rafmacalaba/datause-encoder-data huggingface dataset:
40

# MMda4Az

The World Bank's economic and social database (BESD) provides comprehensive socio-economic ana financial data for both developed and developing countries. As of March 1990, BESD contained 30 data files from the World Bank International Economics Department, IMF Bureau of Statistics, UN, UNESCO, UNIDO, FAO, and ILO. The annex tables on consumption are based on data from the national accounts (NA) - one of the.data files maintained by the World Bank. NA is updated continuously as new data become available. The developing countries for which consumption data are available range from a minimum of 74 countries for the period 1960-65 to a maximum of 91 countries for that of 1980-85.

Table Consumption 1 presents growth in per capita private consumption using constant 1980 local currency. This paper endorses the view that for purposes of welfare comparisons, weighted averages are more suitable than simple arithmetic averages (see Kakwani, 1990).37 The aggregations in the annex tables are therefore population weighted averages.

# The trend

The group of 60 developing countries with complete data for the period 1960 to 1988 have enjoyed growth in per capita private consumption from the sixties to the eighties, with largest increase during the late sixties. 37Nanak Kakwani, "Growth Rates and Aggregate Welfare: An International Comparison." Forthcoming LSNS Working Paper, (Washington, D.C.: World Bank, 1990).
The central labor adjustment in microenterprises with workers is instead an upward shift in short term labor demand for workers in response to blackouts. Each additional blackout weekday is associated with 0.32 additional non-owner work days and 3.13 additional non-owner work hours in a workweek. On a daily basis, firms with workers statistically significantly increase the number of workers staffed and total worker hours on blackout days, on days that are the second of two consecutive blackout days, and on days with power that follow a blackout day. In addition, total weekly wages paid decrease by 20% for each blackout day in a workweek, but the analysis fails to reject that outages have no effect on within-firm worker hourly wages.

What rationalizes an increase in worker hours and a reduction in wages paid, without any measured effect on average hourly wages at the firm level? The evidence in this paper, though speculative, suggests that firms employing unskilled workers are increasing worker labor inputs and that firms employing skilled workers are driving the reductions in wages paid. The composition of the workforce in this industry is consistent with this interpretation; 91% of the workforce are unskilled (and poorly paid), while the tiny fraction of skilled workers take home 61% of total wages paid.

This paper is divided into six sections. Section 2 introduces the context of the study, providing background on Ghana’s electricity crisis and the garment sector. Section 3 describes the sample and data. Section 4 discusses estimation. Section 5 presents the findings and discusses possible interpretations. Section 6 concludes.

# **2 Context**

## **2.1 Dumsor**

In Ghana, as in many low-income countries, electricity demand often outstrips supply, and power is erratic and frequently unavailable. A recent survey of electricity consumers in twelve African countries found that in all but Rwanda more than half of respondents reported at least one outage per day; in Ghana, that figure is 68% of electricity consumers ( **?** ). Despite both public and private efforts to develop new capacity, major power crises plagued Ghana in 1997-98, 2006-07, and 2012-15. Historically dominated by hydropower, from 2006 to 2016 generation capacity doubled, with thermal now accounting for nearly 60% of installed capacity. Still, the hydro/thermal power generation mix leaves Ghana susceptible to capacity reduction from drought and natural gas supply challenges, and the system suffers from massive distribution losses due to obsolete equipment, a tariff structure that is not cost-reflective, and pervasive non-payment by electricity consumers (including government ministries) ( **?** ).

Dumsor, or Dum So, derives from the words for off and on in the Asante Twi, Akuapim Twi, and

> workers are working on a production process in conjunction with worker labor inputs. **?** , **?** , and **?** present evidence on the labor supply of vehicle and bicycle taxi drivers; **?** consider the labor supply of pear packers; **?** examine labor supply in fisheries; and **?** studies the labor supply of stadium vendors.

4
seats in Lilongwe and all of the seats in the surrounding district.<sup>3</sup> ' Because all parties have both urban and rural constituents, the benefits of liberalization (e.g., improved service), which would initially be felt in urban areas, and the costs (e.g., job losses), affected both the Government and the opposition.

