rafmacalaba/datause-encoder-data huggingface dataset:
4 The 1992/93 was based on the sample design of 1987 which had 22 states. In each state a total of 120 EAs, 48 urban, 12 semi-urban, and 60 rural, were studied. These 120 EAs are allocated over the 12 survey months of the year covering 10 EAs in the ratio of 5:4:1 for rural, urban and semi-urban respectively in each state. Otherwise the sample was similar in design to the 1985/86 survey.

The Sample was weighted on the basis of the probability of selecting an EA within a state and choosing a household within an EA. A diary was given to each household to record flows of income to and expenditure from the household within the reference period or reference month2/. Field staff usually made daily visits to each household to ensure quality of entry and also to help those households who have no one literate to fill in the diary. This information was then transferred on a daily basis to the enumerator's memo book. The enumerator's book was then deposited in the field office where it was checked for quality and consistency by the supervisor.

The survey collected information on the following items on each household:

- (a) Household Demographic Statistics: age, gender, location and type of housing of the household.

- (b) Household Expenditure: Expenditure on all goods and services incurred by the household during the survey period. It also includes all monetary transactions (savings, donations and Esusu3/ contributions)

- (c) Imputed Rent on owner-occupied and rent-free dwelling

- (d) Cash Income: Income from wages, overtime, bonuses, professional fees that is received by the household.

# **3.** THE MEASUREMENT OF WELFARE AND POVERTY

# **3.1 Household Expenditure**

The measurement of household welfare or standard of living is a question which has not been resolved completely (Sen, 1987). There are many ways one could go about addressing this issue depending on the context, need and availability of information. Since quality of life has to

> 2/ "The sampling unit of the survey is the household defined as all persons living under the same roof and having a common eating arrangement. The boarders are included as members of the household and lodgers are excluded. Members of the household who are temporarily absent are included". See "Social Statistics in Nigeria", Federal Office of Statistics (FOS), Nigeria, 1980, p 1.

> 2/ Esusu refers to a system whereby a group of individuals or family gets together either on a weekly, biweekly or monthly basis, and each person contributes a certain sum of money, usually the same amount per individual. This is given on a rotating basis to each individual in the group. This results in a large sum of money which the individual can invest or simply save. It is worth noting that there might be differences in the arrangements in different regions, although fundamentally it is similar in effect.
**Table E.2. Conflict** **<u>and consumption, unweighted regression</u>**

|||||Event|type|Perp|etrator t|ype|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|VARIABLES|||||||||
|Conflict events|-0.035***<br>(0.010)|-0.030***<br>(0.011)|-0.028**<br>(0.012)|<br>|||||
|Conflict events property|||<br>|-0.063**<br>(0.029)|||||
|Conflict events violence|||||-0.005<br>(0.038)|<br>|||
|Insurgents|||||<br>|-0.041**<br>(0.016)|||
|Bandits/criminals|||||||-0.050<br>(0.067)||
|Pastoralists/nomads||||||||-0.011<br>(0.023)|
|Observations|2,125|2,075|2,075|2,075|2,075|<br>2,075|2,075|2,075|
|R-squared|0.142|0.258|0.269|0.269|0.267|<br>0.269|0.268|0.267|
|Number of households|717|717|717|717|717|717|717|717|
|Controls|NO|YES|YES|YES|YES|YES|YES|YES|
|Year FE|YES|YES|NO|NO|NO|NO|NO|NO|
|HH FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Region-roundFE|NO|NO|YES|YES|YES|YES|YES|YES|

_Note_ : Dependent variable is log consumption of household. Data used are from the three waves of the GHS and telephone survey for conflict. No weights are used in any of the regressions. Controls include all household and geographical variables listed in appendix table C.1. Standard errors clustered at the local government area level. Significance: *** _p_ < 0.01; ** _p_ < 0.05; * _p_ < 0.1 xix
As the size of the firm maps directly to managerial ability, we map the appropriate aggregate tax rate to the corresponding managerial ability �. As the estimated tax rate is positively correlated with firm size, managers with higher ability are subjected to higher taxes compared to managers with low ability.

