rafmacalaba/datause-encoder-data huggingface dataset:
Table A.1: Average and Median Output Elasticities, By Sector PANEL A: Average Output Elasticities

|ISIC||Obs.|Capital (<sup>ˆ</sup>_θ_<sup>_k_</sup><br>_it_<sup>) </sup>|<sup>Labor (ˆ</sup><sup>_θl_</sup><br>_it_<sup>) </sup>|<sup>Materials </sup>|<sup>(ˆ</sup><sup>_θm_</sup><br>_it_ <sup>) Returns to Scale</sup>|
|---|---|---|---|---|---|---|
|Rev.2|Sector|(1)|(2)|(3)|(4)|(5)|
|31|Food|390|0.02|0.27|0.75|1.04|
||||[0.25]|[0.35]|[0.21]|[0.23]|
|32|Textiles|364|0.16|0.18|0.78|1.12|
||||[0.14]|[0.22]|[0.19]|[0.10]|
|33|Wood|462|0.08|0.20|0.76|1.04|
||||[0.16]|[0.18]|[0.14]|[0.23]|
|38|Metals|391|0.16|0.20|0.81|1.16|
||||[0.21]|[0.13]|[0.16]|[0.13]|

PANEL B: Median Output Elasticities

|31|Food|390|0.08|0.25|0.76|1.03|
|---|---|---|---|---|---|---|
|32|Textiles|364|0.19|0.16|0.79|1.11|
|33|Wood|462|0.11|0.20|0.77|1.11|
|38|Metals|391|0.21|0.19|0.83|1.16|

**_Source:_** Data for the study comes from the World Bank Regional Project on Enterprise Development (RPED) and Ghana Manufacturing Survey (GMES) from 1992 to 2003.The surveys were conducted by the Centre for the Study of African Economies (CSAE) at the University of Oxford, University of Ghana, and Ghana Statistical Service. **_Note:_** Column (1) refers to number of observations for each production function by sector. Columns (2) - (4) report average (median) estimated output elasticity with respect to each production input for firms in the sector in panel (A) and (B). In panel A, results in brackets report standard deviations (not standard errors). Column (5) reports returns to scale, which is given by the sum of the average (median) elasticities of the three inputs.

indicate that the output elasticities are not statistically different before and after trade openness.<sup>20</sup> This suggests that variations in markups cannot be attributed to shocks to the output elasticities.

Table A.2 reports input shares on materials and laborfor the _ex ante_ and _ex post_ trade liberalization periods. It can be seen that _αit_<sup>_l_decreasedacrossallsectorswiththe</sup> exception of food, whereas _αit_<sup>_m_increasedinallsectorsbutwood.</sup> This indicates that the cost component for labor shares decreased while that for materials increased. Hence, variations in markups are driven by variations in inputs shares. A statistical test shows that the differences are statistically significant.<sup>21</sup> 36
|Dimension|Brief Description|
|---|---|
||This set of indicators is based on countries’ use of internationally accepted and<br>recommended methodologies, classifications and standards regarding data integration.<br>These indicators help facilitate data exchange and provide the foundation for the<br>preparation of relevant statistical indicators. The following methods and standards are<br>considered: System of national accounts in use, National Accounts base year,|
|Dimension 5.2:<br>Standards and Methods|Classification of national industry, CPI base year, Classification of household<br>consumption, Classification of status of employment, Central government accounting<br>status, Compilation of government finance statistics, Compilation of monetary and<br>financial statistics, Business process. Further work could improve the validity of this<br>indicator and reduce the risk that countries may be incentivized to adopt only<br>traditional standards and methods and neglect innovative solutions that may be more<br>valid in the current context.|
||Not included because of lack of established methodology or suitable data sources. A|
|Dimension 5.3: Skills|new indicator drawing on PARIS21 indicators such as statistical society presence and<br>data literacy could be developed and is an area of future work.|
||Not included because of lack of established methodology or suitable data sources. A|
|Dimension 5.4:|new indicator based on textual analysis of NSS reports/websites for references to|
|Partnerships|partner organizations could be developed. This is an area of future work.|
||The indicator is based on PARIS21 SDG indicators (SDG 17.18.3 (national statistical|
|Dimension 5.5: Finance|plan that is fully funded and under implementation). It is included in dashboard, but not<br>in the overall SPI score or index because of insufficient country coverage.|

