rafmacalaba/datause-encoder-data huggingface dataset:
Finally, controlling for land area and crop mix, male farmers have a productivity advantage over female farmers. This is largely explained by their greater access to resources, including agricultural inputs, transportation, credits, unpaid agricultural family labor, and mechanization. Our result therefore highlights the continued need to level the playing field between men and women. Further, our analysis shows that the male productivity advantage persists even after adjusting for agricultural resources and other endowments. The remaining unexplained part may possibly reflect an unequal pattern of division of labor driven by gendered social norms. Analysis based on the Sri Lanka Time Use Survey 2017 suggests that women working in agriculture spend significantly less time on paid employment and more time on unpaid domestic work, whether compared to male workers in agriculture or to their counterparts working outside agriculture (World Bank 2021b). Thus, interventions that could ease women’s household responsibilities, for instance, are likely to help increase women’s agricultural productivity and generate more crop income.

# REFERENCES

- <mark>Aguilar, A., E. Carranza, M. Goldstein, T. Kilic, and G. Oseni. 2015. “Decomposition of Gender Differentials in Agricultural Productivity in Ethiopia.”</mark> _<mark>Agricultural Economics</mark>_ <mark>46: 311–</mark> 34.

- <mark>Akresh, R. 2005. “Understanding Pareto Inefficient Intrahousehold Allocations.” IZA Discussion</mark> Paper 1858, IZA, Bonn, Germany. http://ftp.iza.org/dp1858.pdf.

- <mark>Ali, D., D. Bowen, K. Deininger, and M. Duponchel. 2016. “Investigating the Gender Gap in</mark> Agricultural Productivity: Evidence from Uganda.” _World Development_ 87: 152–70.

- <mark>Appleton, S., and A. Balihuta. 1996. “Education and Agricultural Productivity: Evidence from Uganda.”</mark> _<mark>Journal of International Development</mark>_ <mark>8 (3): 415–44.</mark>

- Backiny-Yetna, P., and K. McGee. 2015. “Gender Differentials and Agricultural Productivity in Niger.” Policy Research Working Paper 7199, World Bank, Washington, DC.

- Barrett, C. B., M. F. Bellemare, and J. Y. Hou. 2010. “Reconsidering Conventional Explanations of the Inverse Productivity–Size Relationship.” _World Development_ 38 (1): 88–97.

- Barrett, C. B. 1996. “On Price Risk and the Inverse Farm Size Productivity Relationship.” _Journal of Development Economics_ 51 (2): 193–215.

- <mark>Bellon, M. R., B. H. Kotu, C. Azzarri, and F. Caracciolo. 2020. “To Diversify or Not to Diversify, That Is the Question: Pursuing Agricultural Development for Smallholder Farmers in Marginal Areas of Ghana.”</mark> _<mark>World Development</mark>_ <mark>125: 104682.</mark>

- Benjamin, D. 1995. “Can Unobserved Land Quality Explain the Inverse Productivity Relationship?” _Journal of Development Economics_ 46 (1): 51–84.

26
_Services PTAs_ Finally, we estimate effects for bilateral trade in individual services products. The approach deploys the same technique as above, i.e. using indicator variables for specific policy configurations, but does so on disaggregated trade flows for individual services sectors (Table 5). With some variation across the individual services sectors, the results confirm our main finding that the trade-enhancing effect of PTAs materialises only through ambitious policy configurations in key areas that pertain to the agreement’s structure, its scope, and meaningful disciplines e.g. on the prohibition of local presence requirements, technology transfers, or nationality requirements for managers. Only in these instances do agreements increase bilateral trade substantially. Moreover, Table 5 shows that aggregate effects are driven by increases in trade in regulation-intensive services such as financial and business services and intellectual property rights. All of these findings are robust to controlling for non-services agreement depth.

