rafmacalaba/datause-encoder-data huggingface dataset:
Chart 10 Share of Economies with Changes in the Policy Interest Rate or QE (Percent of sample per quarter) <!-- Start of picture text --> Increasing Rates (left axis) -100<br>Decreasing Rates or QE (right axis)<br>-75<br>-50<br>-25<br>0<br><!-- End of picture text --> <!-- Start of picture text --> 0 0<br>-25 25<br>-50 50<br>-75 75<br>-100 100<br><!-- End of picture text --> Sources: Authors’ calculations, based on data on changes in policy interest rate and QE programs described in Section II and ending in May 2024.

Notes: Reports share of sample with an increase in the policy interest rate >0.1 percentage point in any quarter in blue and share with decreases in the policy interest rate or an ongoing QE program in red. Includes changes in policy rates for individual euro area countries through 1998, and then changes in the ECB policy rate.

Several patterns are immediately apparent in Chart 10. The share of economies adjusting rates in either direction is not steady. These “waves” suggest that movements in policy interest rates are correlated across countries during certain windows. For example, 92 percent of the sample increased interest rates from 2022q3 through 2023q1 during the post-pandemic tightening (with Japan the exception), while every economy lowered rates in 2008q4 in response to the Global Financial Crisis. There are also periods, however, when there is little correlation in rate changes across economies—such as in 1992q1 when rates increased in 41 percent of the sample, declined in 45 percent, and remained constant in the rest. This was a period after the 1991 global recession, oil shocks around the first Gulf War (1990-91), and collapse of the Soviet Union. It was also just before the ERM crisis, which partly resulted from this divergence in policy rates. These waves in the share of the sample with rates moving in the same direction suggest that the impact of global factors on interest rate movements may change over time.

Another pattern in Chart 10 is the larger share of red (countries cutting rates or doing QE) than blue (countries increasing rates), particularly after 2010. This is consistent with more countries easing policy in the later part of the sample. There is one prominent exception to this trend of fewer rate hikes over time, however: the giant “tsunami” of synchronized rate hikes in the postpandemic period. The most recent tightening phase is the most synchronized period of rate increases since the sample began in 1975. This “tsunami” appears to be a return to the synchronized tightening phases from before 2008—and corresponds to the array of statistics in Section III.1 that suggest the characteristics of the most recent tightening phase are more similar to those in earlier periods rather than the 2008-19 window.

32
In addition to data at the firm level, anonymized person-level data enable us to track inflows and outflows of workers from these firms, and to examine the gender and age composition of the workforce, as well as the monthly salaries paid to workers. Column 1 of Table A8 looks at the proportion of workers who were working in firms in January 2013 who remained in the firm five years later, at the end of December 2017. In the control group, only 47 percent of workers are remained this length of time. The point estimate suggests a 5 percentage point increase in this retention rate in the group treatment firms, but this is not statistically significant. Columns 2 and 3 show that 74 percent of workers are male and the average worker is age 43, with neither treatment having large, nor statistically significant impacts on these worker characteristics. Finally, Column 4 examines the treatment impact on mean worker monthly wages. The group treatment results in a 36,526 COP (3%) point estimate increase, but this is not statistically significant.

