rafmacalaba/datause-encoder-data huggingface dataset:
Table 5: Estimations for 1994-2005 urbanization changes in manufacturing sector

||||DV: Chang|e in urbaniz|ation share fo|r total distri|ct-industry e|mployment|||
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Initial urban share|-0.693+++|-0.743+++|-0.689+++|-0.739+++|-0.694+++|-0.741+++|-0.711+++|-0.754+++|-0.692+++|-0.741+++|
||(0.031)|(0.029)|(0.030)|(0.029)|(0.030)|(0.029)|(0.029)|(0.029)|(0.030)|(0.029)|
|Education level|0.055+++|0.059+++|0.056+++|0.062+++|0.057+++|0.060+++|0.044+++|0.041+++|0.057+++|0.059+++|
||(0.010)|(0.011)|(0.009)|(0.011)|(0.010)|(0.012)|(0.012)|(0.013)|(0.010)|(0.012)|
|Infrastructure level index|0.030+++|0.042++|0.027++|0.039++|0.026++|0.036+|0.020+|0.019|0.022+|0.036+|
||(0.011)|(0.019)|(0.011)|(0.019)|(0.011)|(0.019)|(0.011)|(0.019)|(0.012)|(0.020)|
|Log average wage|||-0.020|-0.031++|-0.019|-0.031++|-0.023|-0.035++|-0.026+|-0.040++|
||||(0.015)|(0.014)|(0.014)|(0.014)|(0.014)|(0.014)|(0.016)|(0.016)|
|Urban/rural wage ratio|||-0.021+|-0.015|-0.020+|-0.016|-0.021+|-0.015|-0.019+|-0.014|
||||(0.011)|(0.010)|(0.011)|(0.010)|(0.011)|(0.010)|(0.011)|(0.010)|
|Log total district land area|||||0.007|-0.013|0.017|0.002|0.009|-0.012|
||||||(0.014)|(0.016)|(0.014)|(0.017)|(0.014)|(0.016)|
|Change in urban population|||||0.235|-0.019|0.194|0.067|0.238|-0.016|
||||||(0.219)|(0.203)|(0.186)|(0.189)|(0.212)|(0.199)|
|Urban build-up [50th,75th]|||||||0.033|0.046++|||
||||||||(0.024)|(0.022)|||
|Urban build-up [75th,100th]|||||||0.082+++<br>(0.027)|0.100+++<br>(0.028)|||
|Log land use intensity|||||||||0.017|0.019+|
||||||||||(0.011)|(0.011)|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State fixed effects|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|Number of observations|1700|1700|1700|1700|1700|1700|1700|1700|1700|1700|
|Adjusted R-squared|0.353|0.381|0.357|0.385|0.358|0.385|0.364|0.391|0.359|0.386|

Notes: Estimations consider changes in district-industry employment shares in urban areas for 1994-2005. Positive values indicate an increase across the period in the urban employment share of the district-industry. Explanatory variables are calculated from the 2001 Census or the 2000 manufacturing surveys as indicated in the text. Change in urban population share is calculated as the change in urban population from the 1991 Census to the 2001 Census. Estimations report standard errors clustered by district, include state and industry fixed effects as indicated, have 1700 observations, and weight observations by the interaction of log district size and log industry size. + significant at 10% level; ++ significant at 5% level; +++ significant at 1% level.
- 19 -

unnecessary to use all of these methods. Rather, each agency should identify its minimum data needs on female farmers and select appropriate methodologies from the following list:

* Agricultural calendars. citing activities by month and indicating the gender and person responsible. These should include important cultural events, such as male initiation ceremonies, which influence who does the work and when.

* Spatial maps. a geographer's tool adapted by other researchers to visually describe a system. Symbols such as R (indicating who is responsible), L (who provides labor), and C (who controls the resource) can be attached to maps of fields or enterprises. By mapping responsibilities in each field, a clearer picture of constraints, participants, and beneficiaries emerges.

* Seasonal labor profiles, representing seasonal labor tasks and estimating personal days per month allocated to each task during an average farming season. These profiles are extremely useful in showing changes in the farming cycle and labor allocation when new crops or techniques are introduced.

* Informal surveys, popularized by farming systems research, include Rapid Rural Appraisal and rural reconnaissance. These are quick, informal, cost-effective ways to describe farming practices, agricultural knowledge, and women's roles in agriculture.

