rafmacalaba/data-use-mentions-extended huggingface dataset:
, for 2006 to 2014. Columns 1-3 provide the form number, its purpose, the number of observations and the record type. Columns 4 and 5 display the share of income tax lers covered by the dierent third-party declarations, distinguishing corporations and the self-employed. Column 6 displays the share of informative declarations which are matched with an income tax declaration. In the last row, the shares in brackets refer to the match rate with the monthly sales tax declarations for 2008-2014. The shares are calculated on the pooled data for all years/months. All declarations identify the reporter and taxpayer by their administration-internal anonymous tax ID, and provide information on the transaction amount, and (where applicable) the amount of the tax withheld. Amounts are accrued. Since January 2012, all declarations must be prepared using the DECLAR@7 software. Sanctions for noncompliance with the obligation to submit informative declarations are specied in the Codigo de Normas y Procedimientos Tributarios. All declarations are annual, except D153, which is monthly. D151 requires reporting of transactions >2.5 mio  annually with a transaction partner, and transactions of >50,000  annually for rent, commissions, professional services or interests. For D150, the withholding rate is 2% and 3% respectively for state and private purchases. For D153, the withholding rate is rm specic, following the schedule in Table II. For more information on the ling of informative declarations, see http://www.hacienda.go.cr/contenido/12997-declaraciones-informativas.
# **Table A1: Sensitivity of Consumption to Income for** **_<u>Open</u>_** **<u>and</u>** **_<u>Closed</u>_ Economies**

|**_All Countries_**|**XR**<br>**Practices**|**Current**<br>**Account**|**Capital**<br>**Account**|**Export**<br>**Proceeds**|
|---|---|---|---|---|
||**[1]**|**[2]**|**[3]**|**[4]**|
|Output× _Open_|**0.707**<br>[0.008]***|**0.744**<br>[0.011]***|**0.773**<br>[0.010]***|**0.793**<br>[0.008]***|
|Output × _Closed_|**1.064**|**0.941**|**0.887**|**0.877**|
||[0.018]***|[0.012]***|[0.011]***|[0.007]***|
|Observations|3,833|3,833|3,833|3,835|
|Countries|88|88|88|88|
|Chi-Square|233.1|103.7|36.48|68.73|
|Wald Statistics|0
# **II. Data**

Sampling from the universe of registered businesses and following a uniform stratified random sampling methodology, the core WBES uses a standardized survey instrument to benchmark the investment climate of individual economies across the world.<sup>6</sup> The survey contains sufficient information to allow for firm performance analyses. The surveys also report detailed information on firm employment, age, industry, ownership, legal status, and the number of establishments.

As noted above, our analysis is designed to explain variation in firm labor growth. We use the percentage growth in the number of full-time employees over the two years prior to the survey as the dependent variable in our regressions because that information was asked of firms in a wider sample of countries than, for example, information about sales growth. In addition, the information provided by firm owners regarding their number of employees is likely to be more accurate than information about their sales, especially for smaller firms that either do not keep good accounting records or are reluctant to fully report their sales (e.g., because of potential tax consequences). For the 89 countries whose firms enter our regressions, the WBES surveys were conducted at different points in time between 2000 and 2009.

We use three firm characteristics as controls in our firm growth regressions: age, size as measured by the current number of employees, and the percentage of shares held by foreign owners. Both firm age and number of employees enter the regressions in logged form. We do not have strong priors about how these characteristics affect firms‘ labor growth, though if older firms are more likely to have reached their equilibrium size, they might have slower growth than younger firms. Percentage growth in employees might be greater for smaller firms, as they are starting from a low base of employees, but larger firms may have advantages, especially in terms of access to finance, that make hiring additional workers easier. It is also unclear whether firms with high shares of foreign ownership would expand
methodological differences between how consumption is measured across the two sources of data. For example, consumption in national accounts, HFCE, is often derived as a residual from GDP. Second, even if they followed the same methodologies, the two series do not have the same scope. In addition to the consumption from households that is captured in the surveys, HFCE includes consumption from non-profit institutions (such as charities, religious organizations, trade unions, and political parties), consumption of financial service intermediaries, and imputed rents for housing (Datt and Ravallion 2002).