|**Table A8: Impact on Employment Composition**<br>Five‐Year Retention:<br>Proportion of Jan 2013<br>|Wor|ker Charac|<br>teristics|
|---|---|---|---|
|workers remaining in<br>firm in Dec 2017|Proportion<br>Male|Mean<br>Age|Mean Monthly<br>Salary (COP)|
|Individual Treatment*During Intervention|0.008|‐0.052|‐38509|
||(0.009)|(0.362)|(29109)|
|Individual Treatment*Post Intervention<br>‐0.031|0.009|0.130|‐37387|
|(0.052)|(0.014)|(0.485)|(36636)|
|Group Treatment*During Intervention|‐0.001|‐0.041|‐36|
||(0.008)|(0.404)|(25805)|
|Group Treatment*Post Intervention<br>0.051|‐0.002|‐0.272|36526|
|(0.056)|(0.010)|(0.467)|(33202)|
|Sample Size (N*T)<br>135|8502|8502|8502|
|Sample Size (N)<br>135|146|146|146|
|P‐value: Individual=Group During|0.472|0.985|0.339|
|P‐value: Individual=Group Post<br>0.170|0.500|0.482|0.117|
|Control Mean<br>0.47|0.74|43.0|1087335|
|Control S.D.<br>0.20<br>Notes:<br>Regressions use PILA data on formal employment, and are for samp<br>Column 1 is a cross‐sectional regression for firms with employment<br>Columns 2, 3 and 4 include firm and time fixed effects, and cluster s<br>*, **, *** denote significance at the 10, 5, and 1 percent levels resp|0.14<br>le of survivin<br>data in both J<br>tandard error<br>ectively.|4.97<br>g firms.<br>an 2013 a<br>s at the fir|418941<br>nd Dec 2017.<br>m level.|

72
infrastructure services, based on the assumption of continued expansion at ‘business as usual’ rates (Figure 9). The projections indicate that under ‘business as usual’ conditions fewer than 20 percent of countries would reach universal access for piped water by 2050, while fewer than 40 percent of countries would reach universal access to electricity by the same year. In a third of the African countries surveyed, universal service for piped water and electricity (if historic trends continue) would not be reached during the current century. The projections for flush toilet and landlines are less credible in the sense that both services are currently experiencing very high growth rates from very low base levels, and these growth rates are bound to slow down as penetration increases, particularly given the high cost of these services relative to the purchasing power of the population. Hence the estimates provided here regarding the time to reach universal coverage are bound to be too optimistic.

Another constraining factor is the fact that an additional trend is further complicating the achievement of universal access for network infrastructure services: namely that of shrinking households. The average African household appears to be getting smaller as GDP per capita rise. At work here is urbanization, declines in fertility, and greater economic resources, which allow nuclear families to disengage from extended households, in part because they no longer need the economies of scale provided by larger households. Because shrinking household size exerts such a strong effect on the need for new connections, countries with higher GDP per capita and lower population growth may not necessarily expect a smaller increase in connection needs than poorer countries, because the gains from lower population growth are often offset by the changes in household sizes (Diallo and Wodon, 2007b).

# **4. Conclusion**

This paper has provided a description of the trend in access rates to basic infrastructure services in sub-Saharan Africa using data from DHS surveys. The results are not encouraging. Access rates for electricity and flush toilets have improved slightly over the last decade. Increase in electricity coverage appears to be driven by rural electrification. Urban electricity coverage has declined slightly in the last 10 years and rural coverage has increased by three percentage points. Landline telephone is the only service where coverage has unequivocally improved, irrespective of geographical location or income group. But access to piped water has declined, and for all four services, among the poorest households access remains virtually inexistent. The 12
Niger, Nigeria, and Uganda. Data for Niger is only available in Round 5 of the Afrobarometer (2011–2013). For the other three countries, four rounds of data are available: 2002–2003, 2005–2006, 2008–2010, and 2011–2013.<sup>15</sup> We successfully classify the grammatical gender structure of the native languages of 99.1 percent of respondents, yielding a data set of 26,546 respondents who speak 175 different native languages.

# **4 Cross-Country Analysis**

## **4.1 Empirical Strategy**

In our cross-country analysis, we examine the association between women’s labor force participation and the proportion of a country’s population whose native language is a gender language, _Genderc_ . Our main empirical specification is an OLS regression of the form: where _LFPc_ is women’s labor force participation in country _c_ (in 2011), _Genderc_ is the proportion of the population of country _c_ whose native language is a gender language, _δcontinent_ is a vector of continent fixed effects, _Xc_ is a vector of of country-level geography controls, and _εc_ is a conditionally mean-zero error term.<sup>16</sup> Standard errors are clustered at the language level (by the most widely spoken language within each country). Our cross-country analysis of the relationship between women’s labor force participation and grammatical gender includes data on 178 countries: all the independent states for which data on women’s labor force participation is available from the World Bank development indicators database.

