rafmacalaba/data-use-mentions-extended huggingface dataset:
Attendants.

Niger Paid employment in non-agricultural activities is taken from the ILO's Yearbook of Labor Statistics 1995 and refers to 1991. The data reflect the working population.

Unemployment data are taken from the ILO's Yearbook of Labor Statistics 1995, refer to 1991 and source l1l, Code 1: _Employment_ office statistics and do not include persons who are already in employment. Central Govemment, Education and Health employment is taken from Ide Gnandou in the Resident Mission in Niger (AF4NI) and relate to 1995. Data on military employment do not include personnel enlisted in the paramilitary, i.e. Gendarmerie (1,400), Republican Guard (2,500) or the national police (1,500).

GDP at current prices is taken from World Tables 1995 and relates to 1993. Wage bill of Central Govemment is taken from Ida Gnandou (AF4NI) in the resident mission and relates to 1995. Calculations of Average Central Govemment wages were reconfirmed with Mr. Gnandou. Data on paid employment in non-agricultural activities is taken from the Intemational Labor Office's Yearbook of Labor Statistics 1995 and refers to 1991.

Nigeria Unemployment data are taken from the ILO's Yearbook of Labor Statistics 1995, refer to 1993 and source 1ll, Code 1: _Employment_ office _statistics_ and do not include persons who are already in employment. Data on Civil Service employment is taken from Nigeria Federal Expenditure Review of May 25, 1995 and relates to 1993. Education employment is also taken from Niceria Federal Expenditure Review of May 25, 1995. Data refers only to employees of the Federal Government. Responsibility over education are divided among all tiers of govemment, with the central govemment having the main responsibility over tertiary education, the state govemments for secondary education and local governments for primary education. Health employment figures are also from Nigeria Federal Expenditure Review of May 25, 1995 and only refer to the emplovees of the Federal GovemmenLi Data on military do not include personnel enlisted in paramilitary corps such as the Port Security Police (2,000
2009, 2014 None, mild, moderate, severe, creme yes<br>Pau EncPop u esla t iona Nacsonal Census De Hogares (EN AHO) 20 15,10 2016 Yes\No 5 do mains.mams_ No s elf-cadf-cu re ._ yes<br>Encuesta Nacional de Programa Presupuestales Yearly from 2014-2017 Yes/No 5 domains. No self-care. yes<br>Encuesta Permanente de Empleo en Lima Metropolitana 2017, 2018 5 domams_No sdf-cue_ yes<br>Demographic and Health Survey 2013, 2014 Yes/No 5 domains. No self-care. yes<br>‘MiddleEast and North Afraa<br>Afghanistan Population Census 2011 Yes/No yes<br>Marocan Population Census 7014 Different catepncical answers yes<br>Tunisia Population Census 2014 Yes/No. if Y es. then WGSS answer scale yes<br>Turkey Population Consus O11 5 dom ams. No sdf-care_ yes<br>West Bank/Gaza Expenditure and Consumption Survey (ECS) 2009 Yes/No 5 dom ains. No self-care. yes<br>Scao-connmmic Conditions of the Palestmian Houscholids’ 2010 YesNo 5 domams_No sdf-cure_ yes<br>Population Census 2017 5 domains. No self-care. yes<br>Sub-Saharan Africa Soqo-connmmic Monitoong of the Palestinian 2013 No, Some, Great diffiity, Couldnot entirely 5 domams_No sef-care_ yes<br>‘Djibouti Engurte Djibouienne anpres desMenages<br>Ghana Study on Global Ageing and Adult Health (SAGE)(EDAM) 20172014 None, mild, moderate, severe, extreme 5 dom ams. No sdf-care yes<br>Malawi Population Census 218 yes<br>Rwanda Population Census 2012 Yes\No 5 domains. No self-care. yes<br>SouthAfiaa Popul a ndtioHousi n g Census 211 yes<br>LivingC onditions Survey (LCS) 2014 yes<br>Tanzania ‘National Household Travel Survey (NHTS) 2013 yes<br>Togo EnqurtePopulation and Housing Census 2012 5 dom ains. No com munication.<br>Uganda surla Transiion vers la Vie Actrve (
a board as well as additional **i l.in-:itiij** caused **by** localized industry-specific spillovers and natural advantages. Drawing upon their model they propose an index of geographic concentration of employment in an industry that controls for industry characteristics, namely for the size distribution of plants in the industry.' Indeed, one of the key advantages of the **EG** index is that it allows for cross-industry comparisons.

