rafmacalaba/datause-encoder-data huggingface dataset:
is associated with a concentration of the labor-intensive industries. This corresponds to the fact that in the late 1980s and early 1990s most FDI came from the Asian economies (in particular, Hong Kong), and was largely directed towards export processing industries in the sectors that China had revealed comparative advantages (The World Bank, 2001).

The results show that the high-tech industries and the labor intensive industries concentrate in the provinces with different characteristics. The initial development level ( _yt j_ , _t_ − 1 ) and the growth of the neighbor provinces ( _GAWi_ , _t )_ play a significantly negative role on the concentration of the chemical industry. It suggests that chemical firms tend to agglomerate in the provinces that are relatively less developed and surrounded by less dynamic neighbors. The concentration of the industry “timber processing, bamboo, cane, palm bibber and straw products” is negatively associated with the initial development level _yt j_ , _t_ − 1 , which shows that the less developed provinces are more attractive to the natural resource-intensive industries, other things being equal. By contrast, the roles of openness, the initial development and the growth of the neighbor provinces are completely different in the determination of the concentration of the two high-tech industries “electronic and telecommunications equipment manufacturing” and “instruments, meters and other measuring equipment manufacturing” from that of the two non high-tech industries. These activities tend to concentrate in the rich provinces with a relatively low FDI to GDP ratio and surrounded by dynamic neighbors.<sup>22</sup>

**Table 4 – The determination of the concentration of some representative industries at the national level**

||Chemical material<br>and products<br>manufacturing (3)|Electronic and<br>telecommunications<br>equipment<br>manufacturing (8)|Instruments,<br>meters and other<br>measuring<br>equipment<br>manufacturing<br>(12)|<br>Timber processing,<br>bamboo, cane, palm<br>bibber and straw<br>products (28)|
|---|---|---|---|---|
|_t_<br>_j_<br>_FDIOVER_ ,|0.061***|-0.084***|-0.122***|0.037|
||(3.18)|(-3.62)|(-4.55)|(1.28)|
|1<br>, −<br>_t_<br>_j_<br>_yt_|-0.1789**|0.213**|0.271**|-0.287**|
||(-2.34)|(2.20)|(2.39)|(-2.33)|
|1<br>, −<br>_t_<br>_j_<br>_GAW_|-0.979**|1.316**|1.249**|-0.016|
||(-2.29)|(2.55)|(2.08)|(-0.02)|
|_t_<br>_j_<br>_GPOP_,|-6.343**|8.259**|3.205|-6.148|
||(-2.39)|(2.62)|(0.86)|(-1.52)|
|Constant|1.918***|-0.289|-0.486|2.042***|
||(6.68)|(0.84)|(-1.21)|(4.68)|
|No. of obs.|219|216|219|219|
|AdjustedRsq.|0.0748|0.0974|0.1088|0.038|

Note: _t_ -students are in parentheses. * significant at 10 percent; ** significant at 5 percent; *** significant at 1 percent.

> 22 Here, we argue that one important factor that attracts FDI is the low labor costs. The relation between FDI and high-tech industry concentration would be an interesting topic for future research.

16
9 <!-- Start of picture text --> Equ I ty F I ows to Lat i n Amer- i ca<br>1._<br>01.2 g~~~~~~~~~~e *r_* OO:, Ijw,t X<br>0.<br>-0.2<br>-0.4<br>-0.2<br>-1<br>1999-01 199E-07 1909.-01 1989-07 1990-01 1990-07 1991-01 1991-0? 1902-01 1992-07<br>0- -<br>+ Mexoico e 9raz11<br><!-- End of picture text --> Fi"ue **3** <!-- Start of picture text --> EqpuTty Floiws to Asia<br>-100<br>90-<br>70-<br>80-<br>40-<br>20-<br>-10<br>-20<br>-30<br>-40<br>19S8-01 18998-07 1089i-01 1889i-07 1890-01 1990-07 19391-01 1001-07 1992-01 1992-07<br><!-- End of picture text --> <!-- Start of picture text --> Figur 4<br><!-- End of picture text -->
22 Salt cavern storage facilities are steadily gaining market share at the expense of traditional storage using depleted gas or oil fields. New storage projects completed in 1995 added about 47 billion cubic feet of working gas capacity and 1,395 million cubic feet of daily deliverability to the storage market. Although salt cavern storage facilities accounted for only about 30 percent of the new capacity, they accounted for 65 percent of the new daily deliverability.

