rafmacalaba/datause-encoder-data huggingface dataset:
- 23 -

East Asian exporters have also paid attention to easy access to foreign exchange for exporters, preshipment credit for working capital, labor costs, training, education, infrastructure, technology, and marketing (for a discussion see Keesing 1988.) Their exports have also benefited from a favorable regulatory environment, support for enterprise development, and a forward-looking industrial policy. They have also dealt successfully with protection abroad to maintain their prospects for market penetration.

# Does Policy Matter?

Trade reform has frequently been included under adjustment lending during the 19808 because of the belief that increased trade can help to minimize the slowdown in growth that often accompanies stabilization. The World Bank Report on Adjustment Lending (1988) suggests that short-term changes in the resource balance (the difference betwcen exports and imports of goods and nonfactor services) and GDP growth have been negatively related. Regression results for developing countries show strongly significant and negative coefficients for the change in GDP with respect to a change in the resource balance in the 1980s. However, expenditure-switching policies induced by relative price changes (for example, as a result of real excho_age rate adjustment), by improving efficiency, are expected to lessen the reduction in output that would result from stabilization measures. If expenditure switching leads to a supply response, a given resource balance improvement could be achieved at less cost in terms of foregone growth than would otherwise be the case.
particular chapter 7). The Bank’s researchers had already started using Project LINK’s multicountry macroeconometric models, which imposed global consistency and incorporated transmission channels through a detailed set of bilateral trade matrices (by 0–9 SITC classification), to perform a number of medium-term (3- to 5-year) and long-term (10-year) simulations under alternative scenarios related to adverse shocks and policy response, such as a sharp oil price increase and coordinated G-7 policy action.

The _Long-Term Outlook_ study was the culmination of these efforts and consultations. It tried to link the various economic, financial, demographic, environmental, and technological forces in play at the global level to the underlying macroeconomic analysis and quantitative projections. It also looked at many important trends that would affect the global economic environment and resulting economic performance, such as world trade, commodity prices, and the real rate of interest in the OECD area. It fine-tuned, supplemented, and integrated the judgments of the Bank’s developing country specialists regarding the prospects for their countries, consolidating them into forecasts for the developing world under alternative scenarios. It tried to draw out implications regarding key policy actions that were needed, as well as possible policy failures. It was meant both as a focus around which certain disparate activities and processes in the Bank could coalesce and as a blueprint for a periodic vision document for use by Bank staff, which could also be shared with the world.

This effort led to the launching, in 1992, of _Global Economic Prospects_ , one of the Bank’s flagship products. Every issue of _Global Economic Prospects_ focuses on a theme of importance to the world economy, particularly developing countries, with longer-term and policy implications. The themes covered in the 1992–2010 period ranged from trade in manufactures (1992) and commodities (1993) to the East Asian financial crisis (1998, 2000); the Doha development agenda (2004); migration and remittances (2006); the next wave of globalization (2007); technology diffusion in the developing world (2008); and the Great Recession (2009, 2010, 2011).<sup>5</sup>

> 5 In 2010, the Bank launched a new flagship with a medium- to long-term range, _Global Development Horizons_ . _Global Economic Prospects_ became an Internet-only publication focusing on short-term forecasts and analysis.

11
conclusions on the robustness of theories that might have some merit in explaining the patterns in the data.

Empirical work on trade credit thus far has been hampered due to a lack of firm-level data on trade credit contract terms. Most studies have used the Federal Reserve Survey of Small Business Financing (SSBF) database of U.S. firms, which has only limited data on credit terms and firm characteristics (e.g., Petersen and Rajan, 1997, and Burkart et al., 2008).

An exception is Ng et al. (1999) who use survey level data on 950 listed U.S. firms to study the determinants and characteristics of trade credit contracts. They document wide variation across industries in credit terms, and find that variables capturing buyer and seller reputation are significant determinants of a firm’s choice to extend credit, consistent with theories that explain credit terms as contractual solutions to information problems concerning product quality and buyer creditworthiness.

Our paper differs from the work by Ng et al. (1999) in several important ways. First, we use unique data on actual trade credit contracts rather than survey based data. Second, we cover a broader set of industries that includes technology firms. Third, our dataset includes trade credit terms not only for US firms but also for international firms. Fourth, we have bilateral contract information, so we can control both for buyer and supplier firm characteristics. Fifth, and importantly, we have multiple contracts for the same firm, rather than a firm-average response, allowing us to include firm fixed effects in our empirical analysis, thereby abstracting from timeinvariant firm characteristics that determine the choice of credit terms. Finally, another important distinction is that the Ng et al. data covers only large, publicly traded firms while our dataset includes suppliers of all size. This is an important difference because the credit terms offered by large firms are likely to be very different than those by small firms, especially when credit is extended by small suppliers to large buyers.

