rafmacalaba/data-use-mentions-extended huggingface dataset:
2021), encompassing the period of rapid growth in the world economy and characterized by increasing integration of production and consumption in the world economy. This period includes the economic integration into the world economy of Japan, the Republic of Korea, and other East-Asian countries and the continuous integration of countries within the European Union. While it covers until 2000, I only use the data until 1990 since this is the year other input-output tables start covering more countries.

For the period 1990 to 2015 I use the Eora MRIO databse (Lenzen et al., 2012, 2013). Eora consists of multi-region input-output tables that cover 190 countries. The version of Eora used in this paper is the simplified version, Eora26, thanks to the 26-sector harmonized classification that allows for comparison across countries and sectors. Finally, for 2015-2021, I use the ADB-MRIO (ADB, 2022) covering 62 economies and 25 sectors. Besides covering fewer countries than the data used for previous years, the ADB MRIO nowcast the most recent years in the data. But these recent years can be used to better understand globalization dynamics up to 2021.

One final remark is that all input-output tables provide data for several sectors that I aggregate up to three aggregate industries (manufacturing, agriculture, and services). The services data tend to be of lower quality than goods data, but I include it for the completeness of the analysis and due to the importance of the services sector. Also, I tend to use the data for the whole period 1965-2021 for most analyses, but some of them, - like the regional analysis, require focusing the work on the period 1990 2015, which covers more countries.

# **Results 3**

This section presents the results, starting with a general look at globalization dynamics since 1965 across sectors and countries. I disentangle the dynamics of several countries for 1965-2021 to provide deep insights. And for a regional perspective, I estimate the globalization dynamics across World Bank regions for 1990-20215. Finally, I disentangle the effect of
enterprises receive almost no support from the government, but report facing problems, particularly in terms of inadequate demand and the recovery of financial dues. In this regard, a first policy implication to be considered is a strengthening of legal mechanisms for such enterprises to ease their cash flows. This would be particularly relevant as these enterprises are most likely to be cash and credit-constrained and their operations would no doubt benefit from timely receipt of payments.

The second point to note is that the revenues and value-addition of these enterprises respond sharply to increases in rural demand. Combining the results from our consumption and enterprise surveys, a 6% increase in monthly per capita household expenditures lead to a 25% increase in enterprise revenues. In our empirical design, consumption increases are driven by rainfall shocks, which are both idiosynratic, and short-term. Indeed, we find little impact of past rainfall shocks on household consumption or enterprise revenues. This suggest that government policies boosting aggregate rural growth and consumption would have significant spillovers on the performance of rural non-farm enterprises.

Third, an interesting facet from the data is the role of credit for these enterprises. While existing literature in development economics has shown that unincorporated microenterprises are most likely to be credit constrained, in our sample, only 10% of the enterprises have any outstanding credit and 8% report facing problems in accessing credit. This suggests that the scale of operation for these enterprises is too small to warrant the usage of credit. Nonetheless, it seams that only 2% of the enterprises have access to bank credit, suggesting that the majority of enterprises requiring credit have to rely on non-bank sources of credit, which are typically costlier. While the numbers are small in terms of percentages, scaling up, this implies that over 200,000 enterprises in the state of Rajasthan operate on non-bank credit. Bringing these enterprises within the formal banking channel can result in significant reductions in the cost of credit faced by these enterprises, and an increase in their overall profitability.

Fourth, our results on rainfall shocks and
of female labor force participation, even when compared to other countries in the Middle East. Second, despite dramatic improvements in women’s human capital over recent decades, their labor market outcomes have been rather stagnant (Winkler & Gonzalez (2019)). Third, internet use increased dramatically during the period under analysis (from 2010 to 2016), with the share of users increasing from 5 to 61 percent of the population. The increase was even larger for mobile broadband access, going from near zero in 2010 to approximately 60 percent of the population in 2016 (Telegeography (2018)). Such large increase, together with the availability of longitudinal data, is crucial to the design of the identification strategy of this article.

