rafmacalaba/data-use-mentions-extended huggingface dataset:
Most of the documented research on marriage payments has examined India, where the widespread dowry phenomenon has become associated with the rise in dowry prices, with important implications for the welfare of women (Rao 1993; Edlund 1999). Although this practice was prohibited as early as 1961, it is impossible to eliminate and has spread and increased.

In a study of rural South India, Srinivasan (2005) provides strong evidence in favor of a preference for sons over daughters. This nonpreference for daughters is justified by the large amounts of dowry a woman’s must pay to the groom’s family. However, women in this study seem to be in favor of this practice, which is primarily considered a way to create bonds among families and to provide protection and not in connection with growing evidence of dowry-related violence. Pande and Astone (2007) use data from the National Family Health Survey of India for the 1992–1993 period to show that dowry is one of the determinants of son preference in rural India.

Comparatively little research has explored marriage transfers in the rest of south Asia, though several studies point to dowry payments now occurring in Bangladesh, Pakistan, and Sri Lanka. Anderson (2000) analyzes the causes of dowry in contemporary Pakistan. He documents three possible explanations for the use of such a payment: a transfer to the groom’s family to acquire a high-quality groom, a compensation payment to the groom for receiving the bride (who is considered a liability for the family because she does not contribute to the household income), and a premortem inheritance to the daughter, which is preserved throughout marriage. The empirical results support the groom-price explanation for urban areas, whereas dowry is considered to be inheritance in rural areas.

An article by Nasrin (2011) examines contemporary dowry payments in Bangladesh and considers dowry to be an asymmetry in gender equity that favors men. Her study of three villages shows that girls are often considered a burden for the family because of the cost of dowries. Parents seem to
before rebounding between 2016 and 2020. Even in the first period of overall employment decline, “new” exporters increased employment, but not enough to offset employment declines in the “never” exporting group. During the employment increase between 2016 and 2020, overall employment increased in the “always”, “new”, and “never” groups, while it decreased in those that stopped exporting. New exporters appear to be the main driver of job creation.

# **5. Empirical Analysis**

## **_5.1 Empirical Approach_**

We are interested in estimating the relationship between exports and employment. All our models are estimated using the 2013-2016-2020 firm panel data.

As a first step, we consider the characteristics which make a firm more likely to export. We begin by estimating the extensive margin to identify what the main drivers of a firm being an exporter are.

exporter are. ′ ′ e e e e i i i i (5.1) ii ii where, e e e e i is a dummy indicating a firm = αα+ XX γγ+ ZZ δδ+ DD + μμ + εε _i_ ’s export status in year _t_ . We alternatively consider the determinants of being a “new” exporter (one that did not export in 2013). We control for a selection of firm characteristics ( i i ), and year dummies ( ). Firm characteristics ii included in i are those that we expect would impact the firm’s export sales and employment. XX , ZZ DD Exclusion restrictions in XX i are characteristics that we would expect to impact a firm’s decision to export but not have any direct impact on its employment levels. Our choices of control variables ZZ are guided by the empirical literature (Dixit and Pal 2010, Abbey et al. 2017, Marques 2015).

To control for firm productivity, we use as proxies the value of machinery and labor costs. The value of machinery is taken as the book value of all machinery
2<br>2<br>0 0<br>-2 -2<br>Central Asia South Caucasus Central Europe Western Balkans<br>2000-10 2011-21 2022-30 2000-10 2011-21 2022-30 2000-10 2011-21 2022-30 2000-10 2011-21 2022-30<br>2000-10 Contribution 2011-21 2000-10 Contribution 2011-21<br>2000-21 2000-10 2011-21 2022-30 2000-21 2000-10 2011-21 2022-30<br><!-- End of picture text --> Sources: Penn World Tables; World Bank Note: ECA = Europe and Central Asia; EMDEs = emerging market and developing economies. RUS = Russian Federation; UKR = Ukraine; TUR = Türkiye. TFP = total factor productivity. Shaded area indicates forecast. GDP weights are calculated using average real U.S. dollar GDP (at average 2010-19 prices and market exchange rates).

A. Bars show period averages of annual GDP-weighted averages. Markers denote the median region, with orange whiskers showing min-max ranges across regions.