In short, the bilateral, multi-contract nature of our dataset is truly unique and is a major improvement on datasets that have previously been used to study the determinants of credit terms used in trade credit. This multi-contract structure of our dataset allows us to abstract from unobserved buyer and supplier firm characteristics, something previous empirical work has not been able to do.

3
Our results speak to the effectiveness of multi-pronged interventions that aim to improve access without sacrificing quality of education. Many education programs implemented in developing countries involve a combination of interventions and target multiple issues at the same time. For example, among all registered voluntary and non-governmental organizations in India, only 16% that self-identify as working within the Education and Literacy sector operate exclusively within that sector.<sup>2</sup> Of those operating exclusively within the Education and Literacy sector, very few operated using a single programmatic strategy.<sup>3</sup> Similarly, of the four main federal “School Education & Literacy Schemes” for primary education in India, only one, the Mid- day Meal Program, focuses on one single intervention.<sup>4</sup> In contrast to the breadth of activities, approaches, and objectives that are commonly implemented to improve education in developing countries, a majority of the studies of education programs involve measuring the effect of one type of intervention in isolation. McEwan (2015) studied 76 RCTs and finds only 5 percent evaluate a “combination” of treatments (authors’ calculations).<sup>5</sup> In addition to RCTs, “high quality” studies involving regression discontinuity (RD) and difference in difference (DD) evaluations also tend to focus exclusively on programs with one intervention implemented in isolation. Damon et al. (2016) reviewed 39 “high quality” RD and

> 2 Authors’ Calculations. We extracted the list of NGOs/VOs through NGO-DARPAN at <u>https://ngodarpan.gov.in</u> Registered VOs and NGOs are required to sign-up online using the portal, run by the Planning Commission of India (PTI 2017). Accessed 12/3/2018.

> 3 After coding all NGOs in the state of Rajasthan that focused exclusively on Education and Literacy we found 2.3% of NGOs focused on research or academia, 4.1% focused on giving financial support to students, 56.1% involved running a school or set of schools, 14.62% provided vocational training to youth or adults, 18.7% were coded as providing multiple types of education programs, and 4.1% were not known.

> 4 See http://mhrd.gov.in/schemes-school.

> 5 McEwan’s literature search of randomized experiments conducted in developing country primary schools from the mid-1970s to 2013 resulted in the following studies that evaluated multiple treatment: He, Linden, and MacLeod (2008), Osendarp et al. 2007, Pradhan, Suryadarma, and Beatty (2011), and Ngyuen (2008).

5
the district of destination than in the district of origin, respectively. Migrating to one of the 73 alternative destinations would, on average, have reduced income and consumption relative to the district of origin. The di ff erence in anticipated income and consumption between actual and hypothetical destinations is strongly signi fi cant. Migrants thus tend to move to districts where consumption and income are higher.

Next we examine whether there are signi fi cant di ff erences in returns to individual character� � istics η sz<sup>h</sup> . Surprisingly, results for income show that η sz<sup>h</sup> is on average lower in the district of destination than in the district of origin. The di ff erence is large enough to be statistically signi fi cant. This implies that better educated, high caste migrants are expected to gain relatively less from migrating to actual destination districts than less educated, lower caste migrants. In � contrast, η sz<sup>h</sup> estimates based on consumption data show an increase relative to the district of origin. This suggests that better educated, high class migrants would gain more from migrating. We also observe a slightly stronger increase for the actual destination than in the alternatives. The di ff erence is not statistically signi fi cant, however.

Di ff erences in relative log income and consumption are displayed next. Predicted relative log income and consumption are generated using the same formula<sup>�</sup> δ s + β<sup>�</sup> s(Es<sup>h−</sup> Es) + �χ s(Hs<sup>h−</sup> Hs) used for log income, except that, by construction,<sup>�</sup> δ s = 0 always. We see that relative income falls between the district of origin and the district of destination while it would have risen in alternative destinations. The di ff erence is statistically signi fi cant. In contrast, relative consumption is higher in the destination district than in the district of origin or in alternative destinations but the di ff erence between actual and hypothetical destinations is not signi fi cant.

