rafmacalaba/datause-encoder-data huggingface dataset:
records and thereby access financing that male-owned businesses may already access. This study aims to empirically test the key hypothesis of whether the relationship between mobile money use and investment varies by the gender of the business owner.

# **3. Data and Summary Statistics**

The main data source used in this paper consists of cross-sectional firm-level surveys for 16 economies across Sub-Saharan Africa conducted by the World Bank’s Enterprise Surveys (ES). Table A2 presents the list of economies. Most of the surveys were conducted between 2015 and 2017 apart from four economies (Ghana, Tanzania, Uganda, and Zambia) for which data were collected in 2012. The ES collect information on a representative sample of formal (registered) private firms with at least five employees operating in manufacturing or services sectors. The ES data are fully comparable across countries and are collected via face-to-face interviews with business owners or top managers by using a global methodology.<sup>4</sup> The data have been widely used by several studies to explore the private sector in developing economies (Paunov, 2016; Besley and Mueller, 2018; Chauvet and Ehrhar, 2018; Hjort and Poulsen, 2019; Falciola et al., 2020).

The ES global methodology includes a consistent definition of the universe of inference, a standardized survey instrument, a uniform methodology of implementation, and a standard sampling methodology. The selection of firms in each country is done by stratified random sampling with three levels of stratification: sector of activity, firm size, and location within the country. Sampling weights are used to correct for unequal probability of selection as well as for ineligibility and non-response.<sup>5</sup> Supervisors and enumerators attend formal training sessions to ensure the best practices are deployed. Several quality control checks are implemented to guarantee the quality of the data throughout the data collection process. Consistency checks are employed for 10 percent and 50 percent batches of the data during the survey so as to allow quick callbacks to respondents to be undertaken when necessary to verify information.

Several aspects of the business environment are measured by the ES: infrastructure, regulations, taxes, finance, corruption, crime, informality, and competition. In addition, firms’ characteristics

> 4 ES data are collected using Computer Assisted Personal Interviewing (CAPI) software.

> 5 More information on the Enterprise Surveys global methodology as well as on the sample design and weights computation for the 16 economies is available on the website http://www.enterprisesurveys.org.

8
# **3. Data**

The data used in this study come from several sources. The source of migration figures is the US Census, which includes very detailed information on the social and economic status and the country of origin of foreign-born individuals living in the United States. We use the information from the 1% sample of the 1990 and the 2000 Census.<sup>9</sup> Each individual observation in the Census has a population weight attached to it, which captures the proportion of the overall US population the observation represents. Thus, foreign-born individuals in our data represent around 4.5 million people in the US. The data also contain information about the individual’s industry of employment and the education level. There are 9 separate education levels listed in the Census, and we divide them into two main categories: (i) college or above and (ii) less than college education. Thus we are able to calculate the stock of migrants from each country of origin in each educational category present in the US in 1990 and 2000.

As a robustness check, we also use information on the migrant’s industry of employment. There are close to 300 industries listed in the Census, and we aggregate them into ten sectors to match the sectoral FDI data. Based on the information on an individual’s age and year of arrival, we construct their date of entry into the US labor market.<sup>10</sup> In this way, we construct the migrant stock for each industry, year and country of origin.

Information on US FDI abroad comes from the Bureau of Economic Analysis, which collects figures on total assets and sales volume of non-bank affiliates of non-bank US companies. In our main specification, we focus on aggregate figures for each host country but in a robustness check we also estimate a model at the sectoral level. The data can be disaggregated into 10 industries which are finance, food, information technology, chemicals, electronic and electrical equipment, machinery, fabricated metals, wholesale trade, transportation equipment, and services. As the industry classification changed in 1998, we include only those industries that were present throughout the whole period. To match the availability of the Census information on the country of origin as well as the timing of the Census data, we focus on figures for 57 host countries for the years 1990 and 2000.