The location and ownership of salt cavern storage reflect the commercial focus of storage operators in deregulated gas markets. Most salt cavern storage is in market centers and hubs. For example, 13 of 19 salt cavern facilities in the Southwest are in market centers. Independent operators control 50 percent of salt cavern storage facilities, giving them a 12 percent share in daily deliverability, though only an 8 percent share in working capacity.

# **Financial gas market**

The opening of the gas industry to competition and the development of natural gas spot markets have generated price volatility that was absent in the tightly regulated industry of the past. As industry participants started to look for ways to minimize price risk through financial instruments, markets responded by offering financial natural gas contracts used for hedging, speculation, and arbitrage.

There is now a well-developed financial natural gas market. Financial intermediaries and natural gas marketers offer customized financial instruments that transfer risk among industry participants. In addition, two organized exchanges offer several standardized natural gas futures and options contracts used by traders and industry participants to minimize price risk in many gas delivery locations. These contracts have promoted efficiency in the natural gas industry as market participants have taken advantage of arbitrage opportunities in locational prices and regional natural gas markets have become more nationally integrated.

# _Financial gas contracts_

Financial gas contracts are used to manage two types of risk in the natural gas market, price and basis risk. Price risk is generated by the volatile spot market prices of natural gas. Basis risk is the risk of change in the price differential between locations, time periods, and qualities of gas deliveries and between natural gas and other commodities.

Seven major types of financial gas contracts have been developed in the U.S. financial gas market, each using a different technique to manage price and basis risk:<sup>9</sup>

- _Futures contract -_ a legal agreement between a party that opens a position on the futures market to buy or sell natural gas and the commodity exchange. The party

- 9 The definitions of financial gas contracts draw on U.S. Department of Energy 1994 and NYMEX 1996.
- Tibshirani, R. (1996). Regression selection and shrinkage via the lasso. _Journal of the Royal Statistical Society B_ , 58(1):267–288.

- van Donkelaar, A., Martin, R. V., Brauer, M., Hsu, N. C., Kahn, R. A., Levy, R. C., Lyapustin, A., Sayer, A. M., and Winker, D. M. (2016). Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors. _Environmental Science & Technology_ , 50(7):3762– 3772.

- Vollebergh, H. R. J., Dijkgraaf, E., and Melenberg, B. (2005). Environmental Kuznets curves for CO2: heterogeneity versus homogeneity. _Ssrn_ , (November):39.

- Wagner, M. (2015). The Environmental Kuznets Curve, Cointegration and Nonlinearity. _Journal of Applied Econometrics_ , 30(6):948–967.

- White, H. (1994). _Estimation, inference, and specification analysis_ . Cambridge University Press.

- White, H., White, and Halbert (1980). Using Least Squares to Approximate Unknown Regression Functions. _International Economic Review_ , 21(1):149–70.

- World Bank (1992). World Development Report 1992. Development and the Environment. Technical report.

- World Health Organization (2016). Ambient Air Pollution: A global assessment of exposure and burden of disease. _World Health Organization_ , pages 1–131.

- Zou, H. (2006). The Adaptive Lasso and Its Oracle Properties. _Journal of the American Statistical Association_ , 101(476):1418–1429.

35
17 from all crops in region _i_ :

# **V.B.2 Manufacturing Production**

We assume that manufacturing production uses labor as its sole input. Manufacturing output _qi_<sup>_M_</sup> in region _i_ is a simple linear function of regional manufacturing employment ( _ni_ ), with a multiplicative constant given by productivity parameter _Ti_<sup>_M_.</sup> As in agriculture, the manufacturing good produced in a region _i_ can be either consumed locally or shipped to another region _n_ , with the latter incurring an iceberg trade cost _τni_<sup>_M≥_1.Given these</sup> assumptions, marginal-cost pricing (due to perfect competition) implies that the unit price of the manufacturing good produced in _i_ and offered for sale in _n_ must be: where _wi_<sup>_m_is the wage rate of mobile workers in region</sup><sup>_i_.</sup>

