rafmacalaba/datause-encoder-data huggingface dataset:
|AR-RENEWABLE ENERGY IN RURAL MARKETS|XiaopingWang|
|---|---|
|AR-RENEWABLE ENERGY IN RURAL MARKETS|XiaopingWang|
|AR-RENEWABLE ENERGY IN RURAL MARKETS|XiaopingWang|
|AR-RENEWABLE ENERGY IN RURAL MARKETS|XiaopingWang|
|AR-RENEWABLE ENERGY IN RURAL MARKETS|XiaopingWang|
|AR-RENEWABLE ENERGY IN RURAL MARKETS|XiaopingWang|
|AR-RENEWABLE ENERGY IN RURAL MARKETS|XiaopingWang|
|AR-RENEWABLE ENERGY IN RURAL MARKETS|XiaopingWang|
|AR-RENEWABLE ENERGY IN RURAL MARKETS|XiaopingWang|
|AR-RENEWABLE ENERGY IN RURAL MARKETS|XiaopingWang|
|AR-RENEWABLE ENERGY IN RURAL MARKETS|XiaopingWang|
|AR-RENEWABLE ENERGY IN RURAL MARKETS|XiaopingWang|
|AR-RENEWABLE ENERGY IN RURAL MARKETS|XiaopingWang|
|ANDHRA PRADESH DISTRICT POVERTY INITIATIVES PROJECT|Parmesh Shah|
|ANDHRA PRADESH DISTRICT POVERTY INITIATIVES PROJECT|Parmesh Shah|
|ANDHRA PRADESH DISTRICT POVERTY INITIATIVES PROJECT|Parmesh Shah|
|ANDHRA PRADESH DISTRICT POVERTY INITIATIVES PROJECT|Parmesh Shah|
|ANDHRA PRADESH DISTRICT POVERTY INITIATIVES PROJECT|Parmesh Shah|
|ANDHRA PRADESH DISTRICT POVERTY INITIATIVES PROJECT|Parmesh Shah|
|ANDHRA PRADESH DISTRICT POVERTY INITIATIVES PROJECT|Parmesh Shah|
|ANDHRA PRADESH DISTRICT POVERTY INITIATIVES PROJECT|Parmesh Shah|
|RAJASTHAN DISTRICT PRIMARY EDUCATION PROJECT|Prema Clarke|
|RAJASTHAN DISTRICT PRIMARY EDUCATION PROJECT|Prema Clarke|
|RAJASTHAN DISTRICT PRIMARY EDUCATION PROJECT|Prema Clarke|
|RAJASTHAN DISTRICT PRIMARY EDUCATION PROJECT|Prema Clarke|
|RAJASTHAN DISTRICT PRIMARY EDUCATION PROJECT|Prema Clarke|
|RAJASTHAN DISTRICT PRIMARY EDUCATION PROJECT|Prema Clarke|
|RAJASTHAN DISTRICT PRIMARY EDUCATION PROJECT|Prema Clarke|
|RAJASTHAN DISTRICT PRIMARY EDUCATION PROJECT|Prema Clarke|
|Second National HIV/AIDS Control Project|Mariam Claeson|
|Second National HIV/AIDS Control Project|Mariam Claeson|
|Second National HIV/AIDS Control Project|Mariam Claeson|
|Second National HIV/AIDS Control Project|Mariam Claeson|
|Second National HIV/AIDS Control Project|Mariam Claeson|
|Second National HIV/AIDS Control Project|Mariam Claeson|
|Second National HIV/AIDS Control Project|Mariam Claeson|
|Second National HIV/AIDS Control Project|Mariam Claeson|
|Second National HIV/AIDS Control Project|Mariam Claeson|
|Second National HIV/AIDS Control Project|Mariam Claeson|
|ROAD MAINTENANCE AND DEVELOPMENT|Marianne Kilpatrick|
|ROAD MAINTENANCE AND DEVELOPMENT|Marianne Kilpatrick|
|ROAD MAINTENANCE AND DEVELOPMENT|Marianne Kilpatrick|
|ROAD MAINTENANCE AND DEVELOPMENT|Marianne Kilpatrick|
|ROAD MAINTENANCE AND DEVELOPMENT|Marianne Kilpatrick|
|ROAD MAINTENANCE AND DEVELOPMENT|Marianne Kilpatrick|
|ROAD MAINTENANCE AND DEVELOPMENT|Marianne Kilpatrick|
|ROAD MAINTENANCE AND DEVELOPMENT|Marianne Kilpatrick|
- Dijkstra, L. and Poelman, H. (2014). A harmonised definition of cities and rural areas: the new degree of urbanisation. Regional Working Paper. European Comission Directorate-General for Regional and Urban Policy.