Table 5: Impact of Policy Clusters in Individual Services Sectors, 2000-16

||(1)<br>**FinIns**|(2)<br>**OthBusi**|(3)<br>**IPR**|(4)<br>**FinIns**|(5)<br>**OthBusi**|(6)<br>**IPR**|
|---|---|---|---|---|---|---|
|Structure (most ambitious)|0.3944|0.2045|1.0139<br>∗∗|∗|||
|Structure (shallow)|0.0018|0.0010|0.0065||||
|Scope I (ambitious)||||0.3242<br>∗∗∗|0.2523|0.4089<br>∗|
|Scope I (shallow)||||-0.1286<sup>∗∗</sup>|0.0196|0.0290|
|EU Pair|0.9817<sup>∗∗∗</sup>|1.4664<sup>∗∗∗</sup>|0.0013|0.9861<sup>∗∗∗</sup>|1.4678<sup>∗∗∗</sup>|0.0013|
||(7)|(8)|(9)|(10)|(11)|(12)|
||**FinIns**|**OthBusi**|**IPR**|**FinIns**|**OthBusi**|**IPR**|
|Scope II (most ambitious)|0.6328<br>∗∗∗|0.3221<br>∗∗∗|0.1161||||
|Scope II (least ambitious)|0.0023|0.0015|0.0097||||
|Disciplines (ambitious)||||0.7319<br>∗∗∗|0.2501<br>∗∗∗|0.1864|
|Disciplines (shallow)||||-0.0953|0.0204|-0.0354<sup>∗</sup>|
|EU Pair|0.9778<sup>∗∗∗</sup>|1.4661<sup>∗∗∗</sup>|0.0015|0.9788<sup>∗∗∗</sup>|1.4629<sup>∗∗∗</sup>|0.0021|
|Observations|222,856<br>|230,095<br>|204,868|222,856<br>|230,095|204,868|
|Exporter-year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Importer-year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-pair FE|Yes|Yes|Yes|Yes|Yes|Yes|

_Source_ : Authors’ estimations using WB-DTA 2.0 and ITPD-E data. Dependent variable: bilateral services trade _Xijt_ . PPML estimation with three-way clustered standard errors. Other policy patterns controlled for but coefficients not reported.

36
literature on transition economies (Blanchard and Kremer 1997; Roland and Verdier, 1999). We use our panel data to examine enterprise recovery from each of these shocks.

We begin in Section 2 by discussing the data and examining the sources of funds available to enterprises and households to pay for repair or replacement of lost assets. In Section 3, we use random capital shocks we generated as a part of the project to assess the value of access to liquid capital in the recovery process. We study the timeline of recovery in Section 4, and conclude in Section 5 section with a discussion of policies that might quicken the recovery of microenterprises following a major disaster.

# **Section 2: Firm Losses and Sources of Recovery Funds**

Even in normal circumstances, credit and insurance market failures loom large for business owners in developing countries (e.g. Banerjee and Duflo, 2005). A variety of informal mechanisms have evolved to overcome these market failures, but aggregate shocks such as economic crises and natural disasters can limit the effectiveness of informal financing and risk-coping mechanisms. In particular, group-based informal insurance arrangements are ineffective, as the incomes of a household’s risk-pooling partners also fall (Lustig, 2000). Already limited access to formal credit may become even more limited. As a result, it is of interest to ask: What resources do owners of microenterprises and small firms use to replace buildings and equipment that are destroyed or damaged in major disasters? And, how quickly are the enterprise owners able to replace assets lost in the disaster?

# _The survey data_

We provide some answers to these questions using three surveys we conducted with enterprise owners and wage workers along the southern coast of Sri Lanka after the tsunami. Each of the three samples contains information on 400 to 600 individuals: 130 to 200 individuals suffering asset losses from the tsunami, an equal number of individuals 6
**Figure 4: Labor inflow and outflow in Guangdong (1978-2013)**

<!-- Start of picture text --> 180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>1978 1985 1990 1995 2000 2005 2010<br>-20<br>Gross migration outflow Gross migration outflow<br>Net migration inflow<br>10000 persons<br><!-- End of picture text --> Data Source: Guangdong Bureau of Statistics, 2014.

