rafmacalaba/data-use-mentions-extended huggingface dataset:
informal firms, conditional on sector and geography, exhibit linking patterns similar to those of the smallest quartile of formal firms in the administrative data. This assumption addresses concerns that informal firms might have different linking patterns compared to larger formal firms operating in the same sector and location (e.g. due to internal economies of scale (Grant and Startz, 2022)).<sup>6</sup> Nevertheless, informal firms might encounter additional obstacles specific to informality.<sup>7</sup> To account for this, we implement additional sensitivity checks that account for alternative scenarios with lower linking probabilities between the formal and informal sector.

We use the revised network to answer the question of interest: How do spatial patterns of trade change when informal firms are accounted for? First, we find that sectors and regions with the highest levels of informality have more outlinks in the revised network relative to the baseline network. The spatial inequality in outlinks declines by 7% and the prominence of urban hubs like Nairobi and Mombasa decreases by 5 percentage points. We show that while this decrease in inequality of outlinks is driven by an increase in both inter-county and intra-county trade, intracounty trade rises by a larger margin. Moreover, we find that once informal firms are accounted for, the number of trade relationships between counties is more sensitive to the strength of social ties between them, measured using both online friendship networks and migration inflows.

Next, we simulate the pass-through of domestic and import shocks using both the baseline network estimated from the structural model and the revised network that incorporates informal firms. We find that ignoring informality leads us to underestimate the average impact of domestic shocks and overestimate the average impact of import shocks. In particular, when using the revised network, we find that domestic output shocks have a more pronounced negative effect on sector-regions with higher levels of informality, compared to results based on formal sector data alone. Our results suggest that a 1 percentage point decrease in the formal sector share corresponds with an
**Figure 3: Host Country of Current Migrants (Left) and Expected Duration of Absence (Right)**

<!-- Start of picture text -->
1.5%,<br>South  2.3%,  36‐205 1‐4<br>4.9%,<br>Korea Other<br>Turkey 25‐36<br>13.7%,<br>Kazakhsta<br>n<br>5‐8<br>13‐24<br>77.6%,<br>Russia 9‐12<br><!-- End of picture text -->

_Notes: Listening to the Citizens of Uzbekistan Baseline Survey, Author’s calculations_ The expected duration of absence of migrants varies substantially. About 58 percent of households expect that migrants will return in on year or less. About 15 percent of households expect migrants to be away for two years or more. There is a strong seasonal component to migration flows in Uzbekistan, and at the time of the baseline survey, about 48 percent of migrants had been away for between one to four months.

Most households are in touch with migrant members several times a week or more, and nearly 32 percent of households are daily in touch with their members currently abroad. Less than 5 percent of households speak with migrant members once a month or less, and less than 1 percent are not in contact at all. The most common means of communicating with migrants is over the phone, accounting for about 72 percent of households with migrants. The second most common means of communication is smartphone and computer apps with text and/or video capability (e.g. Skype or WhatsApp, though the questionnaire did not ask about the specific apps used). Less than 2 percent use other means, such as SMS or physical mail.

13
<!-- Start of picture text --> Bank study “Supply andDemandfor Child Care Servicesin Turkey’ (World Bank, 2015) from 603 center-based<br>(whether through the investment or operational grants to facilities).<br>For the demand-side model we make use of the Turkey EU-SILC (Survey of Income and Living Conditions)<br>neighborhood, and increased affordability of child care given the operational grant (which leads to reduced<br>prices in the market through a pass-through assumption) or through the vouchers.<br><!-- End of picture text -->

|(investment) and operational costs and certain prices and enrollments. All of these data come from the<br>empirical data set, but are treated in the model as hypothetical cases (as ifinvestments in these centers have<br>notyetbeenmade).Aprobability ofinvestment is calculated for each service provider inthe baseline. We call<br>grants (ofvarying sizes) and to seewhich ones lead to a higher capacity increase.<br>initial probability to invest, given their (i) price per child, (ii) setup cost ifthey were to open up the center<br>now,(iii)monthlyvariablecosts,and(iv)totalnumberofchildrenenrolled.Netpresentvalueofeachchild|
|---|
services in their production, in response to increased product offerings in the service sectors. Summary statistics for all the variables are presented in Table 1.

