rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 4.1: As labor intensive industries matured, wages rose**

**and low skill manufacturing** **<u>jobs relocated to other lower wage countries</u>**

<!-- Start of picture text --> 25.0<br>23.0<br>21.0<br>FRA<br>19.0<br>DEU<br>17.0<br>KOR<br>15.0<br>JPN<br>13.0<br>GBR<br>11.0<br>USA<br>9.0<br>SGP<br>7.0<br>5.0<br>1980 1989 1998 2004<br>Source: UNIDO, 2011<br>employment (%)<br>Share of manufacturing jobs in total<br><!-- End of picture text --> China‘s spectacular display of structural transformation in flying geese style shares many common characteristics with successful East Asian countries but it has at least three several distinctive ones. One is the _sheer size_ of China‘s relatively unskilled labor pool that absorbed _nearly all_ the labor intensive jobs shed by the successful but much smaller East Asian economies when rising wages pushed them into more sophisticated industries which were aligned better with their upgraded endowment structure. Another is how China took advantage of its large labor force to become the world‘s largest producer and exporter of labor-intensive manufactures. And yet another is the speed with which China achieved entry into labor-intensive manufactures and, in the last 4 – 5 years, into more technologically sophisticated manufactures. All three factors bode well for low-income countries in general, but especially for those in Sub-Saharan Africa which de-industrialized after liberalization.

China‘s large labor-intensive manufacturing sector and its export orientation have had two effects: One, they have _nearly exhausted China’s relatively unskilled labor surplus_ while the global demand for these products is rising with certainty. East Asia‘s size is near-negligible relative to China‘s labor force in labor intensive industries with non-trivial implications. Two, they have potentially _created enormous employment spillovers for poorer, labor-abundant developing countries_ . Rising wages in the near future will force China to graduate from laborintensive industries and move up the ladder to more capital-intensive and technology-intensive industries. China‘s upgrading will leave a huge space, much larger than the leading geese, for many low-income countries to enter a labor-intensive industrialization phase. We will refer to this pattern as the **_Leading Dragon Phenomenon_** . If India, Brazil, Indonesia and other large 43
26 food crop producers in northem Uganda, which is also less accessible, appear not to have benefited from recent growth.

Periods of economnic stagnation and recession also systematically affect some groups more than others. In Zimbabwe, for example, the increase in rural poverty during 1990/91 and 1995/96 was felt most keenly among the commercial farmers (Table 12). Disentangling exactly why some suffered more than others is a difficult undertaking. Some farmers might have suffered more than others from the drought (an issue taken up by Alwang and Mills, 2001 and discussed below). It is also likely that the fall in incomes among commercial farmers was due to the decline in real tobacco prices, estimated by Townsend (1999) to be -2.5 percent per annum during 1990 and 1996/97. Other features of real price changes during the period identified by Townsend (notably the increase in the real price of cotton and continued government intervention in the maize market) may also explain why the smallholder group of farmers have not suffered as much as the commercial farmers during this episode of drought and economic decline.

**Table 12: Zimbabwe, incidence of rural poverty by farming category, 1991-1996**

|Expenditure /adultequivale|nt<br>_1990_|_/91_|_1995/_|_96_|_Percentag_|_echangein:_|
|---|---|---|---|---|---|---|
||_Mean_||_Mean_||||
||_consumption_<br>_(Z$1990/month)_|_Poverty_<br>_headcount_|_consumption_<br>_(Z$1990/month)_|_Poverty_<br>_headcount_|_Mean_<br>_consumptio_|_Poverty_<br>_nheadcount_|
|Communal|65.54|38.5|50.17|52|-0.23|35.1|
|Small scalecommercial|93.15|18.7|65.95|34.4|-0.29|84.0|
|Large scale commercial|99.21|16.3|76.85|27.4|-0.23|68.1|
|Resettlementareas|57.51|47|46.47|50.6|-0.19|7.7|
|Rural|69.6|35.8|54.29|48|-0.22|34.08|

Source: Alwang and Ersado (1999)

