when it comes to measures of uncertainty.

# **C. Municipal-level estimates for sampled municipalities.**

We now turn to municipality-level statistics, which the survey is not designed to estimate reliably. All reported estimates in this section pertain to in-sample municipalities only, so that the direct and SAE estimators are compared over the same municipalities. We begin by comparing measures of uncertainty for direct estimates and small area estimates. We present these results for all four labor market statistics in Table 4 . The estimated mean squared error is always substantially smaller for the small area estimates than the direct estimates. This difference is largest – in percentage terms – for male labor force participation, where the estimated mean squared error is around a third as large as the direct estimate, roughly equivalent to effectively tripling the size of the sample.

The second row reports coverage rates, which is the share of municipalities for which the true census value is within 1.96 standard errors of the point estimate. Despite the smaller estimated mean squared error for the small area estimates, the coverage rates are also higher, again across all four indicators. For female labor force participation in particular, there is a significant increase in coverage rates from 87 to 98 percent in the small area estimates. This suggests that uncertainty for the direct estimates is underestimated and uncertainty for the small area estimates may be slightly overestimated. Therefore, for reach of the four indicators, we multiply the MSE of the municipal direct estimates by a constant greater than one to replicate the coverage rate of the small area estimates. When coverage rates are equalized in this way, the estimated MSE falls by a factor of four to five for labor force participation.<sup>22</sup> <u>Table 4 - Measures of Uncertainty and Coverage for Municipal Estimates</u>

||(1)<br>Male<br>LFP|(2)<br>Female<br>LFP|(3)<br>Male<br>Unemp|(4)<br>Female<br>Unemp|
|---|---|---|---|---|
|**Average estimated mean**<br>**squared error (x1,000)**|||||
|<br>Direct|9.329|5.494|0.288|0.161|
|SAE|3.331|4.991|0.179|0.093|
|**Average estimated mean**<br>**squared error (x1,000) with**<br>**equalized coverage rates**|||||
|<br>Direct|13.895|24.116|N/A|N/A|
|SAE|3.340|5.042|0.179|0.094|
|**Median estimated relative SE**|||||
|Direct|0.122|0.156|N/A|N/A|
|SAE|0.068|0.143|0.480|0.494|

> 22 Coverage rates cannot be equalized for unemployment rates because of the large share of direct estimates that are zero.

17
|LEAM1|Measure<br>of overall<br>trade openness<br>S:Leamer<br>(88)|
|---|---|
|LEAM2|Measure of overall<br>trade<br>intervention<br>S:Leamer<br>(88)|
|LIT|Literacy<br>rate<br>in<br>1960<br>S:WBSI|
|MIX|Dummy variable<br>for mixed government<br>S:Gastil,<br>Barro|
|MSG|Growth of<br>import<br>share<br>S:WBNA|
|MTX<br>(L)|Ratio of<br>import taxes<br>to imports<br>S:IMFGFS, IMFIFS|
|MP|Measure of openness based on import penetration<br>S:Residuals of regres.<br>of M on RGDP60(&sqr),AREA,<br>POP|
|M|Import<br>share of GDP<br>S:WBNA|
|OECD|Dummy for OECD|
|OIL|Dummy for OPEC|
|PI|Average inflation of GDP deflator<br>S:WBNA|
|POP70|Population in 1970<br>$:SH|
|PRI|Primary enrollment<br>rate<br>1960|
||$:Barro<br>from UNESCO,ILO|
|PRJ|Primary enrollment<br>rate 1970<br>$:Barro<br>from UNESCO,ILO|
|REVC|Number of revolution and coups per year<br>S:Barro<br>from Banks|
|RGDPxx|Real GDP per capita<br>in 19xx<br>$:SH|
|SCOUT|Dummy for outward orientation based<br>S:Syrquin and Chenery<br>(88)|
|SEC|Secondary enrollment<br>rate<br>1960<br>S:Barro<br>from UNESCO, ILO|
|SED|Secondary enrollment<br>rate<br>1970<br>$:Barro<br>from UNESCO,ILO|
|SGOV|Real Government Consumption Share<br>$:SH|
|SINV|Real<br>Investment<br>Share<br>S:SH|
|soc|Dummy<br>for socialist economy<br>S:Barro<br>from Gastil|
|SST<br>(L)|Ratio Social Security taxes<br>to GDP<br>S: IMFGFS|
|STDD|Standard Deviation of GDC<br>(growth domestic<br>credit)<br>S:IMFIFS|
|STDI|Standard Deviation of<br>PI<br>(inflation)<br>S:WBNA|
|TAX<br>(L)|Ratio central government tax revenue to GDP<br>S$:IMFGFS|
|TEX<br>(L)|<br>Ratio total government expenditure to GDP<br>S: IMFGFS|
|TRD|Ratio total<br>trade<br>(X+M)<br>to GDP<br>|
||S:WBNA|
|XSG|GrowthofexportshareofGDP|