This article is structured as follows. Section 2 provides a review of the literature on the impacts of information and communication technologies (ICT) on labor market outcomes, with a focus on women in developing countries. Section 3 describes the data sources and main trends in labor market indicators and internet penetration, and section 4 discusses the identification strategy. Section 5 presents the results, and section 6 concludes.

# **2 Digital technologies and women’s labor outcomes**

This article contributes to the literature on the labor market impacts of internet access. The internet can affect labor market outcomes through different channels. It could contribute to improve labor market efficiency, by increasing access to information about job vacancies, lowering the recruiting costs for employers and increasing the quality of job matching (Autor (2000)). On the other hand, since this technology increases the economies of scale of job applications for potential workers, it could also increase the screening costs of firms significantly and lower matching quality (Oyer & Schaefer (2011)).

Empirical literature finds, in general, positive impacts of internet access on labor market outcomes. In the United States, unemployed individuals who search for a job online found a job 25 percent faster than those who search offline, even after accounting for other differences
intuition is akin to the idea of marginal decreasing returns to capital. Consistently with the low density of robots, the median Indonesian firm employs a much smaller amount of capital per worker than its counterpart in the same 2-digit ISIC sector in China and Mexico (Figure A2 in Appendix).<sup>38</sup> The second (non mutually exclusive) possible explanation hinges on the lack of relevant skills. By filling skill shortages, robots may induce large productivity gains that more than compensate the labor replacement effects. In contexts where the quality of labor is relatively low, robots can provide substantial improvements to the production process as they enable firms to achieve higher product standards.<sup>39</sup> This paper provides suggestive evidence that this type of mechanism may be at play in Indonesia where the penetration of robots significantly increases markups and international trade integration, which are enabled by higher production standards. This hypothesis is also consistent with the relatively low quality of skills in Indonesia as proxied by high-school students’ test scores through the OECD Programme for International Student Assessment (PISA 2018).<sup>40</sup> From a policy perspective, the evidence of positive inter-industry employment spillovers provides a possible rationale to facilitate or even incentivize the adoption of automation technologies among manufacturing firms in Indonesia. Further research will be needed to support the external validity of our findings and the related policy implications. In particular, firm-level information on the adoption of automation technologies in developing countries would be a crucial first step towards that end. In addition, why we do not see more Indonesian firms adopting robots given the high returns to adoption remains an important question for future research.<sup>41</sup>

> 38The computation is based on firm-level data from different waves of the World Bank Enterprise Survey. Specifically we compute the median real value of the capital stock per worker (in international dollars) for all of the manufacturing industries the data allows. The data is available for Indonesia in 2009, for Mexico
# **1 INTRODUCTION**

The body of knowledge in the field of export survival analysis is growing with the development of new concepts that explain and capture the dynamics of the process, but also with the slowly increasing availability of relevant data. This paper contributes to the latter by bringing to light previously unreleased customs transactions data from the Lao PDR. These data lend themselves rather well to survival analysis of the kind carried out in only a few countries so far, but also form an interesting window into the composition of, and the mechanisms behind, exporter behavior in a small developing country such as Laos. The paper borrows from the export survival analysis methodology in work by Cadot, Iacovone, Rauch and Pierola (2010), which investigates survival mechanisms among the exporter populations of Malawi, Mali, Senegal and Tanzania using very similar data sets, and which additionally serves as a rare and valuable reference for comparisons.

Ever since the seminal work of Besedes and Prusa (2006), it has been known that new exporting spells in developing countries tend to be short-lived, with high “death” rates: several other studies have confirmed that the median duration of export spells is very short, typically two to three years (Obashi 2010). Efforts by others using both panel and customs data (Nitsch 2009, Alvarez 2008) have revealed that such spells play a critical role in the process of export growth, showing that besides volume growth (intensive margin) and diversification (extensive margin), there is also a fundamentally important “sustainability margin”, carved out of the intensive margin, which focuses on the extent to which new products and destination flows survive in later periods.

