rafmacalaba/datause-encoder-data huggingface dataset:
number also started importing under only the FTA scheme. The number of observations that changed from “MFN” to “only FTA” is the smallest, accounting for only 0.1 percent; this is even smaller than for the case of “both.” <u>Table 3</u> reports the means and medians of the log-differences of importing establishmentproduct-level changes in import prices (import unit values) from 2007 to 2011. In this table, we restrict the sample to observations that existed in both 2007 and 2011 and that used the MFN scheme in 2007.

In the case of “both”, we calculate the price changes for the MFN and FTA schemes separately. The “nominal” row shows a relatively large increase in import prices for observations that changed in status to “only FTA.” The median also shows a positive change in observations that changed in status to the FTA scheme under “both.” These results are unchanged when we deflate import prices by using the commodity-level consumer price index (normalized to 1 in 2007) obtained from the Bureau of Trade and Economic Indices (Ministry of Commerce) of Thailand. The results for real import price changes are shown in the row titled “real.” In sum, a relatively large increase in import prices is observed for products for which the status changed to importing under the FTA scheme, although the absolute magnitude of the price rise is small.

# III. Empirical Results

This section reports our estimation results concerning the effects of FTA use on import prices. We first present our basic estimation results to indicate how the results change when we control for 28
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22
# **Frequency of deposits and withdrawals by account holders**

Adults with a formal account by number of transactions in a typical month (%) <!-- Start of picture text --> 100% Transactions<br>6 or more<br>3–5<br>1–2<br>None<br>DEPOSITS WITHDRAWALS DEPOSITS WITHDRAWALS<br>HIGH-INCOME ECONOMIES DEVELOPING ECONOMIES<br><!-- End of picture text --> _Note:_ Because of “don’t know” and “refuse” responses, the categories do not sum to 100 percent. _Source:_ Demirguc-Kunt and Klapper 2012.

# **How account holders access their accounts**

Adults with a formal account by most common mode of withdrawal used (%)

## HIGH-INCOME ECONOMIES

<!-- Start of picture text --> 69<br>ATM<br>23<br>Financial<br>institution Do not withdraw<br>DEVELOPING ECONOMIES Retail store<br>Person associated<br>with bank<br>ATM<br>39<br>54<br>Financial<br>institution<br><!-- End of picture text --> <!-- Start of picture text --> Source: Demirguc-Kunt and Klapper 2012.<br><!-- End of picture text --> The majority of adults with a formal account in developing economies make deposits or withdrawals only one to two times in a typical month (figure 1.15). They may access their accounts only to withdraw monthly or semimonthly wages (deposited by an employer). In high-income economies, by contrast, more than half withdraw money from their accounts six or more times in a typical month. ATMs and electronic payment systems (debit cards, electronic bill payments, and the like) facilitate more frequent access to accounts. Indeed, adults with a formal account in high-income economies report most commonly using ATMs for withdrawals. Those in developing economies report most commonly making withdrawals over the counter in a branch of their bank or financial institution (figure 1.16).

In recent years the proliferation of “branchless banking” has received growing attention as a way to increase financial access in developing economies, particularly among underserved groups.<sup>21</sup> One mode of branchless banking centers on bank agents, who often operate out of retail stores, gas stations, or post offices. By taking advantage of existing infrastructure and client relationships, this way of operating makes it more cost-efficient to expand financial access. Bank agents can also be mobile, making daily or weekly rounds among clients. Few account holders report relying on bank agents (whether over the counter at a retail store or from some other person associated with their bank) as their main mode of withdrawal or deposit. But in several Asian economies—including Bangladesh, the Lao People’s Democratic Republic, Nepal, and the Philippines—more than 10 percent of account holders already report using bank agents, and this share is expected to grow globally. Over time the Global Findex data can serve as a benchmark for studies and policy interventions examining the effect of bank agents on financial access.

