rafmacalaba/datause-encoder-data huggingface dataset:
**Table 16. Empowerment outcomes (ITT), disaggregated for women, 2010, 2011 and 2012 cohorts**

||Baseline mean for|Pooled 2010‐2012 Coh<br>IPSW model<br>|orts<br>|Older||
|---|---|---|---|---|---|
||young women|Baseline mean for women|Young women|women|Difference|
||[Std Dev]|[Std Dev]|(1)|(2)|(3)|
||0.621|0.704|0.058|0.021|0.037|
|Any money of your own (1=Yes)|[0.485]|[0.457]|(0.042)|(0.042)|(0.058)|
|Control over Earnings (1=Yes), if|0.507|0.712|0.099|0.110*|‐0.011|
|respondent has earnings|[0.500]|[0.453]|(0.082)|(0.064)|(0.104)|
|Control Over Savings (1=Yes), if|0.626|0.536|‐0.050|0.060|‐0.110|
|respondent has savings|[0.484]|[0.499]|(0.069)|(0.050)|(0.089)|
|Control over household spending (#|1.822|4.940|0.053|0.158|‐0.104|
|categories)|[2.695]|[3.446]|(0.201)|(0.275)|(0.369)|
|Mobility (# places visited in last|4.321|4.418|‐0.012|0.283|‐0.295|
|month out of 9)|[1.597]|[1.733]|(0.129)|(0.172)|(0.222)|
|Any Mentor Related to Work (1=Yes)|0.511|0.546|0.119***|0.078|0.041|
||[0.500]|[0.498]|(0.045)|(0.051)|(0.072)|
|Entrepreneurship Score (0‐32)|14.790|14.251|0.951**|0.137|0.814|
||[6.262]|[6.317]|(0.468)|(0.596)|(0.773)|
|Financial Literacy (1=Yes)|0.591|0.577|0.053|0.012|0.042|
||[0.492]|[0.494]|(0.048)|(0.053)|(0.072)|
|Self‐Regulation Score (0‐16)|10.346|10.195|0.266|0.484**|‐0.218|
||[2.418]|[2.318]|(0.207)|(0.196)|(0.283)|
|Self‐Confidence Score (0‐27)|18.885|18.967|0.386|0.479|‐0.093|
||[3.337]|[3.364]|(0.279)|(0.327)|(0.459)|
|Clustered Standard Errors|||Yes|Yes|Yes|

Notes: Standard errors (reported in brackets) clustered at the event level where possible. *,**, and *** denote significance at the 10% level, 5% level, and 1% level. Younger women (aged 16 to 24) compared to older women (age 25 to 35).

39
# **Annex 1:**

## **China: Structural Change in Urban Manufacturing Employment, by Subsector, 2002-2009: Sunset subsectors identified.**

|unit 1000persons|2002|2009|Differences|% change|
|---|---|---|---|---|
|**Total Manufacturing inurban units**|**29807.5 **|**34919.0**|**5111.5**|**17.1**|
|**Subsectors sorted by % change in employment**|||||
|**Sunset sectors**|||||
|Chemical fibers manufacturing|263.4|205.0|-58.3|-22.1|
|Textiles|2841.6|2334.4|-507.1|-17.8|
|Printingand Record Medium reproduction|493.5|423.8|-69.7|-14.1|
|Tobaccoprocessing|233.5|203.2|-30.3|-13.0|
|Non-metallic Mineral Products|2116.0|1893.1|-222.9|-10.5|
|Other Manufacturing|601.4|571.7|-29.7|-4.9|
|Petroleumprocessingand coking products|565.5|553.9|-11.6|-2.0|
|Chemical raw material andproducts|2213.3|2180.9|-32.4|-1.5|
|Smeltingandpressingof ferrous metals|1900.6|1921.3|20.7|1.1|
|Papermakingand Paper Products|592.4|617.5|25.1|4.2|
|Beverage manufacturing|740.3|792.7|52.5|7.1|
|General Purpose Machinery|1921.3|2064.7|143.4|7.5|
|Timber,Wood,Bamboo,and strawproducts|267.7|296.6|28.9|10.8|
|**Growing sectors**|||||
|Medical andpharmaceuticalproducts|844.9|980.1|135.2|16.0|
|Metal Products|897.5|1065.1|167.6|18.7|
|Rubberproducts|377.6|453.4|75.8|20.1|
|Foodprocessing|977.4|1187.4|210.0|21.5|
|Special Purpose Machinery|1400.6|1718.9|318.3|22.7|
|Transport Equipment mfg|2319.4|2962.4|643.0|27.7|
|Foodproduct manufacturing|621.8|811.0|189.2|30.4|
|Smeltingandpressingof non-ferrous metals|755.6|1001.9|246.3|32.6|
|Plasticproducts|606.8|807.1|200.3|33.0|
|Machineryfor cultural activityand office work|464.8|666.5|201.8|43.4|
|Electrical Machinery& Equipment|1441.4|2131.1|689.7|47.8|
|Stationaryand educational and sporting goods|294.6|451.8|157.1|53.3|
|Garments,other fiberproducts,footwear|1336.2|2064.9|728.7|54.5|
|Leather,Fur,Feather & relatedproducts|578.6|906.5|327.9|56.7|
|Electrics and Telecommunications|1623.8|3250.8|1627.0|100.2|
|Furniture Manufacturing|180.5|364.4|183.9|101.9|