<!-- Start of picture text --> The regional distribution of<br>907d ,_ votes (i.e., rather than an urban/rural<br>807 split) and the large rural population in<br>607 X Malawi (see Figure 8) also meant that<br>50Zd- 1_both the Government and the<br>307d i . * ~ opposition parties depend on rural<br>107 07Z : _ _ _ _ _ U-areas _ _ _ _ _ _ _ _ for support, something that had<br>0 -- ,c a significant impact on the design of<br>=. a°.D = O <J~o ereform. One result was that the<br>Government retained the right to<br>Rural Population in 1998 (as percent of total) (as percent of total) percent of total) of total) total) require license holders to provide<br>Bank (2002).<br>service in rural areas (Law, Section<br><!-- End of picture text --> Figure 8: Rural Population in 1998 (as percent of total) (as percent of total) percent of total) of total) total) Source: World Bank (2002).

> 21a). This has been true even for cellular companies. For example, the licenses issued to the first two cellular companies, MTN and Celtel, required that the companies (i) expand coverage to over 50 district, trading and tourist centers; (ii) provide service 10 km on either side of 5 major trunk roads; and (iii) provide 160 community service telephones in rural areas. The licenses layout a 60-month schedule for expanding service and the companies are required to pay fines of

> up to US$400,000 if they failed to meet these requirements.

> 31 One factor that might have affected the 1999 vote compared to the 1994 vote was that the civil service was

> located primarily in Lilongwe. The large number of civil service layoffs and the small pay increases that followed democratization might have made the UDF relatively more unpopular in the 1999 vote in Lilongwe. For example, in 1997, civil servants went on strike for seven weeks to try to get larger pay increases, although they eventually

> backed down. However, the reverse might have also been true, if the UDF had tried to place its supporters in the

> civil service. In addition, President Muluzi apparently tried to pressure civil servants to vote for the UDF. For

> example, the Economist Intelligence Unit (1998) reported that in a speech in southern Balaka district on June 7,'

> 1998, President Bakili Muluzi wamed that civil servants should support the ruling United Democratic Front (UDF).

> They report that he said 'Remain where you are and eat your neutral professionalism. My government is a political

> government. Unless you identify yourself with the UDF, you will get nothing from me.'

26
- 33 -

# Table / Profit and loss accounts. 1984-86

|||Amount||As sh|are of loans<br>advances|and|
|---|---|---|---|---|---|---|
|I tem|1984<br>(|1985<br>TK million|1986<br>)|1984|1985<br>(percent)|1986|
|Income|35.38|65.69|89.90|19.9|26.7|27.1|
|Loan operationi|23.39|35.56|43.91|13.2|14.5|13.2|
|Other operations|||||||
|(investments in|||||||
|fixed- and Fhort-<br>term deposits)|11.99|30.13|45.99|6.8|12.2|13.9|
|Expenditures|31.13|65.27|90.99|17.5|26.5|27.4|
|Cost of funds for|||||||
|loan operation|8.10|14.19|11.94|4.6|5.7|3.6|
|Cost of funds for<br>other operations|7.86|15.43|19.09|4.4|6.3|5.8|
|Cost of administra-|||||||
|tion of loan<br>operation|15.17|35.65|59.96|8.6|14.5|18.1|
|Profits|4.90|0.46|-0.75|2.8|0.2|-0.2|
|Loan operations|0.12|-14.28|-27.99|0.1|-5.8|-8.4|
|Other operations|4.13|14.70|26.90|2.3|6.0|8.1|
|Sundry income|0.65|0.05|0.34|0.4|..|0.1|

Sources: Grameen Bank, Annual Report, 1984-86;<sup>Mahabub Hossain (February 1988).</sup>
in Afghanistan’s economy in recent years. It is thus relatively straightforward to estimate the change in informal sector activity as a weighted difference between the change in nighttime light intensity and the change in cell phone traffic among registered firms.

Non-traditional sources of information are also used to measure the intensity of conflict at the local level, and to control for potentially confounding circumstances. The US Pentagon has made available data on conflict-related casualties with a high level of spatial disaggregation, as well as information on the presence of international and Afghan troops at the district level. Based on information from international donor agencies, it is also possible to estimate the level of foreign aid at the district level.

The analysis is conducted with the district as the unit of observation, as this is the lowest level of spatial disaggregation for which all the relevant data are available. The estimates are for the period 2012-16. The initial year is the starting point of a rapid deterioration of the security situation in Afghanistan. The final year is the last one for which the Pentagon made spatially disaggregated data on conflict-related casualties available.