# **_Distribution, poverty and location_**

The panel analysis of Ethiopian and Ugandan households provides strong empirical evidence that location is important in determining how growth influences income distribution. Other countries also experienced strongly divergent pattems in inequality across regions. In _Ghana,_ for example, inequality fell sharply in Accra, Urban Savannah, and Rural Forest, while it increased sharply in the Coastal zone and Rural Savannah. The stability in the overall Gini in _Zambia_ (at just over 0.5 in 1993 and 1998) also gives a misleading impression of little distribution change. In fact consumption inequality increased sharply in both urban and rural areas. But because mean living standards improved in rural relative to urban areas, the overall Gini remained unchanged (Table
# **Table 4 – Determinants of productivity volatility**

Dependent variable: Standard deviation of annual growth rate of output value per worker

||2001<br>(<br>01<br>ˆβ<br>)|2005<br>(<br>05<br>ˆβ<br>)|Difference<br>( β<sup>ˆ</sup><br>∆<br>)|
|---|---|---|---|
|Firm size (Reference: less than 50)||||
|50-100|-6.184***<br>(-10.09)|-6.258***<br>(-13.70)|-0.074|
|100-500|-10.033***|-8.974***|1.059|
||(-18.66)|(-21.82)||
|More than 500|-16.154***|-13.694***|2.460|
|Firm age (Reference: less than 5 years)|(-26.05)|(-27.79)||
|5-10 years|-5.218***<br>(-11.00)|-5.042***<br>(-14.71)|0.176|
|10-20 years|-6.922***|-7.917***|-0.995|
||(-13.20)|(-21.85)||
|More than 20 years|-7.833***|-9.148***|-1.315|
||(-14.89)|(-20.43)||
|Main part of paid-in capital (Ref.: Hong Kong SAR, China||||
|Macao SAR, China; and Taiwan, China, capital)||||
|State capital|2.352***<br>(4.42)|2.574***<br>(5.03)|0.222|
|Collective capital|1.631***<br>(3.25)|1.879***<br>(3.83)|0.248|
|Corporate capital|1.159**|2.789***|1.630|
||(2.29)|(7.23)||
|Private capital|-0.323<br>(-0.67)|0.167<br>(0.47)|0.490|
|Foreign capital|-3.555***<br>(-5.98)|-2.060***<br>(-4.54)|1.495|
|Ratio of long-term investment to output value|0.057***<br>(7.80)|0.012*<br>(1.68)|-0.045|
|Ratio of intangible assets to output value|0.011<br>(1.38)|0.002<br>(0.33)|-0.009|
|Ratio of export delivery value to sales total output value|0.023***<br>(4.49)|-0.004<br>(-1.08)|-0.027|
|Ratio of new product to output value|-2.320*<br>(-1.94)|-5.277***<br>(-6.99)|-2.957|
|Ratio of labor, unemployment insurance to output value|-1.274***<br>(-4.17)|-0.368<br>(-0.98)|0.906|
|Ratio of medical and retirement insurance to output value|<br>0.292***<br>(6.59)|<br>-0.116<br>(-1.63)|-0.408|
|Coastal province|<br>-2.947***<br>(-7.91)|<br>-4.261***<br>(-13.83)|-1.314|
|Constant|59.093***<br>(77.37)|59.160***<br>(97.77)|0.067|
|_R_<sup>_2_</sup>|0.027|0.027||
|Number of observations|49146|78182||

Note: t-statistics in brackets. *** significant at 1%; ** significant at 5%; * significant at 10%.

15
**Table 5. Baseline results: Two-stage least squares estimation**

||[1]|[2]|[3]|
|---|---|---|---|
|2nd stage: Output informalit|y|||
|Real GDP per capita (in logs)|-3.86***<br>|-3.89***<br>|-3.28*<br>|
||(1.49)|(1.51)|(1.68)|
|Control of Corruption|-0.09|-0.10|-0.04|
||(0.56)|(0.57)|(0.60)|
|Regulatory Quality|0.09|0.09|0.14|
||(0.65)|(0.65)|(0.66)|
|Rule of Law|0.17|0.17|0.19|
||(0.57)|(0.57)|(0.58)|
|Commercial bank branches|-0.16***<br>(0.05)|-0.15***<br>(0.04)|-0.20***<br>(0.07)|
|1st stage: Bankingsector develop|ment|||
|Commercial bank branches in the region|0.88***<br>(0.17)|||
|Distance-discounted commercial bank branches (regional)||0.81***<br>(0.15)||
|Distance-discounted commercial bank branches (global)|||2.99***<br>(0.70)|
|Observations|2,155|2,155|2,157|
|R-squared|0.99|0.99|0.99|
|Kleibergen-Paap rk LM statistic|22.59***|23.70***|17.66***|
|Cragg-Donald Wald F statistic|26.31|31.17|18.29|