We then turn to di ff erences in subjective welfare. The equivalent of<sup>�</sup> δ s is used as for log income. We begin with subjective perceptions regarding the adequacy of total income. Relative to their district of origin, the average subjective satisfaction with total income is found to rise 21
their networks. Member States are also required under the MOU to, among others, work towards the inter-operability of their networks, enter into public private partnership agreements to ensure cross border connectivity and refrain from actions that would prevent or distort competition in the sector<sup>32</sup> .

# **Cross-Border Connectivity Project**

Within the context of the ITU/EU work, the first World Bank harmonization study, and the ECOWAS Technical Group on GSM Roaming work, ECOWAS and WATRA initiated a cross-border connectivity project. The aims were to identify connectivity gaps and regulatory, commercial and policy obstacles to cross-border connectivity and develop a strategy addressing these gaps<sup>33</sup> .

According to the study, traffic is often routed through third party countries (mostly Europe and the United States), making cross-border telecom services costly. There is also reliance on older technologies for intra-regional and long-haul traffic that raises concerns on speed, capacity and costs. More operators rely on satellite for both the exchange of national and sub-regional traffic. The SAT-3 submarine cable, introduced in 2002, offers the most capacity and international connectivity, but it is run by incumbents that exert monopolistic pressures on prices and limit the access of countries not directly connected to the cable. The high tariffs have held back the utilization of the system.

According to the report some of the capacity constraints could be alleviated by alternative infrastructure providers who run other distribution networks such as transport, electricity, oil and gas and deploy their own communications networks along their longhaul routes to service their own needs. There are currently two cross-border fiber routes of this form in use: the Manantali Hydro Electric Power project linking Mali and Mauritania with Senegal and the Chad-Cameroon pipeline.

In addition to the technical obstacles, the report states that coordination and cooperation among ECOWAS member states is limited. The lack of cooperation is found at all levels: between national regulators, between national governments and

> 32 Source: allafrica.com.

> 33 Spintrack AB. 2005: “Cross-border Connectivity Initiative in the ECOWAS Region - ECOWAS Telecommunications Policy Note” and “Cross-border Connectivity Initiative in the ECOWAS Region – Strategies and Recommendations Report”.

25
Table S.5: Robustness checks for Table 1

|Dependent Variable:||log (Loa|nsji)||log (1 + Loansji)|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|log Migrationij|0.150<sup>∗∗∗</sup>|0.134<sup>∗∗∗</sup>|0.203<sup>∗</sup>|0.162<sup>∗∗∗</sup>||
||(0.038)|(0.042)|(0.105)|(0.036)||
|log (1+Migration)ij|||||0.236<sup>∗∗∗</sup><br>(0.036)|
|log Distanceij|−0.790<sup>∗∗∗</sup>|−0.711<sup>∗∗∗</sup>|−1.271<sup>∗∗∗</sup>|−0.833<sup>∗∗∗</sup>|−0.235|
||(0.115)|(0.151)|(0.367)|(0.120)|(0.155)|
|log (1+remittances)ij|||0.015<br>(0.032)|||
||polityi−polityj|||||−0.427<sup>∗∗∗</sup><br>(0.103)||
|(Colonial Link)ij|1.335<sup>∗∗∗</sup>|1.422<sup>∗∗∗</sup>|0.673|1.155<sup>∗∗∗</sup>|1.027<sup>∗∗∗</sup>|
||(0.204)|(0.224)|(0.543)|(0.201)|(0.182)|
|(Language)ij|0.294<sup>∗</sup>|0.722<sup>∗∗∗</sup>|−0.266|0.440<sup>∗∗</sup>|0.094|
||(0.160)|(0.228)|(0.635)|(0.165)|(0.138)|
|(Legal origin)ij|0.156|0.414<sup>∗∗∗</sup>|0.041|0.168|0.018|
||(0.111)|(0.134)|(0.309)|(0.117)|(0.114)|
|(Border)ij|−0.551<sup>∗</sup>|−0.702<sup>∗∗</sup>|0.966<sup>∗∗</sup>|−0.752<sup>∗∗</sup>|−0.007|
||(0.305)|(0.321)|(0.409)|(0.305)|(0.354)|
|Fixed-Efects|i and j|ig and j|i and j|i and j|i and j|
|N|1,628|1,628|213|1,512|2,362|
|L|17|17|16|17|17|
|B|175|175|45|154|181|
|Estimator|OLS|OLS|OLS|OLS|OLS|

Standard errors are clustered at the borrowing country level. N , L and B denote number of observations, number of lending countries and number of borrowing countries. All columns, except for column (2), include country fixed effects for countries i and j. Column (2) includes fixed effects for lending country j and for ig, where ig implies that country i is the borrowing country and the lending country belongs to group g. We distinguish three groups of lending countries indexed by g: Anglo-Saxon countries, continental Western European countries and other. Data on remittances is taken from Docquier, Rapoport and Salomone (2012). Polity is the Polity 2 indicator of the Polity IV dataset.