**22**

MEASURING FINANCIAL INCLUSION
component or measurement error leads to substantial drops. Hence, the figure indicates that in both Russia and in Poland, the living standards are more stable than the measured data would indicate. Nevertheless, even after correcting for measurement error or transitory shocks, individuals in Poland and especially in Russia face considerable fluctuations in living standards.

Table A2 in the appendix shows the size of expenditure shocks as well as their breakdown in transitory and permanent components for different demographic groups in Poland. Table A3 shows the same information for Russia. The tables show that for most demographic groups, neither the size nor the composition of the shocks is significantly different from the overall mean. However, there are a few notable exceptions. Both in Russia and in Poland, individuals with access to land face larger shocks that tend to be less permanent, but these differences are only statistically significant in Poland. In Poland, the size of the shock (as a fraction of expenditure) increases with income, while it displays a U-shaped pattern in Russia. In both countries, households with household heads aged 51-64 with higher education face the smallest shocks, but this difference is only significant in Poland.

<!-- Start of picture text --> Figure 5: Median absolute shocks after correction for<br>transitory component and measurement error.<br>Russia Russia Poland Poland<br>Income Shocks Expenditure Shocks Income Shocks Expenditure Shocks<br>st 50 g 60 - 60 . 60<br>5~0 5 .5 50 .50<br>o a 0 0<br>40 . 40 ° 40 | 40<br>CD 303<br>S 30 30<br>0 0~~~~~~~~~~~~~~~~<br>. 20 l 20 . 20 E20 l<br>U 10 l l 10 lo 10 * 10<br>42 0 0 42<br>b £O C ca b , b '<br><!-- End of picture text -->

Note: Shocks are measured as log changes (times 100) and are in deviation of the national mean. The standard error on the measured median absolute shock is less than 2 percentage points in all cases. The standard errors on the median absolute shock without the transitory component are about 10 for income and expenditure shocks in Russia and about 5 for shocks in Poland. The standard errors of the median absolute shock without measurement error are about 30 and 5 respectively.

23
Table 5: Number of censorship events by media type as a function of demand for news consumption

||(1)<br>**Cen**<br>|(2)<br>(3)<br>**sorship events targeting**:<br><br>|
|---|---|---|
||**TV**|**Radio**<br>**Newspapers**|
|TV audience<br>|0.0231**<br>||
|size|(0.0107)||
|Radio audience||0.00881|
|size||(0.0195)|
|Newspaper audience||0.0982**|
|size||(0.0380)|
|Economic constraints|-0.0340|-0.00419<br>0.0525|
||(0.0233)|(0.0242)<br>(0.0389)|
|Legal constraints|-0.0305|0.0208<br>0.0263|
||(0.0196)|(0.0204)<br>(0.0326)|
|Political constraints|0.0237|0.0783***<br>0.0130|
||(0.0166)|(0.0174)<br>(0.0282)|
|Public corruption|-0.915|-0.0915<br>-2.011|
||(1.101)|(1.160)<br>(1.851)|
|GDP pc (ln)|0.0574|0.479*<br>0.140|
||(0.211)|(0.221)<br>(0.356)|
|Population (ln)|0.259|2.819**<br>2.102|
||(1.018)|(1.076)<br>(1.712)|
|_cons|-5.474|-54.69**<br>-38.98|
||(17.81)|(19.12)<br>(30.13)|
|Country FEs|√|√<br>√|
|Year FEs|√|√<br>√|
|_N_|576|576<br>576|
|_Countries_|51|51<br>51|
|_Years_|12|12<br>12|

Standard errors in parentheses * p<0.1, ** p<0.05, *** p<0.01 Table 6 presents the findings of time-series cross-sectional Poisson and OLS models with country and year fixed effects, given that our dependent variables are counts of total censorship events as well as events affecting our outlet-specific categories per country-year. These models test our second set of hypotheses that go beyond the media targets of censorship and also capture the types of “tools” that censors use to control information. In line with Besley and Prat (2006), 41
<!-- Start of picture text --> 2) qi E j<br>8 ;<br>8000 ; E i<br>o;Ei<br>5} ’ |<br>4000 : E : —_ Developing<br>E E j High-income<br>2005aa 2010 a a20152015 2020 a2025|2025||<br><!-- End of picture text -->

