rafmacalaba/data-use-mentions-extended huggingface dataset:
registration dates.<sup>11</sup> Both these checks revealed the matching process to be accurate at matching firms which had formalized – it was only a handful of cases where individuals had registered at different addresses that we had not detected.

# **5. Results**

We begin by discussing implementation of the different interventions, and use our survey data to examine whether this resulted in changes in knowledge or in inspection frequencies. We then examine whether these interventions changed the rate of business closure, and then ultimately, the impact on different aspects of formalization. To estimate treatment effects, we estimate versions of equations (2) and (3) for different outcomes. Since baseline data are not available for the inspector

> 11 The survey firm was only able to re-contact 1166 firms in doing this check (after accidentally inserting a skip pattern which skipped asking the ALF and CNPJ numbers for most firms in the full follow-up survey), and so given this large attrition rate we do not use these data for measuring the formalization outcome, but instead rely on administrative records, only using these data as a cross-check to ensure our administrative matching procedure is reliable.

17
14 uniform reliance upon higher levels of government to finance lower level spending. The majority (six) of the ten entries show increases in proportions with the large decline in the Thailand percentage attributable to the previously noted change in education finance.

Greater information, albeit less convenient for comparative purposes, can be obtained in studies of local government finance within individual zountries. Using such data sources eoes, of course, produce a potential bias since the very fact that transfers are sufficiently important to study will likely mean that the role of grants is greater than would be found in a random sample of countries. Nevertheless, 3ome interesting trends can be observed from such a nonrandom sample.

A variety of data that indicate the relative fiscal importance of grants and shared taxes in ten selected countries for different years and different levels of government are displayed in Table 3. The data for India reveal increased reliance upon shared taxes at the state level during the last half of the 1970s with approximately constant reliance upon grants. At the city level in India, grants and shared taxes are observed to constitute only slightly more than one-quarter of total revenues; however, this reliance upon transfers is greater in the smaller urban - jurisdictions. Furthermore, during the 1974/75 1979/80 period, real per capita grants to metropolitan local governments in India grew at an average rate of 9.3 percent whereas in the smaller cities the growth rate was 19.0 percent suggesting either a lack of local revenue buoyancy in smaller
Female**|**Male**|**Female**|**Male**|**Female**|
|**_Employment-populatio_**|**_n ratio_**||||||||
|Previous standards|0.569|0.528|0.617|0.528|0.667|0.442|0.687|0.608|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|19<sup>th</sup>ICLS standards|0.542|0.508|0.440|0.411|0.642|0.401|0.563|0.417|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|N|2,193|2,365|5,160|5,504|2,835|3,061|11,195|12,807|
|**_Share of employment i_**|**_n agricultur_**|**_e_**|||||||
|Previous standards|0.115|0.075|0.597|0.420|0.291|0.345|0.819|0.900|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.02)|(0.01)|(0.00)|
|N|<
are taken into account, FHH ─ whether headed by widows or not ─ have significantly lower per capita consumption expenditures. FHH have per capita expenditures that are on average 16 percent lower than male headed households; for households with a widowed female head mean per capita consumption is 19 percent lower than that of all other urban households. Again as in rural areas the significant disadvantage associated with female headship vanishes when marital status is taken into account. For households with widowed female heads in urban areas, the negative effects are substantially attenuated only when controls are added for education and physical capital, reducing the mean difference to a smaller but still significant 6 percent.<sup>16</sup> In sum, allowing for their smaller household size, I find that FHH are on average poorer than MHH only before controlling for marital status. The analysis suggests that it is FHH headed by widows who are the most impoverished, echoing the findings for Uganda and Zimbabwe (Appleton, 1997; Horrell and Krishnan, 2007). Given the very small sample of rural and urban divorced female heads, I am unable to say anything about this group, though there are indications that they may also be relatively disadvantaged.

A considerable drawback to examining the condition of women with this type of data and analysis is that it is limited to households with female heads. A majority of widowed and divorced women get reabsorbed into male headed households either through remarriage or residence. (The ELIM data indicate that 7 percent of women aged 15 and older who are not household heads are current widows.) Thus, an examination of FHH does not allow me to say anything about a large share of ever-widowed or ever-divorced women. To better investigate the welfare of these women, I now turn to the individual data in the DHS.

# **4. Evidence from individual welfare indicators**

We have seen the difficulty of making assessments of women‘s welfare with standard household level data sources. The paper now turns to a source of data rarely used
plies that a standard deviation increase in the tariff rate increases HHI by 1, 794 × 0.05 = 90 points. The coefficient on the interaction between tariff and fuel export share is positive β3<sup>fuel</sup> = 8, 696 (1,907) indicating that the positive association between tariffs and concentration is much stronger in fuel exporting economies: at the median, a 5 percentage point increase in the tariff rate (the standard deviation) and an 18 percentage point increase in fuel export share (the standard deviation) will raise HHI by about 8, 696×0.05×0.18 = 78 points. The interaction term for ores and metals exports is positive, though much smaller, and the interaction term for food exports is negative. The result is clear that tariffs contribute to import market concentration, but especially in fuel exporting economies.

