rafmacalaba/datause-encoder-data huggingface dataset:
Industrial Classification (ISIC) at the 4-digit level. The total number of firms in the data set across the years is 19,235 and on yearly average the number of observations comes down to 1,282 firms.

In order to measure internet access at the woreda level (equivalent to district or county), this study also uses HCES which is a nationally representative survey collected by the CSA for the years of 2004-5, 2010-11, and 2015-16. The surveys provide extensive socio-economic information about Ethiopian households in individual and household levels. Specifically, the data set includes sociodemographic characteristics of household members such as age, sex, education, health, and labor force participation information. Also, the surveys cover information on household expenditure by main items, housing amenities, assets, and access to infrastructure and service.

For the purpose of measuring internet access at the woreda level, we use household expenditures on communication items including mobile cards and mobile apparatus from HCES. Detailed discussions on construction of the internet access variables will follow in the next section.

The household expenditure data set has the total of 1,496 households across the years and on yearly average, the number of households each year is approximately 499 households. As the firm panel data LMMI only extends until 2014, we use the first two waves from the HCES data, namely 20040 5 and 2010-11.

# **3.1 Constructing Internet Access Measures at the Woreda Level**

The HCES communication questionnaire modules provide household expenditure information on various communication-related items. These include mobile apparatus, mobile cards, telegram, internet, sim cards, fixed line, fax, and email. Of those, we chose mobile cards in estimating a woreda’s mobile communication utilization intensity and internet access. As mobile cards are purchased to use data plans on smartphones, this variable is used to gauge what fraction of a woreda’s population has mobiles, thereby measuring level of internet access.

As Ethiopia introduced the 3G networks in 2008, thus permitting use of smartphones, the running assumption here is that the household expenditure on mobile cards considerably increased after 12
|**Table 4: Percent of workers in ea**<br>|**ch employment cate**<br> <br>|**gory; by countr**<br>|**y, region and in**<br>|**comegroup**|||
|---|---|---|---|---|---|---|
|**Region and Income Level**|<br> <br>|**NON-AGRI**<br>|**ULTURE**||**AGRICULTURE **|**NON-**<br>|
||**wage and salary**|**non-paid**||||**EMPLOYMENT**|
|**(number of countries in sample)**|**employee**|**employee**|**employer**|**own account**|||
|**All Countries(90)**|**26.7**|**1.6**|**1.2**|**8.5**|**21.2**|**40.8**|
|**Low and Middle Income**<br>**Countries(67)**|**22.0**|**1.8**|**1.0**|**9.1**|**25.2**|**41.8**|
|**Region(Low and Middle Income C**|**ountries)**||||||
|East Asia and Pacific(6)|23.3|2.7|1.2|11.2|26.9|34.7|
|Europe and Central Asia(13)|37.8|0.3|1.3|2.5|8.9|49.1|
|(18)|37.3|1.4|2.4|11.7|10.3|36.9|
|Middle East and North Africa(4)|24.6|1.2|2.0|4.5|19.1|48.6|
|South Asia(4)|15.6|2.1|0.4|8.5|27.8|45.7|
|Sub-Saharan Africa(20)|8.8|1.6|0.9|12.5|41.9|34.2|
|**Per Capita GNI**|||||||
|Low Income(17)|11.5|1.3|0.6|11.0|37.1|38.5|
|Lower Middle Income(27)|18.4|2.2|0.7|9.0|27.0|42.7|
|Upper Middle Income(22)|38.2|1.0|2.1|8.4|8.9|41.4|
|High Income(24)|54.4|0.3|2.3|4.8|3.0|35.3|

