rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Women’s participation in the economy of Bangladesh has increased steadily since the country’s independence. Female labor force participation (FLFP) grew from just 4 percent in 1974 to 8 percent in 1984, before jumping to 24 percent in 2000. This trend has continued over the past two decades, partly attributable to a ready-made garment sector that accounts for 80 percent of total export earnings and employs 4.4 million people, including more than 3 million women (Fathi 2019, World Bank 2012a).<sup>1</sup> Yet, at 36 percent in 2017, FLFP in Bangladesh is still below the world average of 47 percent (World Bank 2020). In addition, FLFP has consistently been lower in urban areas than in rural areas and has declined in recent years despite economic growth and increases in women’s education. Between 2010 and 2017, urban FLFP fell from 34.5 to 30.8 percent, while rural FLFP increased from 36.4 to 38.5 percent (BBS 2018).

Many explanations have been offered for the low FLFP, ranging from underreporting of women’s home-based work in labor market surveys (Mahmud and Tasneem 2011) to social and cultural factors that traditional labor market studies omit, such as norms about women’s safety and seclusion (Solotaroff et al. 2019, Kotikula et al. 2019, Węziak-Białowolska et al. 2020). It is also widely recognized that women’s employment is mediated by their roles in the reproductive and care economy and by the patriarchal norms that define their role in society (Khatun et al. 2014). In Bangladesh, women’s care responsibilities increase substantially after marriage and childbirth. Marriage is universal, and the average age at first marriage, 15.9 years (BDHS 2014), has increased very slowly over the past few decades (Zahangir 2015, Shuler and Rottach 2010). The average age of first childbirth is 18; women in the age group 20–24 years have the highest childbearing rate; and more than 90 percent of women give birth to at least one child by age 49 (Roy and Hossain 2017). In other words, many working-age women take on substantial caregiving responsibilities early in their lives and likely face a tradeoff with their other roles in society, including their economic participation.

For women with young children, lack of childcare support can lead to withdrawal from the labor market, especially in patriarchal societies where men are considered breadwinners and women caregivers. Globally, an estimated 606 million working-age women consider themselves unavailable for employment or not seeking a job because of unpaid care work, as opposed to only 41 million men (Gromada et al. 2020). Even if women reenter the labor force after childbirth, they might end up in low-

> 1 Kabeer (2000), Mahmud (2010), Kabeer and Mahmud (2004), and Khosla (2009) have documented the hazardous working conditions in the ready-made garment sector in Bangladesh and argued that women’s disproportionately large employment in this sector is a sign of vulnerability, as it is partly driven by the belief that women are unlikely to take collective action against low pay, occupational health, safety, poor working conditions, and job insecurity.

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control for city characteristics, city FE, and province-year FE. We report two specifications, one without and one with additional controls of pre-turnover and post-turnover dummies to allow for differential patterns of under-reporting for those years. The results are in Table 11.

In columns 1 and 2, we find a large and statistically significant effect for the triple interaction term, implying that the incumbent would be more likely to under-report performance in turnover years when the performance could be attributable to the predecessor and when the predecessor had already been demoted. This result supports our conjecture.

# **8. Conclusions**

Bureaucrats are supposed to deliver performance, and they also have a large say in measuring performance. In this paper, we provide evidence that GDP, the key performance measure of local top bureaucrats, is sometimes subject to manipulation, which could be explained by political incentives stemming from the tournament competition system. Covering the period 2001-2013, we use nighttime lights to infer real GDP growth and detect GDP manipulation in cities. Our empirical analysis shows a robustly strong inverse U-shaped relationship between the extent of GDP growth over-reporting and the tenure of city party secretaries, with the peak stabilizing around the fifth year, their decisive career year. This is consistent with the career-incentive hypothesis that city leaders, when faced with an increasing chance of missing the last train for promotions, are more likely to exaggerate economic performance. Interestingly, the association between bureaucrat career incentives and performance manipulation exists only in regions with a weak institutional environment. We find several pieces of evidence in support of winning tournament competition as being the key motivation for GDP manipulation: when political rivals within the province over-report GDP more, the city over-reports more; this political rival effect is especially pronounced for economically comparable rivals; when the degree of competition among political rivals is particularly fierce, city party secretaries begin to overreport GDP earlier. Additional analysis suggests that over-reporting indeed leads to higher chances of promotion, but the ratchet effect and the chance to scapegoat predecessors limit the extent of overreporting.