To confirm that the interaction effect is due to greater tariff evasion in fuel exporting economies, we use a proxy for tariff evasion as the dependent variable in the same regression. We use the measure of Fisman and Wei (2004, note 6) based on import underinvoicing: where exports are total exports by all countries w of product i to destination country c (one of our importing countries) in year t reported in COMTRADE, and imports are as reported in our customs data. Results are reported in Column 3 of Table 3. The interaction coefficients follow a similar pattern as in Column 2, where the coefficient on the interaction between tariff and fuel export share is positive β3<sup>fuel</sup> = 0.36 (0.17), indicating greater evasion, but the coefficients on the interaction between tariff and ores and metals export share and food export share is negative.<sup>9</sup> Higher tariffs are associated with higher values of the proxy for tariff evasion in fuel exporting economies, but not in ores and metals or food exporting

> 9The import underinvoicing proxy for tariff evasion
states. To identify six parameters, 168 home production moments are calculated,<sup>29</sup> and nonparticipation rates are used. **To identify , the nonparticipation and unemployment proportions are mainly used. Setting this value to zero will place everyone in the labor market.** As mentioned before, the level of a spouses’ AA ss home productivity will cause different time allocation and different incentives for participating in the labor market. As the current specification has three home production types per gender, the model has nine household types. Without any heterogeneity, every couple will make the same decisions in the same labor market and children state. This means there is no variance in home production, conditional on the same labor market and children state. If the home productivity levels are similar, then there will be less variance in home production and maybe not a lot of heterogeneity on the time allocations <mark>from spouses that</mark> participate or not in the labor market. Thus, identification for γ<sup>HH</sup> comes from the mean and standard deviation of home production per gender. Additionally, the NP-NP couples helps MM_γγ WW_γγ , μμ , μμ to identify the proportion of high home producers.

**Given that in the Roma survey one person per household is surveyed regarding time usage, correlation of spouses’ times cannot be used in the current estimation; consequently, the identification of is compromised.** Hopefully, the 168 home production moments provide enough information to estimate accurately these two parameters. Future estimations must include both 33 θθ, ββ spouses’ time allocations. **All the parameters form the vector of parameters, .** To estimate the optimal vector of parameters (θ� ), the method of simulated methods of moments is used as follows: θθ SSM θ�= arg min (6) θ<sup>(QQ(θ) −qqss)′ WW−1(QQ(θ) −qqss),<
emissions. The extent of forest loss in the past 30 years has been unprecedented: on net, the world lost approximately 178 million hectares of forest area between 1990 and 2020 (FAO, 2020). In addition to an important environmental outcome in its own right, satellite-derived deforestation measures are also spatially explicit, local measures of environmental harm that circumvent many limitations associated with administrative data on environmental damage.

We test whether the inclusion of provisions in RTAs aimed at protecting forests and/or preserving biodiversity mitigate the ecological impacts of trade liberalization. We use a novel dataset with detailed information on the content of all RTAs in force and notified to the WTO between 1958 and 2018 (Monteiro and Trachtman, 2020) and combine these data with satellite-derived estimates of annual forest loss for 193 countries. From the set of RTAs that enter into force from 2004 and 2014, we estimate the predicted probability that an RTA includes a forest and/or biodiversity provision via the use of machine-learning techniques and a variety of RTA and group-level characteristics. We then use these predicted probabilities to match RTAs that include these provisions to similar RTAs without them to create an appropriate set of counterfactuals. We construct a panel dataset on total forest loss for all countries that join a given RTA and, using a triple-difference model, test whether aggregate increases in forest loss associated with RTA enactment are lower for RTAs with environmental provisions than those without. We find large, significant net increases in annual forest loss following RTAs without provisions (23%) and that the inclusion of these provisions entirely offsets the rise in forest loss observed in the counterfactual RTA groups after enactment. This effect is largely driven by changes to forest loss in tropical, developing countries with high levels of biodiversity–the locations where deforestation is of greatest concern.

We then investigate the mechanisms through which forestry and biodiversity provisions in RTAs mitigate environmental damage. Analysis using the same matched triple-difference approach indicates that environmental provisions limit agricultural land expansion that otherwise occurs following the entry into force of trade agreements. RTAs without
the included variables. Over the years, one strand of the literature has focused on panel data methods to draw inferences on causality. Studies such as Levine, Loayza, and Beck (2000) and Beck, Levine, and Loayza (2000) use a panel GMM estimator to establish a positive relationship between the exogenous component of financial development and economic growth, productivity growth, and capital accumulation.