# **V.B.3 Fertilizer Endowments**

As in Farrokhi and Pellegrina (2023), we assume that the quantity of fertilizer _Q_<sup>_f_</sup> _i_<sup>owned by agents</sup> who live in region _i_ is simply given as a natural endowment _Fi_ (rather than being produced). Moreover, fertilizer is a tradable homogeneous good, so _in the absence of import restrictions_ its price must follow the law of one price. This implies that fertilizer price _p_<sup>_f_</sup> _i_<sup>in region</sup><sup>_i_must equal</sup> the international fertilizer price ( _p_<sup>_f_</sup> _RoW_<sup>).44</sup> To model the import ban, we allow the Sri Lankan government to restrict fertilizer imports through the use of QRs. To implement these QRs, the government distributes import licenses to middlemen, allowing them to import up to _f_<sup> ̄</sup> kg of foreign fertilizer.<sup>45</sup> If the QRs are binding, then the domestic fertilizer price across Sri Lankan regions ( _{p_<sup>_f_</sup> _i_<sup>_}_</sup> _i_<sup>_I_</sup> =1<sup>)willbeanumber</sup><sup>_pf_</sup> _LKA_<sup>strictly</sup> greater than the international price _p_<sup>_f_</sup> _RoW_<sup>, with the price difference being pocketed by the middle-</sup> men in the form of license rents _QR_<sup>_rent_</sup> :<sup>46</sup>

> 44Variable _p_<sup>_f_</sup> _i_<sup>is defined as the price of fertilizer</sup><sup>_gross_of subsidies (see Section V.D).</sup>

> 45Whether or not there are QRs in place, we assume that Sri Lanka is a net importer of fertilizer, which is consistent with the observed preponderance of imported chemical fertilizer in its agricultural sector. For example, in 2019 Sri Lanka imported 210 million USD in chemical fertilizer while only exporting 480,749 USD.

> 46Note that variable _QRrent_ is the _total_ , country-wide license rent, which is then distributed equally among middlemen. We assume that Sri Lankan middlemen have the same spatial distribution as Sri Lankan mobile workers. As a consequence, regional license rents are given by: _QRi_<sup>_rent_</sup> = _QR_<sup>_rent_</sup> _× Ni_<sup>_m/_(�</sup><sup>_I_</sup> _k_ =1<sup>_N_</sup> _k_<sup>_m_).</sup>
aspirations of small-scale entrepreneurs in developing countries. Do they aspire to grow their businesses? If so, on which dimensions and how far? Are their aspirations realistic? If not, do they converge to realistic aspirations with time? Is there heterogeneity in aspirations across businesses, and if so, what are the determinants of such heterogeneity? Finally, do aspirations predict sales, profits, and forward-looking behavior tied to firm performance and growth, such as product and process innovation?

These are all important outstanding questions for research and policy. The answers can help create a better understanding of a population that is often the target of policies aimed at releasing external constraints, taking for granted (unobserved) entrepreneurial aspirations. They would help policy makers better target their policies, by distinguishing the type of entrepreneur who has the potential and aspiration to grow; from one who has a business to subsist. It would also help reconcile why some seemingly profitable opportunities are not taken up, and why policies aimed at raising aspirations, for example by changing mental models, can be an effective way to break a poverty trap (Bernard, Dercon, Orkin, and Taffesse, 2014; The World Bank, 2015).

This paper addresses these questions with a unique panel dataset on the aspirations for business growth for a representative sample of 1,301 small-scale urban retailers in Jakarta, Indonesia. The panel study involves two waves of data collection, in April 2016 and April 2017 and introduces several novel features. First, it records detailed measures of business performance, practices, and aspirations for growth in four business dimensions: shop size, number of employees, number of customers, and sales. Second, it distinguishes between short-term (in one year) and long-term (in a life time) aspirations. This distinction allows us to a) study the dynamics of entrepreneurial aspirations over a 12 month interval and b) learn about the aspiration horizons entrepreneurs have in mind when they think about the ideal business. Third, it proposes measures for two novel concepts: “imagination failure”, the failure to imagine an ideal business in the long-term, and “planning failure”, the failure to imagine a time-frame to achieve an ideal business. Both imagination and planning failures aim to capture behavioral biases in setting aspirations, which are important for aspirations-based poverty traps (Dalton, Ghosal, and Mani, 2016). Finally, the panel dimension of the data allows us to compare aspirations for growth at baseline with actual growth at endline, and therefore study the extent to which entrepreneurs achieve their aspirations and whether they adjust them with realized outcomes.