**Figure 1:** Three channels of export growth – the intensive, extensive and sustainability margins

<!-- Start of picture text --> Intensive margin: Higher volumes of existing products and destinations<br>New products<br>Export growth Extensive margin:<br>New destinations<br>Sustainability margin:
The Facebook survey consistently estimated more severe outcomes than the in-person survey The national-level Facebook estimates range from about 14 percent to about 250 percent more severe than their PPCS counterparts (see Table 2). For six of the eight core indicators, the difference in estimates between the two surveys is significant at the 5% level. The direction of difference holds in almost all cases if estimates are disaggregated by region, gender, age category (18 to 34 / 35 and older) or reported exposure to at least one conflict event. Estimates do not appear to be systematically more similar for any of the subgroups created by these disaggregations, except for respondents from Gaza. This finding is discussed in more detail in a later section. Nor does the direction of differences between subgroups in the Facebook survey necessarily match the direction of differences between the same subgroups in the PPCS.

**Table 2: The Facebook survey estimated more severe outcomes than the PPCS across eight core**

**indicators**

||**Indicator**|**Indicator**<br>**range **|<br>**PPCS**<br>**estimate**|**Facebook**<br>**estimate**|<br><sup>**Difference**</sup>|**N**<br>**(combined)**|
|---|---|---|---|---|---|---|
|_1_|_Share of people who report personal_<br>_exposure to at least one conflict event_<br>_(Gaza only)_|0-100%|35.0<br>(3.2)|49.7<br>(8.8)|14.7<br>(9.4)|2937|
|_2_|<sup>_Approximate prevalence of moderate or_<
21 Dimaranan, B.V. and McDougall, R.A. (eds.) (2005), _Global Trade, Assistance, and Protection: The GTAP 6 Data Base,_ Center for Global Trade Analysis, Purdue University, West Lafayette.

FAO (2004), ‘The Impact of Cotton Sector Support on developing Countries: A Guide to Contemporary Analysis’, mimeo, Food and Agriculture Organization, Rome, September.

- FAPRI (2005), _U.S. and World Agricultural Outlook 2005,_ Ames IA: Food and Agricultural Policy Institute, January (see www.fapri.org).

Hertel, T.W. (ed.) (1997), _Global Trade Analysis: Modeling and Applications_ , New York: Cambridge University Press.

Hossain, F., C.E. Pray, Y. Lu, J. Huang, C. Fan and R. Hu (2004), ‘Genetically Modified Cotton and Farmers’ Health in China’, _International Journal of Occupational and Environmental Health_ 10: 307-14.

Huang, J., S. Rozelle and M. Chang (2004), ‘The Nature of Distortions to Agricultural Incentives in China and Implications of WTO Accession’, Ch. 6 in _China and the WTO,_ edited by D. Bhattasali, S. Li and W. Martin, London: Oxford University Press.

ICAC (2005), _TheOutlook for Cotton Supply in 2005/06_ , Secretariat of the International Cotton Advisory Committee (ICAC), Washington DC, September.

James, C. (2005), _Global Review of Commercialized Biotech/GM Crops: 2005_ , Brief No.

- 34, International Service for the Acquisition of Agri-biotech Applications, Ithaca

NY.
information and analytical capacity (in the absence of mechanic rules), need to be in place to make the system operational. Another issue with the Colombian system is that the dynamic provisions are cumulated as individual provisions, which means it is specific to a credit and cannot be allocated to cover losses of other credit. Due to these concerns, the Colombian authorities have already come up with plans to make the system more rule-based and to use dynamic provisions as general provisions.<sup>60</sup>

# **Peru**

The Peruvian system was initially introduced in 2000 and revised in 2008.<sup>61</sup> It is a rule-based system with a clear macroprudential objective, and the accumulation and release of the funds is based on GDP growth. The rule dictates two sets of general provisioning requirements according to two states of GDP growth, by specifying two sets of PDs and LGDs by type of loans (mortgage, consumer, and different types of commercial loans) [see equation (6)]. Simply put, in a ―low growth‖ period, banks are required to maintain a lower level of general provision (0.7 or 1 percent, depending on the type of loans); in a ―high growth‖ period, banks are required to increase the level of general provision (to 1.1 to 2.5 percent, depending on the type of loans). The additional funds accumulated in good times can be accessed in bad times to offset increasing specific provisioning. The advantage of the Peruvian system is its systemic perspective, but the data requirement on high-frequency and up-to-date GDP data may pose a challenge in other countries and the relevance of GDP growth vis-à-vis other indicators also may need to be evaluated.