To establish whether there exists a link between the performance of manufacturing firms and liberalization of upstream services sectors, we regress the TFP of a manufacturing firm _i_ operating in industry _j_ at time _t_ on the aggregated _Services_Indexjt-1_ lagged one period or disaggregated indices of services reform. We control for foreign ownership, trade liberalization, firm and year fixed effects. Our principal estimation equation has the following form: Services sectors were not the only item on the post-1991 reform agenda in India. Continued reductions in manufactured product tariff rates occurring during the same period may also have influenced manufacturing productivity. To control for changes in tariff rates, we include lagged output tariffs in the same manufacturing sector ( _Tariffjt-1_ ) and a weighted measure of input tariffs ( _Input tariffjt-1_ ). The weights of the input tariffs are taken from the 1993 input-output matrix, while the aggregation of individual tariff lines to the 2-digit sector level is achieved using the 1990 import weights. The information on tariffs was obtained from the World Bank‟s WITS database.<sup>17</sup> As many studies find that foreign affiliates tend to outperform domestic producers (see for instance, Aitken and Harrison, 1999; Arnold and Javorcik, 2009), we include an indicator for foreign-owned firms, equal to one if the foreign ownership share in firm _i_ is above 10% at time _t_ ( _Foreignit_ ). In an expanded specification, we will allow for differential effects of services reform on domestic and foreign firms by interacting _Foreignit_ with the _Services_Indexjt-1._ The dependent variable is firm-specific, but our variables of interest vary at the sector-year level, therefore, we cluster standard errors at the sector-year level.

> 17 The authors are grateful to Rajesh Mehta for providing tariff data for the years in which the figures were missing from WITS.

19
# **Data**

The data required for the study can be divided into two groups: (i) data needed for the global CGE model and (ii) data needed for GIDD model. A detailed discussion on the data for the global CGE model is available in Timilsina et al. (2010), so in this section we focus data required for GIDD model.

The main sources of data in the GIDD model include: (a) the dataset assembled for the production of the World Bank World Development Report (WDR) for developing countries, which are drawn largely from the Living Standards and Measurement Study (LSMS) and the African Institute for Sustainability and Peace (ISP)-Poverty monitoring group; (b) the Europe and Central Asia (ECA) databank and the different World Bank sources for Eastern Europe countries, and (c) the Luxembourg Income Studies (LIS) database for most of the developed countries.

There are two versions of the GIDD model. The original version includes data for the year 2000, while the second includes updates for the year 2005. In the original version, nationally representative household surveys of various countries nearest to 2000 were chosen. For household surveys of countries valued not in year 2000, the following adjustments were applied in order to “value” them in 2000. Local consumer price index (CPI) in the country was used to adjust income and consumption to 2000 domestic values. Furthermore, the values in the household surveys were converted to international dollars in year 2000 using the Purchasing Power Parity (PPP) factor obtained from the Penn World Tables. A correction factor was also applied to the population weights to make the population level consistent to year 2000.

In the updated GIDD model, the values for 2000 were updated to 2005 using updated population and 2005PPP factor. Also, the mean income at the country level was adjusted to match the latest poverty rates reported in POVCAL<sup>3</sup> . Thus, the poverty rates at the country
alternatives, and/or for whom walking to the newly opened station is not an option (either because of safety or distance considerations).

It is also worth noting, however, that our study has several limitations. Most importantly, perhaps, while our analysis focuses on the impacts of the opening of the MRT system on demand for ride-hailing services in the immediate neighborhoods of newly opened MRT stations, it is silent on impacts elsewhere in the city. In other words, our findings should strictly speaking be taken as strong evidence for _local complementarity_ . The opening of the MRT may, for example, have decreased ride-hailing activity to/from locations near the newly opened MRT stations to locations further away, as people now take an MRT ride, (possibly using a _Gojek_ ride for the first/last mile only) to get to that location, instead of, as before, using ride-hailing for the entire journey.

In this respect it would be particularly interesting for future research to be able to trace back how/where people who are now using ride-hailing services for their first and/or last-mile connectivity to the MRT were using ride-hailing services, possibly in combination with other forms of public transport, before the MRT system opened. Another fruitful direction of future research would be to estimate the impacts of the MRT system on local levels of pollution, greenhouse gas emissions, traffic congestion, and the use of other forms of public transport, as well as to replicate our analysis for other cities in Indonesia, and elsewhere in the developing world where the rise of ride-hailing services predates the construction of rapid mass public transit options, fewer people own their own transport, and where ride-sharing is predominantly motorbikebased.

# **REFERENCES**

- Babar, Y., and G. Burtch. 2020. “Examining the Heterogeneous Impact of Ride Hailing Services on Public Transit Use,” _Information Systems Research_ , 31 (3), 820–834.

- BPS, 2020. Jabodetabek Commuter Statistics (Statistik Komuter Jabodetabek: Hasil Survei Komuter
,571|
|Kenya|3,231|Togo|2,000|
|Lesotho|2,667|Uganda|2,800|
|Malawi|2,889|Zambia|2,083|
|Mali|3,333|Zimbabwe|1,500|

_Source_ : World Bank staff calculations.