Our findings have several implications. First, when bureaucrats’ performance is subject to manipulation, performance incentives could lead to performance manipulation rather than real performance improvement. Second, much can be done for improving the quality of GDP and other government economic statistics. Since GDP is at the core of formulating macroeconomic policies, skewed local growth numbers can mislead the upper-level governments into making wrong policies, resulting in substantial welfare loss. It thus makes sense to make the statistical agency independent 30
implementation dates). Using representative household-level employment data, we find that longer exposure to women PRs significantly increases women’s LFP. We explore possible channels through which this effect may arise. We find evidence of both a direct channel (whereby women PRs allocate more employment to women in public works) and indirect channels (whereby women leaders at the district level facilitate/encourage greater female LFP through the public goods they provide).

Our findings are directly relevant to the current debate on policies for empowerment of women in developing countries. They are also especially pertinent to address the problem of declining labor force participation of women in India in the last decade – despite the huge expansion in economic activity since the liberalization of the economy in the 1990s. They are encouraging, inasmuch as they suggest that public works programs may be a natural way for women PRs to leverage their political power for women’s economic empowerment through a greater supply of job opportunities. They also provide some suggestive evidence that women leaders may be catalysts for greater female demand for LFP because of their emphasis on the provision of public goods preferred by women.

In future work, we will aim to gather additional relevant data to refine this analysis. For one, we will compare the effects of women leaders on female LFP at the within-district level, before versus after the reform. We will also modify our analysis of how public good provision by women leaders affects women’s LFP, by obtaining initial levels of public good provision at the district level for an initial year prior to the enactment of the 73rd Constitutional Amendment. We will also examine how our current findings on public good provision and female LFP are modified by using district (rather than state)-level exposure to women leaders as our instrument.

Overall, our findings so far justify a broader discussion of the ways in which political participation of women can be leveraged to enhance their economic empowerment. Understanding these channels is a key to increasing female LFP and unleashing India’s potential in the global economy.

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Many countries which are net physical exporters in aggregate are net importers at the neighbor level; the correlation in net imbalance between the two measures is only 40 percent. At one end, Rwanda, Kenya, and Senegal are net importers from the world, but net exporters to their neighbors. On the other end, Brazil, Estonia, and Guyana are net exporters to the world, but net importers from their neighbors. In the case of landlocked countries such as Rwanda and Senegal, this discrepancy points toward international imports that must travel through neighboring countries. Since these flows also contribute to imbalances between landlocked countries and their neighbors, this suggests that analysis using country-country level remains inaccurate.

**Corridor-Level Imbalance** At an even finer level, I turn to evidence for geographic imbalances along specific routes or corridors, especially for landlocked countries. Using ports data, Teravaninthorn and Raballand (2009) find that Chad exports are only 30 percent of imports. In a frictionless world where all fronthaul trips are loaded, this suggests a lower bound of 35 percent of trips must be empty. In a more extreme example, Uganda has an imbalance of 10 percent, and the authors suggest this generalizes to other landlocked countries especially in the Sahel. Focusing on the corridor level, Annequin et al. (2010) find that exports from Burkina Faso to the port of Tema, Ghana, vary between 25 percent and 40 percent of imports. This imbalance is reflected in prices, where the average cost of shipping a 20’ container from Tema to Ouagadougou was $4,800, over 2.5 times as expensive as the cost of shipping in the reverse direction.

Looking across multiple corridors into a single country, Nathan Associates Inc. (2013) also finds strong export-import imbalances for countries in West Africa. For example, landlocked Burkina Faso trades through four main foreign ports: Abidjian, Tema, Lomé, and Cotonou. In Panel A of Figure 8, I plot the physical trade imbalances by port for the top four ports used by Burkina Faso. Across all ports, overall exports fall significantly short of imports. In 2011, Burkina Faso exports through Abidjian and Lomé were a quarter of its imports, and the ratio for Cotonou and Tema were even more extreme. A back-of-the-envelope estimate would imply that, for example, trucks traveling between Burkina Faso and Abidjian must be empty at least 37.5 percent of the time. Panel B presents a similar picture for Mali and five main ports of Abidjian, Tema, Lomé, Cotonou, and Dakar. While trade along some ports, such as Tema, appears balanced, demand imbalances are strong along the routes to the other ports. This suggests that aggregating empty trips to the country level may miss the presence of demand imbalances along particular corridors. The same figure for Niger would be even more extreme: As 60 to 80 percent of Niger’s exports by value is dense uranium, its export tonnage is less than 1 percent of its import tonnage to each of its main ports of Abidjian, 18
12 data, _R_ in eqn (5) reduces to the Atkinson-Plotnick measure of reranking, _RAP_ (cf. Van de Ven et al. 2001).<sup>12</sup> If people’s rankings in the distributions of _y_ 0 and _y_ 1 are identical, _RAP_ is equal to zero. A policymaker eager to see mobility would therefore want to see a _nonzero_ value of _R_ Some argue for a broader interpretation of mobility as the evolution over time of people’s incomes over time. Van Kerm (2004) has recently shown that reranking is just one component of this broader interpretation of mobility, with mobility so defined being decomposable into three components: a ‘growth’ component (the mobility that would have occurred if all incomes had risen by the same proportion); a ‘dispersion’ component (the mobility that is attributable to a change in the way total income is distributed); and an ‘exchange’ component (the mobility caused by reranking).<sup>13</sup> Dispersion-related mobility gets reflected in nonzero values of _V_ and _H_ , which from this broader perspective would presumably be regarded as a good thing— irrespective of whether vertical or horizontal redistribution is involved, and irrespective of whether any vertical redistribution is pro-poor or prorich. However broad one’s view of mobility, then, it is clear that the pursuit of mobility may entail substantial amounts of inequality-increasing redistribution.