Turning to other host country characteristics, figures on the population size, GDP per capita in current US dollars and consumer price inflation have been taken from the World Bank’s _World Development Indicators._ For 1990 we use the average of inflation from 1988 through

> 9 Extracts from the Census samples are made available through IPUMS (Integrated Public Use Microdata Series), which is a database maintained by Minnesota Population Center at University of Minnesota (http://beta.ipums.org/usa/index.html).

> 10 We consider the year of arrival and the year of the completion of the highest education degree and take the larger of the two numbers as the year of entry into the US labor market.

6
MINIMUM WAGES IN CAMBODIA 26

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-9-

Results for another set of regresslons are displayed In Table S. The dependent varlable In Table 5 Is net output aggregated at border prices Invarlant across farms and converted Into rubles at two rubles to the dollar. The Independent variables In Table 5 are the same as In Table 4. The difference In estimated coefficlents In Tables 4 and 6 thus shows the effect of the domestic price structure on returns to factors of productlon compared to returns using world prices (but keeping the same technology and physical productivity.) If price differentlation (In the domestic price structure) had succeeded In fully capturing rents associated with superior land quallty, the regresslon coefficient associated with land quality (bonitet) In Table 4 would presumably be small. It Is In fact rather large (.53 for 1986) and precisely measured (t-12.74). When net output Is aggregated with border prices Invariant across farms (Table 5), the contributlon of land quality to variatlon In net output Is even greater (coefficlent - .81, t-20.66). This suggests that the current prices differentiated a% the farm level capture some of the rent associated with hlgh quality land, but not all.

Descriptive data characterizing the farms In the 1987 samples are displayed In Table 6. Table 6 Indicates that farms with different leveos of profitability show little difference In farm size (mean size Is about 2200 hectares of cropland and welghted hayland In each category). Jore profitable farms pay silghtly higher wages but emrioy the same number of hours, spend a little more on fertilizer, command more machinery power, and have a few more animals. The fams of different levels of profitability are remarkable for their similarity In average command of factors of production. They differ more In net output than In Inputs, and the difference Is more pronounced under the domestic price structure than under world prices.

# Marginal Value Products of Land With Current and Reformed Prices

Marginal value products for all factors of production calculated from sample means of the 1987 regressions with actual prices are displayed In Table 7. Marginal
|Romania (1992-2002)|IPUMS International<sup>b</sup><br>|81,397|24,781|76,519|27,408|
|---|---|---|---|---|---|
|Russia (n.a.-2002)|DIOC-E database (OECD)<sup>d</sup>|-|-|9,009,859|2,207,429|
|Rwanda (1991-2002)|IPUMS International<sup>b</sup>|101,652|9,296|124,550|4,210|
|Saudi Arabia (1990-2000)|Labor Force Survey<sup>c</sup>|2,842,783<sup>*</sup>|397,989<sup>*</sup>|3,078,548<sup>*</sup>|577,867<sup>*</sup>|
|Senegal (n.a.-2002)|DIOC-E database (OECD)<sup>d</sup>|-|-|35,285|6,909|
|Serbia/Montenegro (n.a.-2001)|<sup>DIOC-E database (OECD)d</sup>|-|-|713,596|114,268|
|Seychelles (n.a.-2000)|DIOC-E database (OECD)<sup>d</sup>|-|-|3,858|728|
|Singapore (1990-2000)|Statistics Singapore|397,189|30,191|512,515|137,705|
|South Africa (1996-2001)|Statistics South Africa|635,110|101,876|795,066|174,873|
|Sri Lanka (n.a.-2001)|DIOC-E database (OECD)<sup>d</sup>|-|-|14,135|1,729|
|Tanzania (n.a.-2002)|DIOC-E database (OECD)<sup>d</sup>|-|-|161,390|4,185|
|Thailand (n.a.-2000)|DIOC-E database (OECD)<sup>d</sup>|-|-|158,445|14,081|
|Trinidad & Tobago (n.a.-2000)|<sup>United Nations CEPALa</sup>|-|-|28,225|2,004|
|Uganda (1991-2002)|IPUMS International<sup>b</sup>|274,198|835|189,700|6,620|
|Un Arab Emirates (1990-<br>2000)|Labor Force Survey<sup>c</sup>|675,549<sup>*</sup>|98,565<sup>*</sup>|1,160,658<sup>*</sup>|213,445<sup>*</sup>|
|Uruguay (n.a.-2006)|DIOC-E database (OECD)<sup>d</sup>|-|-|68,062|10,773|
|Venezuela (1990-2001)|IPUMS International<sup>b</sup>|493,935|18,243|489,636|37,159|