Notes: Standard errors clustered at the country level in parentheses, *** p<0.01, ** p<0.05, * p<0.1. Here common controls and constant terms are omitted for brevity. All regressions include both country fixed effects and year fixed effects. The data are for the period 2004-18. Commercial bank branches are in per 100,000 adults. GDP per capita is in constant 2010 U.S. dollars. The dependent variable at the second stage is DGE-based output informality (in percent of GDP). See sections 2 and 3 for detailed data descriptions and empirical methods. Data are for 2004-18.

22
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Meeting these illustrative infrastructure targets for Cameroon would cost $1.5 billion per year over a decade. Capital expenditure would account for 74 percent of this requirement. Power and water are the sectors with the greatest spending needs; each will require an estimated $0.4 billion per year to meet the targets specified above. The transport sector will require about $0.3 billion each year to build and rehabilitate roads and other modes of transportation. Around $0.2 billion per annum will be required to provide sufficient connectivity and modern

**Table 15. Indicative infrastructure spending needs in Cameroon for 2006 to 2015**

$ million per year

|Sector|Capital<br>expenditure|O&M|Total<br>needs|
|---|---|---|---|
|ICT|109|64|173|
|Irrigation|88|3|91|
|Power (no-trade scenario)|381|73|454|
|Transport|205|122|328|
|Water supply and sanitation|311|123|434|
|Total|1,095|385|1,480|

_Source_ : Mayer and others 2008; Rosnes and Vennemo 2009; Carruthers and others 2009; You and others 2009. _Note_ : O&M= operations and maintenance.

voice and broadband technologies in the ICT sector. Irrigation will require another $0.09 billion annually (table 15).

Cameroon’s infrastructure spending needs, while high in absolute terms, would absorb only 8.9 percent of GDP, among the lowest shares in the region (figure 27). Investment would account for about 6.6 percent of GDP, around half of what China invested in its infrastructure during the mid-2000s.

Cameroon already spends a sizable amount ($0.9 billion per year) to meet its infrastructure needs (table 16).About 47 percent of that total is allocated to capital expenditure and 53 percent to operating expenditures. Operating expenditure is entirely covered from budgetary resources and payments by infrastructure users.

**Figure 27. Cameroon’s infrastructure spending needs in the regional context**

<!-- Start of picture text --> As percentage of GDP<br>SSA<br>Capex O&M<br>LIC-Fragile<br>LIC- Nonfragile<br>Resource-Rich<br>MIC<br>CEMAC<br>Congo, Dem. Rep.<br>Zimbabwe<br>Ethiopia<br>Central African Republic<br>Kenya<br>Uganda<br>South Africa<br>Cameroon<br>Botswana<br>0 10 20 30 40 50 60 70<br>% of GDP<br><!-- End of picture text --> _Source:_ Foster and Briceño-Garmendia 2009.

_Note:_ LIC = low-income countries; MIC = middle-income countries; ECOWAS = Economic Community of West African States; SSA = Sub-Saharan Africa; GDP = gross domestic product; O&M = operations and maintenance; CAPEX = capital expenditure.