Source: Banister and Cook, Monthly Labor Review March 2011, and NBS, China Labor Statistical Yearbook 2010 (for 2009 data). Sectors marked in yellow are sunset sectors. The NBS stopped publishing statistics on rural enterprises in 2002, thus decomposition of employment by subsector for the entire economy is not available.

54
**Table 2. The role of infrastructure in economic upgrading through GVCs**

|VARIABLES|(1)<br>_DVA_|(2)<br>_DVA_|(3)<br>_DVA_|(4)<br>_DVA_|
|---|---|---|---|---|
|**Backward links**|||||
|_FVAX_|0.0622***|0.0456***|0.0140|0.0250|
||(0.00975)|(0.0142)|(0.0398)|(0.0294)|
|_FVADP_|0.253***|0.289***|0.252***|0.252***|
||(0.0269)|(0.0283)|(0.0267)|(0.0267)|
|_FVAX*Air freight_|1.1e+06***<br>(335,790)||||
|_FVAX*Rail_||2.8e-05<br>(3.11e-05)|||
|_FVAX*Port_|||0.0119<br>(0.00826)||
|_FVAX*Road_||||0.0101*<br>(0.0061)|
|_Constant_|5.846***|5.881***|5.935***|6.006***|
||(0.183)|(0.215)|(0.182)|(0.190)|
|Observations|7,156|6,346|7,276|7,276|
|R-squared|0.855|0.877|0.852|0.852|
|**Forward links**|||||
|DVAR|0.220***|0.190***|0.119**|0.191***|
||(0.0173)|(0.0203)|(0.0503)|(0.0465)|
|FVADP|0.233***|0.264***|0.231***|0.232***|
||(0.0259)|(0.0274)|(0.0257)|(0.0257)|
|DVAR*Air freight|138,451<br>(498,564)||||
|DVAR*Rail||0.0001**<br>(4.7e-05)|||
|DVAR*Port|||0.0222**<br>(0.0097)||
|DVAR*Road||||0.0062<br>(0.0091)|
|Constant|5.065***|5.053***|5.186***|5.152***|
||(0.171)|(0.217)|(0.189)|(0.184)|
|Observations|7,164|6,347|7,284|7,284|
|R-squared|0.872|0.896|0.870|0.870|

Source: OECD ICIO database. *** p<0.01, ** p<0.05, * p<0.1. Notes: Robust standard errors in parentheses. _FVAX_ and _DVAR_ lagged. All level variables in natural logarithms.

# **_Connectivity_**

Table 3 focuses on the role of connectivity. Remarkably, all six variables are significant with the expected positive sign when we looked at GVC integration as a seller. Better internet coverage and a higher LPI with regard to customs procedures, logistics, and overall values as well as shorter export and import times have the capacity to magnify economic upgrading through forward GVC links.

Regarding GVC integration as a buyer, however, we observe that only higher LPI customs values show a positive sign (table 3, column 3). This speaks in favor of connectivity being an important factor for competitiveness of the export sector. Timely, reliable, and efficient customs and other administration procedures benefit, in particular, domestic value added. Thus, this result highlights a further positive impact of trade facilitation agreements.

20
# I. **Introduction**

Does the Dollar and Kraay (2000: 27) view, that 'anyone who cares about the poor should favor the growth-enhancing policies of good rule of law, fiscal discipline, and openness to international trade' apply to Africa in the 1990s? Or is the growth path the reforms induced characterized by increasing inequality, denying benefits to the poorest (Stewart, 1995; Mkandawire and Soludo, 1999; Forsyth, 2000). There is no simple answer to this question, given the real-world complexity of recent African history. The 1990s in Africa witnessed many changes that affected people's lives and livelihoods. In addition to economic and political reforms, external opportunities and constraints shifted during the decade, with many countries experiencing sharp movements in their terms of trade. Some countries faced internal civil strife and political instability. Others had to endure one of the worst droughts of the century. And there have been serious health shocks, such as AIDS and malaria, affecting rich and poor alike. The effects of these changes on growth and poverty were further conditioned by the private and public endowments households possessed-their physical assets, human capital, and their access to infrastructure and public services.