- Donaldson, D. and Storeygard, A (2016). The view from above: Applications of satellite data in economics. _Journal of Economic Perspectives_ , **30,** number 4, 171–198.

- Ebener, S., Murray, C., Tandon, A. and Elvidge, C. C. (2005). From wealth to health: modelling the distribution of income per capita at the sub-national level using nighttime light imagery. _International Journal of Health Geographics_ , **4,** number 1.

- Elbers, C., Fujii, T., Lanjouw, P., Ozler, B. and Yin, W. (2007). Poverty alleviation through geographic targeting: How much does disaggregation help? _Journal of Development Economics_ , **83,** number 1, 198–213.

- Elbers, C., Lanjouw, J. and Lanjouw, P. (2003). Micro–level estimation of poverty and inequality. _Econometrica_ , **71,** number 1, 355–364.

- Elbers, C., Lanjouw, J. and Lanjouw, P. (2005). Crime and local inequality in south africa. _Journal of Economic Geography_ , **5,** 101–118.

- Elbers, C., Lanjouw, P., and Leite, P. (2008). Brazil within brazil: Testing the poverty mapping methodology in minas gerais. Policy Research Working Paper 4513. The World Bank.

- Elbers, C. and van der Weide, R. (2014). Estimation of normal mixtures in a nested error model with an application to small area estimation of poverty and inequality. Policy Research Working Paper 6962. The World Bank.

- Elvidge, C.C., Hsu, F. C., Baugh, K. E. and Ghosh, T. (2013). _National Trends in Satellite Observed Lighting: 1992-2012_ , O. Weng (ed), Global Urban Monitoring and Assessment Through Earth Observation edn. CRC Press.

- Engstrom, R., Hersh, J. and Newhouse, D. (2021). Poverty from space: Using highresolution satellite imagery for estimating economic well-being. _World Bank Economic Review_ .

- Ferreira, F. H. G., Chen, S., Dabalen, A. L., Dikhanov, Y. M., Hamadeh, N., Jolliffe, D. M., Narayan, A., Prydz, E. B., Revenga, A. L., Sangraula, P., Serajuddin, U. and Yoshida, N. (2015). A global count of the extreme poor in 2012 : data issues, methodology and initial results. Policy Research Working Paper 7432. The World Bank.

54
# **Table C.22: Main results using husband’s data**

## **Panel A: Effects on promotions and income (Pooled waves)**

||(1)<br>Wife is<br>supervisor(Dummy)|(2)<br>Husband’s personal<br>income(Taka)|(3)<br>Household<br>income<br>(Taka)|
|---|---|---|---|
|selected|0_._26***|287_._84|475_._79|
||(0_._05)|(778_._84)|(1322_._48)|
|N|350|350|350|
|Nominated mean|0_._06|11427_._69|23436_._92|
|Nominated SD|0_._24|5098_._70|7692_._13|

## **Panel B: Effects on wife’s decision-making power**

||Decisi|on-making (Index)||
|---|---|---|---|
||Pooled waves|Wave 1|Wave 2|
|selected|_−_0_._03|_−_0_._06|_−_0_._14|
||(0_._09)|(0_._18)|(0_._11)|
|N|349|174|175|
|Nominated mean|0_._09|0_._06|0_._13|
|Nominated SD|0_._65|0_._76|0_._50|