5
**Table 4: Techno-economic assumptions used for profitability analysis of distributed solar.**

|Variable|201|4 FIT|2016 FIT (from<br>Net metering (|October 28, 2016) /<br>from July 1, 2016)|
|---|---|---|---|---|
||Value|Source/Comment|Value|Source/Comment|
|Investment cost<br>(overnight)|US$ 1,450 per kW|(NREA, 2016)|US$ 600 (aggressive)<br>– 1,250<br>(conservative) per<br>kW|Assumption based on<br>(IEA, 2016a) data for<br>India|
|Exchange rate|7.15 EGP/US$|Central Bank|18.00 EGP/US$|Central Bank|
|O&M cost per year<br>(including insurance)|2.5 % of initial<br>investment|(NREA, 2016)|||
|Depreciable lifetime|25 years|(NREA, 2016)|||
|Minimum book value|5% of initial<br>investment|Assumption|_Unc_|_hanged_|
|Capacity factor|19.4%|EgyptERA|||
|Share of equity|25%|Assumption|||
|Expected return on equity|12%|(NREL, 2016)|15.75% (aggressive)<br>20% (conservative)|Assumption based on<br>higher inflation|
|Lending rate|4%|Concessional rate<br>assumed in FIT of<br>2014|15.75% (aggressive)<br>20% (conservative)|Central Bank overnight<br>deposit rate|
|Grace period|2 years|Assumption for<br>concessional<br>finance|0 years|Assumption based on<br>market conditions|
|Loan tenor|12 years|Concessional rate<br>assumed in FIT of<br>2014|5 years|Assumption based on<br>market conditions|
|Income tax rate|25%|Ministry of Finance|22.5%|Ministry of Finance|
|Loss carryforward period|0|Other sources of<br>taxable income<br>assumed|_Unc_|_hanged_|

19
<!-- Start of picture text --> wo<br>re)<br>= 0.03 0.28 e 0.02 sad 0.04 er<br>EGoooo — 0.020.03 ° ° = ee9-01 e a a 0.02. ee oi<br>co ° ve ° 6<br>es 0 . 03 02 OL Q 0.1 0.2 0 #o<br>& -0.05 -9,01 9 0.05 0.1 “O08 96 -0.2 -0.1 @0 e, 0,2<br>r=] 0:02 ee -0.02 ° e002 e<br>~<br>n -0.03 -0.03 -0.04<br>Output Gap<br><!-- End of picture text -->
response. This is especially important for empirical analysis because the household surveys in developing countries usually lack reliable information on home production activities.

Although the response of hired labor to agricultural productivity shock is ambiguous a priori and thus can lead to misleading conclusions about the poverty impact of agricultural productivity changes, note that the response of total labor devoted to market work is positive under the plausible assumption that �h > 0; �k > 0. In the empirical analysis, we thus look at both hired labor and total labor devoted to market production as opposed to home production.

Proposition 3: Regardless of its impact on hired labor, an increase in agricultural productivity increases total income in a village. The increase in village income is higher, the higher is the labor supply response with respect to wage (i.e, larger values of �k and �h).

Proof: The net income of each household is the sum of land and labor income from market activities. The total net income of the two households is thus: <u>1��</u> Where 41 = A[1 � �] � and Y is the total village income. The change in village income in response to an agricultural productivity shock can be derived as: 14
# Tracing the Local Impacts of Exports on Poverty and Inequality in Mexico

Carlos Rodríguez-Castelán<sup>a</sup> Emmanuel Vazquez<sup>b</sup> Hernan Winkler<sup>c</sup>