* Group and community interviews, which can be conducted with a group of individuals representing more than one household. These can yield a quick, inexpensive overview of conditions and practices in an area.

* Community portraits. which easily compiled for a variety of project village. These allows one to compare and contrast beliefs and practices across villages.

* Household record keeDing is useful for determining family labor contributions for carefully selected, representative households. The technique can be used in non-literate societies by substituting pictures for various activities.

* Policy inventory technigues give an overview of recent sectoral performance, listing major policies affecting the sector and assessing policy impact -- for example on women engaged in agriculture.

2.13 Rapid Cost-Effective Techniques. Several rapid yet inexpensive methods have been used successfully to diagnose and plan extension programs for female farmers in Africa. One such technique is the informal survey technique of Rapid Rural Appraisal combined with a two to three day field visit to the project area. Site-specific information on female involvement in agriculture can be obtained by questioning local officials and extension agents, especially female agents or home economists. Another useful technique requiring only a few hours is to gather groups of wemen farmers--preferably without the inhibiting presence of their husbands and other male authority
# **D.2 Benchmarking the Magnitude of the Earnings Effects**

In this section we show that the earnings effects are substantial relative to two local benchmarks.

**Minimum wage** : During our study period, minimum wages in South Africa varied by sector and location. Sector- and location-specific minimum wages were either set by the Ministry of Labour or in bargaining councils, where large firms and unions agreed minimum wages that applied to all firms (Budlender et al., 2015; Isaacs, 2016). Table D.4 shows minimum wages for urban areas at the time of the study for several sectors relevant to workseekers in our sample.

**Poverty Lines** : South African poverty research often uses poverty lines based on the cost of purchasing 2100 calories plus the average amount spent on non-food items by households whose food expenditure equals the food poverty line (Budlender et al., 2015; Leibbrandt et al., 2012). Using this definition, the adult monthly poverty line just before the study period was 1,386 South African rand or USD 232 in purchasing power parity terms (Isaacs, 2016, p.22).

The average treatment effect on earnings is equal to 17% of the adult monthly poverty line or 7-9% of the monthly minimum wage at the time of the study.

Table D.4: Benchmarking Earnings Figures to Minimum Wage and Poverty Lines

|_Panel A: South African poverty lines _|_and minimum_|_wages _|_at basel_|_ine_||||
|---|---|---|---|---|---|---|---|
|||Mon|thly|We|ekly aaa|||
||Date|ZAR|USD|ZAR|USD|||
|**Poverty line**||||||||
|Adult|Early 2016|1386|232|320|54|||
|Household (4 people)|Early 2016|5544|927|1279|214|||
|**Minimum wage**||||||||
|Domestic work|2015-2016|2550|427|588|98|||
|Hospitality|2015-2016|2750|460|634|106|||
|Wholesale and retail|2015-2016|3250|544|750|125|||
|Private security/contract cleaning|2015-2016|3500|585|808|135|||
|_Panel B: Benchmarking sample earni_|_ngs and certif_|_cation t_|_reatmen_|_t efects _|_on earnings_|||
|||Wee|kly|As % of|poverty line|As % of min|. wage|
||Date|ZAR|USD|Adult|Household|Hospitality|Retail|
|Baseline mean earnings if employed|Late 2016|562|94.1|1.76|0.44|0.89|0.75|
|Endline mean earnings|Early 2017|159|26.6|0.50|0.12|0.25|0.21|
|Endline mean earnings if employed|Early 2017|518|86.7|1.62|0.41|0.82|0.69|
|Treatment efect|Early 2017|54.1|9.05|0.17|0.04|0.09|0.07|

Calculations assume 1 rand _≈_ 0.167 USD in purchasing power parity terms; 4.33 weeks per month. Household poverty lines assume households of four people with only one earner. Control group respondents work 29 hours per week conditional on being employed; earnings for those in full time work will be higher than mean earnings here. Poverty lines are from Isaacs (2016, p.22) and minimum wages are from the Department of Labor for 2015. Minimum wages are for large urban areas (Area A). They are for hospitality businesses with less than 10 employees and shop assistants in the wholesale and retail sector.

51
FIGURE 2 GHANA; FINANCIAL TAXES

**Mcomure** 1

<!-- Start of picture text --> 14<br>13<br>12-<br>10<br>a a<br>4- -<br>1 M L ii<br>3-<br>2-<br>~V a@r m I<br>1G81 MM~ 1983 1984 19B 196 1G987 1G86<br><!-- End of picture text --> 42
# **Growth-Oriented State and Development-Oriented State**

18. We differentiate a growth-oriented state from a development-oriented state, and we nevertheless recognize that they are complementary.