Considering the aforementioned raw geographic concentration index **Gj** for industry _j_ and its plant size distribution as measured by the Herfindahl index, **_Hj = I_** 1<sup>_z_</sup><sup>**,**where</sup><sup>**zij**</sup> is plant i's share in employment of industry **_j,_** the **EG** index for industry **_j_** in a given time period is defined **by** Equation **1:6** The subtraction of the term **(1** - **_EMIx2)Hj_** in Equation 1 accounts for the aforementioned problem that **Gj** would be larger in industries with just a few large plants even if plants chose their locations randomly. That term corresponds to the level of concentration that would be expected (in the model of Ellison and Glaeser **(1997))** if plants in the industry chose their locations independently and randomly and both industry-specific spillovers and natural advantages played no role. Therefore, the **EG** index measures the concentration of employment in an industry over and above the level of concentration that would have prevailed if plants were distributed completely at random. Greater values of the **EG** index are associated with greater geographic concentration of the industry.<sup>7</sup> Dumais et al. (2002) show that the index can
,130|164,595,130|164,298,452|164,298,452|164,298,452|164,298,452|
|R-squared|0.338|0.338|0.340|0.340|0.156|0.156|0.156|0.156|
|Firm-prod-year FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Dest-prod-year FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Firm-prod-dest FE|YES|YES|YES|YES|YES|YES|YES|YES|

Robust standard errors, clustered at the firm-product level, are in parentheses. The dependent variable _yijp,t_ is a dummy variable equal to 1 if firm _i_ exported (or started exporting) product _p_ to country _j_ in year _t_ . STC=1 if a Specific Trade Concern (STC) on product _p_ has been raised in the WTO. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1

# **5 Robustness analysis**

This section presents a series of robustness checks. First, we use different measures of products’ regulation intensity: continuous STC measure and FDA prior notice requirements. Second, we use an alternative measure of depth to provide further evidence that the positive spillover effects are driven by deeper forms of regional integration. Finally, we include additional fixed effects to control for firm-destination-year effects. All regressions in the robustness analysis include spatial exporting controls, although coefficients are not reported for brevity.

## **5.1 Continuous measure of regulation intensity**

The baseline specification uses a binary variable as a proxy for the regulation intensity of products. We can also construct a continuous measure from the STC data by counting the number of times each product was raised as a concern in the SPS/TBT committees 16
99)**|
|HH head female|-0.033<br>(4.90)**|0.135<br>(16.69)**|
|HH No. of child.<16|0.027|0.005|
||(6.15)**|(1.00)|
|Rural|-0.168|0.005|
||(27.95)**|(0.47)|
|Constant|0.003|-1.024|
||(0.06)|(20.95)**|
|_Observations_|_230334_|_150488_|

Source: Moldova HBS 1998-2002. Absolute value of z statistics in parentheses. * significant at 5%; ** significant at 1%

# **5. JOB CREATION POLICIES IN SEGMENTED LOW-INCOME COUNTRIES**

Despite the deindustrialization process observed in the course of transition, agriculture and industry together remain the largest sectors in the CIS-7 and contribute to in between 30% and 60% of total output and in between 50% and 60% of total employment which would make these sectors the primary concern of governments. Yet, agricultural and industrial policies have been marginalized in the 1990s and today do not appear in any substantial form in government policies plans and programs.

Agricultural policies during the 1990s have focused on land reforms and privatization and in this respect they have been relatively successful. Armenia distributed more than 80% of the arable land to more than 320,000 families starting in 1995 and the 2002 World Bank Poverty Update for this country found a clear inverse correlation between land ownership and poverty. Georgia implemented land reforms in 1992 and 1996 distributing full land rights covering about a third of arable land with an additional 20% of the land leased to large producers. By April 1999, 918,000 hectares had been transferred to 1,026,000 rural households, while 825,000 additional hectares had been
Note. Sampling weights used. T-tests of equality of means were conducted across men and women within each country. Only average shares are reported for the sake of clarity.