> 60 See Fernández de Lis and Garcia-Herrero (2010) for more details.

> 61 The discussion on the Peruvian system benefits from the presentation on the subject by Michel Canta Terreros at the Workshop on Countercyclical Provisioning Systems for the Superintendencia de Bancos del Banco
country and birth cohort is included in the appendix.<sup>13</sup> 10 The 30 countries included in the three rounds of the LiTS used in our study are Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czechia, Estonia, Georgia, Germany, Hungary, Kazakhstan, the Kyrgyz Republic, Kosovo, Latvia, Lithuania, Moldova, Montenegro, North Macedonia, Poland, Romania, the Russian Federation, Serbia, the Slovak Republic, Slovenia, Tajikistan, Türkiye, Ukraine, and Uzbekistan.

11 France, Sweden, and the United Kingdom were included in round 2. Italy and Mongolia were included in rounds 2 and 3. Cyprus was included in round 3. Greece was included in rounds 3 and 4. Algeria, Jordan, Lebanon, Morocco, and the West Bank and Gaza were included in round 4.

12 The differences relate primarily to the duration of primary education, which in many countries was extended at the expense of lower secondary in the 1970s. Thus, an individual who completed primary education (and later dropped out of school) in the 1960s may have completed five years of education. In contrast, an individual who did so in the 2000s may have completed seven years. Given that we use the correspondence tables of 2010, we may be overestimating the years of education of people who completed primary education (and later dropped out) in the 1960s and before.

> 13 <u>Table A.1</u> shows the number of non-missing observations for three selected age cohorts and the total in three rounds of the LiTS by country. As can be expected, the number of observations in the 1940-1949 age cohort decreases rapidly

11
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 5. Road standards in Senegal (as of 2006)**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Under-engineered Correctly engineered Over-engineered<br>Nigeria Benin South Africa Rwanda Uganda Chad Ethiopia Senegal Tanzania Ghana Malawi Cote d'Ivoire Cameroon Burkina Faso Kenya Niger Lesotho Mozambique Madagascar Namibia Zambia Average<br><!-- End of picture text --> _Source_ : Gwilliam and others 2008. Derived from the AICD national database (www.infrastructureafrica.org/aicd/tools/data) using RONET.

The combination of good connectivity and adequate standard selection is well received by users. Therefore, despite the relatively poor conditions of the road network due to deferred maintenance, less than one-third of Senegal’s firms identify transport services as a major constraint for doing business—a rating that is within the average observed among low-income countries. Moreover, according to a recent survey on trade logistics that captures indicators including the availability and quality of the roads, Senegal’s Logistics Performance Index (LPI), at 2.86, is above the regional average of 2.46 (figure 6). The LPI is based on a worldwide survey of

**Figure 6. Senegal’s Logistics Performance Index is the highest in West Africa**

<!-- Start of picture text --> 3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>Source : World Bank 2010d<br>Note: SSA = Sub-Saharan Africa.<br>Logistics Performance Index<br>Senegal Benin Guinea Togo Nigeria Niger Cote
growth are expected to resume once economic activity recovers from pandemic disruptions. After a decade of resilience, potential growth elsewhere in the region is also expected to moderate somewhat (by 0.1 percentage point a year on average) as labor force growth eases.

In ECA and SSA, potential growth is projected to slow somewhat. Investment weakness and diminishing demographic dividends in the rest of the decade are expected to be only partially offset by a moderate pick-up of TFP growth as the adverse shocks of the past decade subside. In ECA, the slowdown in potential growth also reflects the fallout from the war in Ukraine that will depress investment in the region for several years.