Other researchers have tried to address causality using micro data at the industry and firm level. In an influential study, Rajan and Zingales (1998) argue that industries that are naturally more heavily dependent on external finance should benefit disproportionately more from greater financial development than industries that are not naturally heavy users of external finance. As discussed in section 3 of this paper, using data from the U.S. as a measure of industries‟ technological dependence on external finance, they find that financial development has a substantial impact on industrial growth, both through the expansion of existing establishments and formation of new establishments, by influencing the availability of external finance.

Demirguc-Kunt and Maksimovic (1998) use a different approach to examine the relations between external financing and institutions. They directly estimate the external financing needs of each individual firm by using a financial planning model. The model permits them to calculate how fast firms could be expected to grow without external finance but instead only with retained earnings and cash from operations. The extent to which firms are able to grow faster than this internally financed growth rate is a function of the dependence of firm‟s growth on external finance. They then show that the proportion of firms that grow at rates exceeding the nonexternally-financed rate is positively associated with stock market liquidity, banking system size and the perceived efficiency of the legal system.

In a similar vein, Wurgler (2000) also employs industry-level data and computes investment elasticity that shows that countries with higher levels of financial development are better able than countries with lower levels at increasing (decreasing) investment in growing (declining) industries. Love (2003) shows
2017, where children living in female headed households were more likely to be living in extreme poverty, the 2022 nowcasting shows the opposite, 20.7 percent of children living in male headed households are living in extreme poverty, versus17.4 percent of children in female headed households. There are four key explanations for this shift, namely 1. New surveys included from several countries in the Africa region which included different consumption measurement questionnaires; 2. The updated extreme poverty line ($2.15), resulted in significantly different results in child poverty rates based on sex of head of household in a number of countries; 3. The 2022 nowcasted analysis does not include information on sex of head of household for India (the 2018 survey dataset from India did not include this information), whereas the analysis in 2017 did include this information (based on a 2011 survey dataset for India); and 4. For the 2017 dataset for Nigeria all households were coded as being female headed, significantly skewing the results. This highlights the challenge of relying on self-reported headship to understand gender differences in poverty, as it can hide important differences in the types of households in which individuals live.

There are further household characteristics that provide critical information for influencing policies and programs, not least child poverty based on disability status, as well as more nuanced analysis to tease out gender differences – but unfortunately this is not possible at this time using data from the global harmonized database.

8
# **1. Introduction**

Productivity improvement is the foundation of economic growth and, consequently, development and well-being (Hall and Jones, 1999). Understanding the sources of economic growth is crucial to devise policies that efficiently and effectively promote economic development (Kim and Loayza, 2019). The topic has attracted substantial attention from policy makers and researchers alike. While physical and human capital accumulation are important drivers of economic growth, the evidence indicates that productivity growth accounts for most of the cross-country differences in income per capita growth (Easterly and Levine, 2001, and Caselli, 2005).

Measuring productivity is challenging. First, multiple definitions of productivity exist. Labor productivity (output or value added per worker) is commonly used. The measure does not impose extensive data requirement, and its simplicity allows for easy comparability over time and place (sectors and/or countries). However, it depends on more than efficiency: firms can raise labor productivity by investing in capital and not only by improving the use of their resources. Total factor productivity (TFP) is a more precise but harder to estimate measure. It provides an estimate of economic efficiency, accounting for the contributions from labor, capital, and possibly intermediate products. In practice, this implies that TFP is measured as a residual, carrying, therefore, the measurement errors of outputs, inputs, and production functions. Difficulties arise in measuring the contribution of the factors of production, starting with, for instance, estimating the value of capital and skill-adjusted labor. In the words of Hicks (1981), “the measurement of capital is one of the nastiest jobs that economists have set to statisticians,” and this applies to human capital as well. Data problems also extend to the measure of revenue and output. Measuring gross output relies on sales and inventory data, both prone to statistical errors. Moreover, price data are usually not available for different products at the firm, so that inferring quantities from revenues may be inaccurate as products and
- Fischer, G., Nachtergaele, F. O., Prieler, S., Teixeira, E., T ́oth, G., Van Velthuizen, H., Verelst, L. and Wiberg, D. (2012). Global agro-ecological zones (gaez v3. 0) - model documentation. Technical Report. FAO, Rome.

- Freire, S., MacManus, K., Pesaresi, M., Doxsey-Whitfield, E. and Mills, J. (2016). _Development of new open and free multi-temporal global population grids at 250 m resolution_ , Geospatial Data in a Changing World; Association of Geographic Information Laboratories in Europe (AGILE) edn. AGILE.

- Fritz, S., See, L., McCallum, I., You, L., Bun, A., Moltchanova, E., Havlik, P. and Co. (2015). Mapping global cropland and field size. _Global change biology_ , **21,** number 5, 1980–1992.