**Keywords:** International trade, exports, poverty, labor markets, migration **JEL Codes:** F14, F16, I3, D3, J61

_____________________________ The authors wish to thank Raymundo M. Campos-Vasquez, Christoph Lakner, Gladys C. Lopez-Acevedo, and Ambar Narayan for their comments and suggestions. They are also grateful to participants at the World Bank authors workshop on the distributional effects of trade, the World Bank workshop on poverty and trade, and the 5th Congress Sobre Mexico organized by the Universidad Iberoamericana. The authors are indebted to Natalia Volkow and Liliana Martinez of the Microdata Center of the National Institute of Statistics and Geography, Mexico, for their support for data use in compliance with the confidentiality requirements set by Mexican law. This study received funding from the World Bank Multi-donor Umbrella Facility for Trade (UF) Trust Fund TF0A9162. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

a. World Bank, e-mail: crodriguezc@worldbank.org.

b. Center for Distributive, Labor, and Social Studies, Instituto de Investigaciones Económicas, Facultad de Ciencias Económicas, Universidad Nacional de La Plata, e-mail: evazquez@cedlas.org.

c. World Bank, e-mail: hwinkler@worldbank.org.
|**Table A**|**7: Interaction**|**term results**|**for social atti**|**tudes toward**|**s women's rig**|**ht to scarcejob**|**s**|
|---|---|---|---|---|---|---|---|
|Dependent<br>variable:_Female_<br>_Workers_|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Exports (%, cell<br>average)*Men have<br>more right to scarce<br>job (WVS)|0.605***<br>(0.211)|0.617***<br>(0.207)|0.658***<br>(0.213)|0.644***<br>(0.213)|0.671***<br>(0.214)|0.624***<br>(0.215)|0.610***<br>(0.209)|
|Exports (%, cell<br>average)|-1.178***<br>(0.429)|-1.190***<br>(0.419)|-1.272***<br>(0.432)|-1.247***<br>(0.433)|-1.283***<br>(0.438)|-1.177***<br>(0.445)|-1.238*<br>(0.739)|
|Exports (%, cell<br>average)*GDP per<br>capita (logs)|||||||0.009<br>(0.071)|
|No. of Workers|||||||-1.949**|
|(logs)*Men have<br>more right to scarce<br>jobs (WVS)|||||||(0.858)|
|No. of Workers<br>l||1.072***|1.117***|1.213***|1.159***|1.151***|5.168***|
|(ogs)||(0.327)|(0.350)|(0.386)|(0.398)|(0.398)|(1.826)|
|GDP per capita<br>||<br>-12.017|<br>-11.785|<br>-13.799*|<br>-21.442***|<br>-38.168***|<br>-42.889***|
|(logs)||(8.120)|(8.180)|(7.865)|(7.883)|(9.297)|(10.036)|
|Herfindahl-<br>|||-11.842**|-10.150*|-9.926|-11.004|-11.442|
|Hirschman Index||||||||
||||(5.534)|(5.409)|(6.465)|(6.920)|(7.321)|
|Age of Firm (logs)|||<br>-0.519|<br>-1.007**|<br>-0.904**|<br>-0.874**|<br>-0.842*|
||||(0.421)|(0.417)|(0.435)|(0.437)|(0.435)|
|Foreign Ownership<br>(proportion)|||<br>0.351|<br>1.922*|<br>2.104*|<br>2.175*|<br>2.309*|
||||(1.098)|(1.124)|(1.204)|(1.204)|(1.193)|
|Female Ownership<br>Y:1 N:0||||<br>5.461***|<br>5.263***|<br>5.267***|<br>5.206***|
|||||(0.730)|(0.773)|(0.771)|(0.776)|
|Skills Obstacle (0-||||0.069|0.380|0.398|0.405|
|4)||||||||
|||||(0.316)|(0.387)|(0.387)|(0.386)|
|How Much Of An<br>Obstacle:<br>Trnrt?||||<br>-0.432|<br>-0.258|<br>-0.254|<br>-0.251|
|aspo||||(0.266)|(0.298)|(0.300)|(0.299)|
|Firm Purchased||||<br>-0.899|<br>-0.562|<br>-0.520|<br>-0.482|
|Fixed Assets Y:1||||||||
|N:0||||||||
|||||(0.758)|(0.815)|(0.819)|(0.818)|

64
<!-- Start of picture text --> _ 1000<br>£<br>wt<br>5<br>~ 800 @ Expenditure met by HRD<br>E<br>= @ Expenditure met by FCB<br>Cc<br>a<br>a 600<br>Lh}<br>o<br>ie}<br>oi<br>S 400<br>8g<br>fe 200<br>0<br>bh hb ph hp<br>a) =i = ne ie><br>ho in ee toe<br>Oo © =<br>Distribution of scale up expenditure (return periods marked)<br><!-- End of picture text -->