The distribution of inter- and intra-provincial migration differs significantly between the PRD and the rest of the province (table 1). A closer look at the fifth population census data shows that nearly 90 percent of the migrant population concentrates in the Pearl River Delta, of which 50 % in Shenzhen and Guangzhou. For the entire province, the intra-provincial migration and inter-provincial migration accounted for 40 % and 60 % of the total migration population, respectively. However, for the Pearl River Delta, the intra-provincial and inter-provincial migration ratios are 1/3 and 2/3, respectively; while in other cities in the province, intraprovincial migration and inter-provincial migration accounted for 3/4 and 1/4, respectively. In other words, most of the inter-provincial population is concentrated in the Pearl River Delta; while for other cities, the majority of the migrant workers are from within Guangdong. The net migration inflows in Guangdong are mainly the migrants to Pearl River Delta from other provinces.

**Table 1 - The distribution of migrant inflows in Guangdong**

||Numb<br>(|er of migrant<br>10000person<br>|2000<br>inflow<br>s)<br>|Perce<br>(Total migr<br>|ntage<br>ation=100)<br>|Numb<br>(|er of migrant<br>10000person<br>|2010<br>inflow<br>s)<br>|Perce<br>(Total mig<br>|ntage<br>ration=100)<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||Total|Intra-<br>provincial<br>migration|Inter-<br>provincial<br>migration|Intra-<br>provincial<br>migration|Inter-<br>provincial<br>migration|Total|Intra-<br>provincial<br>migration|Inter-<br>provincial<br>migration|Intra-<br>provincial<br>migration|Inter-<br>provincial<br>migration|
|Total|2530.4|1023.9|1506.5|40.5|59.5|3680.7|1530.9|2149.8|41.6|58.4|
|Pearl River|||||||||||
|Delta|||||||||||
|Sub-total|2170.4|753|1417.5|34.7|65.3|3136.3|1115.5|2020.8|35.6|64.4|
|Guangzhou|428.2|213.2|215|49.8|50.2|614.7|314.5|300.3|51.2|48.8|
|Shenzhen|607.1|201.5|405.6|33.2|66.8|851.5|271.9|579.6|31.9|68.1|
|Zhuhai|66.6|30.9|35.7|46.3|53.7|72.9|37.2|35.8|50.9|49.1|
|Foshan|240.3|86.2|154.1|35.9|64.1|387|136.8|250.3|35.3|64.7|
|Huizhou|111.8|46.6|65.2|41.7|58.3|205.8|88.6|117.2|43.1|56.9|
|Dongguan|500.9|87.3|413.7|17.4|82.6|658.3|125.1|533.3|19|81|
|<br>Zhongshan|109.9|23.8|86.1|21.7|78.3|176.2|43.8|132.4|24.9|75.1|
|Jiangmen|68.3|36.1|32.2|52.9|47.1|111.1|59.5|51.6|53.6|46.4|
|<br>Zhaoqing|37.4|27.4|10|73.3|26.7|58.8|38.2|20.6|65|35|
|Other cities|360|271|89|75.3|24.7|544.3|415.4|129|76.3|23.7|

Data source: China Bureau of Statistics, 2005; Guangdong Bureau of Statistics, 2012.

7
or children with literate/illiterate parents arise primarily because children are going to different schools—if we compare two children from different households studying in the _same_ school, these gaps are small and insignificant (Figure 1, Figure 2).

- For parental wealth, the learning gaps are never more than 15 points or 0.10 standard deviations (Urdu, Pakistan) once look at the difference between two children in the same school with different parental wealth and control for other child and parental characteristics in a multivariate regression.

- Similar patterns hold for differences by father’s literacy and mother’s literacy for Pakistan; in the case of India, the effect of father’s literacy drops to 0 for Mathematics and –5 points for Hindi, but mother’s literacy remains as important.

- In contrast, the adjusted and unadjusted gender gaps are very similar with girls performing worse in both subjects for India, and better in all subjects for Pakistan, particularly for Urdu and English.

The data from Pakistan offer a unique opportunity to shed some light on the differences between public and private schools in rural areas—a topic that is often debated and remarked upon in the popular press and media reports. One popular perception is that of private schools as “fly-bynight” operators that survive because parents are illiterate and easily influenced. In contrast to this view, the data show a large and significant difference in both the unadjusted and adjusted gaps for all subjects between private and public schools (Figure 3). These gaps are largest for English, where private schools outperform public schools by 150 knowledge points, or almost one standard deviation, but they are also large for Urdu (100 points) and mathematics (75 points). Surprisingly, there is almost _no decrease_ in the gap once we control for family wealth, parental literacy, the age and gender of the child and the village that the school is located in.