In SAR, LAC, and MNA, potential growth is projected to be broadly unchanged in 202230. SAR benefited from demographic tailwinds over the past decade, but these are expected to fade in the remainder of the 2020s; this is expected to be offset by a recovery in TFP growth. Labor force growth is expected to continue declining in LAC, but this

> 5 Throughout this paper, potential growth projections for 2022-30 are predicated on population size and composition in line with the medium fertility scenario of the UN Population Projections, trend improvements in education and health outcomes, and investment growth constant at its long-term average.
2020-21, investment data are compiled from national statistical agencies and Haver Analytics, while the capital stock is estimated from investment data by the perpetual inventory method using historical average depreciation rates.<sup>16</sup> Potential TFP growth is defined as the fitted value of a panel fixed effects regression for 33 advanced economies and 92 EMDEs for 1983-2020 of Hodrick Prescott-filtered trend of actual TFP growth (the Solow residual) on determinants of productivity. These include GDP per capita relative to advanced economies, education (secondary school completion rate), the working-age share of the population, and the five-year moving average real investment growth (as in Abiad, Leigh, and Mody 2007; Bijsterbosch and Kolasa 2010; Feyrer 2007; Turner et al. 2016).<sup>17</sup> To allow for nonlinearities in the productivity dividends

> 15 The potential growth estimates may be biased if the assumption of constant returns to scale is not valid (Dribe et al. 2017). For a detailed discussion of drawbacks of growth accounting, see Dieppe and Kilic Celik (2021). That said, the approach is widely used for its conceptual simplicity and ease of interpretation.

> 16 Implicitly, this approach does not account for the possibility that inefficient investment is written off during downturns. Hence, it may overstate the capital stock during downturns.

> 17 The results are robust to using GDP per capita instead of GDP per capita in percent of advanced-economy GDP per capita. GDP per capita relative to a frontier (advanced economies) is used here to proxy the catch-up effect highlighted in the literature on stochastic frontier analysis (Growiec et al. 2015).

32
and Sleuwaegen, L. and Goedhuys, M. (2002), respectively analyzing 141 and 129 manufacturing firms in Côte d’Ivoire, also found negative correlations between firm growth and both firm size and age; finally, running GMM-SYS panel estimates covering census-based Ethiopian manufacturing firms over the period 1996-2003, Bigsten and Gebreeyesus (2007) showed how the negative relationship between size and age on the one hand and firms’ employment growth on the other is significant and robust to sample selection and unobserved firm heterogeneity<sup>21</sup> .

# **<u>4.2.2 Credit rationing</u>**

Credit constraints and lack of financial capital in general should limit the rate of entry of new businesses, and their likelihood of survival and rate of growth (see Xu, 1998; Carpenter and Petersen, 2002; Becchetti and Trovato, 2002; Aghion, Fally and Scarpetta, 2007). However, some recent studies have shown that the role of credit rationing has been somewhat over-emphasized and that entrepreneurial saving plans may be able to overcome borrowing constraints (Cressy, 1996 and 2000; Parker, 2000; Hurst and Lusardi, 2004). The risk of overstating the hindering role of credit constraints is particularly high in questionnaire analyses where nascent or newborn entrepreneurs are asked to list their main difficulties in starting and/or running a new firm; in fact, they have the self-indulgent tendency to indicate a lack of external financial support as the main _cause_ of their problems, while in most cases this is just a _symptom_ of more fundamental deficiencies internal to the firm.

At any rate, as already discussed in Section 4.1, new entrepreneurial initiatives in the DCs are credit-rationed in the vast majority of cases due to lack of collateral, informational asymmetries and largely imperfect local capital markets. For instance, Goedhuys and Sleuwaegen (2010), in a study investigating 947 small and medium entrepreneurial firms in several manufacturing firms in eleven Sub-Saharan African countries<sup>22</sup>