rafmacalaba/datause-encoder-data huggingface dataset:
(Balima et al., 2020). To capture the between-study heterogeneity while controlling for withinstudy dependence, a multilevel model<sup>13</sup> is most appropriate (see, e.g., Doucouliagos and Laroche, 2009; Doucouliagos and Stanley, 2009). The multilevel model accounts for within-study dependence through the inclusion of a random individual effect for each study, hence the reference to a multilevel random effect model. More specifically, the following Equation 1 is considered: where _effectij_ stands for the _i_<sup>_th_</sup> estimate from the _j_<sup>_th_</sup> study on the effect of income shocks on conflicts; _SEij_ for the standard error (or accuracy of the estimate) of _i_<sup>_th_</sup> estimate from the _j_<sup>_th_</sup> study. _λj_ is the study level random effect and _εij_ is a disturbance term. _β_ 1 stands for the ”true value” of the effect of income shocks on conflicts. _β_ 0 _SEij_ captures the ”noise” or very tendency from researchers to prefer results that are statistically significant, and thus to make use of alternative estimation techniques and/or model specifications to get high significance levels, notably in the face of a small sample. If the number of observations increases indefinitely, the standard error _SEij_ will tend towards zero (i.e., the smaller the standard error, the more accurate and reliable the estimate), and the estimated effect of income shocks will get closer to _β_ 1. In other words, if there is no publication selection bias, the collected estimates will vary randomly around the ”true value” _β_ 1, regardless of the standard error. We correct for heteroscedasticity owing to differences across studies in the sample size and model specifications used by dividing Equation 1 by _SEij_ , which becomes Equation 2: where _tij_ represents the collected t-values. We then assess the existence of **Type I publication selection bias** by testing the null hypothesis that the intercept in Equation (2) is equal to zero ( _β_ 0 = 0), which is also known as the ’Funnel Asymmetry Test’ - ’Precision Effect Test’ (FAT-PET). Basically, if the intercept is statistically significant, it means that the collected estimates do not vary symmetrically and randomly around the ”genuine effect” ( _β_ 0 _SEij_ in Equation 1 has an influence). In this case, it is as if part of the evidence (estimates in the literature) is missing, resulting in a

> 13In the present case, a model allowing for heterogeneity both at the study (or paper) level and the estimate level.

15
|**Table 1: Counterfactuals of the lik**<br>Sample Size|**elihood of operating a b**<br>Realized Outcome<br>(Percent inBusiness)|**usiness**<br>Mean (s.d.)<br>(Percent chan<br>if in alternati<br>Counterfact|Median<br>ce of business<br>ve treatment)<br>ual Outcome|Treatment Effect<br>according to<br>counterfactual|Experimental<br>Treatment Effect<br>and 95% interval.|
|---|---|---|---|---|---|
|**Control Group Firms**||||||
|New Applicants<br>516|50.4|90.6 (16.4)|100|40.2|37.3 [32.6, 42.0]|
|Existing Applicants<br>169|76.3|90.4 (18.3)|100|14.1|19.6 [13.5, 25.7]|
|**Treatment Group Firms**||||||
|New Applicants<br>291|95.9|50.7 (29.9)|50|45.2|37.3 [32.6, 42.0]|
|ExistingApplicants<br>205|96.6|55.1(29.5)|50|41.5|19.6[13.5,25.7]|
|Notes: Last column experimental|estimates are from Tabl|e 2 of McKenzie|(2015)|||

|**Table 2: Counterfactuals of the nu**<br>Sample Size|**mber of employees co**<br>Realized Outcome<br>(Mean number<br>of employees)|**nditional on op**<br>Mean (s.d.) T<br>Cou<br>Nu<br>if in a|**erating a business**<br>rimmed mean (s.d.)<br>nterfactual Outcome<br>mber of employees<br>lternative treatment|Median<br>)|Treatment Effect<br>according to<br>counterfactual|Experimental<br>Treatment Effect<br>and 95% interval.|
|---|---|---|---|---|---|---|
|**Control Group Firms**|||||||
|New Applicants<br>515|6.9|23.2 (31.4)|18.3 (12.5)|15|16.3|2.2 [1.1, 3.3]|
|Existing Applicants<br>169|7.6|23.9 (19.7)|20.9 (13.3)|20|16.3|2.5 [1.0, 4.0]|
|**Treatment Group Firms**|||||||
|New Applicants<br>290|9.7|3.8 (5.6)|3.0 (3.1)|2|5.9|2.2 [1.1, 3.3]|
|ExistingApplicants<br>205<br>Notes: Last column experimental e<br>Trimmed mean drops counterfactu|10.2<br>stimates are from app<br>als above the 95th pe|3.7(5.5)<br>endix Table 11a<br>rcentile|2.9(2.4)<br>of McKenzie (2015)|2|6.5|2.5[1.0,4.0]|