Notes. Exact census years are reported between parentheses after the country name, ‘n.a.’ indicated that census data are unavailable. In the last four columns, an asterisk (superscript) indicates that the education-gender structure was unavailable in the census. In most cases, we used the structure of the other census year, adjusting for the change in human capital in the resident population. For GCC countries, we used the structure observed in Saudi Arabia (only available for the total immigration stock). Data sources:<sup>a</sup> United Nations’ Economic Commission for Latin America and the Caribbean (http://www.cepal.org).<sup>b</sup> See Minnesota Population Center (2010) and <u>https://international.ipums.org.</u><sup>c</sup> Data for GCC countries: for Saudi Arabia see _Population and Social Statistics_ at <u>http://www.cdsi.gov.sa, for the United Arab Emirates see</u> _Statistic Reports-Census 2005_ at http://www.economy.ae, for Qatar see _Labour Force Sample Survey_ at http://www.qsa.gov.qa, for Bahrain see _Labour Market Indicators_ at <u>http://blmi.lmra.bh, for Oman see Periodic</u> _Labour Force Survey_ at <u>http://www.moneoman.gov.om</u> and for Kuwait see _Microdata of the Labor Force Survey_ at <u>http://scs.mop.gov.kw.</u><sup>d</sup> Please refer to "Dumont, Jean-Christophe & Gilles Spielvogel & Sarah Widmaier (2010). Les migrants internationaux dans les pays développés, émergents et en développement : élargissement du profil, Questions sociales, emplois et migrations, n. 114."
**Table 5. Time use (minutes in 24 hours period) and caregiving**

|||**Midline**|**Survey**|||**End**|**line Surve**|**y**|
|---|---|---|---|---|---|---|---|---|
|||Control|Treatment -|Control||Control|Treatme|nt - Control|
||Obs|Group<br>Mean|Coeff.|_p-value_|Obs|Group<br>Mean|Coeff.|_p-value_|
|**Minutes spent by beneficiary woman**|||||||||
|Personal care|944|738|-19 ***|_0.004_|880|734|9|_0.257_|
|Study|944|0|0|_0.277_|880|1|-1|_0.391_|
|Chores|944|364|-109 ***|_0.000_|880|349|9|_0.384_|
|Caring others|944|107|-9|_0.102_|880|100|3|_0.725_|
|Work|944|55|140 ***|_0.000_|880|67|-20 **|_0.028_|
|Social|944|95|1|_0.857_|880|117|-3|_0.700_|
|Other|944|78|-3|_0.543_|880|72|3|_0.691_|
|**Minutes spent by husband**|||||||||
|Personal care|731|698|-21 **|_0.042_|684|685|-9|_0.473_|
|Study|731|0|1|_0.327_|684|0|2|_0.159_|
|Chores|731|6|-4|_0.137_|684|2|0|_0.730_|
|Caring others|731|22|9 **|_0.029_|684|16|-2|_0.653_|
|Work|731|377|36 **|_0.027_|684|415|4|_0.822_|
|Social|731|181|-8|_0.476_|684|205|-12|_0.338_|
|Other|731|154|-19 **|_0.033_|684|117|16 *|_0.071_|
|**Minutes caring for the youngest child in the**|**household**||||||||
|Beneficiary woman|944|1280|-128 ***|_0.000_|880|1277|18|_0.445_|
|Grandmother or female HH member adult|944|28|44 ***|_0.000_|880|39|-22 **|_0.023_|
|HH member girl (<15)|944|37|33 ***|_0.000_|880|21|-8|_0.244_|
|Female neighbor|944|5|33 ***|_0.000_|880|5|-2|_0.485_|
|Male adult|944|11|8 **|_0.020_|880|11|-3|_0.515_|
|Other|944|14|10|_0.109_|880|12|3|_0.666_|