**Figure 4: Output Loss (Taxes) Due to Crime and Corruption by Firm Size**

<!-- Start of picture text --> 8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>less than 5 5 to19 20 to 99 100 or more Aggregate<br>Number of Employees<br>Crime (Loss + Security) Corruption Total<br>Loss (% of Sales)<br><!-- End of picture text --> This leaves us with calibrating the credit market parameter � and the managerial distribution function ��. �. To do this, we adopt an approach that is similar to earlier research that considers heterogeneity in firm productivity and jointly calibrates them to match the moments discussed below.<sup>16</sup> We assume the managerial ability function follows a log-normal distribution with location parameter � and variance parameter �. The three parameters ��, �, �� are calibrated to match three moments that are important in the context of the model. The first two moments that we target are the size distribution of firms which ties to the distribution of managerial ability and hence pins down � and �. We use the information from the Enterprise Survey of 2014 to arrive at the size distribution of firms in Afghanistan which is shown in Figure 5.

Finally, we need a moment that captures the level of financial markets. Levine (2005) surveys the rich theoretical and empirical literature that has studied the relationship between financial development and growth. In the context of our model, we specifically want to measure the effect of frictions that create an obstacle for managers to borrow capital. As discussed in Amaral & Quintin (2010), _intermediated capital_ which is defined as the capital raised by managers over their personal savings in the credit markets is closely related to the financial market parameter �. A manager is able to borrow more capital at higher values of � which essentially reduces his reliance on savings in the first period and there is more intermediated capital

> 16 For an early example see Restuccia and Rogerson (2008), Amaral and Quintin (2010) and Buera _et al._ (2011) who consider heterogeneity in firm productivity in the presence of financial frictions.

23
spillovers. Manufacturing sectors with MNCs operating within regional or global value chains are more prone to generate knowledge transmission to the host economy, but the automatic creation of domestic linkages may be hampered by market failures, such as information asymmetries, low scale, and quality constraints.

The remainder of the paper is structured as follows. The next section discusses the data and presents summary statistics on the set of high-growth firms and the transmission channels of FDI spillovers. Section 3 presents the empirical strategy and results. Section 4 discusses the robustness checks. Concluding remarks are offered in section 5.

# **2. Data and Descriptive Statistics**

I employ the WBES to characterize firm growth, identify high-growth firms, and measure the existence of channels to promote FDI spillovers across the developing world. The WBES is a collection of comparable firm-level surveys of a representative sample of the economy’s private sector. The dataset contains information for 71,000 firms in 122 developing economies. The complete list of countries and the years when each survey was conducted are shown in Annex 1.<sup>4</sup> The surveys provide basic characteristics of the firms and cover a broad range of business environment topics including access to finance, corruption, infrastructure, crime, competition, and firm-level performance measures. Crucially, the surveys ask firms about their employment in the previous fiscal year and three fiscal years before.

# **a. Identification and Characterization of High-Growth Firms**

There are at least three issues that need to be considered when characterizing firm performance: i) the indicator of growth; (ii) the measure of growth; and (iii) the period under study. The indicator of growth refers to the variable in which growth is observed. The most commonly used indicators in the high-growth firms’ literature are sales and number of employees (Daunfeldt et al. 2013). Because I am interested in high-growth firms’ contribution to job creation, I use the number of permanent, full-time employees of the firm as our growth indicator.<sup>5</sup>

> 4 The raw WBESD includes information for around 125,000 firms in 139 countries, including various waves of surveys for many countries. For the purposes of this analysis, we retain the latest survey conducted in each country and only economies classified as low- and middle-income countries by the World Bank Group.

> 5 Permanent, full-time employees are defined as all paid employees and managers that are contracted for a term of one or more fiscal years and/or have a guarantee renewal of their employment contract and that work a complete shift or more per day.

- 5 -
<!-- Start of picture text --> (a) (b)<br>40 14 —— I|mpTExpT<br>— —ImpQExpT<br>35 1.35 ------~ImpTExpQ<br>z5©<br>=] 30 fo}5 13<br>a2<br>325 B 125<br>=-2£3°s<br>520 2 12<br>&<br>= 15 g 1.15<br>10 14 OO ee<br>—— I|mpQExpQ —<br>5 —— Pigou Tax Scenario 405<br>20 40 60 80 100 120 140 20 40 60 80 100 120 140<br>Time Time<br><!-- End of picture text -->
Table 6: Estimating the effect of migrants’ legal status on hourly wages after return, unconditional on return migration

