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25
# **2 Related Literature**

Previous literature has documented the effects of many weather shocks, including temperature, precipitation, NDVI, windstorms, and soil quality, on economic activity (Dell et al., 2014). These papers study short-run effects, such as on labor (Graff Zivin and Neidell, 2014), on agriculture (Schlenker and Lobell, 2010; Deschênes and Greenstone, 2007), and long-run effects, such as on education and health (Groppo and Kraehnert, 2017; Graff Zivin et al., 2018; Maccini and Yang, 2009), on economic growth (Dell et al., 2012a).

We contribute to the growing literature that studies the effect of weather shocks on consumption, poverty, and vulnerability to poverty. Dell et al. (2012b) use a world sample from 2000 to show that countries are, on average, 8.5 percent poorer per 1 degree warmer weather. Other within-country evidence shows that rainfall deficits affect rice farming households negatively in Ethiopia, who lower non-food expenditures to meet their food expenditures (Essama-Nssah et al., 2011). Baquie and Fuje (2020) find that only nonassistance consumption reduces, and households decrease food consumption in response to the growing season rainfall deficit. Vulnerability to poverty increased by 40 percent in Kenya (Christiaensen and Subbarao, 2004), and 42 and 21 percent of households in rural and urban areas, respectively, were vulnerable to poverty in Ethiopia in response to rainfall shocks (Hill and Porter, 2017). Vulnerability to poverty was also estimated to be higher in drought-prone areas vis-à-vis other ecological zones in Ethiopia (Skoufias et al., 2021). Pape and Wollburg (2019) show an increase in rural poverty and hunger due to vegetation (NDVI) deficits in Somalia (also a conflict-affected nation).

Interestingly, Letta et al. (2018) shows a reduction in rural household income and a slowdown in income convergence due to temperature shocks in Tanzania. In a forthcoming paper, Calcutt et al. (forthcoming) use multiple satellite-based measures of weather to find that food consumption is more strongly impacted by weather shocks in the growing season than aggregate consumption, and poorer households with less education in remote areas in dry agro-ecological zones are most affected in four Sub-Saharan Africa countries. Contrary to their findings, we see a decrease in households’ per capita total consumption expenditure due to bad NDVI and temperature shocks. While both food and non-food consumption were negatively affected due to adverse NDVI anomalies in 2018, households reduced non-food consumption to save themselves from food insecurity when faced with extremely hot temperatures.

While many different measures of drought have been used in the recent decade (Calcutt et al. forthcoming, for instance, use multiple measures), a consensus is yet to emerge 5
time and may have a dynamic effect (i.e. the initial variation in RTA depth may affect future variations in depth and the outcome variable), implying a second possible source of bias.<sup>26</sup> Therefore, we follow De Chaisemartin and d’Haultfoeuille (2020) and perform a robust two-way fixed effects estimation of the trade effect of the number of legally enforceable provisions in RTAs. Since we are interested in the dynamic effect, we specifically adopt the estimator proposed by De Chaisemartin and D’Haultfoeuille (2020). The intuition behind this estimation is that, to avoid negative weights that may bias standard two-way fixed effects estimators, one should compare the outcome change from _t −_ 1 to _t_ + _l_ for only the first time switchers’ (i.e. firm-destination pairs at first change in RTA depth occurred at _t_ ), to the outcome change of firm-destination pairs whose treatment has remained stable until _t_ .<sup>27</sup> We present the results of the estimation with _l ∈_ [0 _,_ 3] in Table 11.<sup>28</sup> In line with the event study approach reported in Figure 2, the depth of RTAs has a weak positive effect on the export of firms in the year of the first variation in RTA depth (i.e. _t_ ). The effect of RTA depth becomes strongly significant the year after _t_ + 1, increases in magnitude at time _t_ + 2 and vanishes at _t_ + 3. While the pattern is the same as in the standard event study approach reported in the previous section, the point estimates in Table 11 differ with respect to those reported in Figure 2. However, such a difference is small if standard errors are considered; suggesting the presence of a (very) small negative bias in the standard two-way fixed effects event study approach.

# **Conclusion**

This paper studies the effect of deep trade agreements on the export performance of firms in 31 developing countries. We show a moderate but statistically significant effect of RTA depth on the exports of the _average_ firm. Namely, one additional legally enforceable provision boosts the export of firms by 0.3%. This implies that moving from _shallow_ to _deep_ RTAs leads to a 3.6% increase in firms’ exports. This average effect is however strongly heterogeneous across firms with different characteristics. Large and GVC firms are more positively affected by deep RTAs. For firms belonging to the top-quartile of size distribution, moving from _shallow_ to _deep_ RTAs implies 4.8% increase in their exports. For GVC firms importing and exporting from/to the same country, moving from _shallow_ to _deep_ RTAs implies a 9.6% increase in their exports. Conversely, small and less productive firms are negatively affected by deep RTAs as they suffer the higher degree of competition induced by deep RTA at destination. These results

> 26Notice that this second source of bias is already taken into account in the event study exercise where we focus exclusively on the first variation in RTAs depth.