21
|Populated area<br>-2.414|(1.004) **|
|---|---|
|Municipal capital<br>-0.536|(0.628)|
|Hospital<br>-0.631|(0.671)|
|School<br>2.550|(0.727) ***|
|Travel time to:<br> <br>||
|Populated area<br>0.274|(0.417)|
|Municipal capital<br>-0.146|(0.242)|
|Hospital<br>-0.438|(0.281)|
|School<br>1.007|(0.297) ***|
|Demand for mobility||
|No. of trips to:||
|Buy food<br>-1.239|(0.472) ***|
|Buy goods<br>0.105|(0.167)|
|Go to work<br>0.785|(0.602)|
|Do business<br>-0.295|(0.469)|
|Visitfriends/relatives<br>-0.156|(0.383)|
|Use of public bus to reach:||
|Populated area<br>1.015|(0.712)|
|Municipal capital<br>1.298|(0.493) ***|
|Hospital<br>1.675|(0.521) ***|
|School||
|Use of vehicle to reach:||
|Populated area<br>1.365|(0.796) *|
|Municipal capital<br>0.170|(0.530)|
|Hospital<br>-0.427|(0.518)|
|School<br>1.707<br>|(0.928) *|
|Transport equipment||
|ownership||
|Bicycle<br>0.366|(0.399)|
|Motorcycle<br>0.508|(0.398)|
|Vehicle<br>-0.168|(0.525)|
|Education||
|No. of students||
|All<br>1.003|(0.608) *|
|Boys<br>0.739|(0.533)|
|Girls<br>0.878|(0.535) *|
|Preschool<br>-1.191|(0.437) ***|
|Elementary<br>-0.261|(0.519)|
|Secondary<br>0.334|(0.458)|
|University<br>-0.007|(0.165)|
|Health||
|No.ofsick HH members<br>-0.613|(0.500)|
|Economic benefits||
|No. of HH members engaged in:||
|Agriculture<br>0.098|(0.671)|
|Industry<br>0.057|(0.062)|
|Commerce<br>0.109|(0.185)|
|Service<br>0.321|(0.151) **|
|Public sector<br>0.950|(0.371) ***|
|HH monthlyincome<br>0.290|(0.223)|

Note: Standard errors are shown in parentheses. *, **, *** indicate the statistical significance at 10%, 5% and 1%, respectively.

28
<!-- Start of picture text --> Total carbon emission output, Total carbon emission output,<br>projection for bottom 20 projection for second quintile<br>Historical Historical<br>Pessimistic Pessimistic<br>BAU BAU<br>Opportunistic Opportunistic<br>2000 2005 2010 2015 2020 2025 2030 2000 2005 2010 2015 2020 2025 2030<br>Year Year<br>Total carbon emission output, Total carbon emission output,<br>projection for third quintile projection for fourth quintile<br>Historical Historical<br>Pessimistic Pessimistic<br>BAU BAU<br>Opportunistic Opportunistic<br>2000 2005 2010 2015 2020 2025 2030 2000 2005 2010 2015 2020 2025 2030<br>Year Year<br>Total carbon emission output, Total carbon emission output,<br>projection for top 20 projection for the world<br>Historical Historical<br>Pessimistic Pessimistic<br>BAU BAU<br>Opportunistic Opportunistic<br>2000 2005 2010 2015 2020 2025 2030 2000 2005 2010 2015 2020 2025 2030<br>Year Year<br>0.35 8<br>7<br>0.25 6<br>5<br>CO2 Billion Tons 0.15 CO2 Billion Tons 4<br>3<br>0.05 2<br>40<br>3.5 35<br>3.0 30<br>2.5 25<br>20<br>2.0<br>CO2 Billion Tons CO2 Billion Tons 15<br>1.5<br>10<br>1.0 5<br>17 80<br>16 70<br>15<br>60<br>14<br>50<br>13<br>CO2 Billion Tons CO2 Billion Tons 40<br>12<br>11 30<br>10 20<br><!-- End of picture text --> Figure 11: Future carbon under three scenarios broken down by income quintiles.