33
<!-- Start of picture text --> Distribution of Transport Cost-Distance Elasticity<br>Country HS6<br>0.6<br>204<br>=<br>ao<br>0.2<br>oo. * i i ols i<br>-0.1 0.0 0.1 0.2 03 -0.1 0.0 0.1 0.2 0.3<br>Distance Elasticity<br>Note: There are 66 and 3,973 Country and HS6 estimates, respectively.<br><!-- End of picture text -->
# **2.4 Irrigation investments**

Government irrigation investments enabled the establishment of both small- and large-scale irrigation projects across the country. Initially, extension workers from the Ministry of Agriculture managed the schemes. However, with structural reforms for reduced government intervention in the 1980s, farmers were organized into cooperatives to take up all management responsibilities (Nkhata, Jumbe, & Mwabumba, 2014). Thus, government irrigation policy shifted from government-owned schemes to farmer-managed schemes (GoM 2001). Increasingly, more land is being used for irrigation farming. As of 2016, about 41,053 hectares of land benefiting 348,572 smallholder farmers was used for irrigation farming (Chafuwa, 2018). Despite the increases, the irrigated land is estimated at only 36 percent of the potential 407,862 hectares (draft National Irrigation Policy, 2022). Recent government irrigation investments with credit financing include, among others, the ongoing Shire Valley Transformation Project supported by the World Bank in Nsanje and the Linga irrigating scheme in Nkhatabay supported by the African Development Bank. During the dry season, individual farmers also privately perform irrigated farming, often in wetlands and along rivers. This practice, known locally as "dimba cultivation," is a supplement to rain-fed agriculture for farming households having access to such land. This study considers only access to and use of government irrigation investments.

# **3 Data and methodology**

# **3.1  Data sources**

The study uses Living Standards and Measurement Study (LSMS) data for Malawi; panel data for the years 2013, 2016 and 2019. The 2013 survey was conducted between April and October 2013; the 2016 survey field work happened between April 2016 and April 2017, and the 2019 survey was conducted between April 2019 and March 2020. The surveys included individual and community questionnaires collecting data on agriculture, employment, and consumption assets, among others, to explain the living conditions of Malawians. In Malawi the surveys are conducted by the National Statistical office with support from the World Bank.