- Fujii, T. (2010). Micro-level estimation of child undernutrition indicators in cambodia. _World Bank Economic Review_ , **24,** number 3, 520–553.

- Fujii, T. and van der Weide, R. (2020). Is predicted data a viable alternative to real data? _World Bank Economic Review_ .

- Gibson, J., Le, T. and Kim, B. (2017). Prices, engel curves, and time-space deflation: Impacts on poverty and inequality in vietnam. _World Bank Economic Review_ , **31,** 504–530.

- Goldberger, A. S. (1962). Best linear unbiased prediction in the generalized linear regression model. _Journal of the American Statistical Association_ , **57,** 369–375.

- Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova,
been out of work and school for the last six months, and have been residing in the National Capital District (NCD)/Port Moresby for at least one year. To verify eligibility, representatives of the survey firm also conducted interviews with community leaders, and declared applicants ineligible if a consensus emerged that they have misrepresented any of these three criteria.<sup>9</sup> As the program has become better known over time, greater numbers of youths began to apply to the program. To balance the interests of competing groups, each neighborhood group was allocated a certain number of places, and the program strived for a 50:50 ratio of men to women applicants. Participants were selected randomly from the pool of eligible applicants, separately by neighborhood group and gender.

- (iv) Successful applicants were given the Eligibility Baseline Survey (EBS) that collects a richer set of information compared to ESS (see Annex 2 for a more detailed description of various surveys). They were then offered a 40‐hour Basic Life Skills for Employment Training (BLST) administered over the course of one week. Each BLST participant took a literacy and numeracy test, which was used to determine the quarter of each intake that is then streamed into the SDES component. Usually, many more than a quarter of the participants are literate, so only those with top scores and strong participation in the BLST were allowed to proceed to the next stage of the SDES component.

- (v) Participants were enrolled in either Component 1 (YJC) or Component 2 (OJT) of the Project. Participants in Component 1 (the YJC) were offered to undertake two months (40 days) of public works, such as cleaning the sides of roads. When youths completed the two‐month employment, they completed the program. This component of the Project was offered to approximately 12,500 eligible urban youths over the course of seven years, equivalent to approximately 80 percent of total program participants.

- (vi)
# **1. Introduction**

Agriculture remains the main economic activity in Timor-Leste. The sector provides subsistence to a majority (72 percent in 2007, 52 percent in 2014, Figure 1) of the population, and accounts for over 90 percent of total exports (mainly coffee).<sup>2</sup> Yet it represents only 11.4 percent of the GDP. This suggests that, given the number of people employed in the sector, most farmers in the country remain locked in low-productivity, subsistence agriculture.

**Figure 1: Distribution of the population by poverty status and sector, 2007–2014**

<!-- Start of picture text --> 90 83<br>80<br>72<br>70 62 63<br>60 55<br>52<br>48<br>50 45<br>38 37<br>40<br>28<br>30<br>17<br>20<br>10<br>0<br>Poor Non-poor Total Poor Non-poor Total<br>2007 2014<br>Agriculture Non-agriculture<br>Population (%)<br><!-- End of picture text --> _Source:_ 2007 and 2014 Timor-Leste Survey of Living Standards A majority (83 percent in 2007, 63 percent in 2014) of the poor depend on agriculture for their livelihood (Figure 1). Across the three regions of the country, agricultural households tend to be concentrated in the west and east (61 percent in 2014); and among agricultural households, poverty tends to be more prevalent in the west than in the east or the center (Figure 2). Because most of the poor depend on agriculture, the sector is widely regarded as a major channel through which poverty can be reduced in Timor-Leste. This notion is supported by historical evidence that agriculture has played an integral role in successful poverty reduction elsewhere in Asia, and that growth in
8.7|50.1|9.0|60.1|
|Second 10|2.1|8.9|2.7|34.5|
|Third 10|3.1|14.8|3.5|39.3|
|Fourth 10|4.7|12.3|6.0|24.6|
|Fifth 10|5.8|11.2|8.1|23.0|
|Sixth 10|10.1|8.1|10.0|24.1|
|Seventh 10|13.4|9.6|15.4|22.8|
|Eighth 10|18.2|13.0|15.7|21.3|
|Ninth 10|22.5|10.2|18.7|14.8|
|Top10|11.4|19.6|10.9|19.1|
||100.0||100.0||

Notes: Households ranked into decile groups on the basis of predicted per capita household expenditure (excluding remittances). For those households receiving internal remittances (from Guatemala), column (2) shows the percent of total per capita household expenditure (including remittances) coming from internal remittances. For those households receiving international remittances (from USA), column (4) shows the percent of total per capita household expenditure (including remittances) coming from international remittances. See equation (2) and text for predicted income equation.

Source: Calculated from Guatemala 2000 ENCOVI Survey.