|(1)<br>Migrate|(2)<br>Illegal|(3)<br>Return|(4)<br>Log of<br>hourly wage|
|---|---|---|---|
|Age<br>0.012***|-0.074**|-0.014**|-0.001|
|(0.001)|(0.034)|(0.006)|(0.005)|
|Age squared<br>-0.000***|0.001**|0.000***|0.000**|
|(0.000)|(0.000)|(0.000)|(0.000)|
|No education<br>0.019***|0.485***|-0.030|-0.445***|
|(0.004)|(0.117)|(0.021)|(0.026)|
|Primary or preparatory<br>0.032***|0.476***|0.049**|-0.419***|
|(0.004)|(0.125)|(0.022)|(0.025)|
|Secondary<br>0.017***|0.360***|0.034*|-0.285***|
|(0.003)|(0.101)|(0.017)|(0.020)|
|Rural<br>0.009***|0.256***|0.009|-0.028*|
|(0.003)|(0.083)|(0.015)|(0.016)|
|Oil prices<br>0.013***<br>(0.000)||||
|Diplomatic exchange|-0.229**<br>(0.102)|||
|Confict||0.047***<br>(0.013)||
|Illegal status|||-0.232***<br>(0.053)|
|Legal status|||0.203***<br>(0.044)|
|Observations<br>14,879|1,955|2,071|8,054|
|Parental controls<br>Yes|Yes|Yes|Yes|
|Decades of migration dummies<br>Yes|Yes|Yes|No|
|Labor market controls in Egypt<br>No|No|No|Yes|
|Predicted log wage: Non-mig.: 1.515; Illegal mig.=|1.329; Legal mi|g.=1.794||

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Notes_ : Equation 1 is the probability of temporary migration. Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models the logarithm of hourly wages upon return. For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use the diplomatic exchange, which is the diplomatic representation of each country of destination of Egyptian migrants in Egypt at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange and is equal one if there is evidence of diplomatic exchange at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality on the logarithm of hourly wages, unconditional on return migration. The predicted logarithm of hourly wages for non-migrants, illegal migrants, and legal migrants are reported in the last row.

40
allocation method, because the targeting strategy was being refined up until the program was stopped in 2021.

The final PMT was chosen based on the above evaluation criteria and program considerations, namely a tradeoff between model accuracy and complexity. We find that, in general, more complex models (i.e., including more variables) only marginally improve targeting accuracy. However, the inclusion of more information raises the cost of obtaining information and administering the program. Therefore, we propose a parsimonious model (i.e., one that uses less variables) that provides high targeting accuracy.

# 4. Results

To arrive at a final model for the Sudan PMT, we split the data into a training and testing set using a 75% training and 25% testing split. In an effort avoid overfitting on in-sample characteristics of the sample data, all modeling decisions, including any variable transformations or groupings, were based on the training data before arriving at a small number of potential models that were applied to the testing data to explore model performance on unseen, out-of-sample observations. This is done in an effort mimic how the PMT would be applied in practice and to understand out-of-sample predictive performance of the PMT. After exploring model performance on out-of-sample data, the top performing models were selected and estimated using the complete sample of data.

In an effort to improve predictive accuracy of the Sudan PMT, we analyzed the raw household survey data and selected and transformed variables to better capture the relationship with per capital household consumption and the country context.<sup>17</sup> For all categorical variables, we explored each category for intuitive groupings that were related to household income using cross-tabulations and boxplots. For continuous variables, we explored non-linear relationships and, if non-linear relationships were present, we applied transformations, so that the relationship between continuous predictors and the outcome variable of log per capita household consumption was approximately linear. Examples of these analyses are found in Annex 4.

Table 3 below presents the main results of the Sudan national PMT. During the modeling process, we found that a more parsimonious model, i.e., one with less variables and parameters, performed similar to the complete model, which included all candidate variables. Thus, the final model presented below retains the

> 17 For example, the level of schooling exhibited a stronger relationship with household consumption than years of schooling, due to grade repetitions increasing years of schooling, but not the level earned.

12
Sánchez-Martín, Escribano and de Arce, 2014. “How regional integration and transnational energy networks have boosted FDI in Turkey” Mazzanti M.R. and Biancardi, A., 2005. “Institutional Endowment and Regulatory Reform in Turkey’s Natural Gas Sector”. Chapter 8 inHoekman and Togan, _Turkey : economic reform and accession to the European Union._ Washington, DC . World Bank. Centre for Economic Policy Research, 2005.

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Sayek, S., 2007. “FDI in Turkey: the investment climate and EU effects”. _The Journal of International Trade and Diplomacy_ 1 (2), Fall 2007: 105-138 Saivetz, C., 2009. “Tangled Pipelines: Turkey's Role in Energy Export Plans”. _Turkish Studies_ , Volume 10, Number 1, March 2009 , pp. 95-108(14). Routledge.