We find that on average entrepreneurs in our sample aspire towards positive business growth on all dimensions. In the long term, the average entrepreneur sees her ideal business 95% larger in size, with 42% of more employees, and 54% more customers. The average time horizon to achieve these 2
<mark>Chang, W., L. Díaz-Martin, A. Gopalan, E. Guarnieri, S. Jayachandran, C. Walsh (2020). "What works to enhance women’s agency: Cross-cutting lessons from experimental and quasiexperimental studies." J-PAL Working Paper.</mark> <mark>Chernozhukov, V., M. Demirer, E. Duflo and I. Fernaldez-Val (2018). “Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments.” NBER Working Paper No. 24678.</mark> <mark>Croke, K. E. Mvukiyehe, and J. Quattrochi (2024). “From Workfare to Well-being? Short- and Medium-run Impacts from a Randomized Controlled Trial in Egypt.” Mimeo.</mark> <mark>Crumpton, B. and N. Elnahass (2023). “Building a bright future: the power of preschool education in Djibouti.” World Bank Arab Voices Blog September 27, 2023.</mark> <mark>Dadzie, C. and E. Ofei-Aboagye (2021). “Opportunities for Youth and Women’s Participation in Ghana’s Labour-Intensive Public Works Program.” World Bank Jobs Working Paper No. 61.</mark> <mark>Datt, G. and M. Ravallion (1994). “Transfer Benefits from Public-Works Employment: Evidence for Rural India.”</mark> _<mark>Economic Journal</mark>_ <mark>104(427): 1346-69.</mark> <mark>Dean, J. and S. Jayachandran (2019). “Changing Family Attitudes to Promote Female Employment.” AEA Papers and proceedings 109: 138-42.</mark> <mark>Dupas, P., J. Robinson and S. Saavedra (2020). “The daily grind: cash needs, labor supply and self-control.”</mark> _<mark>Journal of Economic Behavior and Organization</mark>_ <mark>177: 399-414.</mark> <mark>Field, E., R. Pande, N. Rigol, S. Schaner, and C. T. Moore (2021). “On Her Own Account: How Strengthening Women's Financial Control Impacts Labor Supply and Gender Norms.”</mark> _<mark>American Economic Review</mark>_ <mark>111 (7): 2342-75.</mark> <mark>Franklin, S., C. Imbert, G. Abebe, and C. Mejia-Mantilla (2023). “Urban Public Works in Spatial Equilibrium: Experimental Evidence from Ethiopia</mark> _<mark>.” American Economic Review</mark>_ <mark>(forthcoming).</mark> <mark>Gaddis, I. and S. Klasen (2014). “Economic development, structural change, and women’s labor force participation: A reexamination of the feminization U hypothesis.”</mark> _<mark>Journal of Population Economics</mark>_ <mark>27: 639–681.</mark> <mark>Galasso, E. and M. Ravallion (2004). “Social Protection in a Crisis: Argentina’s Plan Jefes y Jefas.”</mark> _<mark>World Bank Economic Review</mark>_ <mark>18(3): 367- 399.</mark> <mark>Gazeaud, J., N. Khan, E. Mvukiyehe, and O. Sterck (2022). “With or Without Him? Experimental Evidence on Gender-Sensitive Cash Grants and Trainings in Tunisia.” World Bank Policy Research Working Paper 10132.</mark> 29
10 Figure 1

# Inflation in Argentina (percent, logarithmic scale)

<!-- Start of picture text --> 10000<br>1000<br>100<br>10<br>1<br>1981 1984 1987 1990 1993 1996 1999 2002<br><!-- End of picture text --> Note: 1996-2001 average = -0.3 percent annually.

Table 2 Standard of Living Indicators

||PPP per<br>capita|Under-5<br> <br>mortality|Education<br>index|
|---|---|---|---|
||income (a)|(per 10000)||
|Argentina|12090|22|0.92|
|Brazil|7320|40|0.83|
|Chile|9110|12|0.90|
|Colombia|5890|28|0.85|
|Mexico|8810|36|0.84|
|Korea|17340|9|0.95|
|Philippines|4220|41|0.91|
|Thailand|6330|33|0.84|
|Middle income countries|5650|38|n.a.|

Source: World Bank, World Development Report 2002; UNDP, Human Development Report 2001 a. 2000 purchasing power parity dollars

# **_II.  Origins of the crisis_**

Diagnosing the causes of the Argentine economic collapse is important because of the implications for future policy directions. By now several economists have prepared analyses of the causes. Most agree that it was a combination of several influences that, like a “perfect storm,” brought about what might be called the 3D collapse: default, devaluation, and depression. There is considerable divergence of opinion, however, on the relative importance of each of the main factors.
Taken together, these estimates suggest that 5–54 million people in Pakistan may lack access to electricity (table 1).