**Table S3. Mapping of SPI Indicators to SDG Indicators**

||**SPI Indicator**|**SPI**<br>**Dimension**|**SDG**|**Data Source**|
|---|---|---|---|---|
|**1**|Availability of Comparable Poverty<br>headcount ratio at $1.90 a day (5 year<br>moving average of availability)|Dimension<br>1.5: Data use<br>by<br>international<br>organizations|SDG<br>1.1.1|World Bank's PIP|
|**2**|Availability of Mortality rate, under-5<br>(per 1,000 live births) data meeting<br>quality standards according to UN<br>IGME (5 year moving average of<br>availability)|Dimension<br>1.5: Data use<br>by<br>international<br>organizations|SDG<br>3.2.1|Child Mortality Metadata<br>from UN IGME|
|**3**|Quality of Debt service data according<br>to World Bank|Dimension<br>1.5: Data use<br>by<br>international<br>organizations|SDG<br>17.4.1|Debt Reporting Metadata<br>from World Bank|
|**4**|Safely Managed Drinking Water|Dimension<br>1.5: Data use<br>by<br>international<br>organizations|SDG<br>6.1.1|Availability of Safely<br>Managed Drinking Water<br>data for use by JMP|
|**5**|GOAL 1: No Poverty (5 year moving<br>average of availability)|Dimension<br>3.1: Social<br>(SDG 1-6)|SDG 1|UN Global SDG Indicators<br>Database|

41
bringing women into the labor force adds an economic benefit from the diversity over and above the benefit of simply having more workers (Ostry, Alvarez, Espinoza, and Papageorgiou, 2018).

# **ix) Real minimum wage**

Overall, the results indicate a positive relationship between the real minimum wage ( _rminw_ ) and female labor force participation in Colombia from 1960 to 2018, although the effect varies over time with a quadratic trend (Figure 16). In the 1960s, a 1% increase in the real minimum wage led to a 0.20pp increase in female labor force participation. In the 1970s, 1980s, and 1990s, the coefficient increased to 3.13pp, 8.06pp, and 11.76pp, respectively, indicating a much stronger effect of the _rminw_ on female labor force participation. These findings may be attributed to increased women's education access during this period, improved economic growth, and expanded opportunities for women in the labor market. Policy changes to reduce income inequality and promote gender equality have also contributed to this higher effect. In the 2000s, the coefficient remained high, although with wide confidence intervals, at 11.01pp, but decreased to 3.36pp in the 2010s, which could be linked to changes in economic policies and labor market conditions. Another factor to consider is the role of women in balancing work and family responsibilities, which could have affected their labor force participation in recent years.

**Figure 16: Effect of the real minimum wage on the female labor force participation rate**

<!-- Start of picture text --> 20.0<br>10.0<br>0.0<br>-10.0<br>-20.0<br>1960 1970 1980 1990 2000 2010 2020<br>Range of variation Minimum wage<br>Percentage points<br><!-- End of picture text --> Source: Own calculations.

The relationship between the minimum wage and female labor force participation can be influenced by the difference in reservation wages between women and men and by the presence of children and 26
24 EC. It is difficult to envisage such a possi.bility in Hungary, not only because capital is not the abundant factor, but also because of the tremendous balance of payment and foreign debt problems the country currently faces, with even tighter exchange controls likely to be imposed in the future.

Finally, mention should be made of two sectors for which the changes in the regulatory environment implied by EC-92 are quite cc itradictory and the same may have a substantial impact on Hungary's economy: air transport and tourism services.