B. Figure shows the percent deviation between the Global Economic Prospects report forecasts released in June 2022 (World Bank 2022c) and January 2020 (World Bank 2020c). For 2023, the January 2020 baseline is extended using projected growth for 2022.
heterogeneity. Findings suggest that country-level characteristics—including public spending efficiency and capital scarcity—are relevant, as well as macroeconomic factors. Methodological and sample differences may also explain some variation in results across studies. That said, the evidence on the impact of public investment to growth in EMDEs is relatively meagre, owing in large part to associated data constraints. Thus, further research—including that provided in this paper—is needed to better inform how public investment can stimulate economic growth and mobilize private investment.

# **3. Methodology and data**

## **3.1. Data and sample**

The database used in the estimation of the macroeconomic effects of public investment draws from several sources. Public investment, private investment, and capital stock data are from the International Monetary Fund’s Investment and Capital Stock Dataset (IMF 2021a). Public debt data are retrieved from the World Bank’s Fiscal Space Database (Kose et al. 2022). Public investment efficiency data are obtained from several sources: IMF (2021b) Fiscal Monitor database, Devadas and Pennings (2018), and Dabla-Norris et al. (2012). Potential output data are sourced from Kilic Celik et al. (2023). GDP and inflation series are from the IMF’s World Economic Outlook database. Labor productivity and total factor productivity data are from Penn World Table 10.01. The resulting dataset comprises up to 129 EMDEs, spanning the period 19802019 (see Appendix table A1 for the sample composition).<sup>12</sup>

## **3.2. A new approach to identify public investment shocks based on cyclically adjusted public spending**

In this paper we apply a new approach to identify public investment shocks. The methodology builds on the work of Alesina et al. (1998), Alesina and Ardagna (2010), and related studies that assess the macroeconomic effects of changes in cyclically adjusted fiscal variables. Conceptually, the approach is consistent with the literature
sectoral GDP<br>3.5 4.0<br>3.0 3.5<br>2.5 3.0<br>2.5<br>2.0<br>2.0<br>1.5<br>1.5<br>1.0<br>1.0<br>0.5 0.5<br>0.0 0.0<br>1980 1985 1990 1995 2000 2005 2010 2015 1991 1996 2001 2006 2011 2016<br>Government Business Infrastructure Housing Business sector Whole economy<br><!-- End of picture text --> _Source:_ Author estimates using NBS data The above estimates include an estimate for the stock of R&D capital. The stock of this type of capital is, however, quite small. Using the revisions to the estimates GFCF published in 2016 and the estimate of the average depreciation rate for R&D expenditures in the United States, which Li and Hall (2017) put at 28% per year, the stock of R&D capital can be estimated for China. Over the past two decades, the stock has risen sharply but still amounts to only slightly below 4% of GDP, accounting for about 21⁄2% of the capital stock in the business and government sectors (Figure 9).

17
based procurement records for a sample of 10,319 tenders from the same district-level procuring entities between FY2011-12 and FY2016-17. FY2011-12 serves as the baseline and FY2017-18 as the endline year, by which all three agencies exclusively procured through e-procurement. A survey of 600 procurement officials in the three agencies served to capture their perceptions of the new e-procurement system and to measure management practices. We also surveyed 600 firms that participated in public works contracts of the three agencies across 16 districts, to understand their transaction costs and perceptions about the e- procurement system.

10. Identifying the causal effects of e-procurement is challenging because e-procurement has been introduced and phased in at the discretion of implementing authorities and their choices may be strategic, for example trying to minimize the exposure of corrupt practices. The central identification challenge in our non-experimental setting is that the district-level procuring entities (PE) in our sample had the discretion to choose when and for which tenders to adopt e-procurement. This poses the risk of bias due to i) selfselection of PEs into e-procurement, ii) strategic sorting of tenders into e-procurement within each PE, iii) potential spillovers between electronic and paper-based tenders (for example because officials focus their attention on electronic tenders), and iv) potential learning effects to take place for procuring entities adopting the new system at different speeds. We combine Coarsened Exact Matching with fixed effects regressions in order to approximate causal effects. We run two parallel specifications. First, we match tenders within procuring entities but across years. Second, we match tenders within the same years, but across procuring entities that have similar baseline dependent variable values. In each of these cases, we look at the differences in the dependent variables of tenders least likely to be influenced by spillover effects. Specifically, we compare manual tenders administered by procuring entities that run mainly manual tenders with electronic tenders administered by procuring entities that run mainly electronic tenders.