> 27See De Chaisemartin and D’Haultfoeuille (2020) for more details on the robust two-way fixed effects estimator.

> 28In Table A10 we show the same estimations including firm’s tariffs as a controls and results hold.

19
Policy Research Working Paper 9399

# **Abstract**

Many developing countries have struggled with realizing sufficient revenues from property tax. However, as developing countries experience economic growth, they are also seeing property values rising, providing a bigger tax base from which to realize revenues. Technology has made tax administration easier and more effective and developing country governments have been improving their quality of governance and considering introducing or enhancing property tax revenue collection to diversify their tax and fiscal revenues. This paper explores the determinants of property tax revenue using data from the United States, Canada, Australia, Chile, and the Organisation for Economic Co-operation and Development for 2006 to 2016, using a fixed effects model. The results show that increases in gross domestic product and population lead to increases in property tax revenue and an increase in federal transfers decreases it. The outcomes of the empirical analysis highlight the statistically significant impacts on property tax collection of a country’s state of development and its demographic, fiscal, and property tax–specific characteristics. A critical question for further research is whether and how the empirical methodologies and specifications as applied to the set of developed economies would be replicated in the context of developing countries.

This paper is a product of the Governance Global Practice and the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank. org/prwp. The authors may be contacted at rawasthi@worldbank.org and tle@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
country (of origin or destination)-year level with information on value and quantity traded (exported or imported). The final data set includes an identifier to differentiate between firms that are only exporters and those that are exporters and importers simultaneously, that we designate as exporter-importers.<sup>2</sup> This exporter-importer status is time-varying. Additional details on the data are provided in the Appendix.

<mark>The final data set excludes annual export flows below 10,000 USD dollars as such small flows may represent the shipping of samples rather than merchandise sold as a true export venture. We will present our results focusing on exporting firms across all sectors in Peru (which we designate as</mark> _<mark>Overall</mark>_ <mark>below) and then focusing on exporting firms in the two specific sectors agribusiness and apparel, whose coverage in terms of HS Chapters or 6-digit products is shown in Table 1.</mark> Note that an exporting firm is defined to be part of the <mark>agribusiness sector or part of the apparel sector if and only if the firm is an i</mark> ntensive exporter in the sector, that is, if that firm’s total value exported of products from that sector accounts for 50 percent or more of the firm’s total exports in a given year.

**<u><mark>Table 1. Definition of Sectors</mark></u>**

|Agribusiness|Grapes, Asparagus (fresh and frozen),<br>Avocados, Coffee, Cacao, Quinoa, Mangos,<br>Bananas, Paprika, Tangerines and Mandarines<br>(HS 080610, 080440, 070920, 071080,<br>090111, 180100, 100850, 100890, 080450,<br>080310, 080300, 080390, 090421, 090420,<br>090422, 080520)|
|---|---|
|Apparel|Chapters 60-63 of HS classification|

To focus on the role of imported intermediate inputs for Peruvian firms’ export performance, we follow the recent literature, namely Arkolakis et al. (2008) and Feng et al. (2012) and use the United Nations Broad Economic Classification (BEC) to identify which imports by

> 2 The data used for this analysis allows the identification of inputs and other goods imported directly by exporters. There may be other inputs and goods imported indirectly through third parties (e.g. distributors and traders); however, these transactions cannot be identified with the data available for the analysis. Importing indirectly may be an efficient strategy for small firms and for the import of non-core inputs as it can reduce fixed costs of establishing relationships with international buyers and may confer some scale-related cost benefits. However, for the purpose of analyzing the degree of integration of exporters into GVCs, transactions covering direct imports are the most critical— firms that are integrated into GVCs would normally source directly, as these sourcing relationships are critical from a quality and technology spillover perspective. Also, since our focus in on export performance, for the purposes of our analysis, we eliminate from the final dataset the firms that are importers-only.

6
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Jensen, M. (2005), <u>http://www3.sn.apc.org/africa/afrmain.htm</u>

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Keller, W. (2001), "International Technology Diffusion," NBER working paper 8573.