19. _A growth-oriented state, over prolonged periods of time, takes policy and resource allocation decisions which create the incentives and effective institutions which will lead to private and public investments, productivity growth, and growth ofper capita income. It avoids to take decisions which undermine such growth. As we shall discuss below, the existence of a bargaining equilibrium among powerful social groups is an essential prerequisites for the emergence and sustainability of a growth-oriented state._ 20. _A development-oriented state_ is a growth-oriented state but is one which, _in addition to taking decisions which enhance growth, also consistently takes decisions which lead to widely shared growth. These include improvements in the welfare of all social groups, and which ensure the economic and environmental sustainability of that growth._ 21. In general, a development-oriented state is stable and sustainable. Its institutional design empowers citizens to control the politicians and the government, enables politicians to control bureaucrats, enables the government to place bounds and safeguards on the behaviors of citizens, and it allows the government to ensure economic stability and encourage development.

22. The bargaining equilibrium requested for passage from growth-orientation to development-orientation in the process of state formation can be successful only if, on the one hand, the poor are organized and represented, and, on the other hand, if the natural resources are effectively managed by civil society organizations which will lobby for their sustainable use. Under these conditions bargaining will also lead to social and environmental policies and programs, such as taxes to increase revenues, land reform, rural and agricultural development, urban development, pension, employment, education, housing, health, environment, etc.

23. This study draws its data from secondary analysis and field observations in the Democratic Republic of the Congo, and it builds on the substance of the above literature. Nevertheless, it takes it a step further by simultaneously presenting the requirements for the formation of a stable and development-oriented state and applying them to the Congo, with implications for World Bank and other donors' re-engagement. A brief historical background of the Congo is in the Annex.

_5_
if _s_ and _m_ refer to a specific services and manufacturing sector respectively, in a country _j_ in a given year. The numerator on the right-hand side of formula (10) refers to the value added contributed directly and indirectly by the factors employed in a services sector, while the denominator measures the value added contributed by factors employed in a manufacturing sector. Therefore, the denominator is not a part of the numerator and the _SII_ measure is not bounded by one, although it is always less than one in the data. It would be bounded by one if we used the gross manufacturing output in the denominator, and the _SII_ of one services sector would likely be negatively correlated to the _SII_ of other goods or services sectors, and so omitted variable bias can be a problem if we do not include all other sectors in our analysis. The strategy we adopt to measure _SII_ as in formula (10) can help us to avoid this problem and keep our specification simple.

It is tempting to use a country’s own services input intensity ( _SII_ ) directly in the regression. But there are a number of issues with such a strategy. _SII_ of a country with underdeveloped services sectors (e.g., financial repression) may not be able to capture the required services input intensity along the manufacturing production possibility frontier. Hence, instead of using countries’ own services input intensities, we use U.S. services input intensity for all the countries under the assumption that the U.S. is among the countries with the least financial and business services transaction costs and frictions. If inter-sectoral linkage is considered as a feature of the production technology, it should be the same across countries in the absence of services under-development. Adopting a similar strategy, Rajan and Zingales (1998) measure industries’ dependence on external funds using only U.S. data for all countries covered by their analysis. Figure 5 shows a scatter plot of the domestic financial services input intensity in manufacturing against the business services input intensity for each of the WIOD countries in 2005. As we expect, U.S. embodied services ratios are among the highest for both financial and business services.

Another problem of using countries’ own services input intensity is a potential endogeneity issue because a country’s embodied services and services development can also be affected by its own manufacturing performance. For example, a country like India with comparative disadvantage in manufacturing may choose to specialize in services, which in turn will promote services development and reduce embodied services due to the weakness of the manufacturing sectors. When we use only U.S. embodied services, the feedback or reverse causality to the U.S. embodied services from other countries’ manufacturing export RCA will be less a concern. In addition, we 15
**_50_**

filng in rel terms mn 1986 such t cmpion increased by 12.3 percet per year during dhe period 198045. it is uiiey dhat aiey price increases led to any cost-push inflation in arcultre prices via an nca in fertilizer pices. This is also reflected in the food price index where the annual cage have been reatively small.