## **Panel C: Effects on assignable goods expenditures and remittances (Pooled waves)**

||Women|Girls|Men|Boys|Remittances|
|---|---|---|---|---|---|
|||(All outcomes as % i|n household inco|me)||
|selected|2_._21*|0_._13|0_._27|0_._78*|5_._13***|
||(1_._14)|(0_._59)|(0_._53)|(0_._46)|(1_._50)|
|N|350|109|350|100|349|
|Nominated mean|5_._71|1_._53|4_._31|1_._37|6_._31|
|Nominated SD|4_._52|1_._63|3_._58|1_._51|9_._39|

Notes: Selected is a dummy variable indicating whether the female sewing-line operator is quasi-randomly selected for the promotion program. Nominated is a dummy variable indicating whether the female sewing-line operator is an eligible, shortlisted runner-up that was quasi-randomly not selected for the promotion program, and represents the comparison group for the selected group. Standard errors clustered at the individual level in parentheses. Controls chosen using pdslasso from wife’s demographic controls and baseline bargaining measures, their squares, and factory, enumerator and month dummies. Where available, the baseline outcome is included in the amelioration set. The sample in Panel C, Column 2 (Column 4) is limited to those respondents with a daughter (son), or a female (male) child in the household.

**Table C.23: Phone surveys—Effects on women’s decision-making and expenditures**

||(1)<br>Involvement in<br>decisions(Index)|(2)<br>Women’s and girls’<br>expenditures<br>(Taka)|(3)<br>Remittances to<br>wife’s family<br>(Taka)|(4)<br>Men’s and boys’<br>expenditures<br>(Taka)|
|---|---|---|---|---|
|selected|0_._14*|518_._90 **|884_._20 **|_−_88_._51|
||(0_._08)|(253_._02)|(443_._16)|(205_._43)|
|N|1610|1610|1605|1610|
|Nominated mean|_−_0_._25|1777_._57|2322_._79|946_._42|
|Nominated SD|0_._98|2511_._91|4681_._98|2206_._44|

Notes: Selected is a dummy variable indicating whether the female sewing-line operator is quasi-randomly selected for the promotion program. Nominated is a dummy variable indicating whether the female sewing-line operator is an eligible, shortlisted runner-up that was quasi-randomly not selected for the promotion program, and represents the comparison group for the selected group. Standard errors clustered at the individual level in parentheses. Controls chosen using pdslasso from demographic controls and baseline bargaining measures, their squares, and factory, enumerator and survey round dummies.

87
# I. Introduction

Government procurement is a major market, accounting for 12% of world GDP in 2019 (Bosio et al. 2022). The COVID-19 pandemic and the ongoing Ukraine-Russian Federation crisis have further emphasized the role of governments in large sectors of the economy, including health, medical equipment and defense.<sup>1</sup> As a result, the use and allocation of government procurement funds have come under increased scrutiny due to their sheer size and importance.

Governments often use public procurement to pursue socioeconomic objectives, such as supporting the development of small and medium-sized local firms or promoting environmentally friendly practices. However, these objectives can come at a cost to economic efficiency, and can result in protectionist policies that limit the participation of foreign suppliers in procurement auctions. These policies can take various forms, including inflating the price bid by foreign firms to provide a preference margin to domestic bidders, requiring products and services to have a certain level of local content, mandating the use of specific technologies, or imposing strict licensing requirements.

The implementation of discriminatory regulations against foreign suppliers can result in significant barriers to trade and can increase the prices at which governments source goods and services. This raises important questions about the extent to which governments around the world implement domestic regulations that discriminate against foreign suppliers and the effects of these regulations on trade. To what extent do governments around the world implement domestic regulations that discriminate against foreign suppliers? And what are the effects of these regulations on trade openness and cross-border government procurement flows?

This paper aims to explore these questions by examining rules that could be characterized as protectionist in government procurement and their impact on cross-border trade. By shedding light on these issues, we contribute to the debate on the role of protectionist policies in government procurement and the broader implications for international trade.

To begin our analysis, we introduce a novel dataset about domestic laws that discriminate against foreign suppliers of goods and services in government procurement. For 140

> 1 According to reports from the U.S. GAO (Government Accountability Office 2021), the U.S. federal government spent $35 billion on medical supplies and pharmaceuticals in fiscal year 2020 alone to treat COVID-19 patients (half of the total increase in federal spending between 2019 and 2020). The Chinese government has spent over $21 billion on COVID-related expenditures between 2020 and 2022. The U.S. government has spent over $19 billion on defense under authorizations following the Ukraine-Russia crisis.