Three simple comparisons highlight the dramatic difference in scores between public and private schools:

- The adjusted gap between public and private schools in English is _12 times_ the adjusted gap between rich and poor children.

- The adjusted gap between public and private schools in Mathematics is _8 times_ the adjusted gap between children with literate and illiterate fathers.

- 17 -
economics literature, it also controls for price trends using time series econometric methods common in the study of finance. The paper subsequently maps out the intra-annual monthly variation in food and non-food consumption as observed in nationally representative household consumption surveys that have been conducted throughout the year in each of these three countries around 2009. The fact that each month, a nationally representative slice of the population has been surveyed, makes this possible and has been exploited here, even though each of the households has only been surveyed once. Finally, the seasonal evolution of the country’s (weighted) staple prices is juxtaposed against the intra-annual variation of consumption to explore connections between intraannual patterns in staple food price movements and consumption.

While essentially descriptive in nature, this paper fills an important empirical void in the current understanding of the evolution of food prices and consumption across seasons. By using time series econometric methods to control for price trends, by systematically looking across different food crops, markets (wholesale and retail) and agro-ecological settings in three different countries and using nationally representative household living standard measurement surveys from each of these countries, it updates and enriches the literature. To date, the literature is too often confined to studying seasonality in one country, or even case study areas within a country, and typically relies on unconditional estimates of seasonality, with only few recent studies also linking the current intraannual evolution of food prices in Africa to this of household consumption.<sup>7</sup> This update on the extent of seasonality in African livelihoods also helps guide the renewed public and private policy and investment interest in African agriculture.

The econometric analysis of monthly food price series shows that seasonal movements in maize wholesale prices explain 20 (Tanzania, Uganda) to 40 (Malawi) percent of their monthly volatility, and between 15 to 20 percent for other crops (Tanzania). Peak prices for wholesale maize are on average 30 (Tanzania, Uganda) to 50 (Malawi) percent higher than their monthly troughs and two to three times higher than the seasonal gaps observed for white maize at the South African

> 7 Chirwa, Dorward and Vigneri (2009) provide a useful exception for Malawi.

4
**Figure 3: Outstanding credit in Brazil**

**Figure 4: Credit by borrower type**

<!-- Start of picture text --> 60 Non‐<br>earmarked<br>50 for firms ‐<br>Earmarked<br>11 percent<br>firms ‐ 10<br>40 of GDP<br>Earmarked percent of GDP<br>30<br>20<br>Non‐<br>10 Free Earmarked earmarked<br>for for<br>0 households households<br>‐ 12 percent ‐ 13 percent<br>of GDP of GDP<br>Percent of GDP<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> _Source: Banco Central do Brasil_ Whereas private banks participate in the earmarked credit system, accounting for about 6 percent of the total, the bulk of it comes through public banks: Banco do Brasil, Caixa Economica Federal and BNDES. Banco do Brasil and Caixa Economica Federal dominate the rural credit market and the residential housing lending market, with 55 percent and 73 percent market share, respectively.<sup>4</sup> However, BNDES is the single largest intermediary of earmarked credit—directly and through on-lending via the large private banks—accounting for about half of total earmarked credit. Importantly, BNDES accounts for about threequarters of earmarked credit to firms or one-third of total credit to firms (Figure 5), making the state-owned development bank the single largest provider of credit to the productive sector.

BNDES expanded credit rapidly in the aftermath of the global financial crisis, initially as a countercyclical policy, but continued to expand as economic growth strengthened. During the recent recession BNDES shrunk, and thus it cannot be said to have been countercyclical. BNDES lending is heavily subsidized and based on the regulated long-term interest rate, the Taxa de Juros de Longo Prazo (TJLP). BNDES has primarily funded itself at the TJLP from the federal government and other funds, most importantly the constitutionally mandated Fundo de Amparo ao Trabalhador (FAT). The TJLP has systematically been well below the government’s funding costs and much more stable. The subsidies embedded in TJLP-based lending government borrowing and FAT funds were estimated at R$ 48 billion or 0.76 percent of GDP in 2016.<sup>5</sup> These implicit subsidies have since shrunk as market interest rates have declined, and a new long-term interest rate, the Taxa de Longo Prazo (TLP), has replaced the TJLP for new loans, providing for a lending rate based on the government’s cost of funding.