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24
|and 1981–|**AF10**<br>0.794<br>(0.23)_∗∗∗_<br>-0.060<br>(0.09)<br>0.002<br>(0.02)<br>-0.112<br>(0.11)<br>0.067<br>(0.19)<br>0.140<br>(0.10)<br>2.012_∗∗_<br>61.961<br>-1.623<br>S-GMM<br>109<br>345<br>mpling frame<br>on) and 1981–<br>n parentheses.<br> not reported.<br> percent level.|
|---|---|
|9–2003|**AF9**<br>2.096<br><br>(3.36)<br>-0.251<br>(0.19)<br>-0.546<br>(0.68)<br>2.042<br>(1.93)<br>0.411<br>(0.58)<br>0.302<br>(0.90)<br>1.281<br>0.368<br>IV-2<br>5<br>33<br>ollars. Sa<br>ross-secti<br>eported i<br>sions, but <br>cance at 1|
|arity, 199|**AF8**<br>0.94<br>(0.33)_∗∗∗_<br>-0.142<br>(0.09)<br>-0.031<br>(0.05)<br>-0.051<br>(0.16)<br>0.002<br>(0.27)<br>0.235<br>(0.40)<br>1.416<br>32.775<br>-1.530<br>S-GMM<br>83<br>204<br>-adjusted d<br>999–2003 (c<br> errors are r<br> the regres<br>ates signif|
|rowth pol|**AF7**<br>4.330<br><br>(1.99)_∗∗_<br>-0.513<br>(0.24)_∗∗_<br>0.071<br>(0.19)<br>-1.010<br>(0.72)<br>1.135<br>(0.75)<br>-2.158<br>(1.48)<br>3.245<br>0.026<br>IV-2<br>4<br>40<br>ower parity<br>verages for 1<br>ust standard <br>e included in<br>nd _∗∗∗_indic|
|ants of g|**AF6**<br>1.045<br>(0.32)_∗∗∗_<br>-0.145<br><br>(0.09)<br>-0.019<br>(0.05)<br>-0.066<br>(0.17)<br>0.041<br>(0.25)<br>1.497<br>31.420<br>-1.399<br>S-GMM<br>83<br>204<br>purchasing p<br>y fve-year a<br>relation-rob<br>cations) wer<br>rcent level, a|
|determin|**AF5**<br>3.778<br><br>(1.98)_∗_<br>-0.600<br>(0.27)_∗∗_<br>-0.206<br>(0.16)<br>-0.402<br>(0.31)<br>0.704<br>(0.50)<br>3.446<br>0.088<br>IV-2<br>4<br>69<br>ernational <br>a periodicit<br>nd autocor<br> (all specif<br>nce at 5 pe|
|amental|**AF4**<br>1.467<br>(0.54)_∗∗∗_<br>-0.323<br>(0.17)_∗_<br>-0.074<br>(0.08)<br>-0.014<br>(0.21)<br>0.415<br>(0.26)<br>9.388_∗∗∗_<br>IV-1<br>3<br>37<br>nt 2005 int<br>anced. Dat<br>edasticity a<br>nstant term<br>tes signifca|
|egressions for fund<br>owth rates*|**AF2**<br>**AF3**<br>2.471<br>0.896<br>(1.13)_∗∗_<br>(0.27)_∗∗∗_<br>-0.450<br>-0.038<br>(0.18)_∗∗_<br>(0.09)<br>-0.158<br>-0.009<br>(0.11)<br>(0.03)<br>2.743_∗∗∗_<br>5.750_∗_<br>1.599<br>58.865<br>-1.311<br>IV-2<br>S-GMM<br>3<br>67<br>75<br>353<br>m GDP data in consta<br> size, and is hence unbal<br>re in log form. Heterosk<br> (GMM only) and a co<br> percent level, _∗∗_indica|
|hmark r<br> GDP gr|**AF1**<br>1.441<br>(0.56)_∗∗∗_<br>-0.354<br>(0.15)_∗∗_<br>-0.076<br>(0.06)<br>9.826_∗∗∗_<br>IV-1<br>2<br>42<br>culated fro<br>ize sample <br> variables a<br>ent variable<br>cance at 10|
|Table A.3: Benc<br>2008, with PPP|Institutions<br>Integration<br>Geography<br>Democracy<br>Fractionalization<br>Social capital<br>Human capital<br>_F_<br>Anderson LR<br>Hansen _J_<br>AR(2)<br>Estimator<br>Instruments<br>N<br>*Growth rates cal<br>selected to maxim<br>2008 (panel). All <br>A lagged depend<br>_∗_indicates signif|

35
The spectral density of _Y_ at frequency _ω_ can be written as a function of _D_ , _D_ � _e_<sup>_−iτω_�</sup> = � _I −_ � _B_ 1 _Le_<sup>_−iω_</sup> + _B_ 2 _L_<sup>2</sup> _e_<sup>_−i_2</sup><sup>_ω_</sup> + _. . . BpL_<sup>_p_</sup> _e_<sup>_−ipω_��</sup><sup>_−_1</sup> : Due to the limited time series available for estimation in many EMDEs, the “limited spectral” identification, where _D_ is truncated at the 10-year horizon due to the associated biases when estimating the long-run estimation on finite data (Dieppe et al., 2019, 2021).