Notes: Coefficients are from an OLS regression of the left-hand side variable on a treatment dummy, controlling for strata dummies, survey- time dummies, and baseline traits (see Table 3 note). ***, **, * indicate significance at 1, 5 and 10 percent. Time use data were collected twice at midline and at endline, in weeks 1 and 3 of the weekly survey. The data refer to the previous day (unless it was Friday, in which case Thursday is the reference day). Activities for the main respondent and for the caregiver of the youngest child are collected for each hour of the day (with exception of the hours between midnight-5am which were grouped together).

44
**<u>Appendix table A3: Village fixed effects regressions for area cultivated with summer crops (imagery based)</u>**

||<br>**Intensity**|<br>**Conflict intensity**|
|---|---|---|
|Conflict indicator|-577.364***|-461.845***|
||(14.393)|(13.420)|
|Lag conflict indicator|-874.098***|-577.472***|
||(16.855)|(15.787)|
|Total damaged area||-0.800***<br>|
|||(0.011)|
|Lag total damaged area||-0.993***|
|||(0.013)|
|Winter crop area|-0.640***|-0.719***|
||(0.004)|(0.004)|
|Rayon GDD above 5 degrees|-1.464***|-1.429***|
||(0.180)|(0.163)|
|GDD squared|0.000***|0.000***|
||(0.000)|(0.000)|
|Total rainfall: sowing (mm)|1.413***|1.470***|
||(0.274)|(0.249)|
|Total rainfall: growing (mm)|-1.100***|-0.702***|
||(0.142)|(0.130)|
|Sowing rain squared|-0.006***|-0.006***|
||(0.001)|(0.001)|
|Growing rain squared|0.001***|0.001**|
||(0.000)|(0.000)|
|Sowing zero rain days|0.972|4.355***|
||(1.732)|(1.576)|
|Growing zero rain days|37.623***|30.859***|
||(1.197)|(1.091)|
|Sowing zero rain days squared|-0.155***|-0.243***|
||(0.039)|(0.035)|
|Growing zero rain days squared|-1.114***|-0.896***|
||(0.033)|(0.030)|
|year=2020|-61.460***|-79.488***|
||(13.087)|(11.900)|
|year=2021|-167.837***|-154.500***|
||(11.800)|(10.729)|
|year=2022|-247.974***|-206.403***|
||(15.779)|(14.354)|
|year=2023|-152.950***|-127.412***|
||(13.595)|(12.365)|
|Constant|<br>3,343.763***|<br>3,346.014***|
||(146.351)|(133.054)|
|Mean of dep. var.|1,677.799|1,677.799|
|<br>STD of dep. var.|<br>1,543.028|<br>1,543.028|
|No. of obs. (village councils)|52,780|52,780|
|R<sup>2</sup>|0.958|0.965|

_Source:_ Original table for this paper. _Note:_ Conflict indicators and damaged crop area cover the period from mid-March to end-August in 2022 and 2023 and zero for other years. Standard errors are in parentheses, and * p < 0.10, ** p < 0.05, and *** p < 0.010. GDD = growing degree days.

34
A Duration Analysis of CONALEP the profile of the graduates in each of the three years in terms of all 13 major occupational groups of careers and the six geographical regions of the country. However, the sample is dominated by 1992 graduates who comprise 50% of the sample; 1991 and 1993 graduates each represent 25% (Tables 2 and 3). The sample selection is probabilistic and statistically representative of the universe of graduates in each cohort. For each graduate, three substitutes were chosen from the same career and school.