7
Table 7

|Dependent variable: Fisca<br>Variables|l adjustme<br>(1)|nt when ne<br>(2)|eded based<br>(3)|on Escolan<br>(4)|o et al.(20<br>(5)|14)<br>(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Rigid expenditure minus|-0.032***|-0.030***|-0.033***|-0.034***|-0.023***|0.082**|-0.096***|-0.066|
|structural as % GDP|(0.011)|(0.008)|(0.010)|(0.010)|(0.006)|(0.034)|(0.033)|(0.050)|
|**Economic factors**|||||||||
|GDP growth (t-1)||0.057||0.165|0.217|0.143|0.185|0.160|
|||(1.317)||(1.502)|(1.481)|(1.470)|(1.491)|(1.515)|
|Inflation (t-1)||2.770***||3.252***|3.540***|3.537***|3.575***|3.209**|
|||(0.812)||(1.182)|(1.170)|(1.137)|(1.137)|(1.218)|
|U.S. Interest rate||-0.136***<br>(0.051)||-0.147**<br>(0.065)|-0.158** <br>(0.066)|-0.163*** <br>(0.060)|-0.169**<br>(0.069)|-0.154**<br>(0.059)|
|**Political factors**|||||||||
|Gini|||0.006|0.005|0.004|0.028**|0.005|0.004|
||||(0.009)|(0.013)|(0.013)|(0.014)|(0.013)|(0.012)|
|Rule of law|||-0.208***|0.106|0.141|0.150|0.049|0.114|
||||(0.068)|(0.137)|(0.137)|(0.134)|(0.127)|(0.137)|
|Margin of Majority|||0.385|0.155|0.106|0.228|0.114|-0.342|
||||(0.386)|(0.376)|(0.375)|(0.359)|(0.371)|(0.621)|
|Election's year|||-0.110|-0.083|-0.071|-0.052|-0.079|-0.080|
||||(0.079)|(0.077)|(0.072)|(0.073)|(0.075)|(0.080)|
|**Interactions**|||||||||
|EMEs * Deviation|||||-0.040<br>(0.025)||||
|Gini * Deviation||||||-0.003***<br>(0.001)|||
|Rule of law * Deviation|||||||0.013**<br>(0.006)||
|Majority * Deviation||||||||0.058<br>(0.096)|
|Constant|0.583***|0.893***|0.968|0.270|0.308|-0.810|0.689|0.589|
||(0.130)|(0.190)|(0.667)|(0.937)|(0.919)|(0.979)|(0.930)|(0.845)|
|Observations|182|180|164|162|162|162|162|162|
|R-squared|0.035|0.157|0.100|0.180|0.190|0.211|0.194|0.184|
|Number of ifscode|60|60|53|53|53|53|53|53|
|CountryFE<br>Robust standard errors cl<br>***p<0.01,**p<0.05,*|Yes<br>ustered by<br>p<0.1|Yes<br>country in p|Yes<br>arentheses|Yes|Yes|Yes|Yes|Yes|

- 25 -
# **Table 5. Estimations for the likelihood that the household has requested a loan from a financial institution**

This table shows marginal effects from the estimations of a probit model. The dependent variable is 1 if the household requested a loan from a formal financial institution and zero otherwise. All independent variables are defined in Table 1. In columns (1)-(4), the variable _Remittances_ refers to a dummy for whether the household receives remittances from overseas, whereas in columns (5)-(8) _Remittances_ refers to per capita amounts received in remittances from overseas. Robust z-statistics are shown in brackets. *,**, and *** denote significance at 10, 5 and 1 percent, respectively.

|||Depen<br>|dent variable: D<br>|ummyfor whet<br>|her household req<br>|uested a loan<br>|<br>||
|---|---|---|---|---|---|---|---|---|
|Variables|Remittances:<br>remittances f<br>|dummy for whe<br>rom overseas<br>|ther household<br>|receives<br>|Remittance<br>capita recei<br>|s: the amount<br>ved from ove<br>|of remittance<br>rseas<br>|s per<br>|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Remittances|-0.020|-0.011|-0.040|-0.044|-0.011|-0.007|-0.021|-0.064|
||[-1.58]|[-0.85]|[-1.97]**|[-0.45]|[-1.80]*|[-1.42]|[-2.13]**|[-1.52]|
|Real per capita income|0.001|0.002|0.002|-0.019|0.002|0.002|0.002|-0.017|
||[0.80]|[0.92]|[0.56]|[-1.70]*|[0.82]|[0.95]|[0.59]|[-1.43]|
|House and land ownership|0.018|0.019|0.028|-0.028|0.018|0.020|0.028|-0.033|
||[1.61]|[1.88]*|[1.61]|[-0.31]|[1.62]|[1.95]*|[1.63]|[-0.36]|
|Adults average education|0.006|0.006|0.011|0.019|0.006|0.006|0.012|0.020|
||[3.12]***|[2.97]***|[3.29]***|[0.97]|[3.22]***|[3.06]***|[3.54]***|[1.01]|
|Agriculture|0.010|0.013|-0.004|-0.072|0.010|0.013|-0.003|-0.079|
||[0.70]|[1.04]|[-0.16]|[-0.71]|[0.70]|[1.02]|[-0.13]|[-0.75]|
|Electricity|0.023|0.024|0.042|-0.059|0.024|0.025|0.042|-0.046|
||[1.99]**|[2.32]**|[2.23]**|[-0.62]|[2.02]**|[2.37]**|[2.23]**|[-0.49]|
|Returned migrant|0.019|0.032|0.038|0.258|0.022|0.037|0.046|0.399|
||[0.72]|[1.24]|[0.85]|[0.91]|[0.84]|[1.39]|[1.02]|[1.21]|
|Observations|2353|2353|1423|415|2327|2327|1411|405|
|Pseudo R<sup>2</sup>|0.03|0.06|0.10|0.08|0.03|0.06|0.11|0.09|
|Log likelihood|-587.6|-567.3|-461.2|-251.4|-584.9|-565.0|-458.3|-244.3|
|Department fixed effects|No|Yes|No|No|No|Yes|No|No|
|Municipalities fixed effects|No|No|Yes|No|No|No|Yes|No|
|Household fixed effects|No|No|No|Yes|No|No|No|Yes|