11. We find overwhelmingly positive impacts of e-procurement on a wide range of outcomes tracked. First, e-procurement improves open and fair access
_Services PTAs_ Similarly to section 5, we construct four mutually exclusive binary variables identifying four quartiles of the services-depth distribution, and flexibly estimate the impact on the share of services VA in services and total exports. We obtain heterogeneous effects, with deeper services-PTAs driving our findings: for both services and total exports, PTAs in the fourth quartile have a substantially larger and more significant effect on the share of services VA from PTA partners. Note that in these models we also control for the effect of the non-services-depth of PTAs, which we take into account with the number of non-services provisions in the agreement: these latter appear to have a positive impact on the within-PTA share of services VA, although the coefficient is a significant for services exports only.

# **6.2 Linkages to Investment and Mode 4**

In this section we investigate the heterogeneous impact of specific policy configurations on the share of services VA from PTA partners.

We focus on two of the seven policy areas described in table 1, namely the provisions relating to the ‘other disciplines’ included in services PTAs (Policy Area 5), and the provisions relating to liberalization of so-called ‘Mode 3’ (M3) and ‘Mode 4’ (M4) services supply (Policy Area 6).

As these are the two policy areas that most relate to GVCs and investment, we explore the existence of asymmetric effects between countries at different levels of income. Patterns of multinationals’ (MNE) activity might in fact vary substantially depending on the country of origin of the MNE, and on where foreign suppliers are located. For this purpose we split our sample in high- and low-income countries<sup>23</sup> and create three

> 23As the TiVA data exploited in this part of the work only feature 64 high-, upper-middle-, and lowermiddle-income countries (as classified by the World Bank), we group upper- and lower-middle income countries in one group, which we define ‘low-income’.

40
>extended harvest<br>crops|Yes|Evidence suggests phone surveys can<br>improve data quality. Auxiliary diaries<br>preferable.|Ceballos et al. (2020);<br>Kilic et al. (2021)|
|Crop harvest|No|Standard crop harvest module at crop-plot<br>level likely too heavy for phone surveys in<br>most countries. Crop-level harvest module<br>implemented in e.g. Georgia.<br>Harvest diaries have also been tested but<br>phone surveys unlikely to be suitable to<br>administer such diaries.<br>Cropcuttingrequires in-person visit.|Deininger et al. (2012)|
|Market prices|Yes|Market price data collection appropriate at<br>the community or market level but farm-<br>gate prices at household/farm level likely<br>difficult.|Hermosilla (2017);<br>Hoogeveen et al. (2014)|
|Shocks and<br>damage caused<br>by shocks|Potentially|Phone surveys can be used to reduce recall<br>length which may improve quality of the<br>data. Auxiliary diaries may be beneficial for<br>data collection.|Knippenberg et al. (2019)|

# 4.2. Respondent behavior and effects

The characteristics of respondents in agricultural surveys may differ from national populations in ways that are material for designing and implementing phone surveys for agricultural data collection.

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(0.013)|(0.013)|
|N fertilizer (Q3)|0.1205***|0.1132***|0.1075***|
||(0.013)|(0.013)|(0.013)|
|N fertilizer (Q4)|0.1195***|0.1151***|0.1128***|
||(0.014)|(0.014)|(0.014)|
|N fertilizer (Q5)|0.1417***|0.1457***|0.1469***|
||(0.017)|(0.016)|(0.017)|
|N fertilizer (Q6)|0.1874***|0.1910***|0.1956***|
||(0.020)|(0.019)|(0.019)|
|N fertilizer (Q7)|0.2000***|0.2046***|0.2091***|
||(0.019)|(0.019)|(0.019)|
|N fertilizer (Q8)|0.1494***|0.1577***|0.1705***|
||(0.019)|(0.019)|(0.019)|
|N fertilizer (Q9)|0.1096***|0.1108***|0.1314***|
||(0.023)|(0.023)|(0.023)|
|N|94724|94724|94724|
|Year FE|Y|Y|Y|
|Grid cell FE|Y|Y|Y|
|Country Trends|Y|
5The China Urban Labor Survey comprises three repeated cross-sectional surveys (2001, 2005, and 2010) conducted in five large urban areas (and a sixth was added in 2010). The survey includes local resident and migrant samples drawn from a geographic-based sampling frame in each city based on dwellings (which are not necessarily household units).