xiii
_patrilineal society. (...) The majority of the respondents felt that a man without a child, particularly without a son, will not be remembered in the family; his branch of the family will come to an end. For the same reason, a man who has only daughters may acquire a second wife to enhance the chance of having a son. Clearly, in such a patriarchal system, the perpetuation of the family line is a strong motivation for children.’_ Isiugo-Abanihe (1994, p. 154, from a survey of 3,073 Nigerian couples) Indeed, the desire for many children, especially males, is motivated by descent transmission and the urge to provide continuation to the family lineage. More importantly, these societies are characterized by the fact that only males have the control of family landed property, as documented in Wusu and Isiugo-Abanihe (2006).<sup>6</sup> _‘In most cultures only male children are allowed to share in family land holdings within the context of the extended family structure and communal ownership of land. Since farming is central to economic life, the most economically rewarding reproductive goal a couple could pursue is a large family size, ideally with many male children. Against this backdrop couples dread barrenness, and until a ‘good’ number of male children are born, extended family members exert pressure, which may culminate in the man marrying another wife’_ Wusu and Isiugo-Abanihe (2006, pp. 141–142) In this context, sons are highly valued by women as well because they represent the only way through which they can inherit part of their deceased husband’s property (Fapohunda and Todaro, 1988; Ewelukwa, 2002). This consequently generates pressure for bearing male children as a way to protect themselves in the state of widowhood. It may also lead to competition among co-wives as described in Bledsoe, Banja, and Hill (1998).

_‘In their husbands’ compounds, women seek to establish their security and to gain a competitive edge over present and future co-wives and sisters-in-law by bearing a number of children, especially sons, who will retain rights of residence and inheritance in the compound and will eventually take over its leadership roles.’_ Bledsoe, Banja, and Hill (1998, p. 23)

> 6There are several sources of law in Nigeria: Common law (the _default_ Law, applied only when other sources of law are not applicable), Customary Law, and Islamic law (Oba, 2011). Even though there is some variation in inheritance rules across states and ethnic groups, in most cases only male children are allowed to inherit the father’s property or larger part of it. According to Islamic law, female children are entitled to inherit half of the share of their male siblings.

7
Table 4. Main features of global recessions (by country group)

|||Globa|l recess|ions||Non-|Global|All|
|---|---|---|---|---|---|---|---|---|
||1975|1982|1991|2009|Average|recessions|downturns|years|
|**Advanced economies**|||||||||
|Output|0.2|0.3|1.3|-3.4|-0.4|3.5|1.8|3.3|
|Output per capita|-0.7|-0.3|0.6|-4.0|-1.1|2.7|1.0|2.4|
|Industrial production|-7.8|-2.5|-0.2|-12.4|-5.7|3.6|-0.3|3.0|
|Trade|-4.7|-0.1|3.7|-11.1|-3.1|6.4|1.7|5.9|
|Unemployment rate|1.5|1.3|0.6|2.2|1.4|-0.1|0.0|0.0|
|Output (PPP)|0.2|0.3|1.3|-3.3|-0.4|3.5|1.7|3.3|
|Output per capita (PPP)|-0.7|-0.4|0.6|-3.9|-1.1|2.7|1.0|2.5|
|Credit|0.2|3.1|2.0|0.9|1.6|4.9|3.0|4.7|
|Government expenditure|8.6|3.5|3.6|7.3|5.7|4.2|3.5|4.2|
|Policy rate|-2.4|-1.2|-1.4|-2.0|-1.7|0.1|-0.7|0.0|
|**EMDEs**|||||||||
|Output|4.2|0.9|1.5|1.8|2.1|4.9|4.0|4.7|
|Output per capita|2.0|-1.2|-0.4|0.4|0.2|3.0|2.4|2.8|
|Industrial production|...|...|0.4|-0.2|0.1|5.4|3.1|5.0|
|Trade|5.3|-5.1|2.0|-9.0|-1.7|6.1|3.8|5.6|
|Unemployment rate|...|-0.2|0.3|0.4|0.2|0.0|0.2|0.0|
|Output (PPP)|4.2|1.1|1.9|2.3|2.4|4.9|4.2|4.8|
|Output per capita (PPP)|2.1|-0.9|0.0|0.9|0.5|3.0|2.5|2.9|
|Credit|-0.7|4.5|5.7|17.7|6.8|7.9|9.8|7.8|
|Government expenditure|16.0|-6.2|2.7|11.7|6.0|5.8|6.5|5.8|
|Policy rate|0.1|1.5|0.2|-2.0|0.0|0.0|0.4|0.0|
|**LICs**|||||||||
|Output|0.1|1.0|-0.5|5.9|1.6|3.9|3.6|3.8|
|Output per capita|-2.3|-1.6|-3.3|3.0|-1.1|1.3|0.9|1.1|
|Trade|3.6|-5.6|-1.4|4.6|0.3|6.4|7.5|6.0|
|Output (PPP)|0.5|0.9|-0.2|5.0|1.6|4.0|3.6|3.9|
|Output per capita (PPP)|-1.9|-1.7|-3.1|2.1|-1.2|1.4|1.0|1.2|