Source: Own calculations based on Ethiopia - High Frequency Phone Survey (2020-2023); Malawi - High-Frequency Phone Survey 2020-2024; Nigeria COVID-19 National Longitudinal Phone Survey 2020-2021. Datasets downloaded from https://microdata.worldbank.org/index.php/catalog/hfps/?page=1&ps=15&repo=hfps on January 2, 2024

# **4.2. Employment status and career aspirations**

We estimate that overall, 25 percent of youths is employed as of May/June 2020 and 30 percent of them is not working in their ideal job. Nigeria and Ethiopia report the highest proportions of youths who are not currently engaged in their ideal work activity, accounting for 41 percent of youths in Ethiopia and 31 percent of youths in Nigeria. This share decreases to 10 percent in Malawi ( **Table A2 in the Annex** ). Nonetheless, approximately 81 percent of youths believe they can achieve their dream job, with percentages ranging from 86 percent of youths in Ethiopia and Nigeria to 61 percent in Malawi ( **Table A2 in the Annex** ). Furthermore, about 70 percent of youths know someone from their community who holds their desired occupation, ranging from 72 percent and 69 percent in Nigeria and Ethiopia respectively to 64 percent in Malawi ( **Table A2 in the Annex** ).

10
**Table 4 Macro Determinants of the Shares of Informal employees in the Extended Labor Force. Eastern and Southern Europe, 2004-2009**

|(_a_|_lternativepa_|_nel data est_|_imates,_ _cont_|_inued_)|||
|---|---|---|---|---|---|---|
||[7]|[8]|[9]|[10]|[11]|[12]|
||<br>(semi-|<br>Popul<br>robust standa|<br>ation-average<br>rd errors clus|<br>d panel-data<br>tered on coun|<br>models<br>tries in paren|<br>theses)|
|Employment Protection<br>|3.138***|3.387***|3.343***|3.426***|4.302***|3.216***|
|Legislation strictness, v. 2|(0.571)|(0.647)|(0.749)|(0.820)|(0.469)|(0.683)|
|Govt. Effectiveness<sup>a</sup><br> <sup>a</sup>|-5.097***|-4.401***|-4.301***|-4.003***|-3.397***|-2.926*|
|(Rule of Law in [11])|(1.442)|(1.693)|(1.674)|(1.350)|(1.237)|(1.562)|
|Investment Freedom<sup>a</sup>|-0
081)|(0.094)|
|Constant|10.791***|10.651***|10.933***|10.836***|10.758***|10.650***|10.671***|<br>10.672***|<br>10.762***|
||(0.034)|(0.057)|(0.070)|(0.055)|(0.038)|(0.039)|(0.044)|(0.053)|(0.087)|
|Observations|2,118|2,100|2,114|2,110|2,114|2,118|2,118|2,108|2,100|
|R-squared|0.017|0.178|0.038|0.020|0.032|0.135|0.072|0.074|0.189|

Note: Dependent variable is the log of per capita food consumption expenditure. Standard errors in parentheses. _Differences found to be statistically significant are indicated by level:_<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01. The reference category is the 14-day diary. Model 2 includes main respondent and household characteristics as controls, model 9 includes the interaction of all characteristics with treatment dummies.

42
were used as collateral.

Related, when asked about the primary use of their WEDP-X loans, almost half of the treatment group say they used the loan for marketing materials or services; while 25% say they purchased inputs, and 21% equipment. While marketing may have been an important use of funds, for loans of this size we suspect inputs and equipment may have been a larger use, and that there may have been some reticence to report this use linked to the mistaken belief that business assets might count as collateral for the loan.