The high frequency of the data also allows estimating the response of each of the three sectors to conflicts with varying time lags. In this paper, the impacts from conflict are estimated over each of the four quarters following a surge in the number of casualties. The exercise reveals very different time patterns across sectors. The impact of local surges in conflict on overall economic activity is relatively modest, but their effect on the composition of output is dramatic.

# **The Afghan context**

Following the fall of the Taliban regime in 2001, Afghanistan received massive international security and development assistance. More than 100,000 international troops were stationed in the country until 2012, supporting operations against the ongoing insurgency and assisting in building and training a national defense force. To ensure a path toward self-reliance, the government of Afghanistan and NATO partners agreed in 2010 on a gradual handover of security responsibilities. By 2018, there were only 16,229 international troops in Afghanistan.<sup>3</sup> There was also a substantial decline in military and civilian aid, from 90 percent of GDP in 2009 to 44 percent in 2018.

> 3 See NATO (2018) Resolute Support Mission “placemat” as of November 2018. https://www.nato.int/nato_static_fl2014/assets/pdf/pdf_2018_11/20181112_2018-11-RSMPlacemat.pdf.

4
# **Table AII-2– Post-matching standardized bias of selected covariates (in %)**

||_New Credit_|<sup>_Formal_</sup><br>_Credit_|_Informal_<br>_Credit_|_Training_|_Taxes_|_Guild_|
|---|---|---|---|---|---|---|
|Matching using_(H,Z,Y)_<br>_(small sample - survivors_<br>_only)_|||||||
|_ln Initial Income_|2.3|14.4|10.9|2.1|3.5|1.8|
|_ln Capital Stock_|0.5|25.2|6.8|9.9|3.0|4.7|
|_ln Workers_|2.4|7.0|7.7|2.1|2.6|0.3|
|_Time in Business_|-4.2|1.7|3.6|-2.2|4.1|-5.3|
|_Age_|-2.7|-1.2|6.4|-7.5|4.5|8.5|
|_Prim_|-2.1|-2.2|-1.7|-9.9|0.2|2.1|
|_Sec_|1.4|1.9|0.0|6.9|-0.4|-3.0|
|_High_|1.6|3.0|2.3|4.4|0.0|0.0|
|Matching using_(H,Z,Y)_<br>_(small sample - all)_|||||||
|_ln Initial Income_|3.0|11.8|2.0|7.1|-0.8|-1.4|
|_ln Capital Stock_|2.5|23.0|0.7|9.8|3.2|4.6|
|_ln Workers_|4.9|7.8|4.6|3.2|2.0|0.9|
|_Time in Business_|-0.6|4.6|-1.2|2.1|3.9|-5.9|
|_Age_|-0.1|0.9|0.2|-3.9|3.6|-1.7|
|_Prim_|-0.9|-5.3|0.0|-7.6|0.9|1.6|
|_Sec_|-0.6|4.7|-1.1|0.4|-0.8|-1.5|
|_High_|2.4|2.0|2.6|9.2|-0.7|-1.0|

_Notes: * not used for matching. All the results correspond to Nearest Neighbor matching procedure._ 25
# **Table 4. Distribution of households in Guatemala receiving international remittances, ranked by decile group according to predicted per capita household expenditure (excluding remittances)**

|_Rank_|_Households receiving_<br>_international remittances_<br>_(from USA)_|_International remittances as_<br>_percent of total per capita_<br>_household expenditure_<br>_(including remittances)_|
|---|---|---|
|(decile)|(1)<br>(percent)|(2)<br>(percent)|
|Lowest 10|11.8|62.1|
|Second 10|4.1|37.0|
|Third 10|3.0|31.5|
|Fourth 10|4.6|32.5|
|Fifth 10|10.7|25.9|
|Sixth 10|10.1|31.3|
|Seventh 10|14.1|24.0|
|Eighth 10|12.2|20.9|
|Ninth 10|17.8|15.9|
|Top 10|11.6|17.2|
||100.0||

Notes: Households ranked into decile groups on the basis of predicted per capita household expenditure (excluding remittances). Column (2) shows the percent of total per capita household expenditure (including remittances) coming from international remittances.