Both of these views—that it is _∆ G_ that ultimately matters, or that it is mobility that ultimately matters—are hard to square with the fact that many persist in expressing reservations about economic growth and trade reforms on the grounds they are often divisive and create inequality. This despite evidence that—at least on the face of it—seems to suggest this is not typically the case. These critics often point to the ways in which people with different characteristics seem to benefit differently from economic growth, those with assets—including

> 12 This is equal to _G_ 1 minus the concentration index for _y_ 1, people being ranked by _y_ 0 in the latter and by _y_ 1 in the former. 13 Applying his decomposition to data for Belgium, Germany and the USA, Van Kerm finds considerable evidence of mobility broadly defined, especially in the US, and finds that the exchange component is by far the most important driver of mobility in all three countries.
20. **Operational Involvement** . As part of the ESW, a quick review of water-related Project Appraisal Documents (PADs) approved since 2000 has been made using the water project database of the World Bank‘s Independent Evaluation Group (IEG). Searches were carried out with terms such as ―water reallocation‖ and ―water transfer‖. A few project documents mentioned that water reallocation would be considered as a measure of demand management. This was the case, for example, in the India Maharashtra Rural Water Supply and Sanitation Project approved in 2004, with the aim of reducing the need for additional infrastructure for water supply and sanitation. The Brazil Ceara Integrated Water Resources Management Project approved in 2000 included a study of intersectoral water allocation in selected watersheds. The project that most explicitly addressed and supported irrigation water reallocations to higher value uses, including environmental and industrial uses, was the China Xinjiang Turpan Water Conservation Project approved in 2010. With the introduction of basin-wide monitoring and management of consumptive use, it intended to ensure that reallocations would take place in terms of consumptive use, and that compensation programs for farmers giving up water supplies were formulated and implemented.

21. However, beyond the relatively few projects that explicitly mention water reallocations and transfers, there are many others that may implicitly affect allocations to irrigated agriculture, including projects supporting inter-basin or even inter country transfers and involving dams. Also projects promoting efficiency improvements in irrigation systems by lining canals and providing incentives for switching to improved irrigation technologies with the aim of moving the ―saved‖ water to other purposes would fall into this category. Furthermore, many urban water supply projects may tap into water resources that directly or indirectly have benefited other (including agricultural) uses downstream. Often project preparation does not consider at all, or does not try to qualitatively or quantitatively assess, the economic costs and benefits associated with such reallocations.

22. Quantitative assessments are more often carried out when projects provide additional water for irrigated agriculture. As part of the economic analysis, the direct economic benefits (DB) resulting from the additional water to irrigated agriculture are estimated. However, the project documents often do not discuss in any detail the assumptions and model specifications on which the estimates have been based. Many of the issues pointed out later in this paper for assessing FDB related to the transfer of irrigation water _away_ from agriculture also apply the case for assessing DB associated with the transfer of water _to_ agriculture.

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- Train, K.: 1986, _Qualitative Choice Analysis: Theory, Econometrics, and an Application to Automobile Demand_ , MIT press.

- Urga, G. and Walters, C.: 2003, Dynamic Translog and Linear Logit Models: a Factor Demand Analysis of Interfuel Substitution in US Industrial Energy Demand, _Energy Economics_ **25** (1), 1–21.

- Webster, M., Paltsev, S. and Reilly, J.: 2008, Autonomous Efficiency Improvement or Income Elasticity of Energy Eemand: Does it Matter?, _Energy Economics_ **30** (6), 2785–2798.

- Woodland, A.: 1993, A Micro-econometric Analysis of the Industrial Demand for Energy in NSW, _The Energy Journal_ **14** (2), 57–89.