# (i) Air Transport

# Three aviation issues affect Hungary:

a) Non-EC countries are worried that fifth freedom rights (the right of a carrier based in one country to offer services between two foreign countries) they have with individual EC countries may be impdired now that the EC is likely to act as a single country. Negotiation of 'anding rights with a body representing 12 countries instead of one implies a change of bargaining power in favor of the European partner. Hungary's airline, Malev, w.ll be in a very weak bargaining position with the EC.

b) The disappearance of tax-free shopping on intra-EC traffic improves the competitive position of non-EC members as a tourist destination. Airport authorities may raise EC landing fees to compensate for the disappearance of a very lucrative source of revenue. Ir sum, the post-1992 regime max divert some tourists away from the EC to alternative, Hungarian destinations.

c) On the other hand, intra-European air transport deregulation should lead, according to different studies,41 to a 10 to 20% reduction in most intra-EC air fares.'2 This implies some trade diversion against Hungary (less tourist arrivals, etc.) unless air fares to and from Hungary are also reduced.

Because of the critical importance of air transport to services exported by Hungary to the EC-12 and of the lack of competitiveness of Malev, the privatization of this airline should be a high priority. A recent World Bank i" A new directive, which is a part of the EC-92 program, is geared to conferring freedom of providing banking services by EC-based financial institutions, but this privilege will not be extended to third countries' financial firms unless those countries pass a reciprocity test.

3 According to the last EIU Hungary Country Report, foreign debt stood at $20.7 bn at the end of December 1989. The debt-service ratio is currently higher than 40%. See EIU(1990).

4 See Acence Europe, October 21, 1989. According to a study of the Netherlands Economic institute air fares on intra-EC travel could increase by 10% on this account and estimates that Greece could see its income from tourism drop by 10%. See also Financial Times, March 9, 1990.

4l See International **Herald** Tribune, June **1,** 1988; Commission(1988), p.98.

42 **EC transport ministers decided** in **early December 1989 that airlines flying** between community **destinations will be able** to **cut fares by up to 20% from January 1, 1990 without needing approval from countries at both ends of the** route, **but only from one of the two. See** International Herald Tribune, December 12, 1989.
Page 21

# CHAPTER THECE-SCOPE FOR ACTION

Under what circumstances are commercial<sup>banks at all likely to resume normal</sup> lending to Sub-Saharan Africa, and<sup>what are the prospects that these conditions</sup> will materialize? What innovations<sup>might accelerate these changes and alter the</sup> context of the discussion? What<sup>particular opportunities are available to</sup> commercial banks and how are these<sup>constrained by the borrowing capacities of</sup> the African countries themselves?<sup>In considering the prospects for increased</sup> commercial bank lending to Africa,<sup>with an emphasis on long-term lending, a</sup> starting point would be to define<sup>the objectives of new lending and target orders</sup> of magnitude.

The statistical profile of Africa's<sup>long-term debt stocks and debt service</sup> payments over the next five years<sup>will depend largely on factors relating to</sup> current outstandings. Important<sup>in this determination will be the debt</sup> performance of the four sovereign<sup>borrowers (C6te d'Ivoire, Nigeria, Gabon,</sup> Congo) who hold the bulk of the<sup>region's long-term commercial bank debt.</sup> Totalling $12.5 billion, their debt<sup>represents 80 percent of long-term public</sup> and publicly-guaranteed debt of $16 billion, and 60 percent<sup>of the total long-</sup> term commercial bank debt of $22 billion<sup>reported by the World Bank Debt Tables.</sup> Non-flow factors such as more accurate<sup>debt information, exchange rate</sup> fluctuations, reschedulings of interest<sup>arrears, market-based debt reductions</sup> (through conversions and discounted<sup>trading) and negotiated debt cancellations</sup> may also have an impact on these statistics.