1
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37
in a pattern that is somewhat contradictory to that examined in developed-world economies (for example, Haltiwanger (2012) and Eslava and Haltiwanger (2014)) on the relative role of SMEs in job destruction and firm dynamics. This pattern is further sensitive to the cut-off used: while the pattern in figure 2 is more ambiguous, figure 3 displays a pattern whereby both the vast majority of exiting firms and job losses are constituted among firms with 250 or fewer employees.

How do these estimates compare to those reported in similar studies? Although strict comparison is difficult owing to diversity in method and data used and periods covered in the various studies, it may be worth comparing the exit rates reported in similar studies where available. Pavcnik (2002) finds an annual rate of 5% exit for manufacturers in Chile over the period 1979 to 1986. Meanwhile, Bartelsman et al (2004) report firm entry and exit rates for a broader set of mostly OECD countries, based on either census or sample data. For Colombia and Chile, based on manufacturing census data, they report exit rate of about 4% per annum over the period 1989 to 1999<sup>11</sup> . Although their estimate for Chile is roughly similar to ours, the estimate for Colombia is twice what we report in this paper. They also report estimates for Argentina over the period 1997 to 1999, revealing an exit rate of about 4.5 percent per annum for broader sector of the economy (and 4% for manufacturing firms), almost four times the estimates we report in this paper. Their estimates for Mexico of 4% exit rate per annum over the period 1989 to 1999, however, is almost half what we report in this paper. Similarly our estimates for Romania and Slovenia are roughly comparable to what they report for these two countries over the period 1996 to 1999. Using a panel data of about 200 firms per country, Söderbom et al. (2006) report exit rates of about 5% for Kenya and about 9% for Tanzania over the period 1994 to 1999. Although our estimate for Kenya is far lower, that of Tanzania is almost similar. All in all, the rough comparison reveals that our estimates of exit are slightly more conservative, as we discuss above.

# **<u>Estimation of predictors of firm exit</u>**

We analyze the predictors of firm exit by estimating the following model from equation (1), where x is a function of various firm- (F), market- (L) and country-level factors (C, R), written as: Resulting in a general estimation of:

> 11 Period for Colombia survey is between 1989 to 1997

16
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21
of the borrower’s debt obligations and hence his net worth; through various contractual frictions, the change in net worth influences the expected return to lending (for more on this transmission mechanism, see Bernanke and Gertler, 1995). We show that this transmission mechanism is mitigated as investment projects become more liquid. The key to this result is as follows: absent an interbank market for investment projects, banks face a tradeoff between investment and liquidity. Consequently, changes in the expected return to investment (for example, through changes in the net worth of the borrower) may change the tradeoff between investment and liquidity, thus affecting the equilibrium amount of investment. In the presence of a liquid interbank market for investment projects, there is effectively no tradeoff between investment and liquidity: since all projects have positive net present values (NPV), all projects are implemented, regardless of small changes in their NPV induced by changes in monetary policy.

Finally, we interact our aggregate measure of connectedness with a structural vector auto-regression (VAR) for the U.S. economy, and produce impulse responses to a monetary policy shock conditional on different levels of connection. We check the robustness of our results also using a Factor-Augmented VAR (FAVAR) model. We find that the responses of the real variables to a monetary policy shock are dampened as the level of the connection between the financial sector and the real economy decreases.

This paper is linked to several strands of the literature. First, it is related to the literature dealing with measurement of financial intermediation and its characteristics. Philippon (2012) provides evidence on the quantitative importance and the cost of financial intermediation in the U.S. in the last 130 years. Greenwood and Scharfstein (2013) analyze the growth of the share of finance on GDP in the U.S. while Philippon and Reshef (2013) analyze the growth of the share of finance for several developed countries. Philippon and Reshef (forthcoming) propose evidence on the evolution of the wages in the financial industry for the period 1909-2006.<sup>7</sup> Second, this paper is related to the analysis of the relation between financial firms and the real economy. Mesonnier and Stevanovic (2012) use micro-level financial data to construct

> 7See also the survey on Financial Intermediation by Gordon and Winton (2003).