21
# **2.1.2. Characteristics of LSMS Surveys**

The LSMS program started in the 1980s, and it has been an important tool in measuring poverty and studying households’ characteristics in developing countries. LSMS surveys have two main characteristics: (i) multi-topic questionnaires, consisting of many modules to gather information on education, health, employment, consumption, non-farm enterprises, durable goods, housing, and migration, and (ii) extensive quality control procedures, such as the extensive use of screening questions and response codes, help resolve most inconsistencies in the data before they reach the NSOs.

LSMS surveys normally contain three types of questionnaires. The household questionnaire is to collect the detailed information on household members, covering diverse topics and focusing on the consumption modules. The community questionnaire is included in many countries’ LSMS programs (see the last column in Table 2) to gather the information on local conditions common to all households in the same community. Like the household questionnaire, the community questionnaire contains some specific modules asking respondents about the conditions of nearby health facilities, schools, markets, and infrastructures such as roads, means of communication and the availability of electricity. To conclude, some countries also design the price questionnaire to collect information on prices of commonly purchased goods from households.

To improve the quantity and quality of gender-disaggregated data and track targets for key Sustainable Development Goals (SDGs), the LSMS+ program has been launched to collect individual-disaggregated information on labor, non-farm enterprises, and ownership of and rights to land, dwelling, financial assets, and mobile phones. LSMS+ surveys have been implemented in 5 countries: Cambodia, Ethiopia, Malawi, Nepal, and Tanzania.

# **2.2. Transport-Related Questions in LSMS Surveys**

All the surveys in our LSMS research sample contain some transport questions, which are synthesized by modules or sections in the household and community questionnaires separately. For the household questionnaires, Table 3 summarizes the availability of the common modules of expenditure, durable goods such as vehicle ownership, and access to facilities, and Table 4 lists the numbers of available transport questions by module in the household questionnaires. For the community questionnaires, Table 5 summarizes the availability of community questionnaire and the questions regarding roads, public transport, and relevant changes of transportation in the community. Some uncommon questions in the household and community questionnaires customized by individual country are listed in Annex 5. Annexes 1 to 4 contain the details of the common transport questions in household and community questionnaires for the 12 countries in LSMS collection and 18 countries in LSMS-tagged collection accordingly.

# **2.2.1. Transport Questions in Household Questionnaires**

The typical LSMS household survey contains the following three types of transport questions: 7
-0.409 -0.373 Constant <u>[0.229] [0.224]</u> Observations                                                       15970                                                            15970 Standard errors in brackets<sup>*</sup> _p_ < 0.05,<sup>**</sup> _p_ < 0.01,<sup>***</sup> _p_ < 0.001

# References

Abu-Bader, S., and E. Ianchovichina (2018). “Polarization, Foreign Military Intervention, and Civil Conflict.” _Journal of Development Economics_ (2018). <u>https://doi.org/10.1016/j.jdeveco.2018.06.006</u> Alderson, A. S., and K. Doran. “Global Inequality, Within-Nation Inequality, and the Changing Distribution of Income in Seven Transitional and Middle-Income Societies.” In _Inequality Beyond Globalization: Economic Changes, Social Transformations, and the Dynamics of Inequality_ , edited by C. Suter, 183-200. New, Brunswick: Transaction Publishers, 2011.

- Alderson, A. S., and K. Doran. “How Has Income Inequality Grown? The Reshaping of the Income Distribution in LIS Countries.” In _Income Inequality: Economic Disparities and the Middle Class in Affluent Countries_ , edited by J. C. Gornick and M. Jäntti, 51-74. Stanford, CA: Stanford University Press, 2013.

- Alderson, A. S., J. Beckfield, and F. Nielsen. “Exactly How Has Income Inequality Changed? Patterns of Distributional Change in Core Societies.” _International Journal of Comparative Sociology_ 46 (2005): 405-423.