For India in particular, researchers have documented a third source of differences. They have noted that the gap between the two sources of data was small during the 1950s and 1960s, but the divergence between the series has grown since. For instance, Kulsehrestha and Kar (2005) note that the gap between the two sources of consumption data was 5 percent (with mean consumption from surveys being lower than that from national accounts) for fiscal year 1957/58, however, this gap had grown to 38 percent by 1993/94. The authors also note that the source of the increase in the discrepancy between consumption in national accounts and consumption in surveys are nonfood items. Similarly, Mukherjee and Chatterjhee (1974) find small differences between the two sources of data in the decade leading up to 1963/64, with consumption in surveys on average lower than consumption in national accounts. They also note that the surveys record a lower share of non-food items relative to national accounts. For food items the difference in consumption between the national accounts and surveys has been relatively small (Kulsehrestha and Kar (2005), Sundaram and Tendulkar (2003), Minhas (1988)).

Following the recommendations of the UN System of National Accounts starting in 1993, the CSO in India added a new item to consumption in national accounts, financial intermediation services indirectly measured (FISIM). FISIM is a measure of the value of financial intermediation. It is calculated as the
021|0.006|
|(% of total)|(0.090)|(0.040)|(0.032)|(0.030)|(0.031)|(0.035)|(0.037)|(0.038)|(0.052)|
|<br>Plots that Use Pesticide (% of total)|-0.290*|-0.257***|-0.095|-0.124|-0.210**|-0.116|-0.144|-0.179*|-0.467***|
||(0.164)|(0.095)|(0.086)|(0.090)|(0.094)|(0.110)|(0.105)|(0.093)|(0.164)|
|Constant|8.520***|2.216**|0.886|0.697|0.067|0.209|0.905|0.760|1.847*|
||(2.037)|(1.085)|(0.734)|(0.824)|(0.718)|(0.760)|(0.704)|(0.615)|(1.077)|
|Observations|2749|2749|2749|2749|2749|2749|2749|2749|2749|

49
!-- Start of picture text --> 38%<br>Personal savings 35%<br>21%<br>Savings from business/reinvested profits 37%<br>Friends or family other than spouse (loans or gifts) 6% 8%<br>6%<br>Spouse (loans or gifts) 7%<br>Commercial banks 4%<br>4%<br>Microfinance institutions 3%<br>3%<br>Input supplier 1% 4%<br>0% 10% 20% 30% 40%<br>% firms citing as main source<br>Main source of capital investments Main source of working capital<br><!-- End of picture text --> Source: Author calculations using survey of KUR borrowers Although most KUR borrowers were previously unbanked, there are some borrowers who previously accessed unsubsidized commercial lending and switched to KUR. Between business creation and receipt of

**14**
# **Figure 4 Labor moving to lowerproductivity services**

# **Figure 5 Relative labor productivity**

(ratio of sectoral to aggregate labor productivity) (share of total employment) <!-- Start of picture text --> Trade, restaurant, hotel<br>Non-market services<br>Finance, real estate, business services<br>0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>1978 1983 1988 1993 1998 2003 2008<br><!-- End of picture text --> Source: GGDC 10-Sector Database, Timmer et al. (2015); authors’ calculations.

<!-- Start of picture text --> Trade, restaurant, hotel<br>Non-market services<br>Financial, real estate, business services<br>10<br>8<br>6<br>4<br>2<br>0<br>1978 1983 1988 1993 1998 2003 2008<br><!-- End of picture text --> Source: OECD; authors’ calculations. Note: A higher STRI indicates a more restrictive regime.

In the decade after the global financial crisis, aggregate labor productivity growth in China weakened to 7.4 percent per year, from 9.0 percent in 1999-2008. A slowdown was observed across sectors but services experienced the largest decrease from an annual average of 8.1 percent in 1999–2008 to 4.6 percent in the most recent decade. Lower aggregate productivity growth has been partly due to China’s economic rebalancing away from foreign demand and industry toward higher domestic consumption and services. Today resources are being reallocated from agriculture and, since 2013 and at a much slower pace, from industry into services.