Source: Adams (2005a: Table 7).
<!-- Start of picture text --> oO<br>oO<br>B<br>Ba<br><!-- End of picture text --> <!-- Start of picture text --> Capital Flows, % Decreaseas a Share of GDP (Top Bar)<br>0 2 4 6 8 10 12 214 «16<br>1998-99<br>2008-09<br>) 10 20 30 40 50 60 70 go<br>Oil Shock, % Decrease in Termsof Trade (Bottom Bar)<br><!-- End of picture text --> <!-- Start of picture text --> ane nega Bo<br>salen<br>10.5 8<br>2008 Q3<br>$5 + 1999Q2 -3<br>19870 oosas<br>os 199801 “ied 199901 2<br>ae 19980 Za pony 7<br>1998Q3<br>95 “12<br>2009 Q1 oos.aa<br>**5 u<br>Bice ceA ch cv<br><!-- End of picture text -->
among children in eligible households.<sup>23</sup>

# **5 Further Evidence**

While the primary objective of our study is to understand how the _Oportunidades_ transfer changed student preferences over high school tracks, here we want to assess whether the observed changes in preferences ultimately lead to better educational outcomes. Technical options display on average lower tuition and transportation costs than the vocational ones, but they require higher scores to be admitted. Therefore, more financially constrained students might _de facto_ reduce the probability of being assigned to their first option in the attempt to pursue a more affordable one. For this purpose, we study whether the cash transfers increase the probability of being assigned to the first option. Column 1 in Table 9 shows a 4 percentage point increase, but the effect is not statistically significant. Final assignment depends both on student’s ranking and the score in the COMIPEMS exam. Since the estimates reported in Table 4 do not show any effect on the exam, we treat the COMIPEMS score as a plausibly exogenous variable. Including score fixed effects increases the size of the coefficient but the effect, that corresponds to about 0.12 standard deviation of the dependent variable, is not statistically significant (column 2). Yet, this effect is likely to vary with the extent of the financial constraints, as measured by distance from students places of residence to the closest vocational option. In fact, we find that the coefficient is larger and statistically significant (at the 5% level) for students who live further away from schools offering vocational programs (column 3).

By relaxing the financial constraints for some students, cash transfers are likely to improve the allocation of students among school tracks, and this in turn should lead to better outcomes later on. Because we lack individual-level data on graduation, taking a standardized achievement test (ENLACE) in 12th grade is used as a proxy for graduation. Only students on track to graduate at the end of the school year are registered to take the exam. Previous studies [De Janvry et al., 2013; Estrada and Gignoux, 2014] present evidence that this is a good proxy, in particular because there is no evidence that schools administer the exam strategically. On average, 44% of the students in

> 23As an additional test, we use the school-level June pass rates in the academic year 2005-2006 in order to further corroborate that students above the eligibility threshold do not prefer easier schooling options. Results (available upon request) are consistent with the estimates reported in Table 8.

22
**Figure 4.**

<!-- Start of picture text --> Optimal versus actual nitrogen application rates<br>Cereal-Root Crop FS<br>0 100 200 300 400<br>Optimal application rates(kg)<br>at acquisition cost at market price<br>300<br>200<br>100<br>0<br><!-- End of picture text --> Source: generated by authors using STATA

# **4.6 Fertilizer subsidies and fertilizer profitability**

Throughout most of Nigeria’s recent history, fertilizer subsidies have been a dominant component of agricultural input programs; accounting for substantial shares of government capital spending on agriculture (Mogues et al., 2012). In 2012 (when our second round of data was collected) the Nigerian government began a new fertilizer program. Prior to 2011 (and when our first round of data was collected), the subsidy program in existence was called the Federal Market Stabilization Program (FMSP). Under the FMSP, each Nigerian state government would submit a request to the federal government for a certain quantity of subsidized fertilizer it wanted to procure. Depending on the federal agricultural budget, the federal government then determined the total amount of subsidized fertilizer to be allocated to each state (Takeshima and Nkonya, 2014). The federal government then procured and sold fertilizer to the state governments with a 25 percent subsidy. States, as well as LGAs often added their own subsidies to the 25% provided by the federal government. This amount varied across states and often ranged between 0 and 50 percent (Banful et al. 2010). Consequently depending on a farmer’s state and local government of origin, the rate for subsidized fertilizer under the FMSP typically ranged between 25 and 75 percent. Anecdotal evidence indicates that a substantial portion of subsidized fertilizer leaked into commercial markets 27
ZAMBIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Zambia’s population could be reached by a GSM signal on a commercially viable basis if measures were taken to dismantle regulatory barriers and promote competition to increase the market (figure 12).