- Alvaredo, F., Assouad, L., and T. Piketty. “Measuring Inequality in the Middle East 1990-2016: The World’s Most Unequal Region?” _Review of Income and Wealth_ (2018). <u>https://doi.org/10.1111/roiw.12385</u>

- Amiel, Y., and F. A. Cowell. _Thinking about Inequality: Personal Judgment and Income Distributions_ . Israel and London School of Economics: Ruppin Institute, 1999.

- Anand, S., and P. Segal. “What Do We Know about Global Income Inequality?” _Journal of Economic Literature_ 46 (2008): 57-94.

- Anand, S., and P. Segal. “The Global Distribution of Income.” In _Handbook of Income Distribution_ , Vol. 2A, edited by A. B. Atkinson and F. Bourguignon, 937-979. Amsterdam: Elsevier, 2015.

Arab Barometer (2019). <u>https://www.arabbarometer.org/</u> 34
more investment and growth in the sector (Table A8). More promising results can be observed in the financial sector. The subsidy there could increase the GDP growth rate by 0.4 percentage point in 2008 and about 0.8 percentage point in 2015. Reductions in the unemployment rate would be modest, between 0.2 and 0.3 percentage points (see Table A9).

The combined effect of the investment subsidy in the three sectors considered here would only add around 200,000 new jobs between 2007 and 2015. The almost totality of these jobs would be created through the stimulus in the financial sector. The stimulus in agro-industry would generate less than 10,000 jobs and the net impact in the mechanics and electronics industrial sector would be nil. Yet, the stimulus package would increase the government deficit by 0.2 to 0.5 percent of GDP over the short term (see Table A10).

The main message here is that the Government would need to be conservative in terms of the direct impact that the Emergence Program has on employment. Clearly, there is a need to analyze the impact of the stimulus package on other sectors, but in general, it seems that reaching the target of 500,000 new jobs would require considerable public resources. As discussed in World Bank 2007 (Chapter 1), the value added of the Emergence Program could be more in terms of its impact on economic diversification, and through this channel, innovation/self-discovery and productivity growth. These effects were not captured in the simulations. The important role of productivity growth, however, was illustrated above.

**An integrated package** .  Not surprisingly, an integrated package that combines the various policies discussed above can have important impact on economic growth and employment creation. More specifically, this policy package would include improved internal efficiency in the education system, higher growth in total factor productivity resulting from policies that promote innovation within firms, social security reforms that keep pay-roll taxes constant and increase domestic savings, and the support to high-value added emergent sectors.  Under this package, GDP growth could accelerate above 6.5 percent per year. Its level is higher by 1 percentage point to its reference scenario level during the whole investigation period. The aggregate unemployment rate would be reduced by 3 percentage points in the medium run (Table A11).  Highly skilled unemployment will continue to

23
Policy Research Working Paper 8457

# **Abstract**

The informal sector in developing economies is a significant source of livelihood for a sizable portion of the population. This study uncovers the effect of poor water infrastructure on the productivity of informal firms. This is achieved using firm-level data for 12 developing economies between 2009 and 2014. The findings indicate that an increase of one standard deviation of the total duration of water shortages in a month can lead to annual average losses of about 14.5 percent of the monthly sales per worker for the average informal firm in the sample that uses water for business activities.

This paper is a product of the Global Indicators Group, Development Economics. 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 author may be contacted at aislam@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
**Table 13:** **<mark>Proportion of undernourished children who fall into the bottom 20 and 40 percent of predicted values for all children</mark>**