Following Baumol (1967), the decline in productivity growth as the economy matures is almost inevitable but, in China
.257*|2.853***|2.923***|
|productivity|(0.791)|(0.787)|(0.764)|(0.671)|(0.874)|(0.875)|(1.179)|(1.843)|(0.931)|(1.081)|
|Observations|103,839|103,013|89,059|71,779|74,087|78,411|97,011|73,647|100,740|82,939|
|Importer-Industry-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Author's analysis based on data described in text. Notes: See Table 3.
- The effect on GDP is in the long run (meaning a generation or so) because that is the amount of time

- required for physical capital to accumulate in response to higher employment rates (which will depend on financial market openness, domestic savings, etc.). More generally in standard Solow-Swan-style growth models, the effect of higher employment rates on economic growth has two parts:<sup>8</sup> 1. The _direct_ boost to GDP from more workers. This occurs relatively quickly, but is somewhere between half and three-quarters of the total gains, depending on the economy (the labor share of income ).

- 2. The _indirect_ boost to GDP, when higher female employment encourages capital accumulation. This occurs ββ

- relatively slowly, and typically accounts for between a quarter to half of the total gains (1- ).

- The “long-run” timeframe is needed to capture both direct and indirect effects. This is discussed more in Section 5. ββ

In more concrete terms, this means that the basic GEGI can be interpreted to answer two counterfactuals:

- Backward-looking: suppose a previous government had implemented policies (a generation ago) to bring female employment rates up to men’s, then GDPPC would be _GEGI%_ higher today than it is currently.

- Forward-looking: suppose the government enacted policies today to bring female employment rates up to men’s. Then GDPPC in the long-run (a generation or so from now) would be _GEGI%_ higher than otherwise.<sup>9</sup>

# **2.3 Data – Basic GEGI**

I calculate the GEGI using the employment-to-population ratio of 15-64-year-olds by gender, as in Equation 2. Employment data are available for around 185 countries. The data are taken from Pennings (2020), who in turn takes the survey data (not projections) from the ILO series “Employment-to-population by sex and age (%) – Annual”, Age (Youth,Adults): 15-64,
surveys. Exploring this topic in the Latin American context is important for several reasons. First, prior to the mid-2000s, large territorial income differences within the Latin American counties have been documented in the literature, including between Mexico’s North and South (Aroca, Bosch, and Maloney, 2005; Gonzalez Rivas, 2007), Colombia’s peripheral and core regions (Galvis and Maisel Roca, 2010 and 2012; Burger, Hendriks and Ianchovichina, 2022), and Peru’s coastal and internal areas (Escobal and Ponce, 2011). Many studies conducted in the first half of the 2000s also question the idea of territorial income convergence in the region (Aroca et al. 2005; Bosch, Aroca, Fernandez, and Azzoni, 2003) or show that convergence has occurred at a very slow pace (Serra, Pazmino, Lindow, Sutton and Ramirez, 2006; Soto and Torche, 2004).

Second, territorial differences in income may explain a sizable share of overall inequality in a country (Elbers, Lanjouw, Mistiaen, Özler and Simler, 2004). Using survey data for eleven Latin American countries for the 2000s, Acemoglu and Dell (2010) investigate the sources of labor income inequality within and between countries in the Americas. They show that the within-municipality differences in labor income are five times the between-municipality differences, which in turn are twice the size of the between-country differences in labor income in the region. Skoufias and Lopez-Acevedo (2009) also find large differences in poverty rates and household incomes across geographic regions,

> 1 In turn, poverty traps may influence territorial growth through increased social fragmentation, underinvestment, and political instability and conflict (Alesina and Perotti, 1996).

> 2 The countries are Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, Honduras, Mexico, Panama, Paraguay, Peru, and Uruguay.