About 40 percent of capital expenditure funding comes from the federal and state governments and from state-owned enterprises (SOEs). Cameroon relies on private flows for about 35 percent of total capital spending on infrastructure in the ICT, power, and water sectors. Official development assistance (ODA) from the member states of the Organisation for Economic Co-operation and Development (OECD) plays accounts for about 21 percent of total capital spending, mostly for transport, water, and power. Non40
**Table 9:  Imports by Product and by Regional Market** (in x 2001 rubles)

|Good|Central F|ar East|North|Siberia|South|Urals|Moscow|St.<br>Petersb.|Tumen|North-<br>west|
|---|---|---|---|---|---|---|---|---|---|---|
|AGR|4.6|1.4|0.8|4.3|5.0|7.6|7.9|2.0|1.3|0.8|
|AIR|0.3|0.1|0.1|0.4|0.3|0.6|0.9|0.1|0.2|0.1|
|CHM|12.5|4.0|2.8|18.8|9.7|25.5|47.9|8.9|4.9|3.6|
|CNM|2.7|0.5|0.4|3.3|2.8|4.6|11.6|2.2|0.8|0.7|
|COL|0.6|0.2|0.2|1.3|0.4|1.2|0.6|0.2|0.2|0.2|
|CON|8.0|4.0|3.4|10.2|10.0|20.4|15.3|4.7|16.4|1.7|
|CRU|1.5|0.6|0.2|2.2|1.4|5.1|0.6|0.9|1.1|0.0|
|ELE|0.3|0.1|0.1|0.4|0.3|0.8|0.5|0.1|0.2|0.1|
|FIN|1.6|0.6|0.5|2.2|1.6|3.7|4.2|0.8|1.7|0.3|
|FME|9.5|1.2|0.8|3.1|7.8|11.7|13.4|4.3|2.9|2.8|
|FOO|19.1|7.2|3.0|14.4|17.9|25.3|79.1|36.2|4.2|7.7|
|GAS|0.1|0.0|0.0|0.1|0.1|0.3|0.2|0.0|0.2|0.0|
|HEA|0.9|0.4|0.3|1.3|1.0|2.3|2.8|0.5|0.9|0.2|
|HOU|3.5|1.4|1.1|4.6|3.9|8.0|10.7|1.8|2.2|0.7|
|MWO|35.8|14.3|8.2|18.0|31.1|60.3|198.5|49.6|20.1|10.9|
|NFM|3.9|2.1|1.5|13.2|4.7|10.9|9.2|3.5|2.2|1.2|
|OIL|4.1|1.1|1.0|4.5|4.0|9.0|6.4|1.6|3.3|0.8|
|OTH|1.7|0.6|0.5|2.2|1.7|4.1|3.7|0.8|2.0|0.4|
|OTI|1.2|0.3|0.2|1.1|1.1|2.1|1.5|0.4|0.6|0.2|
|PST|0.1|0.0|0.0|0.1|0.1|0.2|0.2|0.0|0.1|0.0|
|RLW|0.1|0.0|0.0|0.2|0.1|0.3|0.3|0.1|0.2|0.0|
|SCI|0.0|0.0|0.0|0.0|0.0|0.1|0.1|0.0|0.0|0.0|
|TMS|0.8|0.3|0.2|1.0|0.8|1.8|2.0|0.4|0.7|0.2|
|TPP|2.7|0.6|1.1|0.7|3.6|2.0|22.1|7.3|0.1|2.8|
|TRD|1.0|0.4|0.3|1.5|1.1|2.6|2.4|0.5|1.5|0.2|
|TRK|0.4|0.1|0.1|0.5|0.4|0.9|0.9|0.2|0.5|0.1|
|TRO|0.1|0.0|0.0|0.2|0.2|0.3|0.3|0.1|0.2|0.0|
|CLI|25|10|8|32|29|56|84|13|13|5|
|Total|142|52|35|142|140|268|528|140|81|41|

See Tables 1 and 2 for sector and region definitions.

Source: Roskomstat unpublished surveys and authors' calculations.