To assess the spectral density within a frequency band, the spectral power can be integrated between _ω_ = [ _<u>ω</u> , ω_ ̄]. The shock that maximizes the variance of labor productivity over the desired frequency is the eigenvector associated with the largest eigenvalue of _SY Y_ ( _ω_ ), see (Uhlig, 2003). The frequency band of interest here is from 10- _∞_ years so that technology shocks are identified as the shock that dominates very long-run productivity developments, while excluding short-term developments such as those at business-cycle frequencies from the maximization problem.

# **3.2 Assessing the impact of technology shocks in a pooled panel environment**

Initially, in order to understand the typical impacts of technology shocks across countries, a simple pooled panel estimation with fixed effects is used. This is performed separately across advanced economies and EMDEs, particularly since the variance of productivity is three times larger in EMDEs than in advanced economies, given assumptions required on common parameters for panel estimations. For each economy, the VAR consists of the log-level of labor productivity, logemployment per capita (including self-employment), the log share of investment (gross-fixed capital formation), and separately consumption in GDP, and consumer price inflation.

Macroeconomic aggregates such as GDP and employment are from the World Bank’s World Development Indicators (WDI) database and The Conference Board’s Total Economy Database (TED) for employment. Employment and GDP data are extended where available with WDI and ILO employment estimates and Penn World Table 9.1 GDP data where possible. The majority of economies have full data coverage over the same period over which the growth accounting components are available, and the average sample length is 40 years for EMDEs and 45 years for advanced economies. Hence, here, annual data is used to estimate the VARs. This choice reduces the degrees of freedom in the VAR estimation, while at the same time significantly lengthening the period over which the VAR is estimated for many EMDEs. The span of the data is critical for identifying 8
|**Table 7: Impacts on Market P**|**rices**<br>Full Reform|All services<br>barriers|% change f<br>Only non-<br>discrimina-<br>tory services<br>barriers|rom benchm<br>Only<br>barriers<br>against FDI<br>in services|ark equilibr<br>Only<br>uniform<br>tariffs|ium<br>CRTS|SteadyState|
|---|---|---|---|---|---|---|---|
|**IRTS Goods and Services**|-3.0|-3.0|-2.4|-0.7|0.2|-0.8|-7.4|
|**CRTS Goods and Services**|5.3|5.1|4.2|1.1|0.1|1.2|6.2|
|**Business Services**|-5.6|-6.6|-5.0|-1.6|0.8|-2.7|-7.5|
|Communication|-1.6|-2.0|-1.6|-0.4|0.4|-0.9|-3.6|
|Insurance|-9.5|-11.0|-5.0|-5.2|1.5|-5.4|-15.6|
|Banking and other financial services|-5.5|-6.9|-7.4|0.9|1.5|-3.2|-11.6|
|Other services|-6.1|-7.0|-4.9|-2.5|0.5|-2.4|-4.8|
|Road services|-13.5|-14.2|-12.0|-3.0|1.0|-0.8|-11.3|
|Railway transport|-16.7|-17.3|-16.7|-1.3|1.0|-2.8|-14.4|
|Maritime transport|-34.4|-34.8|-32.0|-3.7|1.0|-14.7|-31.6|
|Pipeline transport|-4.6|-5.5|-4.8|-1.3|1.0|4.2|-2.8|
|Airline transport|-5.7|-6.6|-5.7|-1.4|1.0|3.7|-3.8|
|**Dixit-Stiglitz Goods**|1.1|1.8|1.5|0.3|-0.4|0.1|-6.4|
|Beverages & tobacco|1.1|3.3|2.7|0.8|-2.1|0.0|-8.1|
|Other manufactured food|-4.8|1.4|1.2|0.4|-6.1|-5.3|-15.3|
|Printing and publishing|8.6|4.8|4.2|1.9|5.4|2.4|1.3|
|Petroleum|0.6|0.5|0.6|0.2|0.1|0.4|-7.6|
|Chemicals|1.1|-0.5|-0.2|-0.6|1.5|2.4|-5.8|
|Metals and machines|5.2|2.9|2.5|0.5|3.0|1.7|-3.2|
|Non metallic products|5.3|5.2|4.4|1.2|0.2|1.6|-9.2|
|<br>Other manufactures|-4.8|1.2|0.9|-0.1|-5.7|-3.8|-5.4|
|**Agriculture**|3.2|4.0|3.2|0.6|-0.8|-0.2|9.5|
|**Other CRTS**<br>**Source**: Authors' estimates.|5.8|5.4|4.5|1.3|0.4|1.6|5.4|

39