24
preference induced substitution away from non-EACU suppliers and trade diversion slightly dominates in the cases of Kenya and Tanzania, but not for Uganda and Rwanda.

Finally, we examine the impact of varying the seven vectors of elasticities of firm supply with respect to price for imperfectly competitive goods and services sectors. (We have one vector for each of our ten regions, but there are only seven distinct vectors since all four EACU regions are assumed to have identical vectors for these elasticities of firm supply.) In the EACU liberal case, the welfare gains are about 5 percent larger in Kenya, Uganda and Rwanda with larger elasticities of firm supply.<sup>26</sup>

# **7. Conclusions**

In this paper we have developed an innovative multi-region computable general equilibrium model with a focus on Kenya, Tanzania and their trade partners in the East Africa Customs Union. We have assessed three kinds of deep integration for these countries: trade facilitation, services liberalization and reduction of non-tariff barriers. We assessed the impacts of these reforms within the EACU alone; within a Tripartite agreement among the EACU, COMESA and SADC; and multilaterally. The analysis is based on two new data sets of the ad valorem equivalents of the barriers against foreign suppliers of services and the time costs of trade, which we have shown are important to the results.

We estimate that deep integration within the EACU alone will produce benefits, and if expanded to include COMESA and SADC would produce larger benefits for all four of our EACU countries. Schiff and Winters (2003) have found that the largest gains from regional integration come from the deep aspects of the agreements, so our results are consistent with the broader empirical and theoretical literature.

We find that extending the same three reforms multilaterally generates substantially larger gains than EACU only reform, ranging from twice the size of the gains for Kenya to seven times the gains for Tanzania. We estimate that the impact on sector output, however, is substantially greater with multilateral reform, leading to larger adjustment costs of trade liberalization. Despite the fact that the evidence shows that the adjustment costs of trade

> 26 In Tanzania the gains are approximately unchanged. If available varieties in IRTS goods fall, the welfare gain is smaller. In the EACU Central case, possible trade diversion leads to ambiguous results.

30
# **1 Motivation**

Governments invest in roads to foster national integration, promote economic development, and facilitate trade. Between 1995 and 2015, for example, World Bank-supported transport projects amounted to approximately 161 billion USD.<sup>1</sup> Yet, although road investments constitute a major portion of both donor and government capital expenditure, the economic impact of investments in large-scale road infrastructure is debated: while the effects of large economic corridors and rural connectivity programs have mostly been studied in dense, rapidly growing countries in Asia, the impacts of transport investments in lower-density contexts are less studied. In particular, the potentially heterogeneous benefits of such programs to different types of areas—and therefore the impact of such programs on growth as well as inequality—are less well understood.