Note: All variables show percent changes, except in unemployment rates and policy rate, in which percentagepoint changes of these variables are reported. See footnote of Table 3.

52
profitability and investment. Table 1 provides the descriptive statistics for the variables we use in the empirical analysis.

# _4. RESULTS AND DISCUSSION_

Column (1) of Table 2 presents the results from our baseline specification (1), and column (2) presents the results from a specification that also includes measures of trade and FDI liberalization. The estimates in both columns show a negative and statistically significant effect of licenses on firm-level investment. The coefficient of -0.072 on Licenses (Ljt) presented in column (2) suggests that a one standard deviation reduction in the share of products in an industry subject to licensing requirements (which is 0.22, see Table 1) increases the investment rate by 1.58 percentage points. Given that the average investment rate in our sample is 19%, this increase implies an 8.3% increase in the investment rate.

In column (3), we show that the increase in investment following delicensing occurred mainly in financially more developed states. To that end, we construct a dummy variable that equals one if the average ratio of private bank credit to state net product (source: Reserve Bank of India) is above the sample mean. We interact this dummy variable indicating better credit conditions with the license measure. While the coefficient on the main effect of licenses becomes insignificant, the interaction term is negative and significant, suggesting that mainly firms in states with better credit conditions could benefit from delicensing.

In Table 3, we investigate the heterogeneity in the impact of delicensing on firms’ investment by dividing the firms into four quartiles of the initial firm size distribution, measured by the initial real sales or wage bill of the firm, and construct indicator variables equal to one when the firm belongs to a particular quartile. We include interaction terms between the license measure and three quartile dummies in equation (1). The main coefficient on the license term shows the effect of delicensing on investment for the smallest firms. The negative and significant coefficient suggests that the firms in the first quartile benefitted most from delicensing, and that they increased their investment by 3.76 percentage points given a one standard 6
Finally, households could be migrating to regions with higher levels unanticipated mining revenues. Around 5% of the households in the survey moved to another region. Hence, unanticipated mining revenues could be related to individual preferences<sup>44</sup> . But in fact, unanticipated mining revenues do not have a statistically significant impact on the probability of migrating to another region. The results are presented in **Table 4** . These results are consistent with the assumption that unanticipated mining revenues are indeed exogenous.

# **Table 4: Impact of unanticipated mining revenues on the probability of migrating to** **<u>another region by area</u>**

|_Dependent variable:_|Probability|of migrating|
|---|---|---|
||Rural|Urban|
|Unanticipated mining revenues|0.000|-0.000|
||(0.001)|(0.000)|
|Other Controls|YES|YES|
|N|1360|602|

Source: Young Lives Study Round 1 and 2, ENAHO 2002, 2006, Ministry of Economic and Finance Expenditure Statistics for 2002 –2006.