Without attempting to project the<sup>future profile of Sub-Saharan Africa's debt</sup> overhang, but using as a key measure<sup>of progress anticipated future levels and</sup> geoeraohical distribution of commercial<sup>bank disbursements, the following could</sup> form the minimum objectives for commercial<sup>bank long-term lending by the year</sup> *2000: double the aggregate level of annual<sup>disbursements to the private sector,</sup> as narrowly defined by the World Bank,<sup>from their present base of about</sup> $600 million per year (excluding the<sup>statistics for C6te d'Ivoire);</sup>

- achieve a broader distribution of<sup>disbursements to the private sector in</sup> more countries and economic sectors;

bring aggregate disbursements to public<sup>and publicly guaranteed borrowers,</sup> as broadly defined, at minimum to their<sup>1986 levels of $1.2 billion.</sup> Such a performance would bring<sup>the commercial banks to somewhat over half of</sup> their peak new lending level of $4<sup>billion achieved in 1980.</sup>

## A. QPPQORTNITIES AND_COMMERCIAL BORPOWIXG<sup>CAPAGITY</sup>

Sub-Saharan African governments have<sup>accepted the view put forth by the World</sup> Bank's Long Term Perspectives Study<sup>that future development strategies must</sup> emphasize the development of human<sup>resources and improvement in physical</sup>
**Table 4: Results of the decomposition of the Gender Wage Gap**

||**_All Em_**<br>(1|**_ployees_**<br>)|**_Private, Non_**<br>(|**_agriculture_**<br>2)|**_Non-P_**<br>(|**_rivate*_**<br>3)|
|---|---|---|---|---|---|---|
||**Males**|**Females**|**Males**|**Females**|**Males**|**Females**|
|(1)<br>_W_<br>_w _<sup>_L_</sup><br>_j_|1.03|0.71|1.15|0.71|0.90|0.76|
|(2)<br>_j_<br>_H_<br>_j_<br>_S_<br>_s_|0.19|0.28|0.11|0.14|0.28|0.44|
|(3)_rj_|0.38|0.71|0.31|0.60|0.49|0.59|
|(4)<br>⎟<br>⎟<br>⎠<br>⎞<br>⎜<br>⎜<br>⎝<br>⎛<br>+<br>_j_<br>_j_<br>_H_<br>_j r_<br>_S_<br>_s_<br>1|1.07|1.20|1.03|1.08|1.14|1.26|
|(1) x (4)|1.10|0.85|1.19|0.76|1.02|0.96|
|**Wage Gap**|**24.**|**8%**|**42.**|**5%**|**6.3**|**%**|
|Sample Size N|6,536|4,445|3,086|2,348|3,264|2,037|
|Weighted N|240,956|162,608|115,770|86,168|119,027|74,406|

Source: Author’s estimates using 2006 Macedonia LFS data **. *** Includes public and semi-private companies such as utilities (gas, water, and electricity).

**Table 5: Simulations of changes in the Gender Wage Gap after the introduction of a minimum wage [all employees]**

||**_MW=30 Dinars/hours_**<br>_(0.6 the median wage)_|**_MW=40 D_**<br>_(0.8 the m_|**_inars/hours_**<br>_edian wage)_|**_MW=50 Di_**<br>_(media_|**_nars/hours_**<br>_n wage)_|
|---|---|---|---|---|---|
||**Males**<br>**Females**|**Males**|**Females**|**Males**|**Females**|
|(1)<br>_W_<br>_w _<sup>_L_</sup><br>_j_|1.03<br>0.72|1.03|0.76|1.02|0.79|
|(2)<br>_j_<br>_H_<br>_j_<br>_S_<br>_s_|1.07<br>1.19|1.06|1.15|1.05|1.12|
|(3)_rj_|0.19<br>0.28|0.19|0.28|0.19|0.28|
|(4)<br>⎟<br>⎟<br>⎠<br>⎞<br>⎜<br>⎜<br>⎝<br>⎛<br>+<br>_j_<br>_j_<br>_H_<br>_j r_<br>_S_<br>_s_<br>1|0.37<br>0.69|0.31|0.53|0.28|0.44|
|(1) x (4)|1.10<br>0.86|1.09|0.88|1.08|0.89|
|Wage Gap|24.3|21|.1%|19.|0%|
|**% reduction in**<br>**the Wage Gap**|**2%**|**1**|**5%**|**23**|**%**|

Source: Author’s estimates using 2006 Macedonia LFS data **. *** Includes public and semi-private companies such as utilities (gas, water, and electricity).