_Rgwue 3.3: IndonesIa: Dlferences Between Domestic and BorderPricerfor Kerosene Subsidy_ **_(-)._** _Tar (+)_ <!-- Start of picture text --> Om<br><!-- End of picture text --> Mmc revew bnpact cfthe pnce rePomls was relatvely posillve. Hadt de energy pnce refia not been implemanted, uig thie most liklyeb esimates of pnce elasdaity of demand for diesel and Icerosene oqualto -1, tfe admated dwr36 in net esm hin 1985 woWd have been eqlivalent to Rtp bMiin 3567 or 18.5 powofihe govmnt revenues in thie samne year. Givren thie fallng inamsad off oil and tq ra,m ecomonu polide focussed on controllig govenwnent defiici ev ncw fems Isd domidesddwand bmstpnoeerefomuscontributed to acluev 1hebudgesytary&gts Inteof*1e rev:t, eh ffie% isl ad keosn pnc gswr qmte ff ectve

*sn m 1.7 forda&n t csic" o vmeffc. Sao aodisdmof _ ye,noimpno in **Cbp-r** 1.
# **3. Impact of the Japanese earthquake on supply chain reconfiguration**

In this section, we study how the 2011 earthquake impacted imports from Japan and whether it led to more diversification or reshoring of production.

## 3.1 _Impact on imports from Japan_

As a first exercise, we investigate whether importers less dependent on Japan as a source behaved differently from importers more dependent on Japan in the aftermath of the 2011 earthquake. Specifically, we rely on the following specification: J i i i i i i (1) JJ ii ii ii J ln�YY �= αα + θθ + σσ + ββ II(H HHh SSha aa J JJ 2011 ) + εε where i are country ’s imports of HS 6-digit product from Japan for the 15 largest auto producing JJ countries or the 15 largest exporters of final electronics. ii<sup>7,8</sup> The analysis focuses on the largest auto YY HH kk producing countries as those are the countries where lead firms located their production facilities. The countries are selected based on 2010 motor vehicle production data from the Organisation Internationale des Constructeurs d'Automobiles (OICA). As there is no comparable production data for electronics, we select the largest exporting countries of final electronics based on the 2004-2010 data.<sup>9</sup> i ) is an indicator variable taking value 1 if the importer is dependent on Japanese suppliers in the period preceding the earthquake (more than ii percent of total imports in a II(H HHh SSha aa J JJ 2011 product line between 2004-2010, where can take value of 15, 10 and 5). The specification also includes xx i , i and i , which are respectively the importer-sector, importer-time and sector-time fixed effects. xx We expect the coefficient of interest, , to be negative if the shock led to a greater drop in reliance on αα θθ σσ ββ 7 See Table A1 in Annex for the list of countries in the sample.

8 We use product level bilateral trade data from WITS (UN Comtrade) reported at the 6-digit level in the HS 1988/92 classification for the period 2004-2018.

9 Results are robust to alternative selection methods. See Table A2 in Annex for results for the auto industry based on exports of final auto and selection based on imports of intermediates.

9
_Table I11: Comparison of degree of completion of studies by inclusion and exclusion from President Report conditions of agreement, 1982-1989_

|**Degrea of study**<br>**implementation**|**President's Report**<br>**Included**<br>**Exc**|**luded**|
|---|---|---|
|Planned only|140 (53%)<br>106|(64%)|
|Completed**only**|75 (29%)<br>25|(15%)|
|Completed and available|47 (18%)<br>35|(21%)|
|Total'|262<br>166||

Figures in parentheses represent pereentages of planned **studies for** that group.

± No **information** for S studies, of **which t',o were zomplete and aNailable.** 32. Advanced planning. As a proxy for considering the effect of advanced planning on completion of studies, the annexes of the Staff Appraisal Reports were searched to determine the extent to which studies had been described in detail. The studies were classified in four levels of inclusion within the Annex of an SAR: not included; included in the expenditure tables only; included in the key indicators for monitoring and evaluation, which provided a brief outline; included as a separate annex providing detailed Terms of Reference for the study. Table 12 provides information on the nurrber of studies planned, completed and available for these categories and their respective completion rates.