6Unlike the Rural to Urban Migration in China and Indonesia data source, researchers using these data have a clear understanding of the population from which the sample is drawn. These data sources aim to draw a representative sample of rural households, which will provide a representative characterization of rural migrant labor as well.

7The share of the migrant workforce losing employment with this shock depends on how one defines a migrant, and even different publications from the National Bureau of Statistics using the same data source define them differently. At year-end 2008, Chen (2010) projects that the migrant labor force was 225 million rural registered residents, but this number includes migrants working for any amount of time outside of home villages. Migrants may be more or less permanent. Other work using the rural household survey of the National Bureau of Statistics suggests that there were 142 million rural migrants employed long term (for more than six months) outside their home villages at the end of 2008 (NBS 2010).

8An exception is Wang et al. (2009), which suggests that workers from poorer regions shifted more quickly back into agriculture or local nonagricultural employment, so that by June 2009, there was only a 4 percent drop in employment among workers from China’s poor areas.

9The China National Rural Survey (CNRS) dataset includes information from 58 randomly selected villages in six provinces of rural China representative of China’s major agricultural regions (it is “national” under the assumption that these six provinces are nationally representative). The provinces are Hebei, Hubei, Lioaning, Shaanxi, Sichuan, and Zhejiang. Within province, sampling was stratified by county income quintile (as measured by gross value of industrial output
liberalization should be captured in the data we have. Third, we focus on the provisions most relevant to protection against forest loss. Given the high dimensionality of the RTAs, it is possible there are other provisions that are also important for forest conservation or other provisions that may help or hinder the effectiveness of the provisions we examine. We believe further exploration of these other provisions to be a fruitful avenue for future work. Finally, while we develop a useful framework that attempts to account for the endogeneity of trade agreement content, our propensity score matching approach fundamentally relies on the assumption that treatment selection (provision inclusion) occurs on observable characteristics of trade agreements, not unobservable factors that may also impact subsequent changes in net forest loss after enactment. We argue that our framework provides an improvement to existing literature in this arena, but we concede that the nonrandom nature of the content of RTAs will always raise challenges for causal identification. This motivates our country-level overlapping event study that at least partially circumvents this limitation.

Our work provides critical insights into the effectiveness of environmental provisions in mitigating forest loss arising from trade liberalization. The inclusion of such provisions on average _offsets_ forest loss increases observed in trade agreements without environmental provisions. While environmental provisions appear to be an effective tool for mitigating environmental harm arising from trade liberalization, the costs and benefits are not born equally by all signatories. In particular, we find evidence that these provisions function, at least partially, by mitigating growth in agricultural land area. Encouragingly, provision inclusion does not appear to preclude agricultural gains from trade at the intensive margin. These findings suggest potential avenues forward in negotiation of future trade agreements to encourage sustainable growth.

27
<br>transport transport<br>equipment):<br><!-- End of picture text --> Source: World Bank, WITS database.

China is unquestionably the dragon in the global marketplace for low-tech products today. In the four product categories in which it has the highest global market concentration, China‘s share of global exports exceeds 35 percent—and in travel goods and handbags is close to 50 percent (table 4). Most impressive is the rate at which its share has grown. For travel goods and handbags, for example, its share in 1976 was only 1.6 percent.