**Table 2 Distribution of the 1994 Sample by Cohort**

|**Graduation Year**<br>**Cohort**|**Planned**<br>**Selection**|**%**|**Actual**<br>**Selection**|**%**|
|---|---|---|---|---|
|1991|375|25|346|24.7|
|1992|750|50|704|50.3|
|1993|375|25|349|24.9|
|Total|1500|100|1399|100|

_Source_ : CONALEP (1994).

**Table 3 Actual Sample Selection (original and substitutes by cohort)**

||**Selected**|**S**<br>|**ubstitute**<br>|**s**<br>|**Total**|**% vs. 1,500**|
|---|---|---|---|---|---|---|
|Graduation Year Cohort||1<sup>st</sup>|2<sup>nd</sup>|3<sup>rd</sup>|346|23.1|
|1991|268|53|20|5|704|49.9|
|1992|560|96|42|6|349|23.3|
|1993|286|46|15|2|1,399|93.3|
|Total|1,114|195|77|13|||
|Cumulative percentage|74.3%|87.3%|92.4%|93.3%|93.3%||

_Source_ : CONALEP (1999).

The second CONALEP Graduate Tracer Study (CONALEP 1999) was conducted between May and June of 1998 on the basis of a random sample of individuals who graduated between June 1993 and June 1997. The sample is representative of geographical regions, all 29 careers and all cohorts. The difference between the actual sample of 5,574 individuals and the planned sample of 10,000 was due to exogenous factors such as changes in address (3,590 cases); addresses that belonged to different states (651 cases); differences between the number of graduates officially registered and those found in the administrative records (229 cases), and technical careers that had never been offered (7 cases). CONALEP (1998) extensively reviews the sample frame of the second CONALEP Graduate Survey as described by LEVANTA, the 9
where the number of poor declines by about 300 million from using the 2011 PPP data (rather than the 2005 PPP data when estimating the number of poor in 2010).

The timely analyses of Chandy and Kharas, and Dykstra, Kenny and Sandefur certainly suggest that the 2011 PPP data have a significant effect on the global profile of poverty. An important aspect of both though is that because they are extracting data from PovcalNet in 2005 PPP dollars their analyses implicitly embed an adjustment for urban-bias that was estimated based on analysis of the 2005 PPP data.<sup>8</sup> Chen and Ravallion (2010) argue that the 2005 PPP required rural-urban adjustments for China, India and Indonesia. This was done for good reasons. For example, Chen and Ravallion (2008) note that the 2005 ICP survey in China was confined to only 11 cities and therefore they view the ICP 2005 PPP as an urban PPP index for China. To correct for this, they use the ratio of urban to rural national poverty lines to provide a scaling factor to scale rural consumption such that it is expressed in urban price levels.<sup>9</sup> This adjustment alone reduced poverty in China by nearly half in 2005, from 26.4 percent to 15.6 percent. They argue that there is also reason to believe that the 2005 PPP data for India contain an urban bias and make similar adjustments. For Indonesia, a similar adjustment is made to adjust for differences in rural and urban price levels, but not primarily due to suspected bias in the 2005 ICP price collection.<sup>10</sup>

> 8 PovcalNet is perhaps the most commonly used data tool for estimating global poverty counts. It is an online tool, maintained by the World Bank, which allows analysts to specify parameter values such as the global poverty line, and then estimate the number of poor people in the world based on their assumptions. For more details, see: http://iresearch.worldbank.org/PovcalNet/index.htm.

> 9 National poverty lines used in this analysis represent the cost of obtaining basic needs. Within-country differences in poverty lines therefore reflect, in principle, both differences in prices and needs (most typically through the nonfood allowance).

> 10 There are also other countries that have adjustments for urban bias built in. Much of the data in PovcalNet from the Latin America region draws from the SEDLAC database which scales up all rural values by 15 percent to account for urban-rural price differences

> (http://sedlac.econo.unlp.edu.ar/eng/methodology_faqs.php?page=2).

9