# Transmission and distribution costs

T&D costs were estimated based on reported km of T&D lines, excluding LV distribution lines below 1 kV for which no reliable information is available. T&D line data used in the reference year calculations are listed in annex 4. For the purpose of these study, two costs were used: one for lines above 33 kV, and the other for lines between 1 kV and 32 kV. While the cost of building and maintaining T&D infrastructure varies significantly across countries, structure type, voltage, and terrain (desert, flatland, forest, mountains, urban versus rural), it was not possible within the scope of this study to perform a detailed T&D cost analysis in each country. Assumptions, provided by industry specialists, are summarized in Table A1.5.

## **Table A1.5: Assumptions on T&D cost estimates**

|**Component**|**Assumed value (US$/km)**|**Economic life (years)**|
|---|---|---|
|Lines 110kV or above|165,000|50|
|Lines below 110kV down to 66kV|65,000|40|
|Lines below 66 kV down to 1 kV|10,000|30|

_Source_ : World Bank T&D specialists.

For LV distribution lines below 1 kV, costs assumed in setting tariffs in Peru for the five-year period 2013–2017 were used. The regulatory procedure assumed replacement costs of LV networks in six different settings—high-density urban, medium-density urban, low-density urban, peri-urban, 74
increases in agricultural commodity prices [21]. A USDA report describing the factors leading to the food commodity price rise concludes that the run- up in commodity price reflects a trend of slower growth in production and more rapid growth in demand that led to a tightening of world balances of grains and oilseeds over the last decade [66].

Biofuels are considered to be one among several demand-side and supply-side factors responsible for the increase in crop and food commodity prices in recent years [15, 21, 25, 43, 47, 51, 59]. Quantitative estimates of the impact of biofuels on grain prices range from 20% to 60% (see Table 1). The most pessimistic estimate ascribed 70 to 75% of the price rise between 2002 and 2008 to biofuels [47]. This report uses historical data to estimate the elasticity of world prices of agricultural commodities with respect to the price of energy and related inputs to agriculture and with respect to changes in the value of the dollar. Using these elasticities, this report estimates that between 2002 and 2007, higher prices of energy increased export prices of major U.S. food commodities by about 15 to 20 percentage points, and the depreciating dollar increased food commodity prices by about 20 percentage points. These together, it is argued, translate into a 25 to 30% increase in total price. The author argues that depletion of stocks, shifting for food cropland for production of energy crops, government response in the form of food export bans, and speculative activity, which caused prices to rise, were the consequences of the shocks considered with demand for biofuels being the main cause.

Rosegrant estimates the effect of biofuels using a simulation-based approach [59]. He simulates the market equilibrium under two different scenarios, one without high growth in biofuel and another with high growth in biofuel. For the former, he simulates a scenario in which biofuel grows at a rate which was observed between 1990 and 2000. This is the period before the rapid takeoff in
displacement situations as they are happening and to provide updates on how they are evolving (IDMC 2015). These data are not necessarily representative but can be used in conjunction with other methods to triangulate trends. For example, the Swedish NGO, Flowminder, has pioneered the use of de-identified data from mobile operators to track population displacement caused by natural disasters such as earthquakes in Haiti in 2010 and Nepal in 2015, and these techniques may also have applications in conflict-induced displacement crises.<sup>100</sup>

- (f) High-resolution satellite imagery and unmanned drones. High resolutions satellite imagery can be used to map physical structures in refugee and IDP camps including changes to the number and type of these over time, support the remote detection of displaced populations in hard to reach or insecure settings; and conduct rapid assessments during or immediately after a mass displacement (Harvard Humanitarian Initiative 2014). In settings with clearly distinguishable individual structures, these methods can also be reasonably accurate for the purposes of rapid estimation of displaced population (Checchi, et al. 2013).<sup>101</sup> The use of unmanned drones is also becoming more popular as the cost of this technology falls. This technique has been used by UNHCR to update its estimates of IDPs in Somalia’s Afgooye corridor (IDMC 2015) and by IOM to monitor disaster-induced displacement in Haiti, including the use of Unmanned Aerial Vehicles (UAVs) in collaboration with UNOSAT.