4
Table 5. Impacts of the PEP on Crime Reports by Colombians

||Homicides<br>(1)|Threats<br>(2)|Domestic Viol.<br>(3)|Thefts<br>(4)|Sex Crimes<br>(5)|Total Crimes<br>(6)|
|---|---|---|---|---|---|---|
|_Panel A. OLS - Crime Reports b_|_y Colombians_|_(ratesper 10_|_0,000)_||||
|_PEPc × I_[_PostAugust_2018]|-0.037**|-0.083*|-0.102**|-0.011|-0.111**|-0.024**|
||(0.017)|(0.055)|(0.033)|(0.007)|(0.048)|(0.007)|
|FDR q-values|[0.044]|[0.068]|[0.013]|[0.068]|[0.038]|[0.013]|
|Crime Reports Tot|0.931***|0.877***|1.068***|1.011***|0.783***|1.021***|
||(0.021)|(0.126)|(0.056)|(0.034)|(0.108)|(0.040)|
|AdjR-squared|0.928|0.773|0.827|0.973|0.784|0.954|
|_Panel B. 2SLS - Crime Reports b_|_y Colombians_|_(ratesper 10_|_0,000)_||||
|_PEPc × I_[_PostAugust_2018]|-0.049*|-0.186*|-0.130*|-0.016*|-0.166*|-0.044**|
||(0.022)|(0.096)|(0.072)|(0.010)|(0.098)|(0.015)|
|FDR q-values|[0.062]|[0.078]|[0.078]|[0.084]|[0.080]|[0.025]|
|Crime Reports Tot|0.933***|0.877***|1.063***|1.009***|0.782***|1.011***|
||(0.020)|(0.120)|(0.054)|(0.031)|(0.101)|(0.038)|
|Adj R-squared|0.928|0.770|0.826|0.973|0.783|0.953|
|KP F Stat|20.29|21.11|21.27|21.88|21.16|21.46|
|_Panel C. OLS - Crime Reports b_|_y Colombians_|_(ratesper 10_|_0,000) - Excluding_|_Controlsfo_|_r Total Crime_||
|_PEPc × I_[_PostAugust_2018]|0.116***|-0.085*|-0.190***|-0.049**|-0.120**|-0.056***|
||(0.032)|(0.064)|(0.065)|(0.018)|(0.056)|(0.011)|
|FDR q-values|[0.003]|[0.051]|[0.010]|[0.014]|[0.020]|[0.001]|
|AdjR-squared|0.444|0.641|0.657|0.857|0.668|0.833|
|_Panel D. 2SLS - Crime Reports_|_by Colombians_|_(ratesper 10_|_0,000) - Excludin_|_g Controls_|_for Total Crime_||
|_PEPc × I_[_PostAugust_2018]|0.161**|-0.154|-0.177*|-0.036|-0.177*|-0.068**|
||(0.065)|(0.120)|(0.100)|(0.031)|(0.100)|(0.023)|
|FDR q-values|[0.034]|[0.133]|[0.085]|[0.133]|[0.085]|[0.019]|
|Adj R-squared|0.443|0.640|0.657|0.857|0.667|0.833|
|KP F Stat|21.21|21.21|21.21|21.21|21.21|21.21|
|Observations (All Panels)|1,080|1,080|1,080|1,080|1,080|1,080|

_Notes:_ The table presents the results of the OLS and 2SLS estimations of specification (1). All columns include city as well as the set of controls described in section IV. Standard errors are clustered at the city level. False Discovery Rate q-values are presented in square brackets are presented in brackets. To ensure statistical rigor, we consider a coefficient statistically significant only if it remains valid after adjusting for multiple hypothesis testing and accounting for the small number of clusters.

# **V. A Assessing the validity of the identification strategy**

We also present our results in an event study format to measure the effect across time averaged by quarter and by gender. The benefit of this complementary result is twofold. First, since our sample includes many months before the rollout of the PEP regularization program, we can test 26