Note: Public Spending and Unanticipated mining revenues are measured in 100 million soles. Controls: poverty index and total public spending. Robust standard errors in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%. Standard errors are clustered by region. Geographical Regions: 23. Marginal effects calculated at the means.

# **S4.2 Changes in the composition of regional public expenditure**

The results obtained suggest public expenditure in rural areas is not effective due to the inadequate quality and quantity of public services. This theory might help us understand how malnutrition disparities could be increasing between urban and rural areas despite an increase in average public expenditure. Yet, it is also possible that the type of expenditure made in each region drives the results.

> 44 I test this hypothesis by running a reduced form Probit regression. The dependent variable is the probability of moving (a dummy variable that indicates if the child migrated to another region) and the independent regressor is the level of regional mining revenues. I use a poverty index and the level of government spending as controls and I cluster the estimates by region.

18
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This database can provide aggregate statistics at the country level and within-country statistics per rural or urban status, and per quintile of total expenditure. It can also provide information on the types of expenditures per transport subcategories. Further analysis is conducted in a companion paper (Lebrand et al., f _orthcoming_ ).

## **4.2. Vehicle Ownership: SSAPOV Harmonized Data Collection**

The World Bank Sub-Saharan Statistical Development team harmonized the existing household surveys in the Sub-Saharan Africa region by extracting about 200 variables and unifying the definitions and variables names. These variables contain the information on household consumption, employment status, education, and health, and there are also some harmonized variables about the ownership of vehicles.<sup>8</sup> The SSAPOV harmonized ownership data can be used to study the large heterogeneity of car and other vehicle ownership among the Sub-Saharan African countries. The country coverage, survey years, and availability of the vehicle ownership variables of the SSAPOV database are listed in Annex 9.

# **5. Suggestions for Designing the Transport Module in LSMS Survey**

LSMS surveys can capture abundant detailed information not only on the households’ ownership of vehicles, transport expenditures, access to facilities, and travel experience regarding human development, but also on the conditions of road and public transportation in the communities, however, the richness of the questions, options and expenditure subcategories varies a lot across countries. To facilitate the data harmonization and cross-country comparison, it is necessary to design and implement a set of structured questions on transport infrastructures, which can be inserted into LSMS surveys as a standalone module, and a series of questions to collect the travel information on education, health, and employment, which can be integrated into the existing modules.

For the sample countries in this review, Mauritius and Uganda designed an organized standalone transport section in their survey instruments, which can add some insights into our guidelines for questionnaire design. Annex 10 contains the transport section in Mauritius’s 2014 Continuous Multi-purpose Household Survey, which collects some detailed information on household ownership of vehicles and usage of school vans. It also includes the section of transport services and road infrastructure in the household questionnaire and community questionnaire of Uganda’s 2011/12 National Panel Survey.

This section presents two prototype modules: one includes the transport questions that are suggested to be integrated into the existing modules of education, health, and employment in the household questionnaire, the other includes the structured questions on road infrastructure

> 8 At the World Bank, this harmonized data collection can be accessed through Datalibweb by using the “datalibweb” command in Stata. Datalibweb is a data system enabling users to access the most up-todate harmonized and non-harmonized datasets of different collections across Global Practices.

19
very important: households in coffee regions consumed approximately 10 percent more, coffee farmers an additional 10 percent. The time dummy for 1995/96 is not significant, which underlines the importance of the price boom and regional multiplier effects in explaining overall growth. These coffee-related expenditure differentials disappear between 1995/96 and 1999/2000, a period during which overall expenditure levels increased by more than 20 percent, as indicated by the coefficient of the 1999/2000 time dummy.