> 20 For further discussion of these two points, see Lin (forthcoming b) _._

21 The estimation by Angus Maddison (2010) shows that China‘s per capita income in 2008 was 21 percent of U.S. per capita income (measured in purchasing power parity). This income gap indicates that there is still a large technological gap between China and the industrialized countries. China can therefore continue to enjoy the advantage of backwardness before closing up the gap. China‘s current status relative to the United States is similar to that of Japan in 1951, Korea in 1977, and Taiwan, China, in 1975. Annual GDP growth was 9.2 percent in Japan in 1951–71, 7.6 percent in Korea in 1977–97, and 8.3 percent in Taiwan, China, in 1975–95. China‘s development strategy after the reform in 1979 is similar to that of these three economies (Lin forthcoming a).
. This is problematic, since household surveys are notable for their under-reporting of income. From the perspective of the poor, it is the market for unskilled labor that is most important. The poverty impacts of trade policy often hinge crucially on how well the increased demand for labor in one part of the economy is transmitted to the rest of the economy via increased wages, increased employment or both. Further econometric research aimed at discriminating between competing factor mobility hypotheses is urgently needed.

31
TABLE A7: Impacts of text messages on ECD Outcomes, by treatment variations

||(1)<br>ECD full sample|(2)<br>ECDyoung|(3)<br>ECD Old|
|---|---|---|---|
|Panel A: By treatment variation (thematic focus)||||
|Nutrition & Stimulation|-0,00|-0,05|0,02|
||(0,04)|(0,07)|(0,05)|
|Nutrition|-0,02|0,02|-0,05|
||(0,04)|(0,08)|(0,05)|
|Stimulation|0,02<br>(0,04)|0,00<br>(0,08)|0,02<br>(0,05)|
|Observations|2485|774|1711|
|Panel B: By treatment variation (targeted caregiver)||||
|Mother|0,03|0,05|0,02|
||(0,04)|(0,08)|(0,05)|
|Father|-0,01|-0,01|-0,01|
||(0,04)|(0,08)|(0,05)|
|Mother and Father|-0,02|-0,06|-0,02|
||(0,04)|(0,07)|(0,05)|
|Observations|2485|774|1711|

_Note:_ All outcome variables are standardized using the mean and standard deviation of control households. In column 1, the dependent variable is the first principal component of the 4 Denver sub-components for children below 36 months and the first principal component of all 7
<!-- Start of picture text --> Attrition rate on self-employment data<br>2 4 6 8 10 12<br>Survey round<br>SE data - Treat SE data - control<br>Employment data - Treat Employment data - control<br>.15<br>.1<br>.05<br>Cumulative attrition in round<br>0<br><!-- End of picture text -->

# **Appendix 6: Does treatment change which firms have workers?**

Appendix Table 6.1 compares the baseline characteristics of the subsample of treatment and control firms which have a paid employee in round 4 (during the intervention), and in round 12 (the last survey round). This comparison allows us to see the extent to which the wage subsidy changes which firms have paid workers. We see little selectivity in terms of owner’s education, raven, and digit span scores. During the intervention, the firms hiring workers who would not have done so if they were in the control group appear to be smaller (lower profits, lower total assets) and outside Colombo. However, by the time of the last survey, the profit difference has disappeared, and only the geographic difference remains.

53
(0.66)|(0.22)|(-1.36)**|**_(-0.95)_**|(1.17)|
|fiscal|0.002|-0.0002|-6.94e-06|0.0002|0.0001|0.00005|
|decentralization<sup>3</sup>|(0.43)|(-0.95)|(-0.02)|(I.I1)|(1.50)*|(0.58)|
|Constant||1|||4523|157|
|R-square|0.67|0.75|0.78|0.86|0.85|0.93|
|Observations|25|25|25|25|25|25|

Fiscal decentralization measured separately as the percentage local government contribution to construction, maintenance, administration, and road provision in general in the first, second and third order respectively.

Shaded portions significant at 90% level of confidence.

* significant at 85% level of confidence.

** significant at the 80% level of confidence.

# **_Comparison_** _of_ **_Estimated Resultsfrom Korea and the Cross-Country Panel_**

Table 13 gives a summary view of the impact of decentralization on performance for Korea. Overall, our results for Korea are consistent with our cross-country panel estimations. Despite some sign switches for individual coefficients, the estimated models predict the same point for the optimal level of decentralization.

The results for decentralization of administrative functions (figure 9) have the same pattern as those obtained using the cross-country panel. The only difference is that the results of the cross-country panel seem to overestimate the possible gains from 30