2
cooperative model of bargaining and signaling. Predictions from the model are then econometrically tested with survey data collected from the same population that is the focus of the ethnographic interviews. Thus, our paper is unusual in two respects. Firstly, it introduces asymmetric information into a model of household bargaining where domestic violence is used as a signal of the husband's satisfaction with the marriage. Secondly, it combines economic and anthropological methods to rigorously examine violence within the context of Indian marriage markets employing data collected by the authors. We find that marital violence is not only closely linked to low dowry payments, but that a woman who comes from a wealthy family is more likely to be beaten by her husband in an effort to extract higher transfers from her parents.

The paper is structured as follows: Following this introduction Section 2 summarizes the cultural context and the ethnographic information, Section 3 outlines the theoretical model, Section 4 describes the data and lays out the econometric methodology, and Section 5 analyzes the empirical results, provides policy implications and concludes the paper.

# **2. Cultural Context**

To understand why the phenomenon of dowry violence is so widespread, it might help to outline the basic nature of Indian marriage markets4: Marriage is restricted to endogamous groups, i.e.: people are only permitted to marry within a well-defined set of families who make up their sub-caste. Sub-castes usually share a common characteristic that distinguishes them from other groups; for instance, they may all traditionally practice one occupation, or they may follow unique religious practices. The sub-caste we study in this paper is a potter community in the South Indian State of Karnataka. The craft of pottery was their primary source of employment for many generations, but today the majority are casual laborers. Yet, any individual from the community would face large social sanctions if s/he married someone who belonged to a nonpotter caste. Thus, while their caste affiliation may no longer reflect their current occupation, it continues to define them as a social group
# **1. Introduction**

Trade policy has changed fundamentally since the days of structural adjustment and economywide trade reforms. Partly in reaction to the uneven results of trade policy reforms, the focus has shifted to more targeted interventions aimed at reducing trade costs and addressing market failures that inhibit exports. Significant national resources and international assistance are now devoted to trade facilitation and export promotion, and the international development community has galvanized around a new ―aid-for-trade‖ (AfT) mantra as a means of helping low-income countries integrate into the global economy.

The environment in which trade-related assistance is provided has also changed. In times of fiscal austerity, taxpayers increasingly question the justification for large aid flows and, at the very least, demand results and accountability.<sup>2</sup> The development community has struggled to respond to these demands because there is surprisingly little evidence about what works and what doesn’t in the area of trade and industrial policies.

An authoritative survey of trade and industrial policy recently acknowledged that there is hardly any microeconomic evidence to guide specific trade interventions (Harrison and RodríguezClare, 2010). There are several reasons for the disappointing pace at which such evidence has been gathered. Trade policy research has been slow to respond to changing needs. Tariffs continue to occupy center stage in policy research, in spite of their declining importance as trade barriers, simply because they are easy to measure. The aid-for-trade community has in turn been slow to build a culture of rigorous evaluation. For instance, a review of 85 recent World Bank trade-related projects conducted by the authors revealed that only five of them included rigorous evaluation components. Worse, those few evaluations relied on crude before-after comparisons, which are known to be vulnerable to confounding influences. The ―knowledge-market failures‖ identified by Ravallion (2009) have also inhibited rigorous evaluations in the trade context: demanders of knowledge about the effectiveness of trade interventions have inadequate information about the quality of any potential evaluation, especially because there are so few good examples; project
balance wage adjustments with economic realities, even amid the uncertainties brought about by the pandemic. By mitigating the rise in labor costs for already financially vulnerable firms, the decision aimed to pave the way for a more rapid economic recovery.

**In summary, Romania's labor market demonstrated some resilience during the pandemic-triggered crisis, with unemployment rates recovering in 2021 after a moderate increase in 2020.** The wage landscape exhibited positive movement, underscored by a gradual rise in the mean gross wage and measured adjustments in the minimum wage to align with prevailing economic conditions. These factors ultimately contributed to the maintenance of stability and progress within the labor market.