36
Table 9: EFFECTS ON TRANSFERS FROM EMPLOYERS

||Househol|d received trans|fers from...|
|---|---|---|---|
||any<br>employer(s)|female<br>respondent’s<br>employer(s)|male<br>respondent’s<br>employer(s)|
||(1)|(2)|(3)|
|Treatment|-0.039***|-0.035***|-0.013***|
||(0.008)|(0.008)|(0.004)|
|Mean in control|0.091|0.081|0.032|
|Adjusted R-squared|0.068|0.059|0.039|
|Observations|13464|13464|7778|
|_Panel B:_|_Sample of _|_wage-workers in_|_all 3 surveys_|
|Treatment|-0.040***|-0.026|-0.019|
||(0.013)|(0.016)|(0.015)|
|Mean in control|0.133|0.138|0.073|
|Adjusted R-squared|0.079|0.071|0.077|
|Observations|6336|4488|1332|

**Source:** Author’s analysis based on original survey data. **Notes:** The table reports ITT estimates based on specification (1) estimated with OLS. The sample includes observations from ultra-poor households surveyed at the midline and endline surveys. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). *** (**) (*) indicates significance at the 1% (5%) (10%) level. The dependent variable in column 1 is a dummy variable (DV) equal to 1 if either the main female or the male head of the household had worked (in the last 12 months) for an employer who lives in the same village and is reported as a source of transfers for the household. The dependent variable in column 2 (3) is a DV equal to 1 if the main female respondent (male head of the household) had worked (in the last 12 months) for an employer who lives in the same village and is reported as a source of transfers for the household. In Panel B, the sample is restricted to ultra-poor households where either the main female respondent or the male head of the household had worked for an employer in the 12 months preceding the survey.

44
_CHAPTER 6. UNIT LEVEL MODELS: GENERAL INDICATORS_ 48 3. With the generated census vector **y** _d_<sup>(</sup><sup>_l_),calculate the indicator</sup><sup>_δ_</sup> _d_<sup>(</sup><sup>_l_)</sup> = _hd_ ( **y** _d_<sup>(</sup><sup>_l_)).</sup> 4. Repeat step 3 for _l_ = 1 _, . . . , L_ . The MC approximation of the EB predictor of _δd_ is obtained by averaging the indicators over the _L_ simulated censuses: The MSEs of the Census EB estimators may be estimated through a slight modification of the bootstrap procedure described for EB. The difference between the EB and Census EB estimators lies in the fact that the units from the survey cannot be identified in the census. Therefore, in each bootstrap repetition, we cannot generate census vectors **y** _d_<sup>_∗_(</sup><sup>_b_)</sup> , _d_ = 1 _, . . . , D_ , and then take the sample part **y** _s_<sup>(</sup><sup>_b_)</sup> from them. For the Census EB predictors, in step 2, we generate the bootstrap census vector **y** _d_<sup>(</sup><sup>_b_),</sup> _d_ = 1 _, . . . , D_ , using the values of the auxiliary variables from the census, **X** _d_ , _d_ = 1 _, . . . , D_ . In step 3, the true values of the bootstrap indicators are obtained from the bootstrap census vectors, _δd_<sup>_∗_(</sup><sup>_b_)</sup> = _hd_ ( **y** _d_<sup>_∗_(</sup><sup>_b_)</sup> ). In step 4, the bootstrap sample vector **y** _ds_<sup>_∗_(</sup><sup>_b_)</sup> is not extracted from **y** _d_<sup>_∗_(</sup><sup>_b_)</sup> ; instead, it is generated again using the values of the same auxiliary variables, but this time taken from the survey, **X** _ds_ , _d_ = 1 _, . . . , D_ .

Corral Rodas, Molina and Nguyen (2021) extended the model-based simulation experiment of Molina and Rao (2010) to more realistic scenarios with a much better explanatory power of the model, including contextual variables, using much larger area population sizes and much smaller sampling fractions, generating errors from a Student’s _t_ 5 distribution instead of a normal distribution, and also decreasing the overall sample size and the area sample sizes. Additionally, Corral et al. (2021) performed a design-based validation study, using the Mexican Intracensal Survey
There is indirect evidence to suggest that the benefits of these changes have been felt even by the poorest households.

# **4. Affordability of Service**

In all three sectors, the reform process led to changes in the average level of tariffs and the tariff structures faced by utility customers (Table 4.1).