Given its prominent role in financial intermediation to the productive sector, BNDES is central to the question about the impact of earmarked credit on productivity. The next section reviews the extant empirical literature on the impact of government-owned banks in general and of BNDES in particular against the main theoretical paradigms regarding government intervention in credit markets.

> 4 Banco do Brasil had 63 percent market share in agriculture finance among households and an 18 percent market share among firms as of end-2016.

> 5 See Byskov and Clavijo (2017).

5
government failure associated with the choice of industries, as is argued by Pack and Saggi (2006), and the ways in which support is provided.<sup>4</sup> In this paper, we propose an “entrepreneur-led and government-assisted” approach to the establishment of new industries based on our own observation that there are a significant number of potentially competent entrepreneurs in low-income countries. Our approach is consistent with the theoretical proposition of Rodriguez-Clare (2007) that the best policy entails the direct promotion of clustering in the sector in which the country has a comparative advantage.

Our argument is based importantly on our 19 case studies of cluster-based industrial development in Asia and Africa, which are listed in Table 1. All these clusters have been voluntarily formed by private firms, including the four cases in China, without much assistance from the government. Thus, these studies in China are comparable to the other case studies. The studies of four industrial clusters in Japan and Taiwan, China are also comparable because they were developed before these countries became highly industrialized. Table 2 reports the major characteristics of these clusters in terms of the number of firms, average size of employment, the average schooling years of entrepreneurs, and whether their products were exported. Although the average employment size tends to be small in industrial clusters in SSA (cases 8-10, 15-17, and 19), the number of firms is large compared with clusters in Asia. As will be discussed in the next section in more detail, the majority of the private firms in developing countries are located in industrial clusters, which we define as the localization of firms producing similar and related products (e.g., final products and

> 4 A case in point is the failure of the import substitution industrial policy, which provided distorted profit incentives to enterprises (Bhagawati 1978; Krueger 1978; Balassa _et al_ . 1982).  Lin and Monga (2011) propose the simplified industrialization strategy to avoid such failures.

6
concentrated in only one region or city, the value of _Gi_ approaches 1. In other words, the higher the value of _Gi_ , the more concentrated the sector _i_ .

Consistent with the results above on specialization, the capital intensive industries are slightly more concentrated spatially (mainly in PRD) than the labor intensive industries. The declining concentration of resource intensive industries is consistent with the emerging structural diversification in the West Wing and Mountain Area.<sup>7</sup> As the resource intensive industries often locate close to the factor endowments, it is natural to observe a higher spatial concentration than the other two categories (figure 6).

**Figure 6: Spatial concentration of resource, labor and capital intensive industries in Guangdong**

<!-- Start of picture text --> 2006 2007 2008 2009 2010 2011 2012 2013<br>Year<br>All industries Resource intensive industries<br>Labor intensive industries Capital intensive industries<br>.9<br>.8<br>Gini Index .6.7<br>.5<br>.4<br><!-- End of picture text --> Data Source: Guangdong Bureau of Statistics, 2014.

# **5 Double transfer and policy suggestions**

Since the mid-2000s, possibly compounded by the 2008 global crisis, the GDP growth in Guangdong slowed down from some 15 % in the mid-2000s to some 8 % in the recent years. And the net migration inflow to Guangdong declined (figure 4). While disparity (measured by Gini) within Guangdong declined (figure 2), heterogeneity of growth performance within the PRD as well as among the 21 prefecture-level cities emerged. For example, Zhaoqing (PRD), Jieyang (East Wing) and Yangjiang (West) more than doubled their GDP per capita in 2008-13 compared with average annual growth of some 10 % in the province . This begs the questions of whether this showed sign of possible changes of the development pattern of the province,

> 7 The “M-shape” fluctuation of the Gini coefficient of the capital intensive and labor intensive industries in 20092013 is of interest for further research. One possibility is related to the effects of the effects of the fiscal stimulus on the industries.

11