# **6.2 Impacts on Firm Survival**

Table 3 shows that treatment increased the firm survival rate from baseline to endline from 67% in the control group to 83% in the treatment group. Put differently, access to psychometric-appraised loans reduced firm death over three years to 17%, from the controlgroup rate of 33% through the COVID-19 pandemic, conflict in Ethiopia (including in the Oromia region where the study took place), and the related challenges in the economic environment. For illustrative comparison, McKenzie and Paffhausen (2019) use data from small firms in 12 developing countries to estimate firm death rates of 50% over six years. As a rough benchmark, we might interpolate this linearly to a business-as-usual closure rate of 25% over three years — our treatment group faring better than this, our control group faring worse. It appears the loan helped slightly smaller firms to survive: firms that stayed open (closed) had 2.33 (1.41) employees in the control group versus 1.75 (1.73) employees in the treatment group. The additional firms kept alive in the treatment group are roughly evenly distributed across sectors.

There is no significant impact on women exiting entrepreneurship altogether (Column 2), since many of those control-group respondents who closed their original firm reopened a new firm, typically in a different sector (Column 4). These sector switches did not follow a — — clear pattern for example,
included is agricultural domestic support. This was obtained from WTO members noti fi cations during the period 1995-1998 (see Hoekman, Ng and Olarreaga, 2004 for a discussion of the construction of this variable). Domestic support is measured in dollars and is a continuous variable so it enters in log form in the estimation of (8). Only 158 tariff lines at the six digit of the HS are affected by domestic support in at least one WTO member.<sup>15</sup> One may wonder why we do not use estimates of producer subsidy equivalents (PSE) as traditionally done in the literature rather than estimating the AVE of these subsidies. The reason is twofold. First, production data at this level of disaggregation is not available, which precludes calculating PSE. Second, PSE cannot be directly compared to tariffs as they only affect the production side,

> 14 See Shepherd (2004).

> 15 One problem is that China’s agriculture support is not included as it was not a WTO member during that period.

15
and domestic responsibilities and business performance outcomes for male and female entrepreneurs. To shed light on this, the paper links entrepreneurlevel and firm-level data collected from business owners across 50 countries in 2020 and 2021 1According to UNESCO, more than 1.5 billion children were out of school in April 2020, with a quarter of a billion still out of school by the end of October 2020. Moreover, the COVID-19 pandemic has also dampened or removed the possibility of informal childcare through extended family support networks (UNESCO, 2020 and 2021).

2
Main Business City|27%|31%|47%|39%|
||Other City|22%|29%|43%|32%|

Source: Author’s elaboration from Enterprise Survey (2014) Innovation activity in South Asia appears to be geographically concentrated only when considering the novelty of innovation. To explore the impact of agglomeration and following the literature, Table 20 compares employment shares across provinces with innovation activities shares and their concentration measured with the Herfindahl index. Larger concentration of innovation activities in relation to employment implies that there may be some agglomeration effects of innovation activity, as suggested in the literature. In general, however, the table shows that innovation activities tend to be less concentrated than employment, except in Pakistan, and more concentrated when considering higher degrees of innovation 24
# **Model Code**

The code used to generate these analyses is available at: http://doi.org/10.5281/zenodo.4060416

# **Data**

All data used in this study can be downloaded from the cited sources. Each specific country dataset can be found at the following:

**Data S1.** <mark>World Bank, Ethiopia - High-Frequency Phone Survey on COVID-19 2020. (World Bank, Washington, D.C., 2020); https://microdata.worldbank.org/index.php/catalog/3716.</mark> **Data S2.** <mark>World Bank, Malawi - High-Frequency Phone Survey on COVID-19 2020. (World Bank, Washington, D.C., 2020); https://microdata.worldbank.org/index.php/catalog/3766.</mark> **Data S3.** <mark>World Bank, Nigeria - High-Frequency Phone Survey on COVID-19 2020. (World Bank, Washington, D.C., 2020); https://microdata.worldbank.org/index.php/catalog/3712.</mark> **Data S4.** <mark>World Bank, Uganda - High-Frequency Phone Survey on COVID-19 2020. (World Bank, Washington, D.C., 2020); https://microdata.worldbank.org/index.php/catalog/3765.</mark>

24
reported wage and employment data, and the importance of coffee to Ethiopia's economy.7 Coffee constitutes Ethiopia's single most important crop, contributing over half of the agricultural GDP. Coffee is also responsible for nearly 60% of the country's foreign exchange earnings. Teppi is the second largest coffee plantation in Ethiopia, administering 6245 hectares of fully-developed coffee-producing land. In Ethiopia, Teppi is second in size and output to the adjoining coffee plantation of Bebeka.