<!-- Start of picture text --> E E j High-income<br>2005aa 2010 a a20152015 2020 a2025|2025||<br>120 : :<br>100 3 ;<br>E | " I f I I | — Developing<br>; ft 13 High income<br>20 : -—S Ib/ub<br>2005a2010 2015a 2020 ae2025<br><!-- End of picture text -->

<!-- Start of picture text --> 200<br>© 150<br>fo}<br>nN<br>r=<br>&<br>g5 100<br>3<br>€<br>3B<br>> 50<br>0<br>owrwoorwooranaononrwrtwnonrwvoveoaorn”A”ooccoocrcuc6cClClcrecooeoeoeoeeesceeeseeseeseeseNNNNNNNNNNNNBPC OCU OCUCchTOCUCUCchrTrCOCUCchrCOCUCchrOCUchrOCUchNNN NNNTCUhC CNGeeNNNN<br>excludes outside values<br><!-- End of picture text -->
workers’ lifetime welfare rises despite a drop in their wages, because each manufacturing worker understands that there is a probability each year that she will choose to enter the expanding export sector and bene...t from the increased real wages there. A dynamic approach with a full accounting of option value therefore complicates the welfare analysis of income inequality. For example, recall that Ebenstein et al (2009) have argued that o¤shoring to lowwage countries has pushed large numbers of US workers from high-wage manufacturing jobs into lower-wage service-sector jobs. In a dynamic model, these workers may nonetheless bene...t from such o¤shoring, because each manufacturing worker knows that with some probability each year she will move into the service sector anyway; the value of this option is enhanced by any measure that raises the real wage in that sector.

# 4 Directions for Future Research.

Research in the 1990’s undermined the simple Hecksher-Ohlin theory linking trade and inequality, and economists pointed at non-trade factors, such as technology and labor institutions or laws, to explain rising inequality. However, now a number of other channels have been discovered that have led to a vigorous resurgence of the idea that trade can lead to a rise in inequality –with the new features that it can do so through North-North trade; in countries of the South; and within each industry and within each class of workers. These theoretical developments have been in important respects fed by empirical work, and are now in turn giving rise to a rich new empirical literature, partly due to the increasing access to ...rm-level data across an increasing range of countries.

A number of natural directions for future work suggest themselves. The new ideas on consumer-side e¤ects (Fajgelbaum, Grossman and Helpman (2009)) and on higher-dimensional income-inequality e¤ects (Section 2.3.2) await empirical exploration (with the exception of Broda and Romalis (2009) for the former), while the interactions of trade with imperfect contracting have had very little empirical attention. Most empirical work still focuses on 43
- 50

-

# TABLE 2: Relationships of Informal Money-Keepers To Depositors 1988

|Money-keepers|Deposit|ors In|terview|ed|
|---|---|---|---|---|
||Male|Fema|le|Both|
|Kin:|||||
|Spouse|1|10||11|
|Parent|4|5||9|
|Sibling (full)|0|2||2|
|Offspring<br>Other kin|0<br>6|0<br>5|<br>|0<br>11|
|Subtotal, kin|11 (552)|<br>22|(79Z)|<br>33 (692)|
|Non-kin|9|6||15|
|TOTALS|20 (lOOt)|<br>28|(1002)|48 (1002)|
**Table 6: Impact of Reform By Reduction in Number of Procedures**

||Depe|<br>ndent variable:||
|---|---|---|---|
||Low-risk<br>registered business<br>owner dummy|Low-risk<br>wage earner<br>dummy|Low-risk<br>employed<br>dummy|
||(1)|(2)|(3)|
|Reform dummy (SARE)|0.0021|0.0006|0.0045|
||(0.0020)|(0.0038)|(0.0058)|
|Reform dummy*Reduct. in procedures|0.0002*|0.0007**|0.0010***|
||(0.0001)|(0.0002)|(0.0003)|
|R-squared|0.048|0.076|0.095|
|No. of observations|1,392,457|1,392,457|1,392,457|