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efficiency (see e.g. Mengistae and Honorati, 2007).<sup>12</sup> From an econometric point of view, productivity is effectively identified as the residual from the production function. This means that any measurement error or omitted variable bias will be misattributed to differences in enterprise productivity. Despite having a rich and detailed dataset, we cannot control for potentially important variables such as price differences and land usage.<sup>13</sup> In principle, such endogeneity problems could be remedied by means of instrumental variable estimation but, unfortunately, credible instruments are not available in our data.<sup>14</sup> However, Loening et al (2008) check for the potential impact of endogeneity by using local rainfall as a proxy for unobserved demand for non-farm goods (on the grounds that most buyers of non-farm products in rural markets are farmers, and their income is heavily influenced by rainfall) and show that it is a significant when included in the production function, yet does not lead to marked changes in the coefficients on the factor inputs for the rural sample – indicating that the magnitude of potential endogeneity biases is likely to be small. Moreover, there is very little variation in inputs over time, despite frequent shocks. This suggests non-farm enterprises in rural areas do not change their inputs very much, in response to demand shocks. Thus, endogeneity may not be such a big problem, in terms of leading to bias in the OLS estimator. In addition, the available evidence suggests that a rich set of controls may go a long way towards controlling for unobserved productivity. For example, using data on mostly small manufacturing firms in Ghana, Söderbom and Teal (2004) report instrumental

> 12 Mengistae and Honorati (2007) have investigated the impact of the investment climate on allocative efficiency and find that shortage of land, financial constraints, and problems of tax administration affect young and small firms more than larger ones and, consequently, have helped  incumbent firms protect their market shares.

> 13 If prices for outputs and inputs diverge between rural and urban areas, our production function estimates may give misleading estimates of true underlying productivity, presumably overestimating the productivity of urban enterprises, since  price levels in urban areas are usually higher than those in rural areas. As pointed out by Eberts and McMillen (1999), failing to control for land may result in a downward biased estimate of the ruralurban productivity gap, since urban firms are more likely to be constrained for space than are rural firms. A priori, it is thus difficult to sign the omitted variable bias.

> 14 A less well-documented endogeneity problem that may hamper the identification of production function is that of selection bias (see e.g. Ackerberg et al., forthcoming). Loening et al. (2008) show that such bias is not a problem for rural  firms.

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benefit ( ∆ ) as in Equation (3) exceeds the fixed cost, ceteris paribus. Because the net benefit increases with X, we have the following hypothesis, which can be tested using transaction level data rather than HS8 product level data.

**HYPOTHESIS 3** The tax evasion motive through indirect exports should be higher for larger transactions (larger X).

# **4. Empirical strategy and data**

# **4.1. Empirical strategy**

We aim to examine empirically how export VAT taxes affect indirect exports. The dependent variable is the indirect export share, measured in both conservative and liberal versions as defined later in Section 4.2. For explanatory variables, we have the data on export VAT tax rates (t-r), the shares of exports by firm ownership types, and trade regimes (normal or processing trade). As control variables, we include prc_sh (share of exports under processing trade with imported materials), taking share of exports under normal trade as the omitted variable; we also include collpriv_sh (share of exports by collective and private firms) and for_sh (share of exports by foreign firms), with the share of exports by SOEs as the omitted variable. The share of processing trade can also help to control for the value-added content of a product as processing trade with imported materials is usually associated with lower share of domestic value added contents. For simplicity, all of the other minor types of _trade regimes and ownership types are left out of our data._ Because China applies the same VAT rebate rate on a product exported to all of the destination countries, we are able to test the tax evasion hypothesis using data at the product level without importing countries’ information. Our benchmark regressions will be carried out at the HS 8-digit product level (HS8) to test the first two hypotheses, using the following specification: indirect _ shi   0   1(t  r)i  Z γ  ei (5) where the subscript i denotes the Harmonized System (HS, version 2002) 8-digit product<sup>24;</sup> Z

> 24 The Harmonized System, as agreed by the World Customs Organization, is standardized at the

> 6-digit level (HS6). HS8 here refers to the local tariff-line implementation of the HS by China Customs, which differentiates products and duties at a finer level.

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# **Quantifying Spillover Effects from Large Farm Establishments: The Case of Mozambique**<sup>**1**</sup>

Klaus Deininger<sup>†</sup> , Fang Xia<sup>‡</sup> , Aurelio Mate<sup>♯</sup> , Ellen Payongayong<sup>♦</sup> †World Bank, Washington DC ‡ Central University of Finance and Economics, Beijing ♯Ministry of Agriculture, Government of Mozambique

- ♦Michigan State University, East Lansing MI

**JEL Codes Q15, Q18, O19, H41**

> 1 We thank P. Olinto and C. da Maia for encouraging us to undertake this work and continued support, the Ministry of Agriculture for making data available, and D. Byerlee, M. Childress, and G. Myers for insightful comments. The views presented in this paper are those of the authors and do not necessarily represent those of the World Bank, its Executive Directors, or the countries they represent or any of the other institutions they are associated with.