**How high is the statutory minimum wage relative to the formal wage distribution? Analyzing the wage distribution in January 2020 suggests that the minimum wage's value aligns with the 25th percentile of the entire wage distribution** . Table 3 illustrates Romania's gross monthly wage

**Table 3. Distribution of gross monthly salaries in Romania, January 2020.**

|**Statistics**|**Value (RON)**|
|---|---|
|Mean|4,385|
|P1|129|
|P5|515|
|P10|1,054|
|P25|2,231|
|P50 (Median)|3,096|
|P75|5,202|
|P90|8,310|
|P95|10,928|
|P99|22,034|

_Source: Own estimates based on administrative tax data._ distribution as of January 2020. Our analysis encompasses solely those observations that possess complete information concerning gross salary and working hours, constituting approximately 89% of the total observations. Notably, the value of the minimum wage for full-time workers (2,231 RON) is approximately aligned with the 25th percentile of the overall distribution. This means a large proportion of the population earns an income below the minimum wage designated for full-time workers. However, a
We disregard the value of liabilities reported by the destination country because it is often missing, as issuers of tradable instruments typically do not know the location of the holders of these instruments.

In the CPIS database, countries report missing values and zeros. According to the CPIS guidelines, missing values correspond to data that are not available or were suppressed by the reporting country to preserve confidentiality. Because we do not have enough information to assess if the distinction between zeros and missing values is used consistently across reporting countries, we make no assumptions and treat these observations as missing values. One exception is a jump in the number of reported zeros in 2004 and 2005 compared with 2003 and 2006.<sup>26</sup> Most of the zero values in 2004 and 2005 correspond to country pairs for which there are missing observations before and after. We assume that if a country reports missing values for 2003 and 2006, the values for 2004 and 2005 are also missing. Hence, we replace the zero-valued observations in 2004 and 2005 with missing values for the country pairs for which the values for 2003 and 2006 are both missing.

# **A.3. Foreign Direct Investment**

The foreign direct investment (FDI) data come from the IMF’s Coordinated Direct Investment Survey (CDIS) and the United Nations Conference on Trade and Development’s (UNCTAD’s) Bilateral FDI Statistics. Similar to the IMF’s CPIS, the CDIS is a voluntary data collection exercise that assembles data on countries’ direct investment positions. UNCTAD’s Bilateral FDI Statistics provides FDI data collected primarily from national sources and supplemented with data from other international organizations and mirror data (from partner countries). We combine data from both databases because they cover different periods: the UNCTAD data span 2001–12, whereas the CDIS data cover 2009– 18. By combining data from both sources, we obtain FDI data covering the entire 2001–18 period.

> 26 The jump takes the following form. There are
captures the effect of the change in real total expenditures, which is and the second term captures the effect of change in real poverty lines, which is To dissipate any potential confusion, we now provide some additional details on how to compute P(z1, y0). To check whether household h is poor under P(z1, y0) we compare its 2015 total expenditure to the (deflated) poverty line that household h would face in 2017/18, where this poverty line is obtained by considering the 2017/18 price of a calorie and the food share that would have been imputed to household h by the regression (run on 2017/18 data).

# **Decomposition results**

The results of the decomposition exercise for all regions are presented in Table 6. At the national level, the poverty rate increases from 24.8 to 31 percent, which is an increase by 6.2 percentage points. This increase can be decomposed into a 9.9 percentage point increase due to the reduction in real total expenditures and a 3.7 percentage point reduction due to the decrease in real poverty lines. This decomposition reveals that the increase in poverty over the period would have been even higher if measured using an absolute measure of poverty, which keeps the real value of the poverty lines fixed over time. The hybrid nature of the official poverty lines yields a smaller increase in poverty rates, because the real values of poverty lines are smaller in 2017/18 than in 2015. As shown in Table 7, the real price of a calorie is, in all regions, smaller in 2017/18 than in 2015. One likely reason for these lower calorie prices has to do with the reduction in real total expenditures. As shown in Table 6, total expenditures per capita (expressed in 2015 prices) are about 8,600 Egyptian pounds in 2015 but decrease to below 7,400 Egyptian pounds in 2017/18. Hence, the reduction in households’ consumption possibilities provided an