**<u>Table 4.1: Cross-sectoral overview of tariff reforms</u>**

||Electricity|Telecommunications|Water and Sewerage|
|---|---|---|---|
|Real tariff<br>increases|Average tariffs have risen<br>gradually by 21% since<br>reform in 1996.<br>However, the average annual<br>growth rate of tariffs fell<br>slightly from 7.3% in the<br>period 1992-95 to 6.5% in the<br>period 1996-99.|Following reform, the cost of local<br>charges remained broadly constant.<br>Long distance and international<br>tariffs increased significantly<br>between 1996/98, but then began to<br>decline. Following market<br>liberalization in December 2001,<br>tariffs fell by 67% relative to levels<br>prevailing in 1996.|Average tariffs rose instantaneously by<br>20% at the time of reform, and have<br>subsequently been indexed to the dollar.|
|Changes in tariff<br>structure|Only very minor adjustments<br>to the increasing block tariff<br>structure were made.<br>There continue to be four<br>categories of domestic<br>customer, with standing<br>charges of $3-$10<br>differentiated according to the<br>quality of service received in<br>terms of voltage level.|Prior to reform a fixed monthly<br>charge of $3 was levied for local<br>service and all calls were<br>subsequently ‘free’.<br>Following reform, a number of<br>calling plans are available,|Radical simplification of the tariff<br>structure from 150 categories of users to<br>just three (domestic, commercial and<br>industrial)<sup>12</sup>.<br>Prior to reform a fixed monthly charge<br>of $1-$2 was levied with an entitlement<br>to a ‘free’ block of 10 cubic meters of<br>consumption. This fixed charge was<br>eliminated and a unit charge is now<br>levied on all consumption.|

**Figure 4.1: Evolution of average tariff level**

<!-- Start of picture text --> 70<br>60 Electricity (10kWh)<br>50<br>Fixed telephony<br>40 (10 mins)<br>30<br>Water and sewerage<br>20 (m3)<br>10 Cellular telephony<br>(weighted in-coming<br>0<br>Real dollar cents per unit consumption and out-going mins)<br>1992 1993 1994 1995 1996 1997 1998 1999 2000<br><!-- End of picture text --> _Source:_ Barja and Urquiola, 2001.

12 The previous tariff structure for water and sewerage had 135 categories for unmeasured customers and 15 for measured customers. The categories were based on property values, connection diameters, and geographic location. The complexity of the tariff structure often rendered customer classifications somewhat arbitrary.

21
in fuel oil demand in more temperate locales, for heating. This aligns with evidence from Davis and Gertler (2015), who estimate large increases in demand for air conditioning among residential consumers in Mexico (see Jaglom et al. (2014) for the U.S.). While these studies are an important first step, Auffhammer and Mansur (2014) point out that we still have relatively little evidence on the industrial and commercial sectors, limiting our understanding of how firm demand for energy may evolve due to climate change. Such gaps are even more pronounced in LMICs, where current rates of energy access remain relatively low. As climate change increases the benefits of electrification, climate-driven increases in demand for electricity may be even more extreme in LMICs than they are in HICs.

A separate literature examines anticipated impacts of climate change on energy supply (e.g., Muñoz and Sailor (1998) on hydroelectric generation in California, McDermott and Nilsen (2014) on thermal power stations in Germany, Linnerud, Mideksa, and Eskeland (2011) on nuclear in Europe). Although the evidence base on the precise magnitudes of the effect of climate change on energy supply and demand remains extremely limited for LMICs, the existing evidence points to heightened demand for energy precisely at the moments when extreme temperatures are adversely impacting energy supply.

Given contrasting effects of climate change on energy demand and supply and uncertain impacts of higher energy prices on firm production, increasing energy efficiency is a particularly appealing adaptation strategy. Multiple studies in HICs find a positive relationship between managerial quality and energy efficiency, suggesting that improving energy efficiency may increase profits (e.g., Bloom et al. (2010), R. Martin, Muûls, et al. (2012)). A growing number of studies document a similarly positive association between energy efficiency and various measures of firm performance in LMICs (Sahu (2014), L. W. Fan et al. (2017), Grover and Karplus (2020)). Alcorta et al. (2014) estimate the returns on a range of energy investments in manufacturing in LMICs and find that payback periods range from 0.9–2.9 years. Adhvaryu, Kala, and Nyshadham (2020) document substantial cost savings from investments in efficient LED lighting along with even larger gains in productivity from the cooler light 16