> 15Kenya, Nigeria, and Uganda were also included in the first round of the Afrobarometer. However, that data set does not contain detailed information on native languages.

> 16As discussed further below, our results are also robust to the inclusion of additional contemporaneous controls such as log GDP per capita and population. However, such controls might be directly impacted by gender norms and women’s involvement in the labor force, creating a “bad controls” problem and biasing the coefficient of interest (Angrist and Pischke 2008, Acharya, Blackwell, and Sen 2016). We therefore focus on geographic controls — proportion tropical, precipitation, temperature, suitability for the plough, and an indicator for being landlocked — which are plausibly exogenous.

17
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42
# **Table 13. Importance of investor protections**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|-0.0848|-0.0697|||-0.133|-0.105|||
||(0.0534)|(0.0534)|||(0.107)|(0.110)|||
|Export % of sales|-0.00120*|-0.000401|-0.00115|-0.000406|-0.00286**|-0.000887|-0.00288**|-0.000963|
||(0.000705)|(0.000710)|(0.000710)|(0.000710)|(0.00141)|(0.00143)|(0.00145)|(0.00147)|
|Share of sourcing via imports|0.00194**|0.00170**|0.00208***|0.00183**|0.00489***|0.00433**|0.00555***|0.00493***|
||(0.000777)|(0.000810)|(0.000787)|(0.000808)|(0.00159)|(0.00171)|(0.00165)|(0.00174)|
|High-income source country|-0.0594|0.0475|-0.0543|0.0450|-0.118|0.116|-0.123|0.101|
||(0.0732)|(0.0721)|(0.0750)|(0.0743)|(0.156)|(0.158)|(0.161)|(0.166)|
|>250 employees|-0.0405|-0.0256|-0.0389|-0.0294|-0.109|-0.0635|-0.114|-0.0834|
||(0.0643)|(0.0647)|(0.0650)|(0.0653)|(0.129)|(0.133)|(0.133)|(0.139)|
|Cumulative inv. >US$10M|0.0115|0.0491|-0.00857|0.0339|0.0369|0.0922|0.0151|0.0794|
||(0.0645)|(0.0635)|(0.0631)|(0.0624)|(0.131)|(0.135)|(0.130)|(0.134)|
|Years in country|-0.00240|-0.00445*|-0.00210|-0.00418|-0.00337|-0.00817|-0.00341|-0.00847|
||(0.00255)<br>-|(0.00256)|(0.00254)|(0.00257)|(0.00525)<br>-|(0.00574)|(0.00551)|(0.00595)|
|Level of foreign ownership (%)|0.00274***|-0.00106|-0.00228**|-0.000734|0.00618***|-0.00262|-0.00552**|-0.00220|
||(0.00104)|(0.00110)|(0.00102)|(0.00108)|(0.00224)|(0.00234)|(0.00222)|(0.00237)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.016|0.056|0.042|0.081|||||

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How important were the following factors in your foreign owner company's decision to invest in this country? [Investment protection guarantees against expropriation and other risks (e.g. repatriating profits, currency transfers, discrimination, and breach of contract by the government)]". Higher numbers denote greater importance. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

30
**12**

Since the production functions are concave, the Second Order Condition (SOC)<sup>_d_2</sup><sup>_W_</sup> _<_ 0 is _dk_<sup>2</sup> _L_<sup>_<u>d</u>_</sup><sup><u>Φ</u></sup><sup>_d_2</sup><sup>_W_</sup><sup>_<u>dkL</u>_</sup> satisfied and the denominator _dkL_<sup>(which is the same as</sup> _dk_<sup>2</sup> ) is always negative. _dR_<sup>is thus</sup> _L_ of the same sign as the numerator _dR_<sup>_<u>d</u>_</sup><sup><u>Φ</u>.We thus have the following proposition:</sup>

**Proposition 1** . _An improvement in the property right leads to a shift away from cultivation of the short-term crop towards cultivation of the long-term crop_ .