This complexity makes for considerable debate about the relationship between policy, growth and poverty in Africa-a debate that was previously not always well served with hard evidence (Stewart, 1995). This paper sheds light on this debate by utilizing the much-improved data base in Africa, and by addressing three central questions:

- First, what does recent evidence tell us about the evolution of overall poverty and inequality in Africa and its relation with economic growth (and recession)?

- Second, moving beyond the national averages, did particular population groups or geographical regions gain or lose from the episodes of reform-induced growth?

- Third, among the wide array of disparate events and factors affecting growth and poverty trends, which emerge as key in explaining changes in income distribution and poverty?

The paper builds on the results of a recent series of _Poverty Dynamics_ country studies' which exploit recent (1990s) household survey data in Africa. It examines the main factors behind observed poverty trends by first taking a _macro-perspective,_ linking the historical changes in ' Countries were selected based on the availability of comparable measures of consumption and include Ethiopia, Ghana, Madagascar, Mauritania, Nigeria, Uganda, Zambia, and Zimbabwe. The paper also draws
- 54  Meng Y, Brennan A, Purshouse R, _et al._ Estimation of own and cross price elasticities of alcohol demand in the UK—a pseudo-panel approach using the Living Costs and Food Survey 2001–2009. _Journal of health economics_ 2014; **34** :96–103.

- 55  Joyce Dargay, Petros Vythoulkas. Estimation of a Dynamic Car Ownership Model: A Pseudo-Panel Approach. _Journal of Transport Economics and Policy_ 1999; **33** :287–301.

- 56  Angus Deaton. Panel data from time series of cross-sections. _Journal of Econometrics_ 1985; **30** :109–26.

- 57  Seth H. Giertz. Panel Data Techniques and the Elasticity of Taxable Income. 2008.

- 58  Marno Verbeek, Francis Vella. Estimating dynamic models from repeated cross-sections - Google Scholar. _Journal of Econometrics_ 2005; **127** :83–102.

24
interplay between trade shocks and factor adjustment costs. We have tted our model to household survey panel data and plant-level panel data from Argentina and recovered measures of the adjustment frictions faced both by workers and rms. Using the structural parameters, we have simulated the response of the model, both of rms and workers, following a tari cut episode.

Our paper combines labor market frictions with a complex model of capital adjustment costs, which few papers do. This matters for the patterns of responses of investment, employment and wages. Following a tari cut in textiles, factor market frictions amplify the labor market responses so that the long-run responses of real wages are larger than the short-run responses. There is no overshooting of real wages, as in xed capital models. Our results suggest that the cost of adjusting capital is a fundamental driver of these patterns. After losing protection, real wages decline on impact. Moreover, as rms disinvest in response to the negative trade shock, labor productivity continues to decline during the transition, and real wages further decline as the economy approaches the new steady state. The results are also consistent with the recent ndings in the empirical trade literature, notably those reported by Dix-Carneiro and Kovak (2017). By combining capital and labor adjustment costs, our analysis complements the growing literature on the role of factor market frictions in shaping the way employment and labor adjust to the elimination of taris.

# References

Ackerberg, D., K. Caves, and G. Frazer (2015). Identication Properties of Recent Production Function Estimators, Econometrica, vol. 83(6), pp. 24112451.

Artuc, E., S. Chaudhuri, and J. McLaren (2010). Trade Shocks and Labor Adjustment: A Structural Empirical Approach, American Economic Review, 100(3), pp. 100845.

Artuc, E., D. Lederman, and G. Porto (2015). A Mapping of Labor Mobility Costs in the Developing World, Journal of International Economics, vol. 95(1), pp. 2841.

Artuc, E. and J. McLaren (2015). Trade Policy and Skill Premium: A Structural Analysis with Occupational and Sectoral Mobility, Journal of International Economics, vol. 92(2), pp. 278294.

Autor, D., D. Dorn, and G. Hanson (2013). The China Syndrome: Local Labor Market Eects of Import Competition in the United States, American Economic Review, 103(6), pp. 21212168.