13
<!-- Start of picture text --> Age group: 15-24 Age group: 15-65<br>60 60<br>BAll All<br>50 @ Male 50 mi Male<br>40 @ Female 40 TM Female<br>30 30<br>20 20<br>10; |om| 10 i|<br>Malawi Uganda Tanzania Burkina Nigeria Niger Mali (2014) Malawi Uganda = Tanzania Burkina Nigeria Niger Mali (2014)<br>(2016) (2013) (2012) Faso (2014) (2015) (2014) (2016) (2013) (2012) Faso(2014) (2015) (2014)<br>Desire to work more Availability Availability to work immediately Desire to work more Availability Availability to work immediately<br>to work in to work in<br>the last7 the last7<br>days days<br><!-- End of picture text -->
_Table 9: Comparison of official equivalence scale with square root equivalence scale and per capita equivalence scale_

|Household type|Food<br>share<br>imputed|Ratio #<br>calories<br>(%<br>ref<br>HH)|<br> <br>Ratio<br>NON-<br>food<br>share (%<br>ref HH)|HH<br>size|Official<br>equiv.<br>scale (%<br>ref HH)|SQRT<br>equiv.<br>scale<br>(% ref<br>HH)|<br>Per<br>capita<br>equivalence<br>scale (% ref<br>HH)|
|---|---|---|---|---|---|---|---|
|1 male + 1 female|0.51|0.61|0.93|2|0.59|0.71|0.5|
|2 elderly|0.53|0.52|0.88|2|0.50|0.71|0.5|
|1 male + 2 children|0.51|0.73|0.93|3|0.71|0.87|0.75|
|1 female + 2 children|0.51|0.67|0.92|3|0.65|0.87|0.75|
|1 male + 1 female + 1 child|0.49|0.80|0.96|3|0.79|0.87|0.75|
|1 male + 1 female + 2 children|0.47|1|1|4|1|1|1|
|1 female + 3 children|0.48|0.86|0.97|4|0.85|1|1|
|1 male + 1 female + 3 children|0.45|1.20|1.03|5|1.21|1.12|1.25|
|1 male + 2 female + 2 children|0.44|1.27|1.05|5|1.29|1.12|1.25|
|1 male + 1 female + 4 children|0.43|1.39|1.06|6|1.42|1.22|1.5|
|1 male + 1 female + 3 children +<br>elderly|1<br>0.43|1.46|1.07|6|1.49|1.22|1.5|
|1 male + 1 female + 4 children +<br>elderly|1<br>0.42|1.66|1.10|7|1.71|1.32|1.75|
|1 male + 1 female + 5 children|0.42|1.59|1.08|7|1.64|1.32|1.75|

**Source** : Authors’ calculations based on HIECS 2015. **Note** : The calorie requirements considered are 3,400 calories for a male, 2,750 for a female, and 2,000 for a child. The imputed share of food is computed from the coefficients regressed in 2015 in Upper Rural Egypt.

Figure 1 plots the three equivalence scales as a function of household size. For each household size, the two alternative equivalence scales only have one value, whereas the official methodology has several values, because it depends on household composition as well as household size. The main message coming out of Figure 1 is that the official equivalence scale is closer to the per capita equivalence scale than to the square root equivalence scale. This is very clear for larger household sizes, but it is also valid for smaller household sizes. Another message is that household size is the dominant factor in the determination of the official equivalence scale. Indeed, the differences between official equivalence scales at a given household size seem smaller than the difference between average equivalence scales across different household sizes. Together, this suggests that poverty comparisons would not be radically affected when switching from the official equivalence scale towards a more conventional per capita equivalence scale, a move that would simplify the official poverty measurement methodology.