Pakistan also has some of the worst power outages in the world. According to a 2013 World Bank Enterprise Survey, 81 percent of firms reported being affected by outages on average 75 times a month, with the average outage lasting 17 hours. The IFC survey suggests that Pakistani households typically face 16 hours a day of scheduled blackouts (also called load shedding) during the summer and 12 hours a day during the winter (IFC 2015). The latest _Global Competitiveness Report_ ranks Pakistan 115th among 137 economies in the reliability of electricity supply (Schwab 2018). Largely as a result of unreliable electricity supply, per capita electricity consumption in Pakistan stayed flat for almost a decade, after peaking in 2006.

The electricity deficit in Pakistan is attributable to both lack of investment and inefficiencies in every link of electricity supply. Natural gas—the most important indigenous source of energy— was priced at roughly 36 percent of the international benchmark price in fiscal year 2016. Underpricing gas contributes to gas shortages, because it encourages wasteful energy consumption and reduces suppliers’ interest in gas exploration and production. In fiscal year 2016, gas shortage reached about one‐third of already constrained demand (Ministry of Finance 2016). On top of low gas supply, Pakistan lost more than 12 percent of gas to leakages and theft in 2016—many times more than the roughly 1–2 percent in OECD countries.

With domestic gas production faltering, in 2005 the government adopted a gas allocation policy that gives lower priority to the power sector (with gas going first to residential users and the fertilizer sector). To bridge the shortfall, power generators have increasingly turned to diesel and furnace oil. Liquid fuel–based power generation is much more expensive than gas‐based generation: It accounts for 40 percent of total generation but about 70 percent of the total cost.

The high cost of generation has had a knock‐on effect along the electricity supply chain. It increases the need for subsidies to keep prices low. When the government fails to pay subsidies on time, distribution companies cannot pay the generators, which in turn cannot pay oil and gas suppliers, forcing generators to shut down or run at low capacity, resulting in more load shedding.

4
_17_ _Mexico: The 1997 Pension Reform_

|&t^|<br>1.40||0.00||20.00|E||
|---|---|---|---|---|---|---|---|
|>~~00||***|4.75|0.00|0|B||
||t1.70<br>000|***<br>|_1.50_<br>4~00|**|0.00<br>000|**D**<br>A||
||1.60<br>.||0.00<br>.||0.00<br>.|A<br>||
||0.90.0||1.00||0.00|D||
||9 3;1.6|8|0.00||0.00|A||
||1.60<br>168||0.00<br>000||0.00<br>000|A<br>A||
|**nb~k~**|**0.00**<br>.||0.00<br>~.||0.00<br>.|C<br>||
||0**.**0 ~<br>Pv3 1 55|<br>~|**0.0**<br>0.00|**0.00**|0.00|**D30**<br>A||
|>|~~~~1.70||0.50||0.00|D|l|
|~~~~~1.|70||1.00||0.00|Dl||
||1.17||~1.00||0.00|D||
||>1.50||0.99||0.00|D||
||0.95||1.25|****0.00**||**D**||
|Source: CONSAR<br>Notes:<br>*Initial rate is 0.2<br>**Charge on asse<br>***Charge to fall<br>|Tabulations, 8 J<br>% of pay rising t<br>ts to fall over tim<br>over time if parti<br>|uly 1997<br>o 1.7% by M<br>e if participa<br>cipant remai<br>|arch of 1998.<br>nt remains in tha<br>ns in that AFOR|t AFORE.<br>E.||||
|**Memorandum: T**|**ype of Commiss**|**ion**||||||
|A: On pay alone (|7 of 17)|||Av % A:||1.67||
|B: On assets alone|(I of 17)|||Av % B:||4.75||
|C: On real returns|alone (I of 17)|||Av % C:||33||
|D: Mixture of A&|B (7 of 17)|||Av. D (pay|%;asset %):|1.27|1.03|
|E: Mixture of A&|C (I of 17)|||Av. E (pay|%,ROR %)|1.40|20.00|

- **Investor protection.** Each AFORE must establish a specialized unit to respond to questions and claims from workers and employers. More importantly for investor security, the law provides for submission of claims of workers and employers that are not settled by such a unit, to a system of conciliation and arbitration overseen by CONSAR.

- **I nitially Single Class of SIEFORES.** The Pension Systems Law authorizes CONSAR to permit each AFORE to manage and offer to its customers a variety of SIEFORES with different portfolio compositions, provided that each AFORE offers at least one SIEFORE whose portfolio is composed "fundamentally" of securities whose returns are indexed to the Mexican CPI. Mexican legislators felt that to assure public confidence in the system, participants need to be provided with the option of investing in instruments offering a real rate of return. There are no minimum return requirements for the AFORES.
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