This paper provides evidence on the links between large-scale investments in roads of different types and changes in local economic activity and land use (urbanization and cropland). To shed light on this issue, we study a large program of economic corridors and rural road upgrades that has been ongoing in Ethiopia since 1997: the Road Sector Development Program (RSDP). The scale of the program and the context of Ethiopia allow us to contribute to the literature in two ways. First, while most of the evidence about the development impact of rural roads and economic corridors comes from rapidly developing or densely populated countries, we study the impacts of these investments in a setting where both urbanization and industrialization are low. Ethiopia’s urbanization rate of 17% is less than half of the Sub-Saharan Africa regional average of 37% (World Bank, 2015), and at the program’s start, Ethiopia had one of the least-developed road networks and lowest motor vehicle utilization rates in the world (Adamopoulos, 2018). Second, the transport program we study includes both large economic corridor upgrades aimed at connecting cities, as well as smaller-scale rural road improvements. This allows us to examine differential patterns of effects according to the type of transport investment, as reflected in the second part of our analysis.

The large number of roads upgraded and localities connected over the program period allow us to study the impact of the program as a whole, and of the different types of investments separately. The overall objectives of the program were to support the rehabilitation, improvement, and construction of roads with access to ports (particularly important given that Ethiopia is landlocked), roads that link to major economic centers and resources, and roads connecting areas with potential for commercial agriculture (Ethiopian Roads Authority, 2015a).

> In this paper, we ask whether road upgrades promote growth in local economic activity and produce changes in land use, particularly urban land and cropland. We use three empirical approaches: (1) a

> 1Number extracted from the World Bank projects database, accessible here `https://projects.worldbank.org/en/ projects-operations/projects-list?sectorcode_exact=TI`

2
been distribution-neutral for the developing world as a whole. The distributional shifts have raised the elasticity across all line, but only noticeably so over about $2 a day.

# **3. Bulging middles within developing countries?**

For each of almost 100 developing countries, it is possible to assess whether there has been a bulging middle over time. I have constructed the longest spells between two surveys, both using the same indicator, either consumption or income per person.<sup>14</sup> The median year of the first survey is 1991 while the median for the second is 2004. The median interval between surveys is 13 years and the interval varies from three to 27 years. All changes between the surveys are annualized. Again I use both the changes in the population share between $2 and $13 a day and the changes in the FW index of concentration around the median. Recall that the former measure directly reflects growth in the mean, while the latter is a distributional measure; I also examine distributional changes in the former measure. Precise sample sizes vary depending on the variables used in the analysis. Some further notation will be useful. Let the absolute and proportionate annualized differences for the variable _x_ be _d i_ ( _xit_ ) ≡ ( _xit_ − _xit_ −τ _i_ /) τ _i_ and _gi_ ( _xit_ ) ≡ (ln _xit_ − ln _xit_ −τ _i_ /) τ _i_ respectively, for country _i_ looking back from date _t_ over a time interval of length<sup>τ</sup> _i_<sup>. Note that</sup><sup>_g_</sup> _i_ (.) is simply the average growth rate of _x_ over the period, so<sup>_g_</sup> _i_ (<sup>μ</sup> _it_ ) is the rate of growth in the mean, μ _it_ . Also let _Fit_ ( _z_ ) denote the CDF for country _i_ at date _t_ , giving the proportion of the population living in households with consumption or income not exceeding _z_ in $’s per day at 2005 PPP. (So _Ft_ ( _z_ ) is the population weighted mean of _Fit_ ( _z_ ) over all _i_ .) In measuring the size of the middle class in a given country, I will use both the absolute difference _Ait_ ≡ _Fit_ (13) − _Fit_ (2) and the relative difference, _Rit_ ≡ _Fit_ (13) / _Fit_ (2) . Note that _gi_ (<sup>_R_</sup> _it_ ) is also the difference in growth rates for the poverty measures evaluated at $13 and $2 a day.

# _Sizes of the middle class across countries_

Table 4 gives a 5x5 contingency table that summarizes the joint distribution across the two survey rounds of the sizes of the middle class across countries. A strong positive correlation

> 14  When both were available, consumption was generally preferred The only exception was Peru, for which a much longer time period could be covered using income-based distributions.