13
_On the Dangers of Decentralization 17_ 3.15 Figure 3.1 presents a budget financed by taxes proportional to income. The amounv contributed by a region, on a per capita basis, is a simple function of income. Expenditures ' region, always on a per capita basis, are assumed to be independent of income. It follows that poor regions get more from the budget than they contribute to it, and that the reverse is true for the rich regions. Net gains from the budget decrease as income increases.

3.16 Such transfers can take place, even with a regressive tax system and regressive expenditure system (see figure 3.2 below).

_Figure 3.2. Transfers Induced by a Doubly Regressive Budget_ <!-- Start of picture text --> Fur ca ca uibut to bW<br>PM from aeicual hodga<br>Coifbiom to odgso<br>oa" frcm uw<br>Not gaim km do bu"gbo<br><!-- End of picture text --> 3.17 Empirical studies support this hypothesis. Davezies (1989) studied the contribution to and the gains from the French national budget for 90 d6partements; Oliveira (1991) conducted the same exercise for 22 Brazilian states. For Cote d'lvoire, Thailand, and Morocco, similar estimates were mnade for the contributions and the gains of the metropolitan area and of the rest of the country (Davezies, Nicot, Prud'honume 1985; Davezies, Nicot, Prud'homme 1987; Nicot, Letrung, !989). In spite of the conceptual and statistical difficulties of this exercise (wh.ch were solved by the multiplication and generalization of sensitivity analysis), the results emphasized that richer areas do subsidize, through national budgets, poorer areas.

3.18 Table 3.2 summarizes the magnitude of these transfers between the large cities **and** the rest of the countries for the five cases studied.
_3. A MODEL OF STRUCTURAL CHANGE AND INTEGRATION_

# **3.5 Data**

## **3.5.1 Economic activity**

**Administrative level** For Central Asian countries, we choose each location to be at the district administrative level, called _raions_ in most post-Soviet states<sup>12</sup> . There are 174 districts in Kazakhstan, 162 districts in Uzbekistan, and 45 districts in Kyrgyzstan. For China, the third administrative level is chosen with 2048 locations. Recovering the sufficient statistics (amenity, productivity, prices) for each district requires the following observed variables: employment for the export sector (agriculture or manufacturing) versus non-tradable sectors (services, local manufacturing), land area, total population, and estimates for changes in transport costs. Assuming that agriculture will be the sector to benefit from access to new corridors in Central Asian countries, we also collect data on urban and rural populations for each district.

**Land data** Land areas cover currently-used areas for productive activities, excluding large areas that are empty of population and economic activities. For Central Asian countries, we use European Satellite Agency data to keep irrigated land and urban areas for each loction. For China, the land area that is considered to be allocated between manufacturing and services is the built-up area per location.

**Employment in the tradables sector** For Central Asian countries with a comparative advantage in agriculture, employment in agriculture at the district level is not directly available. While some papers use rural shares as a proxy, workers in rural areas can also work in rural services, and such percentages vary across regions. To adjust the shares in rural population, we use available employment surveys, sufficiently representative at the provincial level, to build a correction factor to consider both the share of employment in the population and the share of employment in agriculture in total employment at the provincial level. Rural shares and employment shares in agriculture are positively but imperfectly correlated (Annex D). In China, manufacturing is the sector with comparative advantage. We use employment data collected at the firm level with information on the postal code of the firm and its sector of production. Employees in manufacturing firms are summed at the district level, and adjusted with the share of employees in total population at the national level to obtain the share of employment in manufacturing per district.

## **3.5.2 Transport costs**

Following the economic geography literature, we use changes in transport times to measure changes in iceberg transport costs.

> 12Raions are subnational levels and belong to oblasts, translated as provinces in English

16