29
variety of recent models from the international trade literature that follow a similar path to Wilson's, including the "gravity with gravitas" model of Anderson and Van Wincoop (2003), the Ricardian model of Eaton and Kortum (2002), and the heterogeneous firms model of Chaney (2008). We show that origin and destination fixed effects from a simple gravity regression can be rescaled to produce a measure of connectivity that corresponds to the total pull exercised by each country on the rest of the network. This measure captures the level of costs on bilateral routes, as well as the dispersion of costs across routes. It takes account of all possible nodal connections, even those for which no direct flights are in fact observed. As such, it provides a highly tractable yet informative indicator of air transport connectivity. It has the added advantage of being closely related to the concepts of multilateral resistance and market potential, which are familiar from the international trade and economic geography literatures (Anderson and Van Wincoop, 2003; Head and Mayer, 2011); indeed, our analysis makes it possible to link such measures to the deep structure of networks with bilateral flows, such as air transport or trade.

Although we focus on air transport in this paper, the methods we develop can easily be applied more broadly, in areas such as maritime transport, and even international production networks for manufactured goods. The concept of connectivity that we develop measures "before-the-border" effects, whether endogenous (e.g., transport policies), or exogenous (e.g., geography, and the hub-andspoke structure of the network). In addition to explaining how countries effectively connect to international freight transport and logistics networks, it complements existing indicators of trade and transport facilitation outcomes ("beyond-the-border" metrics such as the _Logistics Performance Index_ or the Doing Business _Trading Across Borders_ data), which are primarily endogenous, and not tied to the geography of the network (Arvis et al., 2007 and 2010).

We implement our model empirically to produce an internationally comparable ACI for the year 2007 as an input into future research work, and a useful policy benchmarking tool. We show that, as expected, 4
# New Urban Residents

<!-- Start of picture text --> Millions per year<br><!-- End of picture text --> <!-- Start of picture text --> 80<br>e<br>60 Less Developed y,<br>.<br>®<br>40<br>——___—s Predicted<br>* eho ee Observed<br>a<br>20<br>at%s More Developed<br>er; > **<br>0<br>1900 1950 2000 2050 2100 2150 2200<br><!-- End of picture text -->
# **Annexes**

# **Annex I: Key Survey Dataset**

**Table A1.1** : Prices of coal and biomass fuels, and brick weight by province*

||Koshi|Madesh|Bagmati|Gandaki|Lumbini|Sudurpachim|Nepal<br>(Weighted avg.)|
|---|---|---|---|---|---|---|---|
|Coal (NRs/kg)|41.7|40.0|46.0|45.5|41.8|45.5|43.4|
|Biomass (NRs/kg)||||||||
|Traditional|8.0|8.0|8.0|8.0|8.2|-|8.04|
|Briquettes/Pellets|-|-|-|-|-|12.0||
|Brick weight (kg/brick)|3.917|3.375|2.500|2.750|2.613|2.529|2.874|

_Note_ : * Because there are only two brick kilns in Karnali province (ILO et al., 2020), we excluded this province from our study. Source: (Timilsina and Malla, 2023)

**Table A1.2** : Type of technology and fuel used by the surveyed kilns

||Technology|Fue type|
|---|---|---|
|1|CK|Coal|
|2|ZZK ID|Coal and sawdust|
|3|ZZK ID|Coal|
|4|FC BTK|Coal, sawdust and rice husk|
|5|ZZK ND|Coal|
|6|ZZK ND|Coal|
|7|HK|Coal, sawdust and bagasse|
|8|ZZK ID|Coal, sawdust, firewood and rice husk|
|9|ZZK ND|Coal and sawdust|
|10|ZZK ND|Coal, sawdust and firewood|
|11|ZZK ID|Coal, sawdust, firewood and rice husk|
|12|ZZK ND|Coal|
|13|ZZK ND|Coal|
|14|ZZK ND|Coal and firewood|
|15|ZZK ID|Coal|
|16|FC BTK|Coal|
|17|FC BTK|Coal and pellet|
|18|ZZK ID|Coal|
|19|FC BTK|Pellet|
|20|ZZK ID|n.a.|
|21|ZZK ID|Coal|
|22|FC BTK|Coal|

_Note_ : CK is clamp kiln, FC BTK is fixed chimney Bull’s trench kiln, ZZK is zig zag kiln, ID and ND are induced draft and natural draft, HK is Hoffman kiln and n.a. is not available. Source: (Timilsina and Malla, 2023) 43
countries accounted for 35% of world exports of intermediate goods, with a rapid acceleration in this share in the 2000s. The growth of capital goods and consumption goods exports has remained relatively stable since the late 1980s. Milberg and Winkler (2010a) report that the share of US imported inputs of materials and services in total non-energy input use grew at an average rate of 2% and 1.7%, respectively, per annum between 1998 and 2006, reaching levels of over 25% in some sectors, including apparel and motor vehicles by 2006.