10
|PPR<br>Title|Working Paper Series<br>Author|Date|Contact|
|---|---|---|---|
|WPSII1 Recent World Bank Activities in Energy|Industry<br>and Energy<br>Department|||
|WPS112AVisual Overview of the World Oil<br>Markets|Kay McKeough<br>Jose Escay<br>Sompheap Sem|||
|WPS113 Public Sector Pay and Employment<br>Reform|Barbara Nunberg|October 1988|J. Cheeseman<br>61703|
|WPS114 Africa Region Population<br>Projections<br>1988-89 edition|My T. Vu<br>Eduard Bos<br>Rodolfo A. Bulatao|October'<br>1988|S. Ainsworth<br>31091|
|WPS115 Asia Region Population<br>Projections<br>1988-89 edition|My T. Vu<br>Eduard Bos<br>Rodolfo A. Bulatao|October 1988|S. Ainsworth<br>31091|
|WPS116 Latin America and the Caribbean<br>Region Population<br>Projections<br>1988-89 edition|My T. Vu<br>Eduard Bos<br>Rodolfo A. Bulatao|October 1988|S. Ains.orth<br>31091|
|WPS117 Europe, Middle East, and North Africa<br>(EMN) Region Population Projections||||
|1988-89 edition|My T. Vu<br>Eduard Bos<br>Rodolfo A. Bulatao|October 1988|S. Ainsworth<br>31091|
|WPS118 Contract-Plans<br>and Public<br>Enterprise<br>Performance|John Nellis|October 1988|R. Malcolm<br>61707|
|WPS119 Recent Developments in Commodity<br>Modeling: A World Bank Focus|Walter C. Labys|October 1988|A. Daruwala<br>33716|
|WPS120 Public Policy and Private Investment<br>in Turkey|Ajay Chhibber<br>Sweder van Wijnbergen|October 1988<br>|A. Bhalila<br>60359|
|WPS121 Commercial<br>Bank Provisioning<br>Against<br>Claims on Developing Countries|Graham Bird|October 1988|i. Holloman-W<br>33729|
graduate studies abroad planned to do so. If migrants – particularly the well-educated – fail to return, then this brain-drain may have serious consequences for future Romanian growth and development.

**Figure 5  Percentage of Migrants and Population with High-School and Tertiary Education**

<!-- Start of picture text --> 70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>High school (pop) Tertiary (pop)<br>High school (emigrants) Tertiary (emigrants)<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br><!-- End of picture text --> _Sources: Eurostat; IASCI; Soros Foundation; Authors’ calculations_

**Figure 6 Firms Reporting Skills as Obstacles**

<!-- Start of picture text --> 100<br>90<br>80<br>Does not apply<br>70<br>Don't know<br>60<br>Very Severe Obstacle<br>50<br>Major obstacle<br>40<br>Moderate obstacle<br>30<br>Minor Obstacle<br>20<br>No Obstacle<br>10<br>0<br>2002 2005 2009 2013<br><!-- End of picture text --> _Sources: Enterprise Surveys, Authors’ calculations_ Despite its importance, both in terms of the number of migrants and its effects on the national economy, Romanian migration is an understudied phenomenon. This paper aims to address part of this shortfall. We will test what triggers migrants’ plans to return to Romania. We will test whether wage differences, likelihood of employment in Romania, duration of migration, family 5
# 4. Construction of a Toxic Pollution Risk Intensity Index

- 4.1. Calculation of Risk-Weighted and Unweighted Releases and Transfers

This section describes how toxic pollution intensity weighted by risk was calculated using the TRI, HHED and LRD databases. This measure enables the comparison of inter-sectoral environmental and health-related risks. Using the multi-index categorization of HHED, each chemical's rating under each index was assigned to one of three toxicological potency groups, Group One being the most hazardous (see Table 2.1). Each of the indices is also assigned to one of four higher levels of aggregation as follows:

- @ acute human health and terrestrial ecotoxicity;

- @ chronic human health and terrestrial ecotoxicity;

- @ acute aquatic ecotoxicity;

- @ chronic aquatic ecotoxicity.

For our purposes two of these categories were chosen to characterize pollutant intensity, these being acute human health and terrestrial ecotoxicity and acute aquatic ecotoxicity. Human and terrestrial ecotoxicity are distinguished from aquatic ecotoxicity because of the significant variation between the toxicological potency of many chemicals to mammalian and fish life. Chronic toxicity was ignored, largely because the evidence for low-dose, long-term effects is contentious. Since the HHED contains more than one index within each of these categories, the most hazardous toxicological potency rating was selected as a 19