||<br>Mod|<br>Stun<br>el 2|<br>**predicted**<br>ting<br>Mod|<br>**values for a**<br>el 3|<br>**ll children**<br>Mo|<br>Wa<br>del 2|<br>sting<br>Mo|<br>del 3|
|---|---|---|---|---|---|---|---|---|
||Bottom 20%|Bottom 40%|Bottom 20%|Bottom 40%|Bottom 20%|Bottom 40%|Bottom 20%|Bottom 40%|
|Benin<br>Burkina|0.267|0.496|0.288|0.515|0.278|0.486|0.280|0.512|
|<br>Faso|0.268|0.504|0.310|0.583|0.331|0.564|0.402|0.661|
|Burundi|0.252|0.483|0.289|0.522|0.298|0.497|0.434|0.596|
|Cameroon|0.352|0.605|0.407|0.663|0.527|0.768|0.561|0.802|
|Congo, Rep.|0.355|0.571|0.419|0.633|0.335|0.536|0.352|0.616|
|Côte d'Ivoire<br>Congo|0.340|0.608|0.401|0.639|0.239|0.460|0.342|0.549|
|,<br>Dem. Rep.|0.303|0.543|0.346|0.603|0.320|0.563|0.314|0.566|
|Ethiopia|0.262|0.481|0.283|0.554|0.297|0.547|0.421|0.630|
|Gabon|0.416|0.713|0.431|0.635|0.294|0.575|0.303|0.504|
|Gambia|0.311|0.553|0.378|0.598|0.306|0.543|0.354|0.592|
|Ghana|0.374|0.646|0.453|0.706|0.203|0.484|0.443|0.679|
|Guinea|0.297|0.544|0.351|0.620|0.352|0.599|0.435|0.638|
|Kenya|0.274|0.520|0.324|0.588|0.448|0.641|0.419|0.628|
|Lesotho|0.355|0.586|0.379|0.668|0.645|0.812|0.634|0.655|
|Liberia|0.287|0.514|0.319|0.581|0.269|0.521|0.441|0.679|
|Malawi|0.253|0.478|0.283|0.526|0.291|0.541|0.284|0.526|
|Mali|0.283|0.514|0.313|0.588|0.289|0.495|0.351|0.574|
|Mozambique|0.286|0.498|0.301|0.537|0.352|0.558|0.442|0.626|
|Namibia|0.341|0.601|0.388|0.621|0.361|0.656|0.382|0.602|
|Niger|0.289|0.531|0.328|0.579|0.271|0.469|0.359|0.568|
|Nigeria|0.346|0.624|0.375|0.649|0.325|0.552|0.370|0.595|
|Rwanda|0.299|0.529|0.328|0.599|0.353|0.567|0.445|0.589|
|Senegal|0.392|0.593|0.474|0.689|0.401|0.637|0.273|0.599|
|Sierra Leone|0.250|0.471|0.264|0.511|0.218|0.492|0.278|0.545|
|Swaziland|0.337|0.556|0.387|0.646|0.370|0.594|0.362|0.636|
|Tanzania|0.274|0.517|0.327|0.581|0.383|0.645|0.415|0.566|
|Togo|0.361|0.607|0.390|0.650|0.338|0.575|0.445|0.641|
|Uganda|0.325|0.586|0.400|0.625|0.473|0.681|0.455|0.668|
|Zambia|0.254|0.477|0.291|0.536|0.337|0.555|0.342|0.537|
|Zimbabwe|0.276|0.486|0.321|0.579|0.355|0.525|0.389|0.546|
|Mean|0.294|0.533|0.329|0.584|0.320|0.548|0.372|0.595|

Note: Data are drawn from DHSs. Means are population weighted. The table lists the proportion of stunted and wasted children who fall into the bottom 20<sup>th</sup> and 40<sup>th</sup> percentiles of predicted values from the regressions with height-for-age and weight-for-height respectively as the dependent variable. For example, 26.7 percent of stunted children in Benin have predicted values that fall into the bottom 20 percent of all predicted values for children in the Model (2) regression with height-for-age as the dependent variable.

50
29 The case of Turkey is particularly interesting since this is a country which **has** been transformed since about 1977 from a relatively low inflation to a high inflation one, a transformation associated with a switch in exchange rate regime from fixed to crawling peg.<sup>14</sup> From the point of view of the real targets approach a very simple story can be told. For reasons which need not be discussed here Turkey had its debt crisis from 1977 to 1979. The real exchange rate appreciated somewhat from 1974 to 1979 and this, together with the need to drastically improve the current account, called for substantial real depreciation. As part of a major stabilization program which eventually included trade liberalization the nominal exchange rate was depreciated by about 70% in 1980. From 1981 the rate was adjusted daily. The net result was a substantial real depreciation - about 30% - by 1984 and more since, and this was quickly followed by a remarkable export boom. This episode was regarded by Bela Balassa (1983) and others as a striking and praiseworthy example of the success of exchange rate policy, above all, as evidence that export supply and demand elasticities were high.

How does this episode look from the point of view of the nominal anchor approach? The exchange rate was fixed to the US dollar up to 1973 and during the period 1960-70 inflation averaged less than 5%. From 1971 to 1977 it averaged 18%. 1980 14 This discussion draws on the study on Turkey by Ziya Onis and James Riedel (to be published) conducted for the World Bank project.
of mines, gold mining accounts for a substantial share of local employment. For example, 15 percent of all employed males who live within 10 km of a large-scale mine worked in gold mining in 2000. This figure rises to almost 20 percent in 2010.