_Note:_ Standard errors in parentheses (clustered at municipality level). Regressions include quarter and municipality fixed effects. They also include 1999 municipality ln GDP per capita, ln number of establishments per capita, ln fixed assets per capita, and ln investment per capita, all interacted with a linear time trend, as well as individual background variables. Significance levels: * 10%, ** 5%, *** 1% 40
TANZANIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 15. Hidden costs in the power and water sector due to inefficiency, 2006**

a. Power <!-- Start of picture text --> 0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>Tanzania low-income countries<br>unaccounted losses collection inefficiencies<br>b. Water<br>0.30<br>0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>Tanzania low-income countries<br>unaccounted losses collection inefficiencies<br>% of GDP<br>% of GDP<br><!-- End of picture text --> _Source:_ Foster and Briceño-Garmendia 2009.

As of 2006, underpricing of water and power services was costing Tanzania about $260 million per year. In the power sector, the average historical cost of producing electricity in Tanzania has been $0.14 per kilowatt-hour over the last decade. By comparison, the average effective power tariff in 2006 was $0.06 per kilowatt-hour. Overall, losses due to underpricing of power were very substantial 1.8 percent of GDP, more than twice as high as those in the peer group (figure 16). However, due to recent tariff increases, this has subsequently dropped to 1.1 percent of GDP; a substantial improvement but still well above the average for the benchmark group. In the water sector, the total production cost of $0.68 per cubic meter is similarly somewhat higher than the average effective tariff is only $0.50. However, the associated burden of approximately 0.02 percent of GDP is not significant in macroeconomic terms.

29
(CMA), the State Administration of Quality Supervision and Inspection and Quarantine (AQSIQ), and even the Commission for Science and Technology and Industry for National Defense. A summary presentation is shown in Figure E below.

Many other central-level agencies are also involved in rural development, though not necessarily in the implementation of interventions that use central fiscal resources. These include mass organizations like the All-China Women’s Federation (ACWF), the Communist Youth League (CYL), and the Supply and Marketing Cooperatives. Of course, last but not least, the most powerful organs defining rural development and its financing are those Party and “hybrid” (Party and government) agencies such as such as the Leading Group for Financial and Economic Affairs and the State Commission on Public Sector Reform (SCPSR).<sup>40</sup>

**Figure E.  Central Government Agencies Involved in** **_Sannong_ Spending**

<!-- Start of picture text --> State Council<br>NDRC MOF<br>MOA MLR MWR SGA SFA LGOPR<br>SEPA CMA MOST MOCA MOH MOE<br><!-- End of picture text --> Source: Adapted from the website of the Central People’s Government of the PRC: http://www.gov.cn/gjjg/2005-08/01/content_18608.htm; Xinhua information.

Responsibilities at the central level are not clearly delineated, and gaps and overlaps of responsibilities and lack of coordination lead to inefficiencies. For example, there are overlapping responsibilities between the NDRC, broadly responsible for planning, and the MOF, responsible for budget planning (and execution). A lack of coordination can lead to a situation where the NDRC’s planning and approval of capital investments are not in line with

> 40 The official English-language name of this committee, often referred to as the “post establishing committee,” is the State Commission on Public Sector Reform.