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Tagliapietra, S., 2012. “Rethinking the EU-Turkey Energy Cooperation”. Review of Environment, Energy and Economics (Re3), September 2012. Available at SSRN: http://ssrn.com/abstract=2154965 32
day in a year. In that case, a 1 _μg/m_<sup>3</sup> increase in the annual average in the next year would be equivalent to increasing the daily average by 30% for an entire month. That is, a 1 _μg/m_<sup>3</sup> increase in the annual average is the difference between a month with daily exposure of 54 _μg/m_<sup>3</sup> and one with daily exposure of 70 _μg/m_<sup>3</sup> . Or consider the level of PM2 _._ 5 measured at the U.S. Embassy in Delhi in 2015. The annual average daily level was 170 _μg/m_<sup>3</sup> for 2015 with the daily average during November and December, the two worst months of the year, at 287 _μg/m_<sup>3</sup> . A 1 _μg/m_<sup>3</sup> reduction in the annual average level would have been akin to reducing the peak during November and December by _≈_ 10%.

Our results indicate that higher levels of pollution have negative consequences for economic activity that are felt in the short-run, consistent with much of the existing microevidence on the consequences of exposure to air pollution. These negative micro consequences appear to accumulate in ways that reduce activity across the economy, resulting in less activity relative to a counter-factual in which pollution exposure was lower. This reduction in activity results in lower rates of year over year change in GDP.

# **5.3 Robustness**

Our results are robust to a variety of sensible specification variations and alternative clustering. We show (Table 5) that omitting the measure of inversion strength does not substantially change our results - in fact it slightly increases our point estimates. Nor does using total inversions over the whole year rather than dividing them into seasons.

To account for potential spatial correlation in weather patterns or pollution we also cluster errors at the state, rather than district level. This substantially reduces the number of clusters, from 515 to 23. Unsurprisingly this reduces our precision but our estimates remain significant at the 5 percent level.

We also test alternative sets of weather controls (Appendix Figure A1). In general our results remain very stable across a wide variety of combinations of our base weather controls. These include specifications that omit all controls except for precipitation, omit all but controls for average temperature, and define temperature as the maximum rather than the average.

To test for robustness against weak instruments we estimate a LIML version of our IV approach (Appendix Table A2). LIML models have been shown to be more robust to weak instruments than 2SLS (Stock and Yogo, 2002). Our results do not change when estimated with the LIML, rather than 2SLS approach.

The reduction in GDP due to higher levels of pollution may be offset by increases in GDP in the following year if the impacts are due to temporary reductions, for example, 13
This is indeed what we found in a sample of Latin American firms between 2006 and 2010. Firms that used EPA services are more likely to enter and survive in world markets, but the level of firms' exports is unaffected. Thus EPAs contribute to aggregate export growth through the extensive rather than the intensive margin, which is to be expected if they are correcting for market failures associated with information externalities.

The finding on the impact of export promotion activities on new exporters is new, but also important for export growth. Indeed, Eaton et al. (2007) showed that in a sample of Colombian firms from 1996 to 2005, new exporters account for almost half of exporting firms in any given year. Those that survive as exporters for more than a year grow rapidly and account for about half of the country’s total merchandise export growth after a decade. Lederman et al. (2011) report that the 1999 cohort of entrants into exporting accounted for almost 40 percent of Costa Rica’s total merchandise exports by 2005. This suggests that firms’ entry into export activities and their survival are as important as exports from incumbent firms for aggregate export growth in the medium term.<sup>2</sup> The existing literature on export promotion and firm entry is thin, but the available evidence suggests that export promotion does little to explain export growth by helping firm entry into export activities. Bernard and Jensen (2004) found that export promotion across states has no statistically significant impact on the probability of exporting in a sample of U.S. manufacturing firms. Görg et al (2008) found that export-promotion grants offered to Irish manufacturing firms have no impact on the probability of exporting, but do affect the level of exports. In a sense, our results contradict these findings, albeit with different data.

> 2 There is also an important literature that shows that the survival of new export “relationships” is an important determinant of export growth, at least in developing countries. But this literature focuses on products at the tariff line level -- see Evennett and Venables (2002) or Besedes and Prusa (2007) for papers showing that growth in the value of new export products or new export markets can account for a large share of export growth in developing countries. Note that a new export product or a new export market does not necessarily imply that a new firm is exporting. Similarly the fact that a new firm is exporting does not necessarily imply that a new product is being exported or that a new export market has been found. Note that there is also a growing literature using tariff line data (not firm data) showing that the intensive margin (i.e., exporting more of the same product) explains most of export growth;  see Felbemayer and Kohler (2006), Helplman, Melitz and Rubinstien (2008) or Amiti and Freund (2010).

3