OSD is typically prevalent in the most fertile land areas of northwest, southwest, and western parts of Ethiopia. A 1984 study estimated the number of cases of OSD in Ethiopia to be 1.4 million, with a population at risk of 7.3 million (or 17.4% of the population). Point prevalence, determined by the skin-snip method, ranges from a high of 84% in the western endemic zone through 60% in the southwest, and a low of 19.5% in the northwest. Therefore, based on the WHO classification of endemicity, there are hyperendemic and mesoendemic regions in Ethiopia. The mean microfilarial load per infected person ranges from 3.6 to 34 microfilariae per mg of skin snip, depending upon the community. Most recently, Aga _et_ **_al._** (1995) reported a microfilarial load of up to 382 microfilaria per mg skin snip in a rural farming village in southwest Ethiopia.

# _Economic Methodology_

We estimate the daily wage equation for plantation workers<sup>8</sup> , which in the semi-log formulation<sup>9</sup> , is given by:

|||**_in_**_W= a, X +a2_ **_0_**_+a3_**_I_**_+_**_e_**|
|---|---|---|
|where,|**_W:_**<br>**_0:_**|Daily
In a similar vein, the traffic information problem can now be addressed using Google Maps, which provides real-time traffic reports for the Dar es Salaam metro region. Although desktop monitors do not show this, Google Traffic continuously produces a map large enough to cover the entire metro region at 100 m resolution. However, Google provides no facility for georeferenced downloads. To mobilize this huge information resource, one of the authors has developed an algorithm that maps region-scale Google Traffic displays into a panel database of traffic measures for a reference grid with arbitrary cell size.

Google Traffic provides a four-color measure of vehicle speed along each 100-meter road link, with separate speed measures for traffic flows in two directions on primary roads. For this study, we match information from Google Traffic with time-compatible NO2 imagery within a grid of spatial cells with a resolution of .01 decimal degrees (approximately 1.1 km). Since Google Traffic data are available continuously, we also construct a database of traffic measures by day and hour for the period March 20 - May 23, 2019. We convert these files to geoTIFF format for grid-based spatial computations. Using the Dar es Salaam region shapefile provided by OpenStreetMaps (OSM), we divide roads into two classes, primary (OSM classes primary and trunk) and secondary (all other OSM classes). Then we overlay Google Traffic’s color-coded pixels on the OSM map and estimate vehicle speeds by road class and color code using trip routings for a sample of realtime Google Traffic information. Table 1 reports our results for two road classes and four color codes.

**Table 1:  Google Traffic indicators and speeds (km/hr) in Dar es Salaam**

|Color|OpenSt<br>Roa|reetMaps<br>d Type|
|---|---|---|
|Indicator|Primary|Secondary|
|Green|45|24|
|Orange|23|16|
|Red|11|7|
|Purple|7|4|
Policy Research Working Paper 9931

# **Abstract**

This paper nowcasts poverty in India, one of the countries with the largest population below the international poverty line of $1.90 per person per day. Because the latest official household survey dates back to 2011/12, there is considerable uncertainty about recent poverty trends in the country. Applying a pass-through and survey-to-survey methodology, extreme poverty (at the $1.90 poverty line) for India in 2017 is estimated at 10.4 percent with a confidence interval of [8.1, 11.3]. The urban and rural poverty rates are estimated at 7.2 and 12.0 percent, respectively. Across a wide range of publicly available data sources, the paper finds no evidence of an increase in poverty between 2011/12 and 2017/18.

This paper is a product of the Development Data Group, Development Economics and Poverty and the Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/prwp. The authors may be contacted at sfreijerodriguez@worldbank.org and clakner@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