42
Table A5 : Estimation of log-linear models of income for calibration of poverty proxy

||**Federal**<br>**District**|**Greater**<br>**Metropolitan**<br>**Area**|**Pampas**<br>**Region**|**Northeast**<br>**Region**|**Northwest**<br>**Region**|**Cuyo**<br>**Region**|**Patagonian**|
|---|---|---|---|---|---|---|---|
|**Overcrowding**|-1.394|-0.523|-0.602|-0.544|-0.473|-0.558|-0.588|
||(6.25)**|(5.85)**|(10.61)**|(7.41)**|(8.04)**|(5.71)**|(6.12)**|
|**Inappropriate**<br>**housing**|-0.441<br>(1.94)|0.070<br>(0.39)|-0.281<br>(3.74)**|-0.106<br>(1.18)|-0.176<br>(1.91)|-0.229<br>(1.99)**|-0.500<br>(5.00)**|
|**Insanitary**<br>**conditions**|-0.597<br>(2.33)**|-0.797<br>(14.69)**|-0.602<br>(13.15)**|-0.496<br>(9.35)**|-0.691<br>(17.27)**|-0.590<br>(7.97)**|0.513<br>(5.72)**|
|**Presence of**<br>**infants**|-0.394<br>(7.00)**|-0.406<br>(13.65)**|-0.475<br>(26.41)**|-0.385<br>(14.68)**|-0.333<br>(18.41)**|-0.381<br>(13.49)**|-0.394<br>(15.34)**|
|**Head of**<br>**household**|-0.587|-0.386|-0.490|-0.435|-0.438|-0.426|-0.416|
|**completes**<br>**primary**<br>**education**|(8.72)**|(9.67)**|(20.85)**|(10.94)**|(14.73)**|(10.82)**|(11.88)**|
|**Constant**|6.423|5.693|5.707|5.362|5.504|5.532|6.028|
||(185.85)**|(182.75)**|(338.88)**|(187.41)**|(269.29)**|(195.98)**|(244.76)**|
|**Observations**|843|2040|5693|2263|4017|2158|2648|
|**R squared**|0.21|0.32|0.29|0.29|0.30|0.23|0.21|

57
Table 4: Factor covariates, trade and distance weight matrices

|Variable|Trade we|ight matri|x|
|---|---|---|---|
||I<br>II|III|IV|
|∆TFP|0.945||3.131|
||(2.840)||(1.920)|
|Uncertainty|-0.149||-0.134|
||(-1.840)||(-1.600)|
|Real interest rate||-0.391|-0.413|
|||(-1.490)|(-1.260)|
|No. of obs.|43<br>45|45|43|
|_R_<sup>2</sup>|0.165<br>0.078|0.034|0.343|
||Distance w|eight matr|ix|
|∆TFP|5.435||4.958|
||(2.940)||(2.530)|
|Uncertainty|-0.221||-0.142|
||(-1.950)||(-1.270)|
|Real interest rate||-0.027|0.127|
|||(-0.070)|(0.360)|
|No. of obs.|43<br>45|45|43|
|_R_<sup>2</sup>|0.285<br>0.134|0.000|0.345|

Notes: The dependent variable is the common factor from the full sample estimates in Table 3. ∆TFP is the first difference of total factor productivity (TFP), Uncertainty is the log of the U.S. economic policy uncertainty index taken from Baker, Bloom and Davis (2016), Real interest rate is the U.S. real short-term interest rate. T-statistics in brackets computed with heteroskedasticity and autocorrelation consistent (HAC) standard errors. The regressions include a constant.

To assess the covariates of the global business cycle in our much broader country sample, Table 4 presents regressions of the estimated common factor on total factor productivity, policy uncertainty, and the U.S. short-term interest rate, taken as a measure of global monetary conditions. The upper panel reports the results obtained using as dependent variable the common factor derived from the model using the trade weight matrix, and the bottom panel reports the results obtained with the factor estimated when using the bilateral distance weight matrix.

The univariate regression results show that total factor productivity is positively correlated with the common factor, corroborating the findings of Crucini, Kose, and Otrok (2011) using data for G-7 countries. Next, the uncertainty index shows a negative sign with a statistically significant coefficient, showing that the adverse effect of uncertainty on output found by Baker, Bloom and Davis (2016) among major advanced countries also holds in our larger country sample. In turn, the U.S. short-term real interest rate is negatively correlated with the global factor under both 20