**Proof** : We have:

_d_<sup>2</sup> _d d_ Because<sup>_<_0,isadecreasingfunctionof</sup><sup>_T_,implying</sup><sup>_>_</sup> _dTdR_<sup>_τ_(</sup><sup>_T, R_)</sup> _dR_<sup>_τ_(</sup><sup>_T, R_)</sup> _dR_<sup>_τ_(</sup><sup>_TS, R_)</sup> _d_<sup>_<u>d</u>_</sup><sup><u>Φ</u></sup> _d F ′ ′_ . Observe _dR_<sup>_>_</sup> _S_<sup>( ̄</sup><sup>_k −kL_)</sup><sup>_−p.F_</sup> _L_<sup>(</sup><sup>_kL_)</sup> _dR_<sup>_τ_(</sup><sup>_TL, R_), which in turns implies that</sup> _dR_<sup>_τ_(</sup><sup>_TL, R_)</sup><sup>_._</sup> � � _′ ′ ′_ that<sup>_d_</sup> _dR_<sup>_τ_(</sup><sup>_TL, R_) is negative and, using the FOC, that</sup><sup>_F_</sup> _S_<sup>( ̄</sup><sup>_k−kL_)</sup><sup>_−p.F_</sup> _L_<sup>(</sup><sup>_kL_) =</sup><sup>_−τ_(</sup><sup>_TS, R_)</sup><sup>_F_</sup> _S_<sup>( ̄</sup><sup>_k−_</sup> _′ kL_ ) _−pτ_ ( _TL, R_ ) _FL_<sup>(</sup><sup>_kL_) is also negative. It follows that</sup><sup>_<u>dk</u>_</sup> _dR_<sup>_<u>L</u>_is always positive: an improvement in</sup> the property right unambiguously leads to a shift away from the short-term crop and towards the long-term crop. The intuition is simple: an improvement in property rights lowers the likelihood of expropriation more for the long-term crop than the short-term crop, requiring a shift toward investment in the long-term crop to restore the optimality of investments.

# **3.2 Investment on parcels within and outside the border of villages**

We now consider that the household has two parcels, one inside the village ( _V_ ) and one outside the village border ( _O_ ). The household decides on the amount of labor _ki_ to allocate to each parcel _i ∈{V, O}_ (with _kV_ + _kO ≤ k_<sup> ̄</sup> ). In this modified setting, we reason with one type of crop and thus consider a single specification of the production function _F_ ( _ki_ ), with _F ′_ ( _ki_ ) _>_ 0 and _F ′′_ ( _ki_ ) _<_ 0. Because the time spent by the household on each parcel will vary, we now consider that the household’s presence on each parcel affects tenure security ("guarding labor" assump-
approach has little analytical force in understanding some major famines of this century. Many of these were not associated with an obvious food availability decline. But even for those famines that may have been, the arithmetic of aggregate food statistics does not explain why similar shortfalls at other times or places did not also result in famines. Nor does it help us understand the incidence of famine mortality within a population.

Instead, Sen advocates study of the entitlements of famine victims. of course, people may avoid or suffer starvation without a change in their entitlements; they may avoid it by illegal means or suffer it by choice. But it is claimed that by focusing on the determinants of individual budgets, the entitlements approach is useful in understanding how specific famines happen, and how they may be avoided. This perspective has clear antecedents in the literature on famines in India (see, for example, Bhatia, 1967, though recognition of the importance of entitlements probably goes back centuries in the subcontinent and elsewhere). Sen has contributed greatly in focusing analysis and policy on the role of entitlements in the causation and relief of modern famines. Some critics since Sen's work on this topic first appeared have taught us another valuable lesson, that even the clarity of Sen's writing is amenable to serious misinterpretation, and so Dreze and Sen offer some further clarification.

But the primary goal of HPA is to show how famine can be prevented by public action. A wide range of such action is discussed, generally focused on the key issue: how to avoid the contractions in entitlements suffered by vulnerable groups. This is not just a matter of avoiding transient distress, though that is ample motivation. Longer term chronic hunger is both a cause and effect of suffering during a famine. Protecting the entitlements of the 14