The state-owned telecommunications incumbent, Zambia Telecommunications Company Ltd. (ZAMTEL), is characterized by inefficiency and an inability to compete with private mobile operators. The operator has become increasingly dependent on state financial support. The ZAMTEL monopoly is responsible for the exceptionally high prices of international voice communication that are observed in Zambia. In response, the government initiated a privatization process that aims to introduce private investment and management practices into the fixed-line sector.

**Table 7. Benchmarking ICT indicators**

||Unit|Resource rich|**Zambia**|Middle-income<br>countries|
|---|---|---|---|---|
|GSM coverage|% population|66.9|**53.0**|85.1|
|International bandwidth|Mbps/capita|4.0|**4.4**|104.0|
|Internet|Subscribers/100 people|0.1|**0.2**|3.0|
|Landline|Subscribers/100 people|19.3|**8.5**|34.8|
|Mobile phone|Subscribers/100 people|11.4|**20.9**|30.0|
|Labor productivity|Subscribers/employee|405.1|**505.8**|756.8|
|Quality of service|Faults/100 main lines|82.4|**90.8**|50.8|
||**Zambia**|Without submarine|cable<br>Other|developing regions|
|Price of monthly mobile basket|**14.6**|11.12||9.9|
|Price of monthly fixed-line basket|**8.9**|13.58||—|
|Price of 20-hour Internet package|**81.5**|67.95||11.0|
|Price of a 3-minute call to the United States|**5.5**|2.59||2.0|
|Price of intra-Africa calls, mean|**1.2**|0.72||n.a.|

_Source:_ Minges and others, 2009 Note: — = data not available; n.a. = not applicable.

21
the i<sup>th</sup> household’s (log) welfare ratio being less than zero<sup>14</sup> from the estimated parameter, and thus the predicted incidence of poverty and the simulated poverty gap and poverty severity measures.

Road access is proxied by the time in hours needed to reach the closest tarred road, at the household level. We control for the education and health channel thanks to two variables: the time to reach the closest primary school and the time to reach the closest health centre, both at the household level. The access to markets is also proxied by the time to reach the closest food market at the household level.

Concerning the labor opportunities channel, we alternatively introduce three types of activities dummies<sup>15</sup> as explanatory variables to control for the household-head labor supply (Kalugina and Najman, 2004); as described in Figure 4.

ECAM II provides detailed information about labor activities carried out within the household but we focus our analysis on the household-head under the assumption he/she is the major contributor to the household's revenues. We follow the literature on road access and labor opportunities as presented in Section 2 as well as the specific design of labor in Cameroon to define our activities dummies.

<!-- Start of picture text --> Household Head<br>Active Inactive<br>Agriculture Non Agriculture<br>Multi-Activity Single Activity<br><!-- End of picture text --> <u>Figure 4: Labor categories</u> 14 Normalizing consumption by the poverty line implies that ln(ci/z) < 0 for poor households; ci : consumption expenditures and z the poverty line.

> 15 Details will be provided in the next sub section.

14
# **Appendix Figure 1B - Germany The main variables during 1997-2010 for firms from Germany**

<!-- Start of picture text --> 9.00% Gross Savings 16% Gross Capital Formation<br>8.00% 14%<br>7.00% 12%<br>10%<br>6.00%<br>8%<br>5.00%<br>6%<br>4.00%<br>4%<br>3.00%<br>2%<br>2.00%<br>0%<br>6%<br>Excess Savings Non-Operating Assets<br>35%<br>4%<br>30%<br>2%<br>25%<br>0%<br>20%<br>-2%<br>15%<br>-4%<br>10%<br>-6%<br>5%<br>-8%<br>0%<br>-10%<br>80% Leverage 25% Liquidity Holdings<br>75%<br>20%<br>70%<br>65%<br>15%<br>60%<br>55% 10%<br>50%<br>5%<br>45%<br>40% 0%<br>Median<br>Asset-Weighted Average<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 19... 19... 19... 20... 20... 20... 20... 20... 20... 20... 20... 20... 20... 20... 20...<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> Excess Savings, Gross Savings, Gross Capital Formation, Non-Operating Assets, Leverage, and Liquidity Holdings, during the period 1997-2010, according to median and asset-weighted average, for the sample of manufacturing firms from Germany.