**Figure 3: Goods Exports from Low and Middle Income Countries by Product Category (% of World Exports), 1970-2008**

<!-- Start of picture text --> 40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Capital goods Consumption goods Intermediate goods<br>Source: Own calculations. Data: UN Comtrade.<br>NB: See Appendix 2 for the definition of the different product categories.<br>1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text --> These figures of offshoring measure trade in inputs and thus may understate the magnitude of trade within global supply chains. Global corporations in the major industrialized countries are not strictly involved in assembly. Much of the import activity in global supply chains is in fully finished goods. In fact, the purpose of corporate offshoring, whether at arm’s-length or through foreign subsidiaries, is precisely to allow the corporation to focus on its “core competence”, while leaving other aspects of the process, often including production, to others. Many “manufacturing” firms now do not manufacture anything at all. They provide product and brand design, marketing, supply chain logistics and financial management services.

Thus an alternative proxy for offshoring may simply be imports from developing countries. We see in _Table 1_ that Japan and the U.S. now rely heavily on goods imports from low-income developing countries (29% and 23% respectively), which we define as the _narrow measure of goods offshoring_ . While the European countries are at much lower levels, all countries have seen more than a doubling of the narrow measure of goods offshoring since 1991 (see CAGR). However, offshore destinations also include developing countries with a higher income level, such as Mexico, Brazil or South Africa. Thus the _broad measure of goods offshoring_ , including imports from all developing countries, shows that developing country imports constitute over half of total imports by Japan (68%) and the U.S. (54%), while the European countries range from 23% in the U.K to only 13% in Denmark.<sup>5</sup> Since services import data by regions of origin are not available for our relevant time period, we define a country’s import share of ‘computer and information services’ plus ‘other business

> 5 The relevant economic groupings of developing countries according to the UNCTAD can be found here: <u>http://www.unctad.org/sections/stats/docs//gds_csirb_c&td-2-9_en.pdf</u>

5
**4 Data**

We use plant-level data from the Annual Survey of Industries **(ASI)** conducted **by** the Central Statistical Organization **(CSO),** a department of the Ministry of Programme Planning and Implementation of the Government of India to compute our -ighiin.-.iti.j indices for all consecutive years in the period **1980-81** to **1999-00,** with the exception of **1995-96** when the survey was not conducted. The length of our data allows us to cover all the Indian reforms of the 1980s as well as the major reform episode of **1991.** The survey covers all factories registered under the Factories Act of 1948. The **ASI** frame can be classified into 2 sectors: the 'census sector' and the 'sample sector'. Units employing more than **100** workers constitute the census sector. Units in the census sector are covered with a sampling probability of one while units in the sample sector are covered with sampling probabilities lower than one (onehalf until **1987-88** and one-third after **1987-88)."** As Bollard et al. (2010) explain in detail in their appendix, there are substantial caveats to the use of these data. Sampling schedule changes in **1997** led to drastic changes in sample size: the number of total plants covered and the number of plants in the census sector dropped sharply.<sup>2</sup> ' There is a sharp increase in the standard deviation of average employment in both the census and the sample sectors, causing us to worry about heteroskedasticity. In order to correct for the noise and heteroskedasticity, we will weight all descriptive statistics and regressions **by** 3-digit industry-year total employment. That is, we will give more weight to larger, presumably less noisy industries.<sup>2</sup> As an alternative to employment weights, we will also use **FGLS** techniques to weight each observation **by** the inverse of its variance relative to industry and year means. That is, we will downweight observations which are very far from industry and year means. We discuss the technique in detail below. In order to measure changes in industrial policy, we use the detailed dataset of industrial policy in India constructed **by** Sharma (2006a,b) and used in Chamarbagwalla and Sharma (2011). This dataset identifies which 4-digit industries underwent reform in terms of freedom from licensing requirements in each year from **1970** to **1990.** Table 1 shows the quantum of de-

> 20Note that the **ASI** data is a series of repeated cross-sections of plants, not a panel of plants, thus it does **not** allow **one** to identify entry and exit.

> 21See Appendix Figures B.1 and B.2.

> 22Appendix Table B.1 shows the weighted averages of the key variables used in our analysis.

**17**
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49