Consumption growth also led to poverty reduction (Table 3). Between 1992 and 1995, poverty among coffee farmers and other rural households in coffee regions was reduced considerably while rural poverty in non-coffee regions increased slightly. During the first half of the 1990s, if not causation, one certainly observes a strong correlation between poverty reduction and booming coffee prices. Between 1995 and 1999, poverty reduction slowed down but continued for coffee farmers and other rural households in coffee regions despite falling coffee prices. Enjoying remarkable growth rate in its consumption levels, the group of rural households in non-coffee regions experienced a concomitant drop of more than 50 percent in its poverty headcount. Urban poverty decreased at a relatively stable pace throughout the entire period.

Table 3: Poverty reduction by household type, 1992-1999

|_Hhld t_|_P0_|_(Headcount inde_|_x)_|
|---|---|---|---|
|_ouseo ype_|_1992_|_1995_|_1999_|
|Coffee farmers|56.4|42.3|27.0|
||_51.0_<br>_61.7_|_36.4_<br>_48.2_|_24.5_<br>_29.6_|
|Rural in coffee|52.6|37.1|28.7|
|regions|_48.4_<br>_56.8_|_31.1_<br>_43.0_|_25.5_<br>_31.8_|
|Rural in non-coffee|52.0|54.9|26.5|
|regions|_46.0_<br>_58.1_|_47.6_<br>_62.2_|_22.6_<br>_30.4_|
|Urban in coffee|34.1|20.5|8.7|
|regions|_20.1_<br>_48.1_|_12.7_<br>_28.4_|_4.7_<br>_12.7_|
|Urban in non-|31.1|17.9|10.2|
|coffee regions|_24.4_<br>_37.9_|_7.4_<br>_28.5_|_3.7_<br>_16.7_|
|Total|_48.6_<br>_54.7_<br>51.7|_38.1_<br>_45.6_<br>41.8|_24.0_<br>_27.6_<br>25.8|

Source: Authors’ calculations.

Note: 95 % confidence intervals in italics (standard errors corrected for survey design).

To complement the household-group-specific averages presented so far, growth incidence curves for coffee farmers and other rural households in coffee regions, the two groups most closely associated with the coffee price changes, are shown in Figures 3-6. The growth incidence curve plots average consumption growth by consumption percentiles and thus 14
estimation models for both were based on same available data, and only in the case of underweight, a satisfying model was found. Nevertheless, wasting and underweight have a significant pairwise correlation of 0.52 in the 2014 EMHDS survey. Figure 2 illustrates, in a scatter diagram, the partial correlation with notable noise between underweight and wasting. As such, one can consider underweight a proxy, albeit an imperfect proxy, for wasting. Stunting, on the other hand, has no correlation to wasting, while there is a high correlation between underweight and stunting (Figure 2).

**Fig 2. Z-score correlation between underweight and wasting, and stunting and wasting**

<!-- Start of picture text -->
-6 -4 -2 0 2 4 6 -6 -4 -2 0 2 4 6 -6 -4 -2 0 2 4 6<br>Weight-for-length/height Z-score Weight-for-length/height Z-score Weight for age Z-score (Underweight)<br>6 6 6<br>4 4 4<br>2 2 2<br>0 0 0<br>-2 -2 -2<br>-4 -4 -4<br>Height for age Z-score (Stunting) Height for age Z-score (Stunting)<br>-6 -6 -6<br><!-- End of picture text -->

# **Are woredas a useful targeting mechanism?**

As pointed out in the introduction, existing literature points to weaknesses in data for geographical targeting, and our estimates fill a current data gap. However, is woreda targeting using SAE the most efficient targeting mechanism? Given available data, a complete analysis of this question is not possible, but woreda targeting can be compared to some other alternatives. A data-driven national targeting strategy requires data for the entire nation that is correlated with undernutrition. The profile of undernutrition, based on correlates is well-documented [9,14,30] and useful, as some of these can be used as targeting instruments, and some can even be argued to have a causal relationship. Some of these might be more efficient targeting instruments as they can be observed locally and do not require a full household survey and imputation models. For instance, access to health facilities could be argued to have a causal impact and could, potentially, be used as a targeting mechanism. Traveling distance to the 10