In Figure 2, we present a map of Ghana in which the shaded area shows all the areas located within 100 km of any mine in the year 2000. Figure 3 does the same thing for the year 2010. The two maps show that urbanization in a large proportion of the country could potentially be affected by proximity to gold mining. We see that the shaded area includes all the regions West of the mines up to Ivorian border and it stretches to the North past Kumasi. In 2000, it spread past Accra and Lake Volta to the East due to the presence of two small alluvial mines there. Because these mines were discontinued in 2010, there is less shaded area in the Eastern part of the country in Figure 3.

# **3.2 Data sources**

In addition to the mining data mentioned in the previous section, we make use of two data sources to construct proxies for proto-urbanization: the 2000 and 2010 population censuses; and night-time illumination data from satellite imagery. We discuss them in turn.

The census data were made available to us at the enumeration area (EA) level. There were 26,708 EAs in the 2000 Ghana census and 37,625 EAs in the 2010 census. In addition, we have a complete map of the EAs from the 2000 census. In this map, EAs in urban localities are grouped together into a single polygon, a town. This affects 64 % of the EAs. We extract information about the GPS coordinates of the center of each of these EAs and towns. In total, we obtain 12,472 such coordinates. For the enumeration of the 2010 census, some of the EAs were split up, but none were merged. This allows us to link them to our map. We are able to link 11,698 of EAs across the two censuses.<sup>4</sup> They cover 96.17 % of all households in 2000, and 98.51 % in 2010.

Within each EA we have data on 10% of all households. In addition to gender, marital status, and relationship to the household head, the two censuses contain, for each adult, a broad characterization of their sector of activity (e.g., mining, agriculture, trade), labor market

> 4In the following, we refer to the set of polygons that we have mapped as ‘EAs’, acknowledging that in some instances, these polygons group multiple enumeration areas as defined for Census purposes.

10
real exchange rate and the terms of trade) do<sup>nothaveasignificanteffectonforeign</sup> direct investment inflows in any of the<sup>samples.It islikelythattheireffectisalready</sup> captured **by** volatility of economic<sup>growth.</sup> The rate of real exchange rate depreciation<sup>islikewiseinsignificantinallthreere-</sup> gressions. This contrasts with the evidence<sup>inGoldbergandKlein</sup><sup>**(1998)**(whofind</sup> that foreign direct investment **by** both Japan<sup>andtheUnitedStatestotheEastAsian</sup> countries in their sample is significantly affected<sup>**by**bilateralrealexchangerates)and</sup> questions the hypothesis in Froot and Stein<sup>**(1991).**Finally,wefindnoevidence that the</sup> quality of governance, as reflected in the<sup>civillibertiesindex,hasanimpactonforeign</sup> direct investment inflows in any sample.

Table **3** shows the results obtained when the<sup>globalvariablesare replacedwiththe</sup> global factor. We conduct this exercise in order<sup>tofacilitatethecomparisonwiththe</sup> results on the heterogeneous model below.<sup>Asexplainedabove,the syntheticglobalfac-</sup> tor is constructed as a weighted average of the<sup>principal componentsof thesixglobal</sup> variables. In the calculation of the global factor,<sup>all six original variablesenterwith posi-</sup> tive weights. Since the significant global<sup>variablescarrynegativecoefficientsin thebasic</sup> regression (see Table 2), it is not surprising that<sup>the'global'principalcomponentalso</sup> presents a negative and significant coefficient (except<sup>inthe industrial countrysample).</sup> As it is measured here, a decline in the synthetic<sup>globalfactorrepresentslessattractive</sup> conditions for investment in international markets.<sup>Theregressionresultsconfirmthat</sup> this acts as a push force for foreign direct investment<sup>to flow intolocal markets.</sup> It is interesting to note that the size of the coefficient on the<sup>globalfactoristhree</sup> times as big in developing than in industrial countries,<sup>andonlysignificantfor developing</sup> countries. This is to be expected given that<sup>internationalmarket conditionsasproxied</sup> **by** the global factor are to a large extent determined<sup>**by**localconditionsinthericher</sup> economies and, particularly, the **US.** In addition,<sup>thesearealsothemostintegrated</sup> economies.

Judging from the regression fit, the global factor appears<sup>to be a goodsummarymea-</sup>

**15**
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Source: A “Research” Database ofInfrastructure Economic Performance, Amtonio Estache and Ana Goicoechea, World Bank Infrastructure Network, Office ofthe Vice President, June 2005,