_Table 12. Planned, comnpleted and available studies wvithin the Annex of SARs, 1982-1989_

||No<br>Irn|c<br>cluded|C<br>O|ost<br>nly|Bri<br>Out|ef<br>S<br>line|epar<br>Ann|ate<br>ex|
|---|---|---|---|---|---|---|---|---|
|Planned only|89|(63%)|45|(47%)|60|(54%)|,2|(66%)|
|Completed onlv|31|(22%)|28|(29%)|40|(36%)|<br>1|(1%)|
|Completed and available|22|(15%)|23|(24%)|11|(10%)|26|(33*)|
|Total<sup>1</sup>|142||96||111||79||

**Figures** in **parentheses indicate completion rates** for **that** group.

I No information for **8** studies, of **which two** were zornplet:e nd asailable 33. Study completion rates were lower for studies that were not included in SARs (37%) than for those included in such reports, but-- surprisingly-- completion rates for studies with separate Annexes was equally low (34%). Apparently, the most effective strategy was costing the study separately within the expenditure tables (53% completion 14
**TABLE 2: COMPARING LONG‐RUN TRANSMISSION ELASTICITIES**

||_Holtham (1988)_<br>_1967:S1‐1984:S2_|_Gilbert (1989)_<br>_1965:Q1‐1986:Q2_|_Borensztein &_<br>_Reinhart (1994)_<br>_1970:Q1‐1992:Q3 _|_Baffes (2007)_<br>_1960‐2005_|_This Study_<br>_1960‐2008_|
|---|---|---|---|---|---|
|**_Non‐energy_**|—|0.12|0.11|0.16|0.28|
|**_Food_**|—|0.25|—|0.18|0.27|
|**_Raw materials_**|0.08|—|—|0.04|0.11|
|**_Metals_**|0.17|0.11|—|0.11|0.25|

**Notes:** Holtham uses semiannual data, Gilbert and Borensztein & Reinhart quarterly, and Baffes along with the present study annual. Gilbert’s elasticities denote averages based of four specifi‐ cations. Holtham’s raw materials elasticity is an average of two elasticities based on two sets of weights. ‘—‘ indicates that the estimate is not available.

**Source:** Holtham (1988), Gilbert (1989), Borensztein and Reinhart (1994), Baffes (2007), and au‐ thor’s estimates.

**_— 13 —_**
dense and denser areas in France for young workers. However, when older workers come from denser areas, they can command much higher wages than similar workers from less dense areas. This effect seems to be driven by labor market experience in denser areas rather than by increased sorting of intrinsically more skilled workers.

De la Roca and Puga (2012) provide the most comprehensive treatment of these urban learning issues to date using Spanish data. They assess agglomeration benefits in terms of both wage levels and wage growth. Their findings suggest the existence of both a level effect of cities on wages (of the same magnitude as those discussed above) and a dynamic effect. Over the long run, workers in large cities seem to gain about as much from each of these two effects. Confirming these findings for developing cities is, of course, a priority.

To sum up, this discussion of agglomeration economies, which focuses mainly on workers and jobs, reaches a number of interesting conclusions. First, larger cities make workers more productive. There is both a static and a dynamic component to these gains. A static elasticity of wages with respect to city population of 0.03 — as typically found in developed economies — implies that a worker receives a 23 percent higher wage when moving from a tiny city with a population of 5,000 to a large metropolis with a population of 5 million. Taking an elasticity of 10 percent, as found by Combes et al _._ (2013) for China, leads to a much more significant gain of 100 percent. Over time, dynamic effects make this urban premium larger. Assuming an extra 3 percent from learning in the medium run of 3 to 10 years, as De la Roca and Puga (2012) find for Spain, yields another 23 percent from the same move. Although longrun gains of 50 to 125 percent are not miraculous, they are nonetheless sizeable.

These numbers are, of course, only an illustration of the magnitudes at stake. They do not constitute a welfare pronouncement. Cities offer pecuniary benefits, such as higher wages in 17
|cw|me<br>Year|||e|ThePoor<br>v'|E<br>MPIs<br>ve|ce<br>Numberof<br>Regions|
|---|---|---|---|---|---|---|
|da<br>[<br>|2005|<br>|028s<br>||sam|sx7w<br>||<br>|<br><br>|<br><br>|
|‘oso|<br>|2010||<br>0250|<br>|s0a8|as|oa<br>|orm|e<br>oon|)<br>|

<!-- Start of picture text --> The Poor MPIs Number of<br>Year v' ve Regions<br>cw me |e RE ce<br>da [2005 | 028s | sam | sx7w |<br>‘oso | 2010 | 0250 | s0a8 | as |oases)orm | oon |<br><!-- End of picture text -->