- (g) Open data initiatives. There are several initiatives to provide free and open data that enable Internet users to independently mine and analyze data and generate customized summaries, charts and visualizations. For example, the Bank has provided free, open access to its development data since the launch of its Open Data Initiative in 2010, however there is little open data on asylum-seekers, refugees and IDPs. JIPS has developed a web-based platform that allows users to explore, analyze and visualize profiling data online, and IDMC has

> 100 See http://www.
study, a gradient boosting algorithm was used to predict the outcomes based on the predictors listed in Tables 3 and 4. Gradient boosting combines shallow and successive decision trees.</mark><sup>25</sup> <mark>Decision trees consist of recursively partitioning (also known as splitting) predictors. The goal of each tree in our model was to maximize the accuracy of prediction. Each decision tree learns successively and improves upon the previous (learning rate). Eventually, predictions are based on a weighted combination of these trees.</mark><sup>26</sup> <mark>The preprocessed data (variable scaling and removal of highly correlated predictors) was split into training (80%) and test segments (20%) for all outcome variables. First, the algorithm was trained on the training segment and then validated on the test segment to determine predictions. The data was 10-fold cross-validated, with the data split into 80% training and 20% test observations randomly, ten times for all algorithms. The average of the cross-validations was taken as the final result. The relative influence of the predictors with the output was estimated using mean decrease Gini impurity coefficient (MDG) in the gradient boosting algorithm.</mark><sup>25</sup> <mark>MDG is the average across all trees of the decrease in Gini impurity for a predictor.</mark><sup>25</sup> <mark>The mean decrease in Gini coefficient is a measure of how each variable contributes to the homogeneity of the nodes and leaves in the resulting hierarchy of decision trees. A higher MDG value shows that the degree of impurity arising from a variable category could be reduced farthest by one variable, and therefore suggests an important associated factor.</mark><sup>25</sup> <mark>The ranking of the top 10 predictors was plotted based on the MDG. Predictors with a higher relative importance value are
is 27.5 percent, compared with the reference category.<sup>31</sup> All results are robust to the inclusion of productivity as an additional control variable (see results in table A1.6 in annex 1).<sup>32</sup>

**Table 5. Average markup differences by ownership structure, relative to domestic, privately** **<u>owned firms</u>**

||**(1)**<br>**Manufacturing**<br>**(%)**|**(2)**<br>**Services**<br>**(%)**|**(3)**<br>**Mining**<br>**(%)**|**(4)**<br>**All sectors**<br>**(%)**|
|---|---|---|---|---|
|Majority state-owned|52.7|13.9|77.2|20.0|
|State-private ownership|35.2|27.5|n.s.|28.9|
|Domestic private+foreign|16.1|27.9|n.s.|22.9|
|Fully foreign-owned|10.5|26.3|n.s.|20.1|
|Public of national and local<br>interest|20.9|14.9|n.s.|21.4|

_Source:_ World Bank staff calculations based on SBS data set. _Note:_ Numbers reflect estimated coefficients (multiplied by 100) for ownership dummy categories—relative to Romanian private firms, the omitted category—in a fixed-effect regression model controlling for firm covariates plus year and NACE 4-digit sector fixed
with high vs. low quality of financial institutions is a mere proxy for the differential effect of corruption across countries with varying levels of the quality of the broader business environment. The additional interaction term controls include interaction terms between corruption (incidence and depth of petty corruption, separately) and the following: Rule of Law (Worldwide Governance Indicators), Gross Primary Education Enrollment rate (WDI), Gross Secondary Education Enrollment rate (WDI), Freedom from Business Regulation (Economic Freedom of the World), Legal Systems and Property Rights (Economic Freedom of the World), and hours of power outages faced by the firm in a typical month (ES). These interaction term controls are in addition the ones between corruption and GDP per capita (logs, PPP adjusted and in constant 2011 international dollars) and between corruption and _Annual Sales_ (firm-size). Data source for GDP per capita is WDI, World Bank. Regression results in table 5 and 6 confirm our prediction. That is, while higher corruption increases the likelihood of a firm being financially constrained, the increase is significantly smaller in countries that have better financial institutions.

# 5.2 _Falsification test_

Next, we provide results from the falsification test. That is, we check if like corruption, the impact of more burdensome regulation and higher power outages on exacerbating firm’s financial constraints is smaller (less positive or more negative) in countries with better quality of financial institutions as measured by Getting Credit sub-index and the presence of credit bureaus. A failure to find such an effect will raise our confidence against endogeneity concerns with our main results. For regulation and in separate regressions, we use two measures – a dummy variable equal to 1 if the firm was inspected by tax officials and 0 otherwise; and percentage of firm’s senior management’s time spent 24
Policy Research Working Paper 7847

# **Abstract**

Combining macroeconomic and microeconomic data and three indicators of international market integration, this paper assesses the degree to which Latin American labor markets are integrated. The results suggest that relative to East Asia, Latin American labor markets are somewhat more integrated, but considerable differences across countries persist. In addition, the evidence indicates that the degree of labor market integration across Latin American borders is significantly less than that of labor markets within Mexico and within the United States in two of the three indicators. These differences may suggest opportunities for efficiency gains from further labor market integration.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region. 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